diff --git a/ckpts/universal/global_step20/zero/20.attention.dense.weight/exp_avg_sq.pt b/ckpts/universal/global_step20/zero/20.attention.dense.weight/exp_avg_sq.pt new file mode 100644 index 0000000000000000000000000000000000000000..be3d1ee5e5e522bfeea0dec689030707a1ec196e --- /dev/null +++ b/ckpts/universal/global_step20/zero/20.attention.dense.weight/exp_avg_sq.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9b7ff1c25aac1d083a74140789d1a2ed03035b1123f2a862d638bea8a8e8c57 +size 16778411 diff --git a/ckpts/universal/global_step20/zero/20.attention.dense.weight/fp32.pt b/ckpts/universal/global_step20/zero/20.attention.dense.weight/fp32.pt new file mode 100644 index 0000000000000000000000000000000000000000..405b2973f13dfd66b44a970a22cd503c9a212c8e --- /dev/null +++ b/ckpts/universal/global_step20/zero/20.attention.dense.weight/fp32.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:168a9feae7b126f0db6a8babd4adec260d368139c62403f8a129e491958bc2a3 +size 16778317 diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ArrayRef.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ArrayRef.h new file mode 100644 index 0000000000000000000000000000000000000000..0461d5953ed8a7783c82402ca4523b0b0a1ad465 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ArrayRef.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Backtrace.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Backtrace.h new file mode 100644 index 0000000000000000000000000000000000000000..bdef9f4a9de439bf8af9a7c5a35a958caa7b8b41 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Backtrace.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..17c4ddd92f1d469abb771ed0392eed0df0508b1a --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions.h @@ -0,0 +1,29 @@ +#include + +// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch] +// Code introduced to avoid cyclic dependency in static dispatch is no longer +// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place, +// to Operators.cpp for supporting multiple backends with multiple kernels. +// +// Note [Avoiding Include Cycles In Static Dispatch] +// In order to avoid #include cycles in the static dispatch build, we've carefully split out +// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h. +// +// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h. +// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods +// all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all +// directly inlined into TensorBody.h. +// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API, +// which include functions that have defaultable optional arguments. +// That requires knowing the full Tensor class definition. +// +// We break the cycle by doing the following: +// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h +// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl., +// - CPUFunctions_inl.h includes everything else +// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class, +// and then it includes CPUFunctions_inl.h. +// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly. +// - This also means that static dispatch build, CPUFunctions.h only needs to +// #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h. +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions_inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions_inl.h new file mode 100644 index 0000000000000000000000000000000000000000..62c3a104f5390beffd12c9e72fe3356f7ab26c1b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUFunctions_inl.h @@ -0,0 +1,576 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunctions_inl.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + . \ + See NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CPUGeneratorImpl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUGeneratorImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..f74c42f44fda584982bc9aa1e4b5d3f8bd4db3b7 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CPUGeneratorImpl.h @@ -0,0 +1,49 @@ +#pragma once + +#include +#include +#include +#include + +namespace at { + +struct TORCH_API CPUGeneratorImpl : public c10::GeneratorImpl { + // Constructors + CPUGeneratorImpl(uint64_t seed_in = default_rng_seed_val); + ~CPUGeneratorImpl() override = default; + + // CPUGeneratorImpl methods + std::shared_ptr clone() const; + void set_current_seed(uint64_t seed) override; + void set_offset(uint64_t offset) override; + uint64_t get_offset() const override; + uint64_t current_seed() const override; + uint64_t seed() override; + void set_state(const c10::TensorImpl& new_state) override; + c10::intrusive_ptr get_state() const override; + static c10::DeviceType device_type(); + uint32_t random(); + uint64_t random64(); + c10::optional next_float_normal_sample(); + c10::optional next_double_normal_sample(); + void set_next_float_normal_sample(c10::optional randn); + void set_next_double_normal_sample(c10::optional randn); + at::mt19937 engine(); + void set_engine(at::mt19937 engine); + + private: + CPUGeneratorImpl* clone_impl() const override; + at::mt19937 engine_; + c10::optional next_float_normal_sample_; + c10::optional next_double_normal_sample_; +}; + +namespace detail { + +TORCH_API const Generator& getDefaultCPUGenerator(); +TORCH_API Generator +createCPUGenerator(uint64_t seed_val = default_rng_seed_val); + +} // namespace detail + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CUDAFunctions_inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CUDAFunctions_inl.h new file mode 100644 index 0000000000000000000000000000000000000000..126d26818c83b9ebad0f72293f84d5e638e03601 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CUDAFunctions_inl.h @@ -0,0 +1,614 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunctions_inl.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + . \ + See NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CachedTensorUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CachedTensorUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..3413341e666b549226911a35dd84460376267f26 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CachedTensorUtils.h @@ -0,0 +1,24 @@ +#pragma once + +#include + +namespace at::caching { + +// Some systems (just cudagraphs currently) will persist a static tensor output +// whose TensorImpl does not change across iterations. For these tensors caching +// dtype conversions is invalid. Additionally, there will be an extra reference +// count to these cached tensors that would prevent buffer inplacing and other +// checks on tensor uniqueness. If we are not using these systems the enabled +// flag will be false and we will avoid the hash lookup. + +TORCH_API bool is_cached_tensor(const at::Tensor& t); +TORCH_API void add_cached_tensor(const at::Tensor& t); +TORCH_API void remove_cached_tensor(const at::Tensor& t); +TORCH_API void set_cached_tensors_enabled(bool enable); + +// For gradient buffer stealing we will adjust the use count of tensors +// which are persisted by cudagraphs, just as we need to adjust reference +// count of tensors with hooks. +TORCH_API size_t adjusted_use_count(const at::Tensor& t); + +} // namespace at::caching diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CollapseDims.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CollapseDims.h new file mode 100644 index 0000000000000000000000000000000000000000..4e25112e7d4490096e6340184a3a4813511f93b6 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CollapseDims.h @@ -0,0 +1,94 @@ +#include +#include + +namespace at { + +/* +[collapse dims] Updates sizes, and strides to reflect a "collapse" of +the info, possibly excluding the optional excludeDim. A "collapsed" version +of the info is the fewest dims that order the tensor's elements in the same +way as the original info. If excludeDim is specified, the collapse is the +fewest dims that order the tensor's elements as the original and preserve the +excluded dimension, unless the tensor collapses to a point. + +This function returns a pair of values. + +1) The (new) index of the preserved dimension if excludeDim is +specified. 0 if the tensor is collapsed to a point. -1 +otherwise. + +2) The new number of dimensions. +*/ +template +inline std::pair collapse_dims( + T* sizes, + T* strides, + int64_t dims, + const int excludeDim = -1) { + TORCH_CHECK( + excludeDim >= -1 && excludeDim < dims, + "expected excluded dim between -1 and dims - 1"); + + int64_t stopDim = (excludeDim == -1) ? dims : excludeDim; + int64_t newIndex = -1; + int64_t oldIndex = 0; + int64_t remappedExcludedDim = -1; + + while (oldIndex < dims) { + // Finds a dimension to collapse into + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + ++oldIndex; + break; + } + + // Collapses dims + for (; oldIndex < stopDim; ++oldIndex) { + if (sizes[oldIndex] == 1) { + continue; + } + + if (strides[newIndex] == sizes[oldIndex] * strides[oldIndex]) { + sizes[newIndex] *= sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } else { + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + } + } + + // Handles excludeDim being set (oldIndex == excludeDim) + if (oldIndex != dims) { + // Preserves excluded dimension + ++newIndex; + sizes[newIndex] = sizes[oldIndex]; + strides[newIndex] = strides[oldIndex]; + remappedExcludedDim = newIndex; + + // Restarts iteration after excludeDim + ++oldIndex; + stopDim = dims; + } + } + + // Handles special case of all dims size 1 + if (newIndex == -1 || (newIndex == 0 && sizes[0] == 1)) { + dims = 1; + sizes[0] = 1; + strides[0] = 1; + + return std::pair(0, 1); + } + + dims = newIndex + 1; + return std::pair(remappedExcludedDim, dims); +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..bdaee888e89bd9e1dd22c54a72d5d1b96affeda1 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradFunctions.h @@ -0,0 +1,29 @@ +#include + +// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch] +// Code introduced to avoid cyclic dependency in static dispatch is no longer +// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place, +// to Operators.cpp for supporting multiple backends with multiple kernels. +// +// Note [Avoiding Include Cycles In Static Dispatch] +// In order to avoid #include cycles in the static dispatch build, we've carefully split out +// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h. +// +// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h. +// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods +// all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all +// directly inlined into TensorBody.h. +// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API, +// which include functions that have defaultable optional arguments. +// That requires knowing the full Tensor class definition. +// +// We break the cycle by doing the following: +// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h +// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl., +// - CPUFunctions_inl.h includes everything else +// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class, +// and then it includes CPUFunctions_inl.h. +// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly. +// - This also means that static dispatch build, CPUFunctions.h only needs to +// #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h. +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..b3b7c537ca6e8ad8fe7a30a5a7af956af7994d6e --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeExplicitAutogradNonFunctionalFunctions.h @@ -0,0 +1,29 @@ +#include + +// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch] +// Code introduced to avoid cyclic dependency in static dispatch is no longer +// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place, +// to Operators.cpp for supporting multiple backends with multiple kernels. +// +// Note [Avoiding Include Cycles In Static Dispatch] +// In order to avoid #include cycles in the static dispatch build, we've carefully split out +// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h. +// +// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h. +// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods +// all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all +// directly inlined into TensorBody.h. +// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API, +// which include functions that have defaultable optional arguments. +// That requires knowing the full Tensor class definition. +// +// We break the cycle by doing the following: +// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h +// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl., +// - CPUFunctions_inl.h includes everything else +// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class, +// and then it includes CPUFunctions_inl.h. +// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly. +// - This also means that static dispatch build, CPUFunctions.h only needs to +// #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h. +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..fde0a471ac0135f1dcb55f78e10d0818c5cff2e2 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions.h @@ -0,0 +1,29 @@ +#include + +// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch] +// Code introduced to avoid cyclic dependency in static dispatch is no longer +// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place, +// to Operators.cpp for supporting multiple backends with multiple kernels. +// +// Note [Avoiding Include Cycles In Static Dispatch] +// In order to avoid #include cycles in the static dispatch build, we've carefully split out +// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h. +// +// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h. +// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods +// all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all +// directly inlined into TensorBody.h. +// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API, +// which include functions that have defaultable optional arguments. +// That requires knowing the full Tensor class definition. +// +// We break the cycle by doing the following: +// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h +// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl., +// - CPUFunctions_inl.h includes everything else +// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class, +// and then it includes CPUFunctions_inl.h. +// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly. +// - This also means that static dispatch build, CPUFunctions.h only needs to +// #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h. +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h new file mode 100644 index 0000000000000000000000000000000000000000..ca2e5233c49e31edd26b144714ca7b9949a50eee --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradFunctions_inl.h @@ -0,0 +1,500 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunctions_inl.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + . \ + See NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..1432163225387fc9614419529c63e4ce402ca239 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions.h @@ -0,0 +1,29 @@ +#include + +// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch] +// Code introduced to avoid cyclic dependency in static dispatch is no longer +// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place, +// to Operators.cpp for supporting multiple backends with multiple kernels. +// +// Note [Avoiding Include Cycles In Static Dispatch] +// In order to avoid #include cycles in the static dispatch build, we've carefully split out +// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h. +// +// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h. +// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods +// all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all +// directly inlined into TensorBody.h. +// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API, +// which include functions that have defaultable optional arguments. +// That requires knowing the full Tensor class definition. +// +// We break the cycle by doing the following: +// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h +// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl., +// - CPUFunctions_inl.h includes everything else +// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class, +// and then it includes CPUFunctions_inl.h. +// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly. +// - This also means that static dispatch build, CPUFunctions.h only needs to +// #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h. +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions_inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions_inl.h new file mode 100644 index 0000000000000000000000000000000000000000..90ffa6b1eb4a9cc5d64851784113a739e385a77e --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/CompositeImplicitAutogradNestedTensorFunctions_inl.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunctions_inl.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + . \ + See NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include + + + diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Context.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Context.h new file mode 100644 index 0000000000000000000000000000000000000000..931cd86e77d984cc3b69aca0516b7c3489320825 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Context.h @@ -0,0 +1,560 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace at { + +class Tensor; + +enum class TORCH_API Float32MatmulPrecision { HIGHEST, HIGH, MEDIUM }; + +class TORCH_API Context { + public: + Context(); + + const Generator& defaultGenerator(Device device) { + c10::DeviceType device_type = device.type(); + initCUDAIfNeeded(device_type); + initHIPIfNeeded(device_type); + if (device_type == at::kCPU) { + return at::detail::getDefaultCPUGenerator(); + } else if (device_type == at::kCUDA) { + return at::detail::getCUDAHooks().getDefaultCUDAGenerator(device.index()); + } else if (device_type == at::kMPS) { + return at::detail::getMPSHooks().getDefaultMPSGenerator(); + } else if (device_type == at::kXPU) { + return at::detail::getXPUHooks().getDefaultXPUGenerator(device.index()); + } else if (device_type == at::kIPU) { + return at::detail::getIPUHooks().getDefaultIPUGenerator(device.index()); + } else if (device_type == at::kPrivateUse1) { + return at::GetPrivateUse1HooksInterface()->getDefaultGenerator( + device.index()); + } else { + AT_ERROR(c10::DeviceTypeName(device_type), " device type not enabled."); + } + } + const AcceleratorHooksInterface& getAcceleratorHooksInterface( + c10::optional opt_device_type = c10::nullopt) { + c10::DeviceType device_type = opt_device_type.has_value() + ? opt_device_type.value() + : at::getAccelerator(true).value(); + if (device_type == at::kCUDA) { + return at::detail::getCUDAHooks(); + } else if (device_type == at::kMPS) { + return at::detail::getMPSHooks(); + } else if (device_type == at::kPrivateUse1) { + return at::detail::getPrivateUse1Hooks(); + } else { + AT_ERROR( + c10::DeviceTypeName(device_type), " device type not an accelerator."); + } + } + Device getDeviceFromPtr(void* data, c10::DeviceType device_type) { + initCUDAIfNeeded(device_type); + initHIPIfNeeded(device_type); + initXPUIfNeeded(device_type); + if (device_type == at::kCPU) { + return c10::DeviceType::CPU; + } else if (device_type == at::kCUDA) { + return at::detail::getCUDAHooks().getDeviceFromPtr(data); + } else if (device_type == at::kXPU) { + return at::detail::getXPUHooks().getDeviceFromPtr(data); + } else if (device_type == at::kPrivateUse1) { + return at::GetPrivateUse1HooksInterface()->getDeviceFromPtr(data); + } else { + AT_ERROR(c10::DeviceTypeName(device_type), " device type not enabled."); + } + } + static bool isPinnedPtr(const void* data) { + return detail::getCUDAHooks().isPinnedPtr(data); + } + static bool hasOpenMP(); + static bool hasMKL(); + static bool hasLAPACK(); + static bool hasMKLDNN(); + static bool hasMAGMA() { + return detail::getCUDAHooks().hasMAGMA(); + } + static bool hasCUDA() { + return detail::getCUDAHooks().hasCUDA(); + } + static bool hasMTIA() { + return detail::getMTIAHooks().hasMTIA(); + } + static bool hasCUDART() { + return detail::getCUDAHooks().hasCUDART(); + } + static long versionCUDART() { + return detail::getCUDAHooks().versionCUDART(); + } + static bool hasCuDNN() { + return detail::getCUDAHooks().hasCuDNN(); + } + static long versionCuDNN() { + return detail::getCUDAHooks().versionCuDNN(); + } + static bool hasCuSOLVER() { + return detail::getCUDAHooks().hasCuSOLVER(); + } + static bool hasHIP() { + return detail::getHIPHooks().hasHIP(); + } + static bool hasMPS() { + return detail::getMPSHooks().hasMPS(); + } + static bool hasIPU() { + return c10::impl::hasDeviceGuardImpl(c10::DeviceType::IPU); + } + static bool hasXLA() { + return c10::impl::hasDeviceGuardImpl(c10::DeviceType::XLA); + } + static bool hasXPU() { + return detail::getXPUHooks().hasXPU(); + } + static bool hasLazy() { + return c10::impl::hasDeviceGuardImpl(c10::DeviceType::Lazy); + } + static bool hasORT() { + return c10::impl::hasDeviceGuardImpl(c10::DeviceType::ORT); + } + // defined in header so that getNonVariableType has ability to inline + // call_once check. getNonVariableType is called fairly frequently + void lazyInitCUDA() { + c10::call_once(thc_init, [&] { detail::getCUDAHooks().initCUDA(); }); + } + void lazyInitHIP() { + c10::call_once(thh_init, [&] { detail::getHIPHooks().initHIP(); }); + } + void lazyInitXPU() { + c10::call_once(thx_init, [&] { detail::getXPUHooks().initXPU(); }); + } + void lazyInitPrivateUse1() { + c10::call_once(thp_init, [&] { + if (isPrivateUse1HooksRegistered()) { + at::GetPrivateUse1HooksInterface()->initPrivateUse1(); + } + }); + } + static const at::cuda::NVRTC& getNVRTC() { + return detail::getCUDAHooks().nvrtc(); + } + + static bool setFlushDenormal(bool on); + + // NB: This method is *purely* whether or not a user requested + // that CuDNN was enabled, it doesn't actually say anything about + // whether or not CuDNN is actually usable. Use cudnn_is_acceptable + // to test this instead + bool userEnabledCuDNN() const; + void setUserEnabledCuDNN(bool e); + bool userEnabledMkldnn() const; + void setUserEnabledMkldnn(bool e); + bool benchmarkCuDNN() const; + void setBenchmarkCuDNN(bool); + int benchmarkLimitCuDNN() const; + void setBenchmarkLimitCuDNN(int); + bool deterministicCuDNN() const; + void setDeterministicCuDNN(bool); + bool userEnabledNNPACK() const; + void setUserEnabledNNPACK(bool e); + + // Note [Disabling Fused SDP Kernels] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // Flash and Memory Efficient SDP kernels are enabled by default. + // However, they can be disabled by setting + // at::globalContext().setUserEnabledFlashSDP(false) flag. + // This is useful for debugging purposes. For example, if you want to + // compare the performance of the flash SDP kernels with the unfused + // kernel, you can disable the flash SDP kernels. By disabling + // the math SDP kernel, you can force your code to use flash kernels. + // The math SDP kernel can be disabled by setting + // at::globalContext().setUserEnabledMathSDP(false) flag. + void setSDPUseFlash(bool); + bool userEnabledFlashSDP() const; + + void setSDPUseMemEfficient(bool); + bool userEnabledMemEfficientSDP() const; + + void setSDPUseMath(bool); + bool userEnabledMathSDP() const; + + void setSDPUseCuDNN(bool); + bool userEnabledCuDNNSDP() const; + + at::LinalgBackend linalgPreferredBackend() const; + void setLinalgPreferredBackend(at::LinalgBackend); + + // Note [Enabling Deterministic Operations] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // Operations in PyTorch that normally act nondeterministically, but have an + // alternate deterministic implementation, should satisfy the following + // requirements: + // + // * Include this comment: "See Note [Enabling Deterministic Operations]" + // + // * Check the value of `at::globalContext().deterministicAlgorithms()` to + // toggle + // between nondeterministic and deterministic implementations. + // + // * Have an entry in the list of PyTorch operations that toggle between + // nondeterministic + // and deterministic implementations, in the docstring of + // `use_deterministic_algorithms()` in torch/__init__.py + // + // `example_func()` below shows an example of toggling between + // nondeterministic and deterministic implementations: + // + // void example_func() { + // // See Note [Enabling Deterministic Operations] + // if (at::globalContext().deterministicAlgorithms()) { + // example_func_deterministic(); + // } else { + // example_func_nondeterministic(); + // } + // } + + bool deterministicAlgorithms() const; + bool deterministicAlgorithmsWarnOnly() const; + void setDeterministicAlgorithms(bool, bool); + bool deterministicFillUninitializedMemory() const; + void setDeterministicFillUninitializedMemory(bool); + + // Note [Writing Nondeterministic Operations] + // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + // Operations in PyTorch that act nondeterministically and do not have an + // alternate deterministic implementation should satisfy the following + // requirements: + // + // * Include this comment: "See Note [Writing Nondeterministic Operations]" + // + // * Include a comment explaining why the operation is nondeterministic. + // + // * Throw an error when `Context::deterministicAlgorithms()` is true. Most + // of the time, this should be accomplished by calling + // `at::globalContext().alertNotDeterminstic()`. However, if the + // nondeterministic behavior is caused by the CuBLAS workspace + // configuration in CUDA >= 10.2, + // `at::globalContext().alertCuBLASConfigNotDeterministic()` should be + // called instead (in this case, a comment explaining why the operation is + // nondeterministic is not necessary). See below for details on these + // methods. + // + // * Have an entry in the list of nondeterministic PyTorch operations in the + // docstring of `use_deterministic_algorithms()` in torch/__init__.py + // + // * Have a test function in `test/test_torch.py` whose name begins with + // `test_nondeterministic_alert_`. Alternatively, if CuBLAS workspace + // configuration is the reason for nondeterminism, the operation should be + // included in the `test_cublas_config_nondeterministic_alert` test. Any new + // tests should ideally follow a pattern similar to the existing ones. + // + // `example_func()` below shows an example of the comments and error-throwing + // code for a nondeterministic operation: + // + // void example_func() { + // // See Note [Writing Nondeterministic Operations] + // // Nondeterministic because + // at::globalContext().alertNondeterministic("example_func"); + // ... + // } + + // Throws an error if `Context::deterministicAlgorithms()` is true + static void alertNotDeterministic(c10::string_view const& caller); + + // Throws an error if `Context::deterministicAlgorithms()` is true, CUDA + // >= 10.2, and CUBLAS_WORKSPACE_CONFIG is not set to either ":16:8" or + // ":4096:8". For more details: + // https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility + void alertCuBLASConfigNotDeterministic() const; + + void setFloat32MatmulPrecision(const std::string& s); + bool allowTF32CuDNN() const; + void setAllowTF32CuDNN(bool); + bool allowTF32CuBLAS() const; + void setAllowTF32CuBLAS(bool); + Float32MatmulPrecision float32MatmulPrecision() const; + void setFloat32MatmulPrecision(Float32MatmulPrecision p); + bool allowFP16ReductionCuBLAS() const; + void setAllowFP16ReductionCuBLAS(bool); + bool allowBF16ReductionCuBLAS() const; + void setAllowBF16ReductionCuBLAS(bool); + at::QEngine qEngine() const; + void setQEngine(at::QEngine e); + static const std::vector& supportedQEngines(); + static bool isXNNPACKAvailable(); + void setCheckSparseTensorInvariants(bool e); + bool checkSparseTensorInvariants() const; + // This method is used to release the original weight after pre-packing. + // It should be called once before loading/running the model. + // NB: By default it is set to true for mobile builds. + void setReleaseWeightsWhenPrepacking(bool e); + bool releaseWeightsWhenPrepacking() const; + + void setDisplayVmapFallbackWarnings(bool enabled); + bool areVmapFallbackWarningsEnabled() const; + + void setDefaultMobileCPUAllocator(); + void unsetDefaultMobileCPUAllocator(); + bool allowFP16ReductionCPU() const; + void setAllowFP16ReductionCPU(bool); + + private: + void initCUDAIfNeeded(c10::DeviceType p) { + if (p == c10::DeviceType::CUDA) { + lazyInitCUDA(); + } + } + void initHIPIfNeeded(c10::DeviceType p) { + if (p == c10::DeviceType::HIP) { + lazyInitHIP(); + } + } + void initXPUIfNeeded(c10::DeviceType p) { + if (p == c10::DeviceType::XPU) { + lazyInitXPU(); + } + } + static bool checkCuBLASConfigDeterministic(); + c10::once_flag thc_init; + c10::once_flag thh_init; + c10::once_flag thx_init; + c10::once_flag thp_init; + bool enabled_cudnn = true; + bool deterministic_cudnn = false; + bool _deterministic_algorithms = false; + bool _deterministic_algorithms_warn_only = false; + bool _deterministic_fill_uninitialized_memory = true; + bool enabled_flashSDP = true; + bool enabled_mem_efficientSDP = true; + bool enabled_mathSDP = true; + bool enabled_cudnnSDP = false; +#ifdef USE_ROCM + bool benchmark_cudnn = true; +#else + bool benchmark_cudnn = false; +#endif + Float32MatmulPrecision float32_matmul_precision = + c10::utils::check_env("TORCH_ALLOW_TF32_CUBLAS_OVERRIDE") == true + ? at::Float32MatmulPrecision::HIGH + : at::Float32MatmulPrecision::HIGHEST; + int benchmark_limit_cudnn = 10; + bool allow_tf32_cudnn = true; + bool allow_fp16_reduction_cublas = true; + bool allow_bf16_reduction_cublas = true; + bool enabled_mkldnn = true; + bool enabled_nnpack = true; + at::LinalgBackend linalg_preferred_backend = + c10::utils::check_env("TORCH_LINALG_PREFER_CUSOLVER") == true + ? at::LinalgBackend::Cusolver + : at::LinalgBackend::Default; +#ifdef C10_MOBILE + bool release_original_weights = true; +#else + bool release_original_weights = false; +#endif + bool display_vmap_fallback_warnings_ = false; + c10::optional quantized_engine = c10::nullopt; + bool enable_sparse_tensor_invariant_checks = false; + bool allow_fp16_reduction_cpu = false; + + Allocator* prev_allocator_ptr_{nullptr}; +}; + +TORCH_API Context& globalContext(); + +static inline void init() { + globalContext(); +} + +TORCH_API Allocator* getCPUAllocator(); + +static inline DeprecatedTypeProperties& getDeprecatedTypeProperties( + Backend p, + ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + p, s); +} + +static inline DeprecatedTypeProperties& CPU(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::CPU, s); +} + +static inline DeprecatedTypeProperties& CUDA(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::CUDA, s); +} + +static inline DeprecatedTypeProperties& HIP(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::HIP, s); +} + +static inline DeprecatedTypeProperties& MPS(ScalarType s) { + return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties( + Backend::MPS, s); +} + +static inline bool hasCUDA() { + return globalContext().hasCUDA(); +} + +static inline bool hasMTIA() { + return globalContext().hasMTIA(); +} + +static inline bool hasHIP() { + return globalContext().hasHIP(); +} + +static inline bool hasIPU() { + return globalContext().hasIPU(); +} + +static inline bool hasXLA() { + return globalContext().hasXLA(); +} + +static inline bool hasMPS() { + return globalContext().hasMPS(); +} + +static inline bool hasORT() { + return globalContext().hasORT(); +} + +static inline bool hasXPU() { + return globalContext().hasXPU(); +} + +// Despite its name, this function returns the number of *CUDA* GPUs. +static inline size_t getNumGPUs() { + // WARNING: DO NOT ADD LOGIC TO HANDLE OTHER DEVICE TYPES TO THIS + // FUNCTION. If you are interested in interrogating the number of + // devices for a specific device type, add that function to the + // relevant library (e.g., similar to at::cuda::device_count()) + if (hasCUDA() && hasHIP()) { + throw std::runtime_error( + "Enabling both CUDA and HIP in ATen is not supported, as HIP masquerades " + "to be CUDA (e.g., when you say CUDA, on a HIP build of ATen, this actually " + "means HIP. Rebuild PyTorch with one or the other disabled."); + } else if (hasCUDA()) { + return detail::getCUDAHooks().getNumGPUs(); + } else if (hasHIP()) { + return detail::getHIPHooks().getNumGPUs(); + } else { + return 0; + } +} + +static inline bool hasOpenMP() { + return globalContext().hasOpenMP(); +} + +static inline bool hasMKL() { + return globalContext().hasMKL(); +} + +static inline bool hasLAPACK() { + return globalContext().hasLAPACK(); +} + +static inline bool hasMAGMA() { + return globalContext().hasMAGMA(); +} + +static inline bool hasMKLDNN() { + return globalContext().hasMKLDNN(); +} + +static inline void manual_seed(uint64_t seed) { + auto gen = globalContext().defaultGenerator(c10::DeviceType::CPU); + { + // See Note [Acquire lock when using random generators] + std::lock_guard lock(gen.mutex()); + gen.set_current_seed(seed); + } + // NB: Sometimes we build with CUDA, but we don't have any GPUs + // available. In that case, we must not seed CUDA; it will fail! + const auto cuda_num_gpus = detail::getCUDAHooks().getNumGPUs(); + if (hasCUDA() && cuda_num_gpus > 0) { + for (const auto i : c10::irange(cuda_num_gpus)) { + auto cuda_gen = globalContext().defaultGenerator( + Device(at::kCUDA, static_cast(i))); + { + // See Note [Acquire lock when using random generators] + std::lock_guard lock(cuda_gen.mutex()); + cuda_gen.set_current_seed(seed); + } + } + } + + const auto xpu_num_gpus = detail::getXPUHooks().getNumGPUs(); + if (hasXPU() && xpu_num_gpus) { + for (const auto i : c10::irange(xpu_num_gpus)) { + auto xpu_gen = globalContext().defaultGenerator( + Device(at::kXPU, static_cast(i))); + { + // See Note [Acquire lock when using random generators] + std::lock_guard lock(xpu_gen.mutex()); + xpu_gen.set_current_seed(seed); + } + } + } + + if (hasMPS()) { + auto mps_gen = globalContext().defaultGenerator(c10::DeviceType::MPS); + // See Note [Acquire lock when using random generators] + std::lock_guard lock(mps_gen.mutex()); + mps_gen.set_current_seed(seed); + } +} + +// When the global flag `allow_tf32` is set to true, cuBLAS handles are +// automatically configured to use math mode CUBLAS_TF32_TENSOR_OP_MATH. +// For some operators, such as addmv, TF32 offers no performance improvement +// but causes precision loss. To help this case, this class implements +// a RAII guard that can be used to quickly disable TF32 within its scope. +// +// Usage: +// NoTF32Guard disable_tf32; +struct TORCH_API NoTF32Guard { + NoTF32Guard(); + ~NoTF32Guard(); + static bool should_disable_tf32(); + + private: + bool changed = false; +}; + +struct TORCH_API ROCmBackwardPassGuard { + ROCmBackwardPassGuard(); + ~ROCmBackwardPassGuard(); + static bool is_backward_pass(); +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Device.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Device.h new file mode 100644 index 0000000000000000000000000000000000000000..6c515580363c9e9aab3ee322678fd0cb0283aec8 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Device.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..35ba9f5284376f0d873b33b3f75f7d0608b0dae7 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch.h @@ -0,0 +1,808 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#ifdef __CUDACC__ +#include // For CUDA_VERSION +#endif + +#ifdef TEMPLATE_SELECTIVE_BUILD +#include +#else +namespace at { +/** + * The method should_include_kernel_dtype() returns true/false + * based on whether the switching code for a specific dtype should be + * included based on build time constants generated from tracing model + * execution. This method will be implmeneted via code-generation and + * included in this file when code-gen is ready. + */ +inline constexpr bool should_include_kernel_dtype( + const char* /*kernel_tag_str*/, + at::ScalarType /*scalar_type*/ +) { + return true; +} +} // namespace at +#endif + +/** + * In the Facebook internal build (using BUCK), this macro is enabled by + * passing in -c pt.enable_record_kernel_dtype=1 when building the tracer + * binary. + */ +#if defined ENABLE_RECORD_KERNEL_FUNCTION_DTYPE +namespace at { +namespace detail { +TORCH_API void record_kernel_function_dtype(std::string name); +} +} // namespace at + +#define RECORD_KERNEL_FUNCTION_DTYPE(NAME, enum_type) \ + at::detail::record_kernel_function_dtype( \ + std::string(NAME) + "$" + toString(enum_type)); +#else +#define RECORD_KERNEL_FUNCTION_DTYPE(NAME, enum_type) +#endif + +#define AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type) \ + do { \ + if constexpr (!at::should_include_kernel_dtype( \ + at_dispatch_name, enum_type)) { \ + AT_ERROR( \ + "dtype '", \ + toString(enum_type), \ + "' not selected for kernel tag ", \ + at_dispatch_name); \ + } \ + } while (0) + +#define AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...) \ + case enum_type: { \ + AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type); \ + using HINT C10_UNUSED = c10::impl::ScalarTypeToCPPTypeT; \ + return __VA_ARGS__(); \ + } + +#define AT_DISPATCH_CASE(enum_type, ...) \ + AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__) + +#define AT_DISPATCH_CASE_QINT(enum_type, scalar_type, ...) \ + case enum_type: { \ + AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type); \ + using scalar_t = scalar_type; \ + using underlying_t C10_UNUSED = typename scalar_t::underlying; \ + const auto& SCALAR_TYPE C10_UNUSED = enum_type; \ + const auto& UNDERLYING_TYPE C10_UNUSED = toUnderlying(enum_type); \ + return __VA_ARGS__(); \ + } + +#define AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + enum_type, scalar_type, bitwidth, qmin, qmax, ...) \ + case enum_type: { \ + AT_PRIVATE_CHECK_SELECTIVE_BUILD(enum_type); \ + using scalar_t = scalar_type; \ + using underlying_t C10_UNUSED = typename scalar_t::underlying; \ + const auto& SCALAR_TYPE C10_UNUSED = enum_type; \ + const auto& UNDERLYING_TYPE C10_UNUSED = toUnderlying(enum_type); \ + C10_UNUSED int bit_width = bitwidth; \ + C10_UNUSED int64_t quant_min = qmin; \ + C10_UNUSED int64_t quant_max = qmax; \ + return __VA_ARGS__(); \ + } + +namespace detail { + +inline at::ScalarType scalar_type(at::ScalarType s) { + return s; +} + +C10_DEPRECATED_MESSAGE( + "passing at::DeprecatedTypeProperties to an AT_DISPATCH macro is deprecated, " + "pass an at::ScalarType instead") +inline at::ScalarType scalar_type(const at::DeprecatedTypeProperties& t) { + return t.scalarType(); +} + +C10_DEPRECATED_MESSAGE( + "AT_DISPATCH_ALL_TYPES_AND_HALF is deprecated, " + "use AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, ...) instead") +inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF() {} + +C10_DEPRECATED_MESSAGE( + "AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX is deprecated, " + "use AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(at::ScalarType::Half, ...) " + "instead") +inline void deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF_AND_COMPLEX() {} + +} // namespace detail + +// The AT_DISPATCH_* family of macros provides the ability to +// conveniently generate specializations of a kernel over all of the +// dtypes we care about in PyTorch. We call it "dispatch" because +// we are "dispatching" to the correct, dtype-specific kernel. +// +// A standard usage looks like: +// +// AT_DISPATCH_ALL_TYPES(self.scalar_type(), "op_name", [&] { +// // Your code here, with 'scalar_t' now defined to +// // be the dtype in question +// }); +// +// There are many variations of this macro, so it's important to +// understand exactly /which/ dtypes you want to get instantiated, as +// well as what the "default" set is. +// +// The default set of dtypes that are instantiated (e.g., by +// AT_DISPATCH_ALL_TYPES) are floating point types (float, double), +// and integral types (int32_t, int64_t, int16_t, int8_t, uint8_t), +// but NOT booleans (bool), half-precision floats (Half) or +// complex number (c10::complex, c10::complex). +// This "cut" is somewhat historical (the default types are the +// ones that TH historically supported), but it also reflects the +// fact that the non-default types are "poorly" behaved (booleans +// are NOT integers mod 2, half precision operations ~essentially +// don't exist on CPU, complex numbers are an experimental application). +// +// Here are the questions you should generally ask to decide which +// dispatch you want: +// +// 1. Is this an integral or floating point specific operation? +// (If so, you'll want one of the FLOATING or INTEGRAL macros.) +// +// 2. Should half be supported? (If you're on CPU, the answer is almost +// definitely no. If you do want support, use one of the AND_HALF +// macros) +// +// Much rarer situations: +// +// 3. Should bool be supported? (You often have to write your kernel +// differently if arithmetic operations are involved.) If so, +// Use AT_DISPATCH_ALL_TYPES_AND along with ScalarType::Bool +// +// 4. Should complex be supported? The answer is almost always no, +// unless you are working on "generic" code that should work on +// all dtypes. +// +// Parameters: +// ----------- +// +// 1. The NAME argument is a "tag" that is used to trace and then +// conditionally compile fragments of the case statements such +// that the kernel functions are specialized only for the dtypes +// that are needed. The NAME parameter *must* be a build time +// const char* (can't be std::string, etc...) +// +// Please ensure that the NAME is unique for every implementation +// or you run the risk of over-including code for the kernel +// functions. There is no risk of missing out on any code, so +// it's mostly a risk of a Type-2 error, and not a Type-1 error. +// +// Switch-like syntax: +// ------------------- +// There is also a switch-case like syntax which is useful if a kernel +// needs to be specialized for particular scalar types +// +// AT_DISPATCH_SWITCH(self.scalar_type(), "op_name", +// AT_DISPATCH_CASE_INTEGRAL_TYPES([&] { +// op_integral(iter); +// }) +// AT_DISPATCH_CASE_FLOATING_TYPES([&] { +// op_floating(iter); +// }) +// AT_DISPATCH_CASE(kBool, [&] { +// op_bool(iter); +// }) +// ); +// +// For each AT_DISPATCH_FOO macro, there is a corresponding +// AT_DISPATCH_CASE_FOO macro which can be used inside of an +// AT_DISPATCH_SWITCH block. + +// NB: the the_type variable is not used, but we have kept it for +// backwards compatibility. It's probably not used by anyone though; +// but we're just being safe (and it doesn't hurt.) Note we must +// use it to shut up warnings about unused store. + +#define AT_DISPATCH_SWITCH(TYPE, NAME, ...) \ + [&] { \ + const auto& the_type = TYPE; \ + constexpr const char* at_dispatch_name = NAME; \ + /* don't use TYPE again in case it is an expensive or side-effect op */ \ + at::ScalarType _st = ::detail::scalar_type(the_type); \ + RECORD_KERNEL_FUNCTION_DTYPE(at_dispatch_name, _st); \ + switch (_st) { \ + __VA_ARGS__ \ + default: \ + AT_ERROR( \ + '"', \ + at_dispatch_name, \ + "\" not implemented for '", \ + toString(_st), \ + "'"); \ + } \ + }() + +#define AT_DISPATCH_CASE_FLOATING_TYPES(...) \ + AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_TYPES_AND_HALF(...) \ + AT_DISPATCH_CASE(at::ScalarType::Double, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES_AND_HALF(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_FLOATING_TYPES_AND_HALF(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_REDUCED_FLOATING_TYPES(...) \ + AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) + +#define AT_DISPATCH_REDUCED_FLOATING_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_REDUCED_FLOATING_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_TYPES_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_TYPES_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, ...) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES_AND2( \ + SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_TYPES_AND2( \ + SCALARTYPE1, SCALARTYPE2, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, ...) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, ...) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_COMPLEX_TYPES(...) \ + AT_DISPATCH_CASE(at::ScalarType::ComplexDouble, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::ComplexFloat, __VA_ARGS__) + +#define AT_DISPATCH_COMPLEX_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_COMPLEX_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_COMPLEX_TYPES_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_COMPLEX_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_COMPLEX_TYPES_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(...) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE_COMPLEX_TYPES(__VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND1(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND1( \ + SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND1( \ + SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND2( \ + SCALARTYPE1, SCALARTYPE2, ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2( \ + SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND2( \ + SCALARTYPE1, SCALARTYPE2, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND5( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, SCALARTYPE5, ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND5( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND5( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + ...) \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE6, __VA_ARGS__) + +#define AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_FLOATING_AND_COMPLEX_TYPES_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_INTEGRAL_TYPES(...) \ + AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Long, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Short, __VA_ARGS__) + +#define AT_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_INTEGRAL_TYPES_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_INTEGRAL_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_INTEGRAL_TYPES_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES(...) \ + AT_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_QINT_TYPES(...) \ + AT_DISPATCH_CASE_QINT(at::kQInt8, at::qint8, __VA_ARGS__) \ + AT_DISPATCH_CASE_QINT(at::kQUInt8, at::quint8, __VA_ARGS__) \ + AT_DISPATCH_CASE_QINT(at::kQInt32, at::qint32, __VA_ARGS__) + +#define AT_DISPATCH_QINT_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_QINT_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_QINT_TYPES_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_QINT_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_QINT_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_QINT_TYPES_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_QINT_BYTE_TYPES(...) \ + AT_DISPATCH_CASE_QINT(at::kQInt8, at::qint8, __VA_ARGS__) \ + AT_DISPATCH_CASE_QINT(at::kQUInt8, at::quint8, __VA_ARGS__) + +#define AT_DISPATCH_QINT_BYTE_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_QINT_BYTE_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_QINT_AND_SUB_BYTE_TYPES(...) \ + AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + at::kQInt8, at::qint8, CHAR_BIT, SCHAR_MIN, SCHAR_MAX, __VA_ARGS__) \ + AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + at::kQUInt8, at::quint8, CHAR_BIT, 0, UCHAR_MAX, __VA_ARGS__) \ + AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + at::kQInt32, \ + at::qint32, \ + CHAR_BIT * sizeof(int), \ + INT_MIN, \ + INT_MAX, \ + __VA_ARGS__) \ + AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + at::kQUInt4x2, at::quint4x2, 4, 0, 15, __VA_ARGS__) \ + AT_QINT_SUB_BYTE_PRIVATE_CASE_TYPE( \ + at::kQUInt2x4, at::quint2x4, 2, 0, 3, __VA_ARGS__) + +#define AT_DISPATCH_QINT_AND_SUB_BYTE_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_QINT_AND_SUB_BYTE_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(...) \ + AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE_COMPLEX_TYPES(__VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, NAME, AT_DISPATCH_CASE_ALL_TYPES_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND(SCALARTYPE, ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(SCALARTYPE, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND(SCALARTYPE, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, ...) \ + AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND2( \ + SCALARTYPE1, SCALARTYPE2, ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2( \ + SCALARTYPE1, SCALARTYPE2, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND2( \ + SCALARTYPE1, SCALARTYPE2, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, ...) \ + AT_DISPATCH_CASE_ALL_TYPES(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND3( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND4( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND5( \ + SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, SCALARTYPE4, SCALARTYPE5, ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND5( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND5( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE6, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND6( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND7( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE6, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE7, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND7( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND7( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND8( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + SCALARTYPE8, \ + ...) \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX(__VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE1, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE2, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE3, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE4, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE5, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE6, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE7, __VA_ARGS__) \ + AT_DISPATCH_CASE(SCALARTYPE8, __VA_ARGS__) + +#define AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND8( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + SCALARTYPE8, \ + TYPE, \ + NAME, \ + ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND_COMPLEX_AND8( \ + SCALARTYPE1, \ + SCALARTYPE2, \ + SCALARTYPE3, \ + SCALARTYPE4, \ + SCALARTYPE5, \ + SCALARTYPE6, \ + SCALARTYPE7, \ + SCALARTYPE8, \ + __VA_ARGS__)) + +#define AT_DISPATCH_CASE_BIT_TYPES(...) \ + AT_DISPATCH_CASE(at::ScalarType::Bits1x8, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Bits2x4, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Bits4x2, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Bits8, __VA_ARGS__) \ + AT_DISPATCH_CASE(at::ScalarType::Bits16, __VA_ARGS__) + +#define AT_DISPATCH_BIT_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_DISPATCH_CASE_BIT_TYPES(__VA_ARGS__)) + +#define AT_DISPATCH_INDEX_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Int, index_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Long, index_t, __VA_ARGS__)) + +// ---------------------------------------------------------------------------- +// DEPRECATED MACROS, DON'T USE THESE +// ---------------------------------------------------------------------------- + +#define AT_DISPATCH_ALL_TYPES_AND_HALF(TYPE, NAME, ...) \ + detail::deprecated_AT_DISPATCH_ALL_TYPES_AND_HALF(); \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_DISPATCH_CASE_ALL_TYPES_AND(at::ScalarType::Half, __VA_ARGS__)) diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch_v2.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch_v2.h new file mode 100644 index 0000000000000000000000000000000000000000..e0764834c02fdea27dd56cd08a283f772bcd7938 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Dispatch_v2.h @@ -0,0 +1,186 @@ +#include + +// This is a new implementation of the AT_DISPATCH macro family from +// ATen/Dispatch.h +// +// The intended usage is: +// +// ScalarType scalar_type; +// +// AT_DISPATCH_V2( +// scalar_type, +// "debug string", +// AT_WRAP([&] { +// ... code to specialize with scalar_t ... +// }), +// kHalf, +// AT_EXPAND(AT_ALL_TYPES), +// ... as many types arguments as needed ... +// ) +// +// For example, given an old style: +// +// AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2( +// kComplexHalf, +// kHalf, +// self.scalar_type(), +// "_local_scalar_dense_cpu", +// [&] { +// scalar_t value = *self.data_ptr(); +// r = Scalar(value); +// } +// ) +// +// You now write: +// +// AT_DISPATCH_V2( +// self.scalar_type(), +// "_local_scalar_dense_cpu", +// AT_WRAP([&] { +// scalar_t value = *self.data_ptr(); +// r = Scalar(value); +// }), +// AT_EXPAND(AT_ALL_TYPES), +// AT_EXPAND(AT_COMPLEX_TYPES), +// kComplexHalf, +// kHalf, +// ) +// +// Notably, it sports the following improvements: +// +// - It is not necessary to specify the arity (e.g., +// AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND{2,3,4,...}) +// when using the macro +// +// - It is not necessary to specify each dtype individually; if +// there is a set of related dtypes and you want to dispatch +// over all of them, you can simply say, e.g., AT_EXPAND(AT_INTEGRAL_TYPES) +// in your argument list. +// +// However, you must remember to wrap the payload body in AT_WRAP, or commas +// inside your lambda will be improperly handled. Furthermore, if you more +// entries to ScalarType than can be supported by this macro, it will fail +// with an obscure error (due to attempting to concatenate AT_AP with +// something that is not a number). +// +// The implementation strategy is to use the count arguments trick +// (e.g., as described in https://stackoverflow.com/a/2124385/23845) +// to discover how many dtypes have been passed, and then dispatch to a +// hand-written macro for each arity that applies as many DISPATCH_CASE as +// necessary. The hand-written macros can be regenerated for other arities +// with the script below. +// +// There is some delicacy in the implementation in controlling when +// macro expansion occurs, mediated with AT_EXPAND and AT_GUARD. I mostly +// relied on GPT4 to help me get it right. + +// Public API macros + +// See documentation above +#define AT_DISPATCH_V2(TYPE, NAME, BODY, ...) \ + AT_DISPATCH_SWITCH(TYPE, NAME, AT_AP_VAR(AT_WRAP(BODY), TYPE, __VA_ARGS__)) + +// This macro lets you pass an arbitrary expression that may contain internal +// commas to another macro without having the commas causing the expression +// to be interpreted as being multiple arguments +#define AT_WRAP(...) __VA_ARGS__ + +#define AT_FLOAT8_TYPES \ + c10::kFloat8_e5m2, c10::kFloat8_e5m2fnuz, c10::kFloat8_e4m3fn, \ + c10::kFloat8_e4m3fnuz + +#define AT_INTEGRAL_TYPES \ + c10::kByte, c10::kChar, c10::kInt, c10::kLong, c10::kShort +#define AT_FLOATING_TYPES c10::kDouble, c10::kFloat +#define AT_BAREBONES_UNSIGNED_TYPES c10::kUInt16, c10::kUInt32, c10::kUInt64 +#define AT_INTEGRAL_TYPES_V2 \ + AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) +#define AT_COMPLEX_TYPES c10::kComplexDouble, c10::kComplexFloat +#define AT_QINT_TYPES c10::kQInt8, c10::kQUInt8, c10::kQInt32 +// NB: not *actually* all types +#define AT_ALL_TYPES AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES) +#define AT_ALL_TYPES_AND_COMPLEX \ + AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES) + +// Helper macros + +#define AT_AP_VAR(N, T, ...) \ + AT_EXPAND(AT_CONCAT(AT_AP, AT_NUM_ARGS(__VA_ARGS__))(AT_WRAP(N), __VA_ARGS__)) +#define AT_CONCAT(a, b) AT_CONCAT_AUX(a, b) +#define AT_CONCAT_AUX(a, b) a##b +#define AT_EXPAND(X) X + +// Ensure we never have too many scalar types for the expansion here to +// support. To bump this, you must regenerate the macros below. +static_assert(static_cast(c10::ScalarType::NumOptions) < 45); + +// Python code to regenerate generate code below: +#if 0 + +num_args = 45 + +nums = ', '.join(str(i) for i in reversed(range(num_args+1))) +args = ', '.join(f'_{i}' for i in range(1, num_args+1)) + +print(f'#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, {nums}))') +print(f'#define AT_NUM_ARGS_AUX({args}, N, ...) N') + +for i in range(1, num_args+1): + args = ', '.join(f'_{i}' for i in range(1, i+1)) + cases = ' '.join([f'AT_DISPATCH_CASE(_{j}, N)' for j in range(1, i+1)]) + print(f'#define AT_AP{i}(N, {args}) {cases}') + +#endif + +// Begin generated code +// clang-format off + +#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0)) +#define AT_NUM_ARGS_AUX(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, N, ...) N +#define AT_AP1(N, _1) AT_DISPATCH_CASE(_1, N) +#define AT_AP2(N, _1, _2) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) +#define AT_AP3(N, _1, _2, _3) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) +#define AT_AP4(N, _1, _2, _3, _4) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) +#define AT_AP5(N, _1, _2, _3, _4, _5) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) +#define AT_AP6(N, _1, _2, _3, _4, _5, _6) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) +#define AT_AP7(N, _1, _2, _3, _4, _5, _6, _7) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) +#define AT_AP8(N, _1, _2, _3, _4, _5, _6, _7, _8) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) +#define AT_AP9(N, _1, _2, _3, _4, _5, _6, _7, _8, _9) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) +#define AT_AP10(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) +#define AT_AP11(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) +#define AT_AP12(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) +#define AT_AP13(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) +#define AT_AP14(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) +#define AT_AP15(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) +#define AT_AP16(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) +#define AT_AP17(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) +#define AT_AP18(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) +#define AT_AP19(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) +#define AT_AP20(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) +#define AT_AP21(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) +#define AT_AP22(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) +#define AT_AP23(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) +#define AT_AP24(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) +#define AT_AP25(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) +#define AT_AP26(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) +#define AT_AP27(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) +#define AT_AP28(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) +#define AT_AP29(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) +#define AT_AP30(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) +#define AT_AP31(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) +#define AT_AP32(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) +#define AT_AP33(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) +#define AT_AP34(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) +#define AT_AP35(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) +#define AT_AP36(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) +#define AT_AP37(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) +#define AT_AP38(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) +#define AT_AP39(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) +#define AT_AP40(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) +#define AT_AP41(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) AT_DISPATCH_CASE(_41, N) +#define AT_AP42(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) AT_DISPATCH_CASE(_41, N) AT_DISPATCH_CASE(_42, N) +#define AT_AP43(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) AT_DISPATCH_CASE(_41, N) AT_DISPATCH_CASE(_42, N) AT_DISPATCH_CASE(_43, N) +#define AT_AP44(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) AT_DISPATCH_CASE(_41, N) AT_DISPATCH_CASE(_42, N) AT_DISPATCH_CASE(_43, N) AT_DISPATCH_CASE(_44, N) +#define AT_AP45(N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45) AT_DISPATCH_CASE(_1, N) AT_DISPATCH_CASE(_2, N) AT_DISPATCH_CASE(_3, N) AT_DISPATCH_CASE(_4, N) AT_DISPATCH_CASE(_5, N) AT_DISPATCH_CASE(_6, N) AT_DISPATCH_CASE(_7, N) AT_DISPATCH_CASE(_8, N) AT_DISPATCH_CASE(_9, N) AT_DISPATCH_CASE(_10, N) AT_DISPATCH_CASE(_11, N) AT_DISPATCH_CASE(_12, N) AT_DISPATCH_CASE(_13, N) AT_DISPATCH_CASE(_14, N) AT_DISPATCH_CASE(_15, N) AT_DISPATCH_CASE(_16, N) AT_DISPATCH_CASE(_17, N) AT_DISPATCH_CASE(_18, N) AT_DISPATCH_CASE(_19, N) AT_DISPATCH_CASE(_20, N) AT_DISPATCH_CASE(_21, N) AT_DISPATCH_CASE(_22, N) AT_DISPATCH_CASE(_23, N) AT_DISPATCH_CASE(_24, N) AT_DISPATCH_CASE(_25, N) AT_DISPATCH_CASE(_26, N) AT_DISPATCH_CASE(_27, N) AT_DISPATCH_CASE(_28, N) AT_DISPATCH_CASE(_29, N) AT_DISPATCH_CASE(_30, N) AT_DISPATCH_CASE(_31, N) AT_DISPATCH_CASE(_32, N) AT_DISPATCH_CASE(_33, N) AT_DISPATCH_CASE(_34, N) AT_DISPATCH_CASE(_35, N) AT_DISPATCH_CASE(_36, N) AT_DISPATCH_CASE(_37, N) AT_DISPATCH_CASE(_38, N) AT_DISPATCH_CASE(_39, N) AT_DISPATCH_CASE(_40, N) AT_DISPATCH_CASE(_41, N) AT_DISPATCH_CASE(_42, N) AT_DISPATCH_CASE(_43, N) AT_DISPATCH_CASE(_44, N) AT_DISPATCH_CASE(_45, N) +// End generated code +// clang-format on diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ExpandBase.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ExpandBase.h new file mode 100644 index 0000000000000000000000000000000000000000..8db6be6a643c8cb60cab8487478f9a2f0c817d8b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ExpandBase.h @@ -0,0 +1,30 @@ +#include + +// Broadcasting utilities for working with TensorBase +namespace at { +namespace internal { +TORCH_API TensorBase expand_slow_path(const TensorBase& self, IntArrayRef size); +} // namespace internal + +inline c10::MaybeOwned expand_size( + const TensorBase& self, + IntArrayRef size) { + if (size.equals(self.sizes())) { + return c10::MaybeOwned::borrowed(self); + } + return c10::MaybeOwned::owned( + at::internal::expand_slow_path(self, size)); +} +c10::MaybeOwned expand_size(TensorBase&& self, IntArrayRef size) = + delete; + +inline c10::MaybeOwned expand_inplace( + const TensorBase& tensor, + const TensorBase& to_expand) { + return expand_size(to_expand, tensor.sizes()); +} +c10::MaybeOwned expand_inplace( + const TensorBase& tensor, + TensorBase&& to_expand) = delete; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/FuncTorchTLS.h b/venv/lib/python3.10/site-packages/torch/include/ATen/FuncTorchTLS.h new file mode 100644 index 0000000000000000000000000000000000000000..b648ef616284f971ab51e2bf9d8e7dd557237bb7 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/FuncTorchTLS.h @@ -0,0 +1,46 @@ +#pragma once + +#include +#include + +namespace at::functorch { + +// NOTE [functorch TLS in pytorch/pytorch] +// +// functorch lives out-of-tree. However, it has some TLS that needs to be +// propagated. The solution for that is we store a pointer to the TLS +// inside pytorch/pytorch and extend FuncTorchTLSBase inside functorch to +// include whatever functorch needs. +// +// We need to store a pointer due to the indirection: +// inside functorch, we will create a subclass of FunctorchTLSBase called +// FuncTorchTLSImpl that actually contains metadata, like the DynamicLayerStack. +// FuncTorchTLSBase doesn't have any metadata because it hasn't been defined +// yet. +// +// Here in pytorch/pytorch, we will pass around FuncTorchTLSBase*, but inside +// functorch, we will assign a FuncTorchTLSImpl* to the FunctorchTLSBase*. +// We can't directly pass around FunctorchTLSBase (without a pointer) because +// FuncTorchTLSImpl does not fit inside a FuncTorchTLSBase by virtue of having +// more elements. +struct TORCH_API FuncTorchTLSBase { + virtual ~FuncTorchTLSBase() = default; + virtual std::unique_ptr deepcopy() const = 0; + + virtual int64_t checkSupportsSingleLevelAutogradFunction() const = 0; + virtual void checkSupportsCppAutogradFunction() const = 0; + virtual void checkSupportsInplaceRequiresGrad() const = 0; + virtual void checkSupportsRetainGrad() const = 0; +}; + +// returns deepcopy of the functorch tls +TORCH_API std::unique_ptr getCopyOfFuncTorchTLS(); + +// sets the functorch tls. always does a deep copy. +TORCH_API void setFuncTorchTLS( + const std::shared_ptr& state); + +// get a mutable reference to the functorch tls +TORCH_API std::unique_ptr& functorchTLSAccessor(); + +} // namespace at::functorch diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/FunctionalStorageImpl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/FunctionalStorageImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..c003fc22babdc46161cbd78dd9b293777bdb549c --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/FunctionalStorageImpl.h @@ -0,0 +1,126 @@ +#pragma once + +#include + +namespace at::functionalization { + +// See Note [Functionalization Pass In Core] + +// ViewMeta is a class used by the functionalization pass to navigate between +// a base tensor and a view tensor. +// For example, if I call `b = a.view1(...)` +// the functionalization pass will generate and store a ViewMeta on b that looks +// like: +// +// ViewMeta( +// [](const Tensor& base, int64_t mutated_view_idx) { +// return base.view1(...); +// }, +// [](const at::Tensor& base, const at::Tensor& mutated_view, +// int64_t mutated_view_idx) -> at::Tensor { +// return at::functionalization::impl::view1_inverse(base, mutated_view, +// ...); +// } +// +// The forward_fn lambda describes how to replay view1 on a tensor. +// +// The reverse_fn lambda describes how, given a tensor that is already a view, +// how to get the corresponding base tensor. See Note [Functionalization Pass: +// View Inverses] for details. +struct ViewMeta { + ViewMeta( + std::function forward, + std::function reverse, + bool is_multi_output = false, + int64_t out_idx = 0) + : forward_fn(std::move(forward)), + reverse_fn(std::move(reverse)), + out_index(out_idx), + is_multi_output(is_multi_output) {} + + std::function forward_fn; + std::function reverse_fn; + // See Note [out_idx in ViewMeta] + int64_t out_index; + + // Tells us if this is a multi-output view + bool is_multi_output; + + // Returns a copy of the current ViewMeta, if out_idx matches the current + // out_index. Otherwise, returns a new ViewMeta with the same forward/reverse + // functions, but a new out index. + ViewMeta to_out_idx(int64_t out_idx); +}; + +// FunctionalStorageImpl is a subclass of StorageImpl used by the +// functionalization pass. It has no underlying data (similar to meta storage). +// It also knows how to reflect mutations to tensors in the absence of a valid +// data pointer. +// +// A storage represents the state shared by (potentially multiple) views of the +// same tensor. For example, in the following code: +// +// b = a.view1(...) +// c = b.view2(...) +// b.add_(1) +// --> storage.add_update(b, {view1_meta}) +// +// The call to add_(1) will result in a call to alias.add_update(b, +// {view1_meta}), queueing up the mutation from b onto the alias. Later, suppose +// c is used in an expression (e.g. you try to print c, or pass it to an +// operator). Doing so will involve "syncing" c. First we apply any pending +// updates to the alias, and then we regenerate c by replaying its views off of +// the updated alias. E.g: +// +// print(str(c)) +// --> c.sync_() +// --> alias.apply_updates() // after this, the alias will be updated to +// reflect the mutation to b +struct TORCH_API FunctionalStorageImpl : public c10::StorageImpl { + public: + struct Update { + // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members) + const at::Tensor new_val; + // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members) + const std::vector view_metas; + }; + + explicit FunctionalStorageImpl(const Tensor& value); + + void add_update( + const Tensor& updated_val, + const std::vector& view_metas); + bool apply_updates(); + const Tensor& base() { + return base_; + } + size_t generation() const { + return generation_; + } + void freeze() { + frozen_ = true; + } + + ~FunctionalStorageImpl() override = default; + + private: + // NB: base_ should always point to a tensor BELOW the current + // functionalization layer. This is mainly to avoid reference cycles. e.g. + // given `b = a.view(...)` Both a.storage_ and b.storage_ are a + // FunctionStorageImpl containing an Walualias, with contains a Tensor + // `base_`. In this case (where a and b are FunctionalTensorWrapper's), base_ + // should point not to a, but to a's unwrapped value, a.value_` See Note + // [Functionalization: Walualias Removal] for a diagram that shows this + // visually. + at::Tensor base_; + std::vector updates_; + // generation_ gets incremented every time a mutation is queued onto the + // alias. It is used to determine if a given tensor is "up to date", or if it + // needs to be regenerated from the alias. + size_t generation_ = 0; + // If frozen, no more mutations are allowed on this storage. Once frozen, a + // storage cannot be unfrozen. + bool frozen_ = false; +}; + +} // namespace at::functionalization diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h new file mode 100644 index 0000000000000000000000000000000000000000..8327d00ece5c0f2b2c1e81f88e54fa0c76d9e97d --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Functions.h @@ -0,0 +1,1427 @@ +#pragma once + +// @generated by torchgen/gen.py from Functions.h + +#ifdef TORCH_ASSERT_NO_OPERATORS +#error This change adds a dependency on native_functions.yaml, \ + meaning the file will need to be re-compiled every time an operator \ + is changed or added. Consider if your change would be better placed in \ + another file, or if a more specific header might achieve the same goal. \ + See NOTE: [Tensor vs. TensorBase] +#endif + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from and \ + see NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +// NOTE: [TORCH_ASSERT_ONLY_METHOD_OPERATORS] +// +// In ATen, certain generated headers files include the definitions of +// every single operator in PyTorch. Unfortunately this means every +// time an operator signature is updated or changed in +// native_functions.yaml, you (and every other PyTorch developer) need +// to recompile every source file that includes any of these headers. +// +// To break up these header dependencies, and improve incremental +// build times for all PyTorch developers. These headers are split +// into per-operator headers in the `ATen/ops` folder. This limits +// incremental builds to only changes to methods of `Tensor`, or files +// that use the specific operator being changed. With `at::sum` as an +// example, you should include +// +// // instead of ATen/Functions.h +// // instead of ATen/NativeFunctions.h +// // instead of ATen/Operators.h +// // instead of ATen/CPUFunctions.h +// +// However, even if you're careful to use this in your own code. +// `Functions.h` might be included indirectly through another header +// without you realising. To avoid this, you can add +// +// #define TORCH_ASSERT_ONLY_METHOD_OPERATORS +// +// to the top of your source file. This way any time the non-specific +// headers are included, the compiler will error out. +// +// Also, be aware that `ops` are not available in all build +// configurations (namely fb-internal) so you must guard these +// includes with `#ifdef AT_PER_OPERATOR_HEADERS`. e.g. +// +// #ifndef AT_PER_OPERATOR_HEADERS +// #include +// #else +// #include +// #endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { + + + +// Special C++ only overloads for std()-like functions (See gh-40287) +// These are needed because int -> bool conversion takes precedence over int -> IntArrayRef +// So, for example std(0) would select the std(unbiased=False) overload +TORCH_API inline Tensor var(const Tensor& self, int dim) { + return at::var(self, IntArrayRef{dim}); +} +TORCH_API inline std::tuple var_mean(const Tensor& self, int dim) { + return at::var_mean(self, IntArrayRef{dim}); +} +TORCH_API inline Tensor std(const Tensor& self, int dim) { + return at::std(self, IntArrayRef{dim}); +} +TORCH_API inline std::tuple std_mean(const Tensor& self, int dim) { + return at::std_mean(self, IntArrayRef{dim}); +} + +inline int64_t numel(const Tensor& tensor) { + return tensor.numel(); +} + +inline int64_t size(const Tensor& tensor, int64_t dim) { + return tensor.size(dim); +} + +inline int64_t stride(const Tensor& tensor, int64_t dim) { + return tensor.stride(dim); +} + +inline bool is_complex(const Tensor& tensor) { + return tensor.is_complex(); +} + +inline bool is_floating_point(const Tensor& tensor) { + return tensor.is_floating_point(); +} + +inline bool is_signed(const Tensor& tensor) { + return tensor.is_signed(); +} + +inline bool is_inference(const Tensor& tensor) { + return tensor.is_inference(); +} + +inline bool _is_zerotensor(const Tensor& tensor) { + return tensor._is_zerotensor(); +} + +inline bool is_conj(const Tensor& tensor) { + return tensor.is_conj(); +} + +inline Tensor conj(const Tensor& tensor) { + return tensor.conj(); +} + +inline bool is_neg(const Tensor& tensor) { + return tensor.is_neg(); +} + +} diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Generator.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Generator.h new file mode 100644 index 0000000000000000000000000000000000000000..48c25e141dcb8c0264ca9435352889c7a250f74d --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Generator.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/InferSize.h b/venv/lib/python3.10/site-packages/torch/include/ATen/InferSize.h new file mode 100644 index 0000000000000000000000000000000000000000..111c7eb8f5fc7cd20a3eb812450324788608c011 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/InferSize.h @@ -0,0 +1,87 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace at { + +// Infers the size of a dim with size -1, if it exists. Also checks that new +// shape is compatible with the number of elements. +// +// templated to handle std::vector and DimVector use cases, see +// below +// +template +inline void infer_size_impl( + InputArrayRef shape, + NumelType numel, + ResultVec& res) { + NumelType newsize = 1; + // N.B. this is an index, not a sym dim! + auto infer_dim = c10::optional(); + for (int64_t dim = 0, ndim = shape.size(); dim != ndim; dim++) { + if (shape[dim] == -1) { + if (infer_dim) { + throw std::runtime_error("only one dimension can be inferred"); + } + infer_dim = dim; + } else if (shape[dim] >= 0) { + newsize *= shape[dim]; + } else { + AT_ERROR("invalid shape dimension ", shape[dim]); + } + } + + if (numel == newsize || (infer_dim && newsize > 0 && numel % newsize == 0)) { + if (infer_dim) { + // We have a degree of freedom here to select the dimension size; follow + // NumPy semantics and just bail. However, a nice error message is needed + // because users often use `view` as a way to flatten & unflatten + // dimensions and will otherwise be confused why + // empty_tensor.view( 0, 0) + // works yet + // empty_tensor.view(-1, 0) + // doesn't. + TORCH_CHECK( + newsize != 0, + "cannot reshape tensor of 0 elements into shape ", + shape, + " because the unspecified dimension size -1 can be any " + "value and is ambiguous"); + res[*infer_dim] = numel / newsize; + } + return; + } + + std::ostringstream ss; + ss << "shape '" << shape << "' is invalid for input of size " << numel; + throw std::runtime_error(ss.str()); +} + +inline std::vector infer_size(IntArrayRef shape, int64_t numel) { + auto res = shape.vec(); + infer_size_impl(shape, numel, res); + return res; +} + +inline at::DimVector infer_size_dv(IntArrayRef shape, int64_t numel) { + auto res = at::DimVector(shape); + infer_size_impl(shape, numel, res); + return res; +} + +inline at::SymDimVector infer_size_dv( + c10::SymIntArrayRef shape, + c10::SymInt numel) { + auto res = at::SymDimVector(shape); + infer_size_impl( + shape, std::move(numel), res); + return res; +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedFallback.h b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedFallback.h new file mode 100644 index 0000000000000000000000000000000000000000..beef24a6ed9c5b8373a0db5bcc16f268b8c18726 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedFallback.h @@ -0,0 +1,25 @@ +#pragma once +#include +#include +#include + +namespace at { + +// If an operator doesn't have a batching rule implemented then we fallback +// to this implementation. The fallback only works on out-of-place operators +// that return only tensors with new memory. (e.g., no in-place operators, no +// view operations). +// +// The fallback effectively takes all of the BatchedTensors in `stack`, slices +// them, and runs `op` on all of the corresponding slices to produce slices +// of the outputs. The output slices then get `torch.stack`ed to create the +// final returns. +// +// The performance of the fallback is not very good because it introduces an +// extra copy from stacking the sliced outputs. Because of this, we prefer to +// write batching rules for operators whenever possible. +void batchedTensorForLoopFallback( + const c10::OperatorHandle& op, + torch::jit::Stack* stack); + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedTensorImpl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedTensorImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..098fbf9d6292fdeb0fff5e3786b471b742540723 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyBatchedTensorImpl.h @@ -0,0 +1,160 @@ +#pragma once + +#include + +#include +#include +#include + +namespace at { + +// We assume this in a few other places in the codebase, +// but there isn't a centralized definition. +constexpr int64_t kVmapMaxTensorDims = 64; + +// The valid vmap levels range from [0, 64). This effectively means that we +// support a maximum of 64 nested vmaps. +constexpr int64_t kVmapNumLevels = 64; + +// Store this number of elements of BatchDims on the stack. Most people will +// probably use <= 5 nested vmaps, but adjust this number as necessary. +constexpr int64_t kBatchDimsStackSize = 5; + +// a BatchDim represents a "private" dimension on a Tensor created inside of +// vmap. It is a (level, dim) tuple, with the `dim` indicating which dimension +// is being vmap'ed over and the `level` being an identifier for which vmap +// said dimension was created inside. The `dim` corresponds to a "physical +// dim" - it is a dimension index on the underlying physical tensor that is +// being vmapped over. +struct BatchDim { + BatchDim(int64_t level, int64_t dim) : dim_(dim), level_(level) {} + int64_t dim() const { + return dim_; + } + int64_t level() const { + return level_; + } + + private: + int64_t dim_; + int64_t level_; +}; + +using BatchDims = SmallVector; +using BatchDimsRef = ArrayRef; + +// A BatchedTensorImpl holds an underlying Tensor and a list of BatchDim +// NB: We use the term "BatchedTensor" to mean a Tensor that is backed with a +// BatchedTensorImpl. +// +// The batch dimensions are treated as being "private"; they are not +// user-visible. For example, in the following Tensor, +// bt = BatchedTensorImpl(ones(2, 3, 5, 7), [(lvl=1, dim=0), (lvl=2, dim=1)]) +// dimensions 0 and 1 are batch dimensions. +// +// bt.sizes() returns (5, 7); bt.sum(0) performs a reduction over the (public) +// dim 0, which is equivalent to dim 3 in the underlying ones(2, 3, 5, 7) +// tensor. +struct TORCH_API BatchedTensorImpl : public c10::TensorImpl { + explicit BatchedTensorImpl(Tensor value, BatchDims bdims); + + // Returns a reference to BatchDims that represent which dimensions of this + // tensor are private. + BatchDimsRef bdims() const { + return bdims_; + } + + // BatchedTensorImpl wraps a Tensor + const Tensor& value() const { + return value_; + }; + + // Given a public dimension index, return the dimension index in the + // underlying value() tensor. For example, if we have + // bt = BatchedTensorImpl(ones(2, 3, 5, 7), [(lvl=1, dim=0), (lvl=2, + // dim=2)]) + // bt.actualDim(0) -> 1 + // bt.actualDim(1) -> 3 + // bt.actualDim(2) -> Error + int64_t actualDim(int64_t dim, bool wrap_dim = true) const; + + // We have to override this because we opted into CustomStrides + IntArrayRef strides_custom() const override; + // Override a bunch of methods inherited from TensorImpl to return error + // messages. + bool is_contiguous_custom(at::MemoryFormat memory_format) const override; + void set_size(int64_t dim, int64_t new_size) override; + void set_stride(int64_t dim, int64_t new_stride) override; + void set_storage_offset(int64_t storage_offset) override; +#ifdef DEBUG + bool has_storage() const override; +#endif + + private: + // see NOTE: [BatchedTensorImpl levels invariant] + void checkInvariants() const; + const char* tensorimpl_type_name() const override; + + Tensor value_; + + // Note: [BatchedTensorImpl levels invariant] + // There is an invariant that the BatchDims must be stored in increasing + // `level` order. That is, for i < j, bdims_[i].level must be less than + // bdims_[j].level. + BatchDims bdims_; +}; + +// NB: We use the term "BatchedTensor" to mean a Tensor that is backed with a +// BatchedTensorImpl. +inline bool isBatchedTensor(const Tensor& tensor) { + return tensor.unsafeGetTensorImpl()->key_set().has(DispatchKey::Batched); +} + +// It is unsafe to call this on a Tensor that is not backed by a +// BatchedTensorImpl. Please use `maybeGetBatchedImpl` whenever possible. +inline BatchedTensorImpl* unsafeGetBatchedImpl(const Tensor& tensor) { + return static_cast(tensor.unsafeGetTensorImpl()); +} + +inline BatchedTensorImpl* maybeGetBatchedImpl(const Tensor& tensor) { + if (!isBatchedTensor(tensor)) { + return nullptr; + } + return unsafeGetBatchedImpl(tensor); +} + +// Returns a bitset. If bit i is set, then that means dim i is a batchdim. +inline std::bitset createBatchDimBitset( + BatchDimsRef bdims) { + std::bitset is_bdim; + for (const auto& bdim : bdims) { + is_bdim.set(bdim.dim()); + } + return is_bdim; +} + +// Creates a bitset for all of the levels present in `bdims` +inline std::bitset createVmapLevelsBitset(BatchDimsRef bdims) { + std::bitset result; + for (const auto& bdim : bdims) { + result.set(bdim.level()); + } + return result; +} + +inline std::ostream& operator<<(std::ostream& out, const BatchDim& bdim) { + out << "(lvl=" << bdim.level() << ", dim=" << bdim.dim() << ")"; + return out; +} + +// Use this to construct a BatchedTensor from a regular Tensor +TORCH_API Tensor makeBatched(const Tensor& tensor, BatchDims bdims); + +// Adds a batch dim to `tensor`, returning a BatchedTensor +TORCH_API Tensor addBatchDim(const Tensor& tensor, int64_t level, int64_t dim); + +// Checks if an inplace operation on self and other is "vmap compatible". +// See NOTE: [vmap-incompatible in-place operations] for the definition of this. +TORCH_API bool inplaceIsVmapCompatible(const Tensor& self, const Tensor& other); + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapMode.h b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapMode.h new file mode 100644 index 0000000000000000000000000000000000000000..a1231088c2b31e3fd8c40ed17ccdd41f36035367 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapMode.h @@ -0,0 +1,26 @@ +#pragma once + +#include + +namespace at::impl { + +// VmapMode contains a thread local count of how many nested vmaps +// we are currently inside. That number is known as the `vmap level`. +// VmapMode is used in the implementation of the Python `torch.vmap` API. +// +// NOTE: this is NOT the c++ api for torch.vmap. That doesn't exist yet. + +struct TORCH_API VmapMode { + // Returns the vmap level, aka the count of how many nested vmaps we're in. + static int64_t current_vmap_level(); + + // Increment the count of nested vmaps. If this causes the vmap level to be + // greater than 0, then it enables DispatchKey::VmapMode on all tensors. + static int64_t increment_nesting(); + + // Decrements the count of nested vmaps. If this causes the vmap level to be + // equal to 0, then it disables DispatchKey::VmapMode on all tensors. + static int64_t decrement_nesting(); +}; + +} // namespace at::impl diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapTransforms.h b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapTransforms.h new file mode 100644 index 0000000000000000000000000000000000000000..97729b3254e7458fa76fed74469bf77ed796fe7d --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/LegacyVmapTransforms.h @@ -0,0 +1,183 @@ +#pragma once + +#include +#include + +namespace at { + +// This file contains abstractions used for transforming *logical* vmap +// arguments into *physical* arguments. (Keep reading for definitions of these +// terms). + +// NOTE: [Logical vs physical args] +// Consider the following vmap. +// vmap(vmap(func, in_dims=(2,)), in_dims=(0,))(torch.ones(2, 3, 4)) +// This would produce a BatchedTensor wrapping a Tensor of size [2, 3, 4], +// with batch dims 0 and 2: +// BatchedTensor(ones(2, 3, 4), bdims=[(lvl=1,dim=0),(lvl=2,dim=2)]) +// +// We say the *logical* view of the tensor has size [3] -- tensors inside +// `func` appear to have size [3]. +// However, the *physical* underlying tensor (the one passed to vmap) has size +// [2, 3, 4]. +// +// This notion of logical vs physical also extends to non-tensor arguments. +// Consider the previous tensor; let's assume the user called +// `torch.sum(tensor, dim=0)` inside of `func`. Then the logical +// dimension they are reducing over is dim 0 but the physical dim is dim 1 +// (the first non-batch dimension) + +// Forward declared; see NOTE: [What is a VmapPhysicalView?] +struct VmapPhysicalView; + +// Most PyTorch operators take 4 or fewer inputs. +constexpr int64_t kVmapTransformStaticInputSize = 4; +using VmapPhysicalViewVec = + SmallVector; + +// Pytorch generally advertises good performance for <= 5 dims. +// (see ATen/core/DimVector.h). We add a few extra dims (~3) for vmap +// dimensions to get 8. Adjust this number as necessary +constexpr int64_t kVmapStaticDimVecSize = 8; +using VmapDimVector = SmallVector; +using VmapSymDimVector = SmallVector; + +// NOTE: [What is an VmapTransform?] +// An *VmapTransform* converts logical views of tensors to physical views. +// +// Batching rules use VmapTransforms to convert logical arguments to +// physical arguments, then call one or more at:: operator that handles the +// physical arguments, and then converts the physical result back to a logical +// argument. + +// VmapTransform for operators that take tensors with multiple batch dims. +// Given one or more logical views on Tensors, `logicalToPhysical` +// permutes all of the batch dims to the front of the tensor, aligns +// and expands the batch dims to match each other (according to their `level`), +// and returns a VmapPhysicalView on the tensor(s). +struct TORCH_API MultiBatchVmapTransform { + static VmapPhysicalView logicalToPhysical(const Tensor& logical_tensor); + static VmapPhysicalViewVec logicalToPhysical(ITensorListRef logical_tensors); +}; + +// VmapTransform for operators that broadcast all inputs. +// Given some logical views on Tensors, `logicalToPhysical`: +// - permutes all of the batch dims to the front of the tensors +// - aligns all the batch dims to the collective levels of all of the tensors. +// If a tensor does not have a batch dim for a vmap level, then it receives +// a size-one dimension for said level. +// - aligns the non-batch dims to have the same dimensionality, adding extra +// size-1 dimensions in between the batch dimensions and the non-batch +// dimensions so that the batch dimensions are lined up from the right. +// +// For example: given inputs of size (B, 2) and (B, 3, 2) where B is the batch +// dimension, BroadcastingVmapTransform returns VmapPhysicalViews that wrap +// tensors of size (B, 1, 2) and (B, 3, 2). +// +// Given inputs of size (B, 2) and (2,), BroadcastingVmapTransform returns +// VmapPhysicalViews wrapping tensors of size (B, 2) and (1, 2). We don't +// actually *need* to return a tensor of size (1, 2) for the second tensor +// because the broadcasting operation takes care of that for us, but we do +// it anyways to keep things simple. +struct TORCH_API BroadcastingVmapTransform { + static VmapPhysicalViewVec logicalToPhysical(TensorList logical_tensors); +}; + +// Forward declared, if you're reading this file head to toe, don't worry about +// it yet. +struct VmapPhysicalToLogicalMap; + +// NOTE: [What is a VmapPhysicalView?] +// VmapPhysicalView represents a physical view on a Tensor. +// +// One can use it to further convert logical dimension indices, logical shapes, +// and more to their physical variants, or convert a new (physical) tensor into +// a logical BatchedTensor. (TODO(rzou): some of these are not yet implemented). +// +// VmapPhysicalView stores a physical tensor with all of its batch dimensions at +// the front and some levels that correspond to said batch dimensions. +// +// The levels bitset specifies which vmap levels correspond to the batch +// dimensions at the front of the tensor. In particular, the number of set bits +// corresponds to the number of batch dimensions on `tensor` and the rightmost +// bit of `levels` specifies the maximum number of nested vmaps we are in at +// this point in time. +// For example, given: +// physical_view = VmapPhysicalView(tensor=ones(2, 3, 4, 5, 6), levels={1, 3}) +// +// Rightmost bit of `levels` is 3 indicating the number of nested vmaps less +// than or equal to 3. +// bitset: 010100 +// ^ +// | +// levels: 012345 +struct TORCH_API VmapPhysicalView { + VmapPhysicalView(Tensor&& tensor, std::bitset levels) + : levels_(levels), tensor_(std::move(tensor)) { + TORCH_INTERNAL_ASSERT(!isBatchedTensor(tensor_)); + } + + Tensor& tensor() { + return tensor_; + } + const Tensor& tensor() const { + return tensor_; + } + + // Maps logical dim indices to physical dim indices. Also does dim wrapping. + // + // For example, given: + // physical_view = VmapPhysicalView(tensor=ones(2, 3, 4, 5), levels={1, 3}) + // + // Then physical_view.getPhysicalDims({0, 1}) returns {2, 3}. + // This is because the size of levels tell us that the first two dimensions + // of `tensor_` are batch dimensions, so a logical dim of `n` is actually + // a physical dim of `n + 2`. + VmapDimVector getPhysicalDims(OptionalIntArrayRef logical_dims) const; + int64_t getPhysicalDim(int64_t logical_dim) const; + + // Returns a VmapPhysicalToLogicalMap object. This can be used for + // mapping a physical tensor to a new logical tensor (BatchedTensor) + VmapPhysicalToLogicalMap getPhysicalToLogicalMap() const; + + // Maps a logical shape to a physical shape by pre-pending the batch + // sizes to the logical shape. + VmapDimVector getPhysicalShape(IntArrayRef logical_shape) const; + + int64_t numBatchDims() const; + + private: + int64_t numLogicalDims() const; + + std::bitset levels_; + Tensor tensor_; +}; + +// Convenience struct used for mapping a physical tensor (a non-BatchedTensor) +// to a logical one (BatchedTensor). It holds some levels that are used to do +// the mapping and assumes that the batch dimensions in the physical tensor all +// occur at the front of the tensor. +struct TORCH_API VmapPhysicalToLogicalMap { + VmapPhysicalToLogicalMap(std::bitset levels) + : levels_(levels) {} + + // Maps a physical tensor to a new logical tensor (BatchedTensor). + // Assumes that all of the "batch dimensions" are at the front + // of the physical tensor. For example, given: + // - x = rank-4 Tensor with size 2, 3, 5, 7 + // - levels = (2, 4) + // Returns: + // - BatchedTensor(x, bdims=[(dim=0,lvl=2), (dim=1, lvl=4)]) + Tensor apply(const Tensor& physical_tensor) const; + + // Given a vector of physical tensors, + // 1. maps each tensor to a new logical tensor. Assumes that all of the + // "batch dimensions" are at the front of the physical tensors. + // 2. stores the new logical tensors back into the passed-in vector. This is + // to avoid additional dynamic allocations. + void applyInplace(std::vector& physical_tensors) const; + + std::bitset levels_; +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/LinalgBackend.h b/venv/lib/python3.10/site-packages/torch/include/ATen/LinalgBackend.h new file mode 100644 index 0000000000000000000000000000000000000000..4617afd0b72c7ce286e61a4d1abe2cc89743024c --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/LinalgBackend.h @@ -0,0 +1,31 @@ +#pragma once + +#include + +#include +#include + +namespace at { + +enum class LinalgBackend : int8_t { Default, Cusolver, Magma }; + +inline std::string LinalgBackendToString(at::LinalgBackend backend) { + switch (backend) { + case LinalgBackend::Default: + return "at::LinalgBackend::Default"; + case LinalgBackend::Cusolver: + return "at::LinalgBackend::Cusolver"; + case LinalgBackend::Magma: + return "at::LinalgBackend::Magma"; + default: + TORCH_CHECK(false, "Unknown linalg backend"); + } +} + +inline std::ostream& operator<<( + std::ostream& stream, + at::LinalgBackend backend) { + return stream << LinalgBackendToString(backend); +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/MetaFunctions_inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/MetaFunctions_inl.h new file mode 100644 index 0000000000000000000000000000000000000000..2111758cb07be2a4ab5bfe932688ed394e53d1e8 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/MetaFunctions_inl.h @@ -0,0 +1,324 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunctions_inl.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + . \ + See NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/NamedTensorUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/NamedTensorUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..c1443b7eaa01b4d3215e14c478a5c38195e0a5c0 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/NamedTensorUtils.h @@ -0,0 +1,215 @@ +#pragma once +#include +#include +#include + +#include +#include +#include + +namespace at { + +using NameVector = SmallVector; + +inline bool has_names(const ITensorListRef& tensors) { + return std::any_of(tensors.begin(), tensors.end(), [](const Tensor& t) { + return t.has_names(); + }); +} + +// Converts dim to an positional index. Errors if `dim` cannot be used to +// refer to any dimension of tensor. +TORCH_API int64_t dimname_to_position(const Tensor& tensor, Dimname dim); +TORCH_API std::vector dimnames_to_positions( + const Tensor& tensor, + DimnameList dims); + +// Unifies two DimnameList to produce a third. This is useful for implementing +// the named inference rule for binary broadcasting operations like add. +// +// There are three main constraints: +// 1) Check matching: Names must match positionally from the right. +// 2) Check misaligned: If a name `n` is in `names`, then it must appear at +// the same index from the right in other. +// 3) The output names are obtained by unifying the names individually from the +// right. +TORCH_API std::vector unify_from_right( + DimnameList names, + DimnameList other, + const char* action = "broadcast"); + +[[noreturn]] inline void reportNYIDimnameOverload(const char* op_name) { + TORCH_CHECK( + false, + op_name, + ": You passed a dimname (string) to this op in place of a dimension " + "index but it does not yet support this behavior. Please pass a dimension " + "index to work around this."); +} + +// [NOTE] Writing name inference rules +// +// Operators that support named tensors are either composed of operations that +// support named tensors or implement some name inference rule. An op that +// implements its own name inference rule generally looks like the following: +// +// Tensor op(...) { +// perform_shape_checks(...); +// # (1) +// auto maybe_outnames = compute_outnames(...); +// auto result = [&]() { +// NoNamesGuard guard; +// return op_impl(...); +// }(); +// # (2) +// propagate_names_if_nonempty(result, maybe_outnames); +// +// Each op has (1) a compute outnames step and (2) a propagate names step. +// +// compute_outnames is responsible for checking that input names match and +// determining what the output names should be. It returns either: +// - {} (if the inputs tensors are all unnamed) +// - non-empty outnames. +// +// propagate_names_if_nonempty propagates the outnames if they exist to the +// result tensors. +// +// The {} case is an optimization; if the user does not use named tensors they +// pay no perf cost for it. + +namespace namedinference { + +const Tensor& propagate_names_if_present_and_nonempty( + const Tensor& result, + c10::optional maybe_names, + bool validate_names = false); +// Propagates `names` to `result` if `names` is not empty. +// `names` can be empty; see [NOTE] Writing name inference rules +// If `names` is not empty, `names.size()` should equal `result.dim()`. +// When in doubt, use this overload instead of the others. +TORCH_API const Tensor& propagate_names_if_nonempty( + const Tensor& result, + DimnameList maybe_names, + bool validate_names = false); + +// Propagates `names` to `result`. Only use this if we are certain that there +// are names to propagate (that names is not empty). +TORCH_API const Tensor& propagate_names( + const Tensor& result, + DimnameList names, + bool validate_names = false); + +// Propagates all names from src to result. +TORCH_API void propagate_names(const Tensor& result, const Tensor& src); + +// Propagates all names except for those at the excluded_idxs. +TORCH_API void propagate_names_except( + const Tensor& result, + const Tensor& src, + IntArrayRef excluded_idxs); + +// Used for reduction ops that have a `keepdim` arg. +TORCH_API void propagate_names_for_reduction( + const Tensor& result, + const Tensor& src, + IntArrayRef excluded_idxs, + bool keepdim); + +TORCH_API void propagate_names_for_expand( + const Tensor& result, + const Tensor& self); + +TORCH_API std::vector compute_cat_outnames( + const MaterializedITensorListRef& tensors); + +TORCH_API std::vector compute_broadcast_outnames( + const Tensor& self, + const Tensor& other); + +TORCH_API std::vector broadcast_to_outnames( + const Tensor& tensor, + const Tensor& reference_tensor, + const char* op_name); + +TORCH_API std::vector compute_matmul_outnames( + const Tensor& self, + const Tensor& other); + +TORCH_API std::vector compute_cdist_outnames( + const Tensor& self, + const Tensor& other); + +TORCH_API std::vector compute_bmm_outnames( + const Tensor& result, + const Tensor& self, + const Tensor& other); + +TORCH_API std::vector compute_squeeze_outnames(const Tensor& tensor); +TORCH_API std::vector compute_squeeze_outnames( + const Tensor& tensor, + std::bitset dims); + +std::vector compute_diagonal_outnames( + const Tensor& tensor, + int64_t dim1, + int64_t dim2); + +// TensorImpl* overloads for Legacy TH/THC code. Use these sparingly. + +TORCH_API TensorImpl* propagate_names_if_nonempty( + TensorImpl* result, + DimnameList maybe_names, + bool validate_names = false); + +TORCH_API TensorImpl* propagate_names( + TensorImpl* result, + DimnameList names, + bool validate_names = false); + +TORCH_API void propagate_names(TensorImpl* result, /*const */ TensorImpl* src); + +TORCH_API inline void propagate_names( + const TensorBase& result, + DimnameList names, + bool validate_names = false) { + propagate_names(result.unsafeGetTensorImpl(), names, validate_names); +} + +TORCH_API inline void propagate_names_if_nonempty( + const TensorBase& result, + DimnameList names, + bool validate_names = false) { + propagate_names_if_nonempty( + result.unsafeGetTensorImpl(), names, validate_names); +} + +TORCH_API inline void propagate_names( + const TensorBase& result, + const TensorBase& src) { + propagate_names(result.unsafeGetTensorImpl(), src.unsafeGetTensorImpl()); +} + +// result = m1 @ m2 + bias +TORCH_API std::vector propagate_names_for_addmm( + const Tensor& m1, + const Tensor& m2, + const Tensor& bias); + +TORCH_API std::vector propagate_names_for_addmv( + const Tensor& mat, + const Tensor& vec, + const Tensor& bias); + +TORCH_API void check_names_for_dot(TensorImpl* vec1, TensorImpl* vec2); + +TORCH_API std::vector compute_baddbmm_outnames( + const Tensor& result, + const Tensor& self, + const Tensor& other, + const Tensor& bias); + +TORCH_API bool are_names_equal(TensorImpl* self, TensorImpl* other); + +} // namespace namedinference + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/NativeFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/NativeFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..e7df75b68718c0453e1476f110dfd269bcb47ee3 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/NativeFunctions.h @@ -0,0 +1,1317 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunctions.h + +#ifdef TORCH_ASSERT_NO_OPERATORS +#error This change adds a dependency on native_functions.yaml, \ + meaning the file will need to be re-compiled every time an operator \ + is changed or added. Consider if your change would be better placed in \ + another file, or if a more specific header might achieve the same goal. \ + See NOTE: [Tensor vs. TensorBase] +#endif + +#if defined(AT_PER_OPERATOR_HEADERS) && defined(TORCH_ASSERT_ONLY_METHOD_OPERATORS) +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider including a specific operator from \ + and see NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS]. +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include 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+#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { + +namespace meta { + + + +} // namespace meta +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/NumericUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/NumericUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..788da64b4e4274bcda9adce01c39902d2aae61ef --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/NumericUtils.h @@ -0,0 +1,203 @@ +#pragma once + +#ifdef __HIPCC__ +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace at { + +// std::isnan isn't performant to use on integral types; it will +// (uselessly) convert to floating point and then do the test. +// This function is. + +template , int> = 0> +inline C10_HOST_DEVICE bool _isnan(T /*val*/) { + return false; +} + +template , int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return ::isnan(val); +#else + return std::isnan(val); +#endif +} + +template ::value, int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return std::isnan(val.real()) || std::isnan(val.imag()); +} + +template , int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return at::_isnan(static_cast(val)); +} + +template < + typename T, + std::enable_if_t, int> = 0> +inline C10_HOST_DEVICE bool _isnan(at::BFloat16 val) { + return at::_isnan(static_cast(val)); +} + +inline C10_HOST_DEVICE bool _isnan(at::BFloat16 val) { + return at::_isnan(static_cast(val)); +} + +template < + typename T, + std::enable_if_t, int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return val.isnan(); +} + +template < + typename T, + std::enable_if_t, int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return val.isnan(); +} + +template < + typename T, + std::enable_if_t, int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return val.isnan(); +} + +template < + typename T, + std::enable_if_t, int> = 0> +inline C10_HOST_DEVICE bool _isnan(T val) { + return val.isnan(); +} + +// std::isinf isn't performant to use on integral types; it will +// (uselessly) convert to floating point and then do the test. +// This function is. + +template , int> = 0> +inline C10_HOST_DEVICE bool _isinf(T /*val*/) { + return false; +} + +template , int> = 0> +inline C10_HOST_DEVICE bool _isinf(T val) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return ::isinf(val); +#else + return std::isinf(val); +#endif +} + +inline C10_HOST_DEVICE bool _isinf(at::Half val) { + return at::_isinf(static_cast(val)); +} + +inline C10_HOST_DEVICE bool _isinf(at::BFloat16 val) { + return at::_isinf(static_cast(val)); +} + +inline C10_HOST_DEVICE bool _isinf(at::Float8_e5m2 val) { + return val.isinf(); +} + +inline C10_HOST_DEVICE bool _isinf(at::Float8_e4m3fn val) { + return false; +} + +inline C10_HOST_DEVICE bool _isinf(at::Float8_e5m2fnuz val) { + return false; +} + +inline C10_HOST_DEVICE bool _isinf(at::Float8_e4m3fnuz val) { + return false; +} + +template +C10_HOST_DEVICE inline T exp(T x) { + static_assert( + !std::is_same_v, + "this template must be used with float or less precise type"); +#if defined(__CUDA_ARCH__) || defined(__HIP_ARCH__) + // use __expf fast approximation for peak bandwidth + return __expf(x); +#else + return ::exp(x); +#endif +} + +template <> +C10_HOST_DEVICE inline double exp(double x) { + return ::exp(x); +} + +template +C10_HOST_DEVICE inline T log(T x) { + static_assert( + !std::is_same_v, + "this template must be used with float or less precise type"); +#if defined(__CUDA_ARCH__) || defined(__HIP_ARCH__) + // use __logf fast approximation for peak bandwidth + return __logf(x); +#else + return ::log(x); +#endif +} + +template <> +C10_HOST_DEVICE inline double log(double x) { + return ::log(x); +} + +template +C10_HOST_DEVICE inline T log1p(T x) { + static_assert( + !std::is_same_v, + "this template must be used with float or less precise type"); +#if defined(__CUDA_ARCH__) || defined(__HIP_ARCH__) + // use __logf fast approximation for peak bandwidth + // NOTE: There is no __log1pf so unfortunately we lose precision. + return __logf(1.0f + x); +#else + return ::log1p(x); +#endif +} + +template <> +C10_HOST_DEVICE inline double log1p(double x) { + return ::log1p(x); +} + +template +C10_HOST_DEVICE inline T tan(T x) { + static_assert( + !std::is_same_v, + "this template must be used with float or less precise type"); +#if defined(__CUDA_ARCH__) || defined(__HIP_ARCH__) + // use __tanf fast approximation for peak bandwidth + return __tanf(x); +#else + return ::tan(x); +#endif +} + +template <> +C10_HOST_DEVICE inline double tan(double x) { + return ::tan(x); +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/PTThreadPool.h b/venv/lib/python3.10/site-packages/torch/include/ATen/PTThreadPool.h new file mode 100644 index 0000000000000000000000000000000000000000..1c910dfb97dce44748c054b457b400b13b1b9fda --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/PTThreadPool.h @@ -0,0 +1,17 @@ +#pragma once + +#include +#include + +namespace at { + +class TORCH_API PTThreadPool : public c10::ThreadPool { + public: + explicit PTThreadPool(int pool_size, int numa_node_id = -1) + : c10::ThreadPool(pool_size, numa_node_id, []() { + c10::setThreadName("PTThreadPool"); + at::init_num_threads(); + }) {} +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/PadNd.h b/venv/lib/python3.10/site-packages/torch/include/ATen/PadNd.h new file mode 100644 index 0000000000000000000000000000000000000000..573d1a7b88ab7367858df90b6adfebfa9b97d5e9 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/PadNd.h @@ -0,0 +1,28 @@ +#pragma once +#include +#include + +namespace at { + +enum class padding_mode { + reflect, + replicate, + circular, + constant, +}; + +static inline c10::string_view padding_mode_string(padding_mode m) { + switch (m) { + case padding_mode::reflect: + return "reflect"; + case padding_mode::replicate: + return "replicate"; + case padding_mode::circular: + return "circular"; + case padding_mode::constant: + return "constant"; + } + TORCH_CHECK(false, "Invalid padding mode (", static_cast(m), ")"); +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Parallel-inl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Parallel-inl.h new file mode 100644 index 0000000000000000000000000000000000000000..a5e682281abe52215288d53fc5c9552d3dd2d483 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Parallel-inl.h @@ -0,0 +1,93 @@ +#pragma once + +#include +#include +#include + +namespace at { + +template +inline void parallel_for( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f) { + TORCH_INTERNAL_ASSERT_DEBUG_ONLY(grain_size >= 0); + if (begin >= end) { + return; + } + +#ifdef INTRA_OP_PARALLEL + at::internal::lazy_init_num_threads(); + const auto numiter = end - begin; + const bool use_parallel = + (numiter > grain_size && numiter > 1 && !at::in_parallel_region() && + at::get_num_threads() > 1); + if (!use_parallel) { + internal::ThreadIdGuard tid_guard(0); + c10::ParallelGuard guard(true); + f(begin, end); + return; + } + + internal::invoke_parallel( + begin, end, grain_size, [&](int64_t begin, int64_t end) { + c10::ParallelGuard guard(true); + f(begin, end); + }); +#else + internal::ThreadIdGuard tid_guard(0); + c10::ParallelGuard guard(true); + f(begin, end); +#endif +} + +template +inline scalar_t parallel_reduce( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const scalar_t ident, + const F& f, + const SF& sf) { + TORCH_CHECK(grain_size >= 0); + if (begin >= end) { + return ident; + } + +#ifdef INTRA_OP_PARALLEL + at::internal::lazy_init_num_threads(); + const auto max_threads = at::get_num_threads(); + const bool use_parallel = + ((end - begin) > grain_size && !at::in_parallel_region() && + max_threads > 1); + if (!use_parallel) { + internal::ThreadIdGuard tid_guard(0); + c10::ParallelGuard guard(true); + return f(begin, end, ident); + } + + c10::SmallVector results(max_threads, ident); + internal::invoke_parallel( + begin, + end, + grain_size, + [&](const int64_t my_begin, const int64_t my_end) { + const auto tid = at::get_thread_num(); + c10::ParallelGuard guard(true); + results[tid] = f(my_begin, my_end, ident); + }); + + scalar_t result = ident; + for (auto partial_result : results) { + result = sf(result, partial_result); + } + return result; +#else + internal::ThreadIdGuard tid_guard(0); + c10::ParallelGuard guard(true); + return f(begin, end, ident); +#endif +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ParallelNativeTBB.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ParallelNativeTBB.h new file mode 100644 index 0000000000000000000000000000000000000000..9193e06ed695233637fe5ee8344777e3e42c799b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ParallelNativeTBB.h @@ -0,0 +1,52 @@ +#pragma once + +#include +#include +#include + +#include + +#ifdef _WIN32 +#ifndef WIN32_LEAN_AND_MEAN +#define WIN32_LEAN_AND_MEAN +#endif +#endif +#include + +#define INTRA_OP_PARALLEL + +namespace at::internal { + +template +inline void invoke_parallel( + const int64_t begin, + const int64_t end, + const int64_t grain_size, + const F& f) { + // Choose number of tasks based on grain size and number of threads. + int64_t chunk_size = divup((end - begin), get_num_threads()); + // Make sure each task is at least grain_size size. + chunk_size = std::max(grain_size, chunk_size); + + std::atomic_flag err_flag = ATOMIC_FLAG_INIT; + std::exception_ptr eptr; + tbb::parallel_for( + tbb::blocked_range(begin, end, chunk_size), + [&eptr, &err_flag, f](const tbb::blocked_range& r) { + try { + internal::ThreadIdGuard tid_guard( + tbb::this_task_arena::current_thread_index()); + f(r.begin(), r.end()); + } catch (...) { + if (!err_flag.test_and_set()) { + eptr = std::current_exception(); + } + } + }, + tbb::static_partitioner{}); + if (eptr) { + std::rethrow_exception(eptr); + } +} + +} // namespace at::internal diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/RedispatchFunctions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/RedispatchFunctions.h new file mode 100644 index 0000000000000000000000000000000000000000..474607d3951bd7d14f2e62635ddc2cb4fb2fa26b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/RedispatchFunctions.h @@ -0,0 +1,24791 @@ +#pragma once + +// @generated by torchgen/gen.py from RedispatchFunctions.h + +#ifdef TORCH_ASSERT_ONLY_METHOD_OPERATORS +#error This change adds a dependency on all pytorch operators, meaning the \ + file will need to be re-compiled every time an operator is changed or added. \ + Consider using the at::_ops::{name}::redispatch() interface by including \ + the specific operator from +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { + +namespace redispatch { + + // aten::_cast_Byte(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Byte(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Byte::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Char(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Char(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Char::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Double(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Double(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Double::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Float(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Float(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Float::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Int(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Int(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Int::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Long(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Long(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Long::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Short(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Short(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Short::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_cast_Half(Tensor self, bool non_blocking=False) -> Tensor + inline at::Tensor _cast_Half(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking=false) { + return at::_ops::_cast_Half::redispatch(dispatchKeySet, self, non_blocking); + } + + // aten::_backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, bool create_graph=False) -> () + inline void __dispatch__backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList inputs, const c10::optional & gradient={}, c10::optional retain_graph=c10::nullopt, bool create_graph=false) { + return at::_ops::_backward::redispatch(dispatchKeySet, self, inputs, gradient, retain_graph, create_graph); + } + + // aten::set_data(Tensor(a!) self, Tensor new_data) -> () + inline void __dispatch_set_data(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & new_data) { + return at::_ops::set_data::redispatch(dispatchKeySet, self, new_data); + } + + // aten::data(Tensor self) -> Tensor + inline at::Tensor __dispatch_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::data::redispatch(dispatchKeySet, self); + } + + // aten::is_leaf(Tensor self) -> bool + inline bool __dispatch_is_leaf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_leaf::redispatch(dispatchKeySet, self); + } + + // aten::output_nr(Tensor self) -> int + inline int64_t __dispatch_output_nr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::output_nr::redispatch(dispatchKeySet, self); + } + + // aten::_version(Tensor self) -> int + inline int64_t __dispatch__version(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_version::redispatch(dispatchKeySet, self); + } + + // aten::requires_grad_(Tensor(a!) self, bool requires_grad=True) -> Tensor(a!) + inline at::Tensor & __dispatch_requires_grad_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, bool requires_grad=true) { + return at::_ops::requires_grad_::redispatch(dispatchKeySet, self, requires_grad); + } + + // aten::retain_grad(Tensor(a!) self) -> () + inline void __dispatch_retain_grad(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::retain_grad::redispatch(dispatchKeySet, self); + } + + // aten::retains_grad(Tensor self) -> bool + inline bool __dispatch_retains_grad(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::retains_grad::redispatch(dispatchKeySet, self); + } + + // aten::_fw_primal(Tensor(a) self, int level) -> Tensor(a) + inline at::Tensor _fw_primal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level) { + return at::_ops::_fw_primal::redispatch(dispatchKeySet, self, level); + } + + // aten::_make_dual(Tensor(a) primal, Tensor tangent, int level) -> Tensor(a) + inline at::Tensor _make_dual(c10::DispatchKeySet dispatchKeySet, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { + return at::_ops::_make_dual::redispatch(dispatchKeySet, primal, tangent, level); + } + + // aten::_unpack_dual(Tensor(a) dual, int level) -> (Tensor(a) primal, Tensor tangent) + inline ::std::tuple _unpack_dual(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dual, int64_t level) { + return at::_ops::_unpack_dual::redispatch(dispatchKeySet, dual, level); + } + + // aten::_new_zeros_with_same_feature_meta(Tensor self, Tensor other, *, int self_num_batch_dims=0) -> Tensor + inline at::Tensor _new_zeros_with_same_feature_meta(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims=0) { + return at::_ops::_new_zeros_with_same_feature_meta::redispatch(dispatchKeySet, self, other, self_num_batch_dims); + } + + // aten::_has_same_storage_numel(Tensor self, Tensor other) -> bool + inline bool _has_same_storage_numel(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::_has_same_storage_numel::redispatch(dispatchKeySet, self, other); + } + + // aten::rename_(Tensor(a!) self, Dimname[]? names) -> Tensor(a!) + inline at::Tensor & rename_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional names) { + return at::_ops::rename_::redispatch(dispatchKeySet, self, names); + } + + // aten::rename(Tensor(a) self, Dimname[]? names) -> Tensor(a) + inline at::Tensor rename(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional names) { + return at::_ops::rename::redispatch(dispatchKeySet, self, names); + } + + // aten::align_to(Tensor(a) self, Dimname[] names) -> Tensor(a) + inline at::Tensor align_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList names) { + return at::_ops::align_to::redispatch(dispatchKeySet, self, names); + } + + // aten::align_to.ellipsis_idx(Tensor(a) self, Dimname[] order, int ellipsis_idx) -> Tensor(a) + inline at::Tensor align_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx) { + return at::_ops::align_to_ellipsis_idx::redispatch(dispatchKeySet, self, order, ellipsis_idx); + } + + // aten::align_as(Tensor self, Tensor other) -> Tensor + inline at::Tensor align_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::align_as::redispatch(dispatchKeySet, self, other); + } + + // aten::align_tensors(Tensor[] tensors) -> Tensor[] + inline ::std::vector align_tensors(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::align_tensors::redispatch(dispatchKeySet, tensors); + } + + // aten::_assert_async(Tensor self) -> () + inline void _assert_async(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_assert_async::redispatch(dispatchKeySet, self); + } + + // aten::_assert_async.msg(Tensor self, str assert_msg) -> () + inline void _assert_async(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view assert_msg) { + return at::_ops::_assert_async_msg::redispatch(dispatchKeySet, self, assert_msg); + } + + // aten::_assert_scalar(Scalar self, str assert_msg) -> () + inline void _assert_scalar(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, c10::string_view assert_msg) { + return at::_ops::_assert_scalar::redispatch(dispatchKeySet, self, assert_msg); + } + + // aten::_functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor + inline at::Tensor _functional_assert_scalar(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token) { + return at::_ops::_functional_assert_scalar::redispatch(dispatchKeySet, self, assert_msg, dep_token); + } + + // aten::_functional_assert_async.msg(Tensor self, str assert_msg, Tensor dep_token) -> Tensor + inline at::Tensor _functional_assert_async(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view assert_msg, const at::Tensor & dep_token) { + return at::_ops::_functional_assert_async_msg::redispatch(dispatchKeySet, self, assert_msg, dep_token); + } + + // aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None) -> () + inline void _assert_tensor_metadata(c10::DispatchKeySet dispatchKeySet, const at::Tensor & a, at::OptionalIntArrayRef size=c10::nullopt, at::OptionalIntArrayRef stride=c10::nullopt, c10::optional dtype=c10::nullopt) { + return at::_ops::_assert_tensor_metadata::redispatch(dispatchKeySet, a, size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*size)) : c10::nullopt, stride.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*stride)) : c10::nullopt, dtype); + } + + // aten::_assert_tensor_metadata(Tensor a, SymInt[]? size=None, SymInt[]? stride=None, ScalarType? dtype=None) -> () + inline void _assert_tensor_metadata_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & a, at::OptionalSymIntArrayRef size=c10::nullopt, at::OptionalSymIntArrayRef stride=c10::nullopt, c10::optional dtype=c10::nullopt) { + return at::_ops::_assert_tensor_metadata::redispatch(dispatchKeySet, a, size, stride, dtype); + } + + // aten::_print(str s) -> () + inline void _print(c10::DispatchKeySet dispatchKeySet, c10::string_view s) { + return at::_ops::_print::redispatch(dispatchKeySet, s); + } + + // aten::sym_constrain_range(Scalar size, *, int? min=None, int? max=None) -> () + inline void sym_constrain_range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt) { + return at::_ops::sym_constrain_range::redispatch(dispatchKeySet, size, min, max); + } + + // aten::sym_constrain_range_for_size(Scalar size, *, int? min=None, int? max=None) -> () + inline void sym_constrain_range_for_size(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, c10::optional min=c10::nullopt, c10::optional max=c10::nullopt) { + return at::_ops::sym_constrain_range_for_size::redispatch(dispatchKeySet, size, min, max); + } + + // aten::_functional_sym_constrain_range(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor + inline at::Tensor _functional_sym_constrain_range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, c10::optional min, c10::optional max, const at::Tensor & dep_token) { + return at::_ops::_functional_sym_constrain_range::redispatch(dispatchKeySet, size, min, max, dep_token); + } + + // aten::_functional_sym_constrain_range_for_size(Scalar size, int? min, int? max, Tensor dep_token) -> Tensor + inline at::Tensor _functional_sym_constrain_range_for_size(c10::DispatchKeySet dispatchKeySet, const at::Scalar & size, c10::optional min, c10::optional max, const at::Tensor & dep_token) { + return at::_ops::_functional_sym_constrain_range_for_size::redispatch(dispatchKeySet, size, min, max, dep_token); + } + + // aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor _make_dep_token(c10::DispatchKeySet dispatchKeySet, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::_make_dep_token::redispatch(dispatchKeySet, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_make_dep_token(*, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor _make_dep_token(c10::DispatchKeySet dispatchKeySet, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::_make_dep_token::redispatch(dispatchKeySet, dtype, layout, device, pin_memory, memory_format); + } + + // aten::refine_names(Tensor(a) self, Dimname[] names) -> Tensor(a) + inline at::Tensor refine_names(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList names) { + return at::_ops::refine_names::redispatch(dispatchKeySet, self, names); + } + + // aten::_use_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank) -> bool + inline bool _use_cudnn_ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank) { + return at::_ops::_use_cudnn_ctc_loss::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank); + } + + // aten::_use_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank) -> bool + inline bool _use_cudnn_ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank) { + return at::_ops::_use_cudnn_ctc_loss_Tensor::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank); + } + + // aten::_cudnn_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor) + inline ::std::tuple _cudnn_ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { + return at::_ops::_cudnn_ctc_loss::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); + } + + // aten::_cudnn_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank, bool deterministic, bool zero_infinity) -> (Tensor, Tensor) + inline ::std::tuple _cudnn_ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { + return at::_ops::_cudnn_ctc_loss_Tensor::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); + } + + // aten::_use_cudnn_rnn_flatten_weight() -> bool + inline bool _use_cudnn_rnn_flatten_weight(c10::DispatchKeySet dispatchKeySet) { + return at::_ops::_use_cudnn_rnn_flatten_weight::redispatch(dispatchKeySet); + } + + // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor + inline at::Tensor _cudnn_rnn_flatten_weight(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { + return at::_ops::_cudnn_rnn_flatten_weight::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); + } + + // aten::_cudnn_rnn_flatten_weight(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional) -> Tensor + inline at::Tensor _cudnn_rnn_flatten_weight_symint(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { + return at::_ops::_cudnn_rnn_flatten_weight::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); + } + + // aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _cudnn_rnn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::_cudnn_rnn::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state); + } + + // aten::_cudnn_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _cudnn_rnn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::_cudnn_rnn::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); + } + + // aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) + inline ::std::tuple> _cudnn_rnn_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::_cudnn_rnn_backward::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask); + } + + // aten::_cudnn_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) + inline ::std::tuple> _cudnn_rnn_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::_cudnn_rnn_backward::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); + } + + // aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor _cudnn_init_dropout_state(c10::DispatchKeySet dispatchKeySet, double dropout, bool train, int64_t dropout_seed, at::TensorOptions options) { + return at::_ops::_cudnn_init_dropout_state::redispatch(dispatchKeySet, dropout, train, dropout_seed, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor _cudnn_init_dropout_state(c10::DispatchKeySet dispatchKeySet, double dropout, bool train, int64_t dropout_seed, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_cudnn_init_dropout_state::redispatch(dispatchKeySet, dropout, train, dropout_seed, dtype, layout, device, pin_memory); + } + + // aten::_debug_has_internal_overlap(Tensor self) -> int + inline int64_t _debug_has_internal_overlap(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_debug_has_internal_overlap::redispatch(dispatchKeySet, self); + } + + // aten::_fused_dropout(Tensor self, float p, Generator? generator=None) -> (Tensor, Tensor) + inline ::std::tuple _fused_dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::_fused_dropout::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::_masked_scale(Tensor self, Tensor mask, float scale) -> Tensor + inline at::Tensor _masked_scale(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, double scale) { + return at::_ops::_masked_scale::redispatch(dispatchKeySet, self, mask, scale); + } + + // aten::native_dropout(Tensor input, float p, bool? train) -> (Tensor, Tensor) + inline ::std::tuple native_dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, c10::optional train) { + return at::_ops::native_dropout::redispatch(dispatchKeySet, input, p, train); + } + + // aten::native_dropout_backward(Tensor grad_output, Tensor mask, float scale) -> Tensor + inline at::Tensor native_dropout_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, double scale) { + return at::_ops::native_dropout_backward::redispatch(dispatchKeySet, grad_output, mask, scale); + } + + // aten::_sobol_engine_draw(Tensor quasi, int n, Tensor sobolstate, int dimension, int num_generated, ScalarType? dtype) -> (Tensor, Tensor) + inline ::std::tuple _sobol_engine_draw(c10::DispatchKeySet dispatchKeySet, const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype) { + return at::_ops::_sobol_engine_draw::redispatch(dispatchKeySet, quasi, n, sobolstate, dimension, num_generated, dtype); + } + + // aten::_sobol_engine_ff_(Tensor(a!) self, int n, Tensor sobolstate, int dimension, int num_generated) -> Tensor(a!) + inline at::Tensor & _sobol_engine_ff_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) { + return at::_ops::_sobol_engine_ff_::redispatch(dispatchKeySet, self, n, sobolstate, dimension, num_generated); + } + + // aten::_sobol_engine_scramble_(Tensor(a!) self, Tensor ltm, int dimension) -> Tensor(a!) + inline at::Tensor & _sobol_engine_scramble_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & ltm, int64_t dimension) { + return at::_ops::_sobol_engine_scramble_::redispatch(dispatchKeySet, self, ltm, dimension); + } + + // aten::_sobol_engine_initialize_state_(Tensor(a!) self, int dimension) -> Tensor(a!) + inline at::Tensor & _sobol_engine_initialize_state_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dimension) { + return at::_ops::_sobol_engine_initialize_state_::redispatch(dispatchKeySet, self, dimension); + } + + // aten::_reshape_from_tensor(Tensor self, Tensor shape) -> Tensor + inline at::Tensor _reshape_from_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & shape) { + return at::_ops::_reshape_from_tensor::redispatch(dispatchKeySet, self, shape); + } + + // aten::_shape_as_tensor(Tensor self) -> Tensor + inline at::Tensor _shape_as_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_shape_as_tensor::redispatch(dispatchKeySet, self); + } + + // aten::dropout(Tensor input, float p, bool train) -> Tensor + inline at::Tensor dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { + return at::_ops::dropout::redispatch(dispatchKeySet, input, p, train); + } + + // aten::dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) + inline at::Tensor & dropout_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { + return at::_ops::dropout_::redispatch(dispatchKeySet, self, p, train); + } + + // aten::feature_dropout(Tensor input, float p, bool train) -> Tensor + inline at::Tensor feature_dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { + return at::_ops::feature_dropout::redispatch(dispatchKeySet, input, p, train); + } + + // aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) + inline at::Tensor & feature_dropout_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { + return at::_ops::feature_dropout_::redispatch(dispatchKeySet, self, p, train); + } + + // aten::alpha_dropout(Tensor input, float p, bool train) -> Tensor + inline at::Tensor alpha_dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { + return at::_ops::alpha_dropout::redispatch(dispatchKeySet, input, p, train); + } + + // aten::alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) + inline at::Tensor & alpha_dropout_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { + return at::_ops::alpha_dropout_::redispatch(dispatchKeySet, self, p, train); + } + + // aten::feature_alpha_dropout(Tensor input, float p, bool train) -> Tensor + inline at::Tensor feature_alpha_dropout(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, bool train) { + return at::_ops::feature_alpha_dropout::redispatch(dispatchKeySet, input, p, train); + } + + // aten::feature_alpha_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!) + inline at::Tensor & feature_alpha_dropout_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, bool train) { + return at::_ops::feature_alpha_dropout_::redispatch(dispatchKeySet, self, p, train); + } + + // aten::abs(Tensor self) -> Tensor + inline at::Tensor abs(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::abs::redispatch(dispatchKeySet, self); + } + + // aten::abs_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & abs_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::abs_::redispatch(dispatchKeySet, self); + } + + // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & abs_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::abs_out::redispatch(dispatchKeySet, self, out); + } + + // aten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & abs_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::abs_out::redispatch(dispatchKeySet, self, out); + } + + // aten::absolute(Tensor self) -> Tensor + inline at::Tensor absolute(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::absolute::redispatch(dispatchKeySet, self); + } + + // aten::absolute_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & absolute_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::absolute_::redispatch(dispatchKeySet, self); + } + + // aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & absolute_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::absolute_out::redispatch(dispatchKeySet, self, out); + } + + // aten::absolute.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & absolute_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::absolute_out::redispatch(dispatchKeySet, self, out); + } + + // aten::angle(Tensor self) -> Tensor + inline at::Tensor angle(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::angle::redispatch(dispatchKeySet, self); + } + + // aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & angle_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::angle_out::redispatch(dispatchKeySet, self, out); + } + + // aten::angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & angle_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::angle_out::redispatch(dispatchKeySet, self, out); + } + + // aten::view_as_real(Tensor(a) self) -> Tensor(a) + inline at::Tensor view_as_real(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::view_as_real::redispatch(dispatchKeySet, self); + } + + // aten::view_as_complex(Tensor(a) self) -> Tensor(a) + inline at::Tensor view_as_complex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::view_as_complex::redispatch(dispatchKeySet, self); + } + + // aten::sgn(Tensor self) -> Tensor + inline at::Tensor sgn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sgn::redispatch(dispatchKeySet, self); + } + + // aten::sgn_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sgn_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sgn_::redispatch(dispatchKeySet, self); + } + + // aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sgn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sgn_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sgn.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sgn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sgn_out::redispatch(dispatchKeySet, self, out); + } + + // aten::chalf(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor chalf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::chalf::redispatch(dispatchKeySet, self, memory_format); + } + + // aten::real(Tensor(a) self) -> Tensor(a) + inline at::Tensor real(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::real::redispatch(dispatchKeySet, self); + } + + // aten::imag(Tensor(a) self) -> Tensor(a) + inline at::Tensor imag(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::imag::redispatch(dispatchKeySet, self); + } + + // aten::_conj(Tensor(a) self) -> Tensor(a) + inline at::Tensor _conj(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_conj::redispatch(dispatchKeySet, self); + } + + // aten::conj(Tensor(a) self) -> Tensor(a) + inline at::Tensor __dispatch_conj(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::conj::redispatch(dispatchKeySet, self); + } + + // aten::_conj_physical(Tensor self) -> Tensor + inline at::Tensor _conj_physical(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_conj_physical::redispatch(dispatchKeySet, self); + } + + // aten::conj_physical(Tensor self) -> Tensor + inline at::Tensor conj_physical(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::conj_physical::redispatch(dispatchKeySet, self); + } + + // aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conj_physical_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::conj_physical_out::redispatch(dispatchKeySet, self, out); + } + + // aten::conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conj_physical_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::conj_physical_out::redispatch(dispatchKeySet, self, out); + } + + // aten::conj_physical_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & conj_physical_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::conj_physical_::redispatch(dispatchKeySet, self); + } + + // aten::resolve_conj(Tensor(a) self) -> Tensor(a) + inline at::Tensor resolve_conj(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::resolve_conj::redispatch(dispatchKeySet, self); + } + + // aten::resolve_neg(Tensor(a) self) -> Tensor(a) + inline at::Tensor resolve_neg(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::resolve_neg::redispatch(dispatchKeySet, self); + } + + // aten::_neg_view(Tensor(a) self) -> Tensor(a) + inline at::Tensor _neg_view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_neg_view::redispatch(dispatchKeySet, self); + } + + // aten::acos(Tensor self) -> Tensor + inline at::Tensor acos(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::acos::redispatch(dispatchKeySet, self); + } + + // aten::acos_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & acos_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::acos_::redispatch(dispatchKeySet, self); + } + + // aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & acos_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::acos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & acos_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::acos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arccos(Tensor self) -> Tensor + inline at::Tensor arccos(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arccos::redispatch(dispatchKeySet, self); + } + + // aten::arccos_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arccos_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arccos_::redispatch(dispatchKeySet, self); + } + + // aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arccos_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arccos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arccos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arccos_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arccos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, bool ceil_mode=False, bool count_include_pad=True) -> Tensor + inline at::Tensor avg_pool1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true) { + return at::_ops::avg_pool1d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad); + } + + // aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> Tensor + inline at::Tensor adaptive_avg_pool1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_avg_pool1d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::adaptive_max_pool1d(Tensor self, int[1] output_size) -> (Tensor, Tensor) + inline ::std::tuple adaptive_max_pool1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_max_pool1d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor + inline at::Tensor add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::add_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::add__Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & add_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::add_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & add_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::add_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_add_relu.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor + inline at::Tensor _add_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_add_relu_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & _add_relu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu__Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _add_relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_add_relu.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _add_relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::_add_relu_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_add_relu.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor + inline at::Tensor _add_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu_Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_add_relu_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & _add_relu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu__Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor + inline at::Tensor add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::add_Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::add_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::add__Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::addmv(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor addmv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmv::redispatch(dispatchKeySet, self, mat, vec, beta, alpha); + } + + // aten::addmv_(Tensor(a!) self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & addmv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmv_::redispatch(dispatchKeySet, self, mat, vec, beta, alpha); + } + + // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addmv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmv_out::redispatch(dispatchKeySet, self, mat, vec, beta, alpha, out); + } + + // aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addmv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::addmv_out::redispatch(dispatchKeySet, self, mat, vec, beta, alpha, out); + } + + // aten::addr(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor addr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addr::redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha); + } + + // aten::addr_(Tensor(a!) self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & addr_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addr_::redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha); + } + + // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addr_out::redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha, out); + } + + // aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::addr_out::redispatch(dispatchKeySet, self, vec1, vec2, beta, alpha, out); + } + + // aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor + inline at::Tensor affine_grid_generator(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, at::IntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator::redispatch(dispatchKeySet, theta, c10::fromIntArrayRefSlow(size), align_corners); + } + + // aten::affine_grid_generator(Tensor theta, SymInt[] size, bool align_corners) -> Tensor + inline at::Tensor affine_grid_generator_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator::redispatch(dispatchKeySet, theta, size, align_corners); + } + + // aten::affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor + inline at::Tensor affine_grid_generator_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, at::IntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator_backward::redispatch(dispatchKeySet, grad, c10::fromIntArrayRefSlow(size), align_corners); + } + + // aten::affine_grid_generator_backward(Tensor grad, SymInt[] size, bool align_corners) -> Tensor + inline at::Tensor affine_grid_generator_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, c10::SymIntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator_backward::redispatch(dispatchKeySet, grad, size, align_corners); + } + + // aten::_is_all_true(Tensor self) -> Tensor + inline at::Tensor _is_all_true(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_is_all_true::redispatch(dispatchKeySet, self); + } + + // aten::_is_any_true(Tensor self) -> Tensor + inline at::Tensor _is_any_true(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_is_any_true::redispatch(dispatchKeySet, self); + } + + // aten::_test_check_tensor(Tensor self) -> Tensor + inline at::Tensor _test_check_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_test_check_tensor::redispatch(dispatchKeySet, self); + } + + // aten::_test_functorch_fallback(Tensor self, Tensor other) -> Tensor + inline at::Tensor _test_functorch_fallback(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::_test_functorch_fallback::redispatch(dispatchKeySet, self, other); + } + + // aten::all.dim(Tensor self, int dim, bool keepdim=False) -> Tensor + inline at::Tensor all(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::all_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::all.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor + inline at::Tensor all(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) { + return at::_ops::all_dims::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::all_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { + return at::_ops::all_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) { + return at::_ops::all_dims_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::all.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::all_dims_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::all.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor + inline at::Tensor all(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::all_dimname::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::all_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::all.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) { + return at::_ops::all_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::allclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> bool + inline bool allclose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) { + return at::_ops::allclose::redispatch(dispatchKeySet, self, other, rtol, atol, equal_nan); + } + + // aten::any.dim(Tensor self, int dim, bool keepdim=False) -> Tensor + inline at::Tensor any(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::any_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::any.dims(Tensor self, int[]? dim=None, bool keepdim=False) -> Tensor + inline at::Tensor any(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) { + return at::_ops::any_dims::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::any_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out) { + return at::_ops::any_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) { + return at::_ops::any_dims_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::any.dims_out(Tensor self, int[]? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::any_dims_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::any.dimname(Tensor self, Dimname dim, bool keepdim=False) -> Tensor + inline at::Tensor any(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::any_dimname::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::any_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::any.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & out) { + return at::_ops::any_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & end, at::TensorOptions options={}) { + return at::_ops::arange::redispatch(dispatchKeySet, end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::arange(Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & end, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::arange::redispatch(dispatchKeySet, end, dtype, layout, device, pin_memory); + } + + // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, at::TensorOptions options={}) { + return at::_ops::arange_start::redispatch(dispatchKeySet, start, end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::arange_start::redispatch(dispatchKeySet, start, end, dtype, layout, device, pin_memory); + } + + // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::TensorOptions options={}) { + return at::_ops::arange_start_step::redispatch(dispatchKeySet, start, end, step, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::arange.start_step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor arange(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::arange_start_step::redispatch(dispatchKeySet, start, end, step, dtype, layout, device, pin_memory); + } + + // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arange_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & end) { + return at::_ops::arange_out::redispatch(dispatchKeySet, end, out); + } + + // aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arange_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & end, at::Tensor & out) { + return at::_ops::arange_out::redispatch(dispatchKeySet, end, out); + } + + // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arange_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) { + return at::_ops::arange_start_out::redispatch(dispatchKeySet, start, end, step, out); + } + + // aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arange_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { + return at::_ops::arange_start_out::redispatch(dispatchKeySet, start, end, step, out); + } + + // aten::_dim_arange(Tensor like, int dim) -> Tensor + inline at::Tensor _dim_arange(c10::DispatchKeySet dispatchKeySet, const at::Tensor & like, int64_t dim) { + return at::_ops::_dim_arange::redispatch(dispatchKeySet, like, dim); + } + + // aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor + inline at::Tensor argmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::argmax::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::argmax_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & out) { + return at::_ops::argmax_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor + inline at::Tensor argmin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::argmin::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argmin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::argmin_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argmin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & out) { + return at::_ops::argmin_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::acosh(Tensor self) -> Tensor + inline at::Tensor acosh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::acosh::redispatch(dispatchKeySet, self); + } + + // aten::acosh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & acosh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::acosh_::redispatch(dispatchKeySet, self); + } + + // aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & acosh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::acosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::acosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & acosh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::acosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arccosh(Tensor self) -> Tensor + inline at::Tensor arccosh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arccosh::redispatch(dispatchKeySet, self); + } + + // aten::arccosh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arccosh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arccosh_::redispatch(dispatchKeySet, self); + } + + // aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arccosh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arccosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arccosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arccosh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arccosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::asinh(Tensor self) -> Tensor + inline at::Tensor asinh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::asinh::redispatch(dispatchKeySet, self); + } + + // aten::asinh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & asinh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::asinh_::redispatch(dispatchKeySet, self); + } + + // aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & asinh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::asinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::asinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & asinh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::asinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arcsinh(Tensor self) -> Tensor + inline at::Tensor arcsinh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arcsinh::redispatch(dispatchKeySet, self); + } + + // aten::arcsinh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arcsinh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arcsinh_::redispatch(dispatchKeySet, self); + } + + // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arcsinh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arcsinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arcsinh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arcsinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::atanh(Tensor self) -> Tensor + inline at::Tensor atanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::atanh::redispatch(dispatchKeySet, self); + } + + // aten::atanh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & atanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::atanh_::redispatch(dispatchKeySet, self); + } + + // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atanh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::atanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::atanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atanh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::atanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arctanh(Tensor self) -> Tensor + inline at::Tensor arctanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arctanh::redispatch(dispatchKeySet, self); + } + + // aten::arctanh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arctanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arctanh_::redispatch(dispatchKeySet, self); + } + + // aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctanh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arctanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arctanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctanh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arctanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a) + inline at::Tensor as_strided(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); + } + + // aten::as_strided(Tensor(a) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a) + inline at::Tensor as_strided_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided::redispatch(dispatchKeySet, self, size, stride, storage_offset); + } + + // aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!) + inline const at::Tensor & as_strided_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); + } + + // aten::as_strided_(Tensor(a!) self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor(a!) + inline const at::Tensor & as_strided__symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_::redispatch(dispatchKeySet, self, size, stride, storage_offset); + } + + // aten::asin(Tensor self) -> Tensor + inline at::Tensor asin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::asin::redispatch(dispatchKeySet, self); + } + + // aten::asin_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & asin_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::asin_::redispatch(dispatchKeySet, self); + } + + // aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & asin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::asin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & asin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::asin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arcsin(Tensor self) -> Tensor + inline at::Tensor arcsin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arcsin::redispatch(dispatchKeySet, self); + } + + // aten::arcsin_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arcsin_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arcsin_::redispatch(dispatchKeySet, self); + } + + // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arcsin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arcsin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arcsin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arcsin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arcsin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::atan(Tensor self) -> Tensor + inline at::Tensor atan(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::atan::redispatch(dispatchKeySet, self); + } + + // aten::atan_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & atan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::atan_::redispatch(dispatchKeySet, self); + } + + // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atan_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::atan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atan_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::atan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arctan(Tensor self) -> Tensor + inline at::Tensor arctan(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::arctan::redispatch(dispatchKeySet, self); + } + + // aten::arctan_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & arctan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::arctan_::redispatch(dispatchKeySet, self); + } + + // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctan_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::arctan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::arctan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctan_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::arctan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::atleast_1d(Tensor self) -> Tensor + inline at::Tensor atleast_1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::atleast_1d::redispatch(dispatchKeySet, self); + } + + // aten::atleast_1d.Sequence(Tensor[] tensors) -> Tensor[] + inline ::std::vector atleast_1d(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::atleast_1d_Sequence::redispatch(dispatchKeySet, tensors); + } + + // aten::atleast_2d(Tensor self) -> Tensor + inline at::Tensor atleast_2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::atleast_2d::redispatch(dispatchKeySet, self); + } + + // aten::atleast_2d.Sequence(Tensor[] tensors) -> Tensor[] + inline ::std::vector atleast_2d(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::atleast_2d_Sequence::redispatch(dispatchKeySet, tensors); + } + + // aten::atleast_3d(Tensor self) -> Tensor + inline at::Tensor atleast_3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::atleast_3d::redispatch(dispatchKeySet, self); + } + + // aten::atleast_3d.Sequence(Tensor[] tensors) -> Tensor[] + inline ::std::vector atleast_3d(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::atleast_3d_Sequence::redispatch(dispatchKeySet, tensors); + } + + // aten::baddbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor baddbmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::baddbmm::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); + } + + // aten::baddbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & baddbmm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::baddbmm_::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); + } + + // aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & baddbmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::baddbmm_out::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha, out); + } + + // aten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & baddbmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::baddbmm_out::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha, out); + } + + // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor bartlett_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::TensorOptions options={}) { + return at::_ops::bartlett_window::redispatch(dispatchKeySet, window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::bartlett_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor bartlett_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::bartlett_window::redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); + } + + // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor bartlett_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::TensorOptions options={}) { + return at::_ops::bartlett_window_periodic::redispatch(dispatchKeySet, window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::bartlett_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor bartlett_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::bartlett_window_periodic::redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); + } + + // aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor + inline at::Tensor batch_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps, bool cudnn_enabled) { + return at::_ops::batch_norm::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); + } + + // aten::quantized_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point) -> Tensor + inline at::Tensor quantized_batch_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) { + return at::_ops::quantized_batch_norm::redispatch(dispatchKeySet, input, weight, bias, mean, var, eps, output_scale, output_zero_point); + } + + // aten::_batch_norm_impl_index(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor, Tensor, Tensor, Tensor, int) + inline ::std::tuple _batch_norm_impl_index(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps, bool cudnn_enabled) { + return at::_ops::_batch_norm_impl_index::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); + } + + // aten::_batch_norm_impl_index_backward(int impl_index, Tensor input, Tensor grad_output, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var_transform, bool train, float eps, bool[3] output_mask, Tensor reservedSpace) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _batch_norm_impl_index_backward(c10::DispatchKeySet dispatchKeySet, int64_t impl_index, const at::Tensor & input, const at::Tensor & grad_output, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var_transform, bool train, double eps, ::std::array output_mask, const at::Tensor & reservedSpace) { + return at::_ops::_batch_norm_impl_index_backward::redispatch(dispatchKeySet, impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace); + } + + // aten::bernoulli(Tensor self, *, Generator? generator=None) -> Tensor + inline at::Tensor bernoulli(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli::redispatch(dispatchKeySet, self, generator); + } + + // aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator, at::Tensor & out) { + return at::_ops::bernoulli_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::bernoulli_.Tensor(Tensor(a!) self, Tensor p, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & bernoulli_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & p, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli__Tensor::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::bernoulli_.float(Tensor(a!) self, float p=0.5, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & bernoulli_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p=0.5, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli__float::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::bernoulli.p(Tensor self, float p, *, Generator? generator=None) -> Tensor + inline at::Tensor bernoulli(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli_p::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias=None) -> Tensor + inline at::Tensor bilinear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const c10::optional & bias={}) { + return at::_ops::bilinear::redispatch(dispatchKeySet, input1, input2, weight, bias); + } + + // aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor + inline at::Tensor binary_cross_entropy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy::redispatch(dispatchKeySet, self, target, weight, reduction); + } + + // aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy_out::redispatch(dispatchKeySet, self, target, weight, reduction, out); + } + + // aten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, at::Tensor & out) { + return at::_ops::binary_cross_entropy_out::redispatch(dispatchKeySet, self, target, weight, reduction, out); + } + + // aten::binary_cross_entropy_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensor + inline at::Tensor binary_cross_entropy_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy_backward::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction); + } + + // aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, grad_input); + } + + // aten::binary_cross_entropy_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, at::Tensor & grad_input) { + return at::_ops::binary_cross_entropy_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, grad_input); + } + + // aten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> Tensor + inline at::Tensor binary_cross_entropy_with_logits(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, const c10::optional & pos_weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy_with_logits::redispatch(dispatchKeySet, self, target, weight, pos_weight, reduction); + } + + // aten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensor + inline at::Tensor bincount(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & weights={}, int64_t minlength=0) { + return at::_ops::bincount::redispatch(dispatchKeySet, self, weights, minlength); + } + + // aten::bitwise_not(Tensor self) -> Tensor + inline at::Tensor bitwise_not(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::bitwise_not::redispatch(dispatchKeySet, self); + } + + // aten::bitwise_not_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & bitwise_not_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::bitwise_not_::redispatch(dispatchKeySet, self); + } + + // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_not_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::bitwise_not_out::redispatch(dispatchKeySet, self, out); + } + + // aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_not_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::bitwise_not_out::redispatch(dispatchKeySet, self, out); + } + + // aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copysign_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::copysign_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::copysign.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copysign_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::copysign_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::copysign.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor copysign(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::copysign_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::copysign_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & copysign_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::copysign__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::copysign.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor copysign(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::copysign_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::copysign_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & copysign_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::copysign__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copysign_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::copysign_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::copysign.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copysign_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::copysign_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_lazy_clone(Tensor self) -> Tensor + inline at::Tensor _lazy_clone(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_lazy_clone::redispatch(dispatchKeySet, self); + } + + // aten::logical_not(Tensor self) -> Tensor + inline at::Tensor logical_not(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::logical_not::redispatch(dispatchKeySet, self); + } + + // aten::logical_not_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & logical_not_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::logical_not_::redispatch(dispatchKeySet, self); + } + + // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_not_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::logical_not_out::redispatch(dispatchKeySet, self, out); + } + + // aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_not_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::logical_not_out::redispatch(dispatchKeySet, self, out); + } + + // aten::logical_xor(Tensor self, Tensor other) -> Tensor + inline at::Tensor logical_xor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_xor::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_xor_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & logical_xor_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_xor_::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_xor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_xor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_xor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::logical_xor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logical_and(Tensor self, Tensor other) -> Tensor + inline at::Tensor logical_and(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_and::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_and_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & logical_and_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_and_::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_and_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_and_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logical_and.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_and_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::logical_and_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logical_or(Tensor self, Tensor other) -> Tensor + inline at::Tensor logical_or(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_or::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_or_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & logical_or_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_or_::redispatch(dispatchKeySet, self, other); + } + + // aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_or_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logical_or_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logical_or.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logical_or_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::logical_or_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor blackman_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::TensorOptions options={}) { + return at::_ops::blackman_window::redispatch(dispatchKeySet, window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::blackman_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor blackman_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::blackman_window::redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); + } + + // aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor blackman_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::TensorOptions options={}) { + return at::_ops::blackman_window_periodic::redispatch(dispatchKeySet, window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::blackman_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor blackman_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::blackman_window_periodic::redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); + } + + // aten::bmm(Tensor self, Tensor mat2) -> Tensor + inline at::Tensor bmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::bmm::redispatch(dispatchKeySet, self, mat2); + } + + // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::bmm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { + return at::_ops::bmm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::broadcast_tensors(Tensor[] tensors) -> Tensor[] + inline ::std::vector broadcast_tensors(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::broadcast_tensors::redispatch(dispatchKeySet, tensors); + } + + // aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a) + inline at::Tensor broadcast_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::broadcast_to::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::broadcast_to(Tensor(a) self, SymInt[] size) -> Tensor(a) + inline at::Tensor broadcast_to_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::broadcast_to::redispatch(dispatchKeySet, self, size); + } + + // aten::_sparse_broadcast_to(Tensor(a) self, int[] size) -> Tensor(a) + inline at::Tensor _sparse_broadcast_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_sparse_broadcast_to::redispatch(dispatchKeySet, self, size); + } + + // aten::cat(Tensor[] tensors, int dim=0) -> Tensor + inline at::Tensor cat(c10::DispatchKeySet dispatchKeySet, const at::ITensorListRef & tensors, int64_t dim=0) { + return at::_ops::cat::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::ITensorListRef & tensors, int64_t dim=0) { + return at::_ops::cat_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cat_outf(c10::DispatchKeySet dispatchKeySet, const at::ITensorListRef & tensors, int64_t dim, at::Tensor & out) { + return at::_ops::cat_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::cat.names(Tensor[] tensors, Dimname dim) -> Tensor + inline at::Tensor cat(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim) { + return at::_ops::cat_names::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, at::Dimname dim) { + return at::_ops::cat_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::cat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cat_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { + return at::_ops::cat_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concat(Tensor[] tensors, int dim=0) -> Tensor + inline at::Tensor concat(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim=0) { + return at::_ops::concat::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, int64_t dim=0) { + return at::_ops::concat_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concat_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { + return at::_ops::concat_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concat.names(Tensor[] tensors, Dimname dim) -> Tensor + inline at::Tensor concat(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim) { + return at::_ops::concat_names::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, at::Dimname dim) { + return at::_ops::concat_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concat.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concat_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { + return at::_ops::concat_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concatenate(Tensor[] tensors, int dim=0) -> Tensor + inline at::Tensor concatenate(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim=0) { + return at::_ops::concatenate::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concatenate_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, int64_t dim=0) { + return at::_ops::concatenate_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concatenate.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concatenate_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { + return at::_ops::concatenate_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concatenate.names(Tensor[] tensors, Dimname dim) -> Tensor + inline at::Tensor concatenate(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim) { + return at::_ops::concatenate_names::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concatenate_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, at::Dimname dim) { + return at::_ops::concatenate_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::concatenate.names_out(Tensor[] tensors, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & concatenate_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Dimname dim, at::Tensor & out) { + return at::_ops::concatenate_names_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::block_diag(Tensor[] tensors) -> Tensor + inline at::Tensor block_diag(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::block_diag::redispatch(dispatchKeySet, tensors); + } + + // aten::ceil(Tensor self) -> Tensor + inline at::Tensor ceil(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::ceil::redispatch(dispatchKeySet, self); + } + + // aten::ceil_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & ceil_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::ceil_::redispatch(dispatchKeySet, self); + } + + // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ceil_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::ceil_out::redispatch(dispatchKeySet, self, out); + } + + // aten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ceil_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::ceil_out::redispatch(dispatchKeySet, self, out); + } + + // aten::chain_matmul(Tensor[] matrices) -> Tensor + inline at::Tensor chain_matmul(c10::DispatchKeySet dispatchKeySet, at::TensorList matrices) { + return at::_ops::chain_matmul::redispatch(dispatchKeySet, matrices); + } + + // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & chain_matmul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList matrices) { + return at::_ops::chain_matmul_out::redispatch(dispatchKeySet, matrices, out); + } + + // aten::chain_matmul.out(Tensor[] matrices, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & chain_matmul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList matrices, at::Tensor & out) { + return at::_ops::chain_matmul_out::redispatch(dispatchKeySet, matrices, out); + } + + // aten::unsafe_chunk(Tensor self, int chunks, int dim=0) -> Tensor[] + inline ::std::vector unsafe_chunk(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t chunks, int64_t dim=0) { + return at::_ops::unsafe_chunk::redispatch(dispatchKeySet, self, chunks, dim); + } + + // aten::chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[] + inline ::std::vector chunk(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t chunks, int64_t dim=0) { + return at::_ops::chunk::redispatch(dispatchKeySet, self, chunks, dim); + } + + // aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[] + inline ::std::vector tensor_split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections, int64_t dim=0) { + return at::_ops::tensor_split_sections::redispatch(dispatchKeySet, self, sections, dim); + } + + // aten::tensor_split.sections(Tensor(a -> *) self, SymInt sections, int dim=0) -> Tensor(a)[] + inline ::std::vector tensor_split_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt sections, int64_t dim=0) { + return at::_ops::tensor_split_sections::redispatch(dispatchKeySet, self, sections, dim); + } + + // aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[] + inline ::std::vector tensor_split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices, int64_t dim=0) { + return at::_ops::tensor_split_indices::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(indices), dim); + } + + // aten::tensor_split.indices(Tensor(a -> *) self, SymInt[] indices, int dim=0) -> Tensor(a)[] + inline ::std::vector tensor_split_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim=0) { + return at::_ops::tensor_split_indices::redispatch(dispatchKeySet, self, indices, dim); + } + + // aten::tensor_split.tensor_indices_or_sections(Tensor(a -> *) self, Tensor tensor_indices_or_sections, int dim=0) -> Tensor(a)[] + inline ::std::vector tensor_split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim=0) { + return at::_ops::tensor_split_tensor_indices_or_sections::redispatch(dispatchKeySet, self, tensor_indices_or_sections, dim); + } + + // aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor + inline at::Tensor clamp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clamp::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clamp.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor + inline at::Tensor clamp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clamp_Tensor::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clamp_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) + inline at::Tensor & clamp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clamp_::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clamp_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) + inline at::Tensor & clamp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clamp__Tensor::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clamp_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clamp.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out) { + return at::_ops::clamp_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clamp_Tensor_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clamp.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out) { + return at::_ops::clamp_Tensor_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clamp_max(Tensor self, Scalar max) -> Tensor + inline at::Tensor clamp_max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & max) { + return at::_ops::clamp_max::redispatch(dispatchKeySet, self, max); + } + + // aten::clamp_max.Tensor(Tensor self, Tensor max) -> Tensor + inline at::Tensor clamp_max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & max) { + return at::_ops::clamp_max_Tensor::redispatch(dispatchKeySet, self, max); + } + + // aten::clamp_max_(Tensor(a!) self, Scalar max) -> Tensor(a!) + inline at::Tensor & clamp_max_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & max) { + return at::_ops::clamp_max_::redispatch(dispatchKeySet, self, max); + } + + // aten::clamp_max_.Tensor(Tensor(a!) self, Tensor max) -> Tensor(a!) + inline at::Tensor & clamp_max_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & max) { + return at::_ops::clamp_max__Tensor::redispatch(dispatchKeySet, self, max); + } + + // aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & max) { + return at::_ops::clamp_max_out::redispatch(dispatchKeySet, self, max, out); + } + + // aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & max, at::Tensor & out) { + return at::_ops::clamp_max_out::redispatch(dispatchKeySet, self, max, out); + } + + // aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & max) { + return at::_ops::clamp_max_Tensor_out::redispatch(dispatchKeySet, self, max, out); + } + + // aten::clamp_max.Tensor_out(Tensor self, Tensor max, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & max, at::Tensor & out) { + return at::_ops::clamp_max_Tensor_out::redispatch(dispatchKeySet, self, max, out); + } + + // aten::clamp_min(Tensor self, Scalar min) -> Tensor + inline at::Tensor clamp_min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min) { + return at::_ops::clamp_min::redispatch(dispatchKeySet, self, min); + } + + // aten::clamp_min.Tensor(Tensor self, Tensor min) -> Tensor + inline at::Tensor clamp_min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & min) { + return at::_ops::clamp_min_Tensor::redispatch(dispatchKeySet, self, min); + } + + // aten::clamp_min_(Tensor(a!) self, Scalar min) -> Tensor(a!) + inline at::Tensor & clamp_min_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & min) { + return at::_ops::clamp_min_::redispatch(dispatchKeySet, self, min); + } + + // aten::clamp_min_.Tensor(Tensor(a!) self, Tensor min) -> Tensor(a!) + inline at::Tensor & clamp_min_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & min) { + return at::_ops::clamp_min__Tensor::redispatch(dispatchKeySet, self, min); + } + + // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & min) { + return at::_ops::clamp_min_out::redispatch(dispatchKeySet, self, min, out); + } + + // aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min, at::Tensor & out) { + return at::_ops::clamp_min_out::redispatch(dispatchKeySet, self, min, out); + } + + // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & min) { + return at::_ops::clamp_min_Tensor_out::redispatch(dispatchKeySet, self, min, out); + } + + // aten::clamp_min.Tensor_out(Tensor self, Tensor min, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clamp_min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & min, at::Tensor & out) { + return at::_ops::clamp_min_Tensor_out::redispatch(dispatchKeySet, self, min, out); + } + + // aten::clip(Tensor self, Scalar? min=None, Scalar? max=None) -> Tensor + inline at::Tensor clip(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clip::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clip.Tensor(Tensor self, Tensor? min=None, Tensor? max=None) -> Tensor + inline at::Tensor clip(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clip_Tensor::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clip_(Tensor(a!) self, Scalar? min=None, Scalar? max=None) -> Tensor(a!) + inline at::Tensor & clip_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clip_::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clip_.Tensor(Tensor(a!) self, Tensor? min=None, Tensor? max=None) -> Tensor(a!) + inline at::Tensor & clip_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clip__Tensor::redispatch(dispatchKeySet, self, min, max); + } + + // aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clip_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & min, const c10::optional & max=c10::nullopt) { + return at::_ops::clip_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clip.out(Tensor self, Scalar? min=None, Scalar? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clip_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out) { + return at::_ops::clip_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clip_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & min={}, const c10::optional & max={}) { + return at::_ops::clip_Tensor_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::clip.Tensor_out(Tensor self, Tensor? min=None, Tensor? max=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clip_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & min, const c10::optional & max, at::Tensor & out) { + return at::_ops::clip_Tensor_out::redispatch(dispatchKeySet, self, min, max, out); + } + + // aten::cudnn_is_acceptable(Tensor self) -> bool + inline bool cudnn_is_acceptable(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::cudnn_is_acceptable::redispatch(dispatchKeySet, self); + } + + // aten::complex(Tensor real, Tensor imag) -> Tensor + inline at::Tensor complex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & real, const at::Tensor & imag) { + return at::_ops::complex::redispatch(dispatchKeySet, real, imag); + } + + // aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & complex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & real, const at::Tensor & imag) { + return at::_ops::complex_out::redispatch(dispatchKeySet, real, imag, out); + } + + // aten::complex.out(Tensor real, Tensor imag, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & complex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & real, const at::Tensor & imag, at::Tensor & out) { + return at::_ops::complex_out::redispatch(dispatchKeySet, real, imag, out); + } + + // aten::polar(Tensor abs, Tensor angle) -> Tensor + inline at::Tensor polar(c10::DispatchKeySet dispatchKeySet, const at::Tensor & abs, const at::Tensor & angle) { + return at::_ops::polar::redispatch(dispatchKeySet, abs, angle); + } + + // aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & polar_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & abs, const at::Tensor & angle) { + return at::_ops::polar_out::redispatch(dispatchKeySet, abs, angle, out); + } + + // aten::polar.out(Tensor abs, Tensor angle, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & polar_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & abs, const at::Tensor & angle, at::Tensor & out) { + return at::_ops::polar_out::redispatch(dispatchKeySet, abs, angle, out); + } + + // aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor + inline at::Tensor constant_pad_nd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) { + return at::_ops::constant_pad_nd::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad), value); + } + + // aten::constant_pad_nd(Tensor self, SymInt[] pad, Scalar value=0) -> Tensor + inline at::Tensor constant_pad_nd_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) { + return at::_ops::constant_pad_nd::redispatch(dispatchKeySet, self, pad, value); + } + + // aten::contiguous(Tensor(a) self, *, MemoryFormat memory_format=contiguous_format) -> Tensor(a) + inline at::Tensor __dispatch_contiguous(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::MemoryFormat memory_format=MemoryFormat::Contiguous) { + return at::_ops::contiguous::redispatch(dispatchKeySet, self, memory_format); + } + + // aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor + inline at::Tensor convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) { + return at::_ops::convolution::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups); + } + + // aten::convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor + inline at::Tensor convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + return at::_ops::convolution::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); + } + + // aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple convolution_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask) { + return at::_ops::convolution_backward::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : c10::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask); + } + + // aten::convolution_backward(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple convolution_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::convolution_backward::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + + // aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor + inline at::Tensor convolution_overrideable(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) { + return at::_ops::convolution_overrideable::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups); + } + + // aten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor + inline at::Tensor convolution_overrideable_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + return at::_ops::convolution_overrideable::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); + } + + // aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) + inline ::std::tuple convolution_backward_overrideable(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask) { + return at::_ops::convolution_backward_overrideable::redispatch(dispatchKeySet, grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask); + } + + // aten::convolution_backward_overrideable(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) + inline ::std::tuple convolution_backward_overrideable_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::convolution_backward_overrideable::redispatch(dispatchKeySet, grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + + // aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor + inline at::Tensor _convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) { + return at::_ops::_convolution::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32); + } + + // aten::_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> Tensor + inline at::Tensor _convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) { + return at::_ops::_convolution::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32); + } + + // aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor + inline at::Tensor _convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled) { + return at::_ops::_convolution_deprecated::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); + } + + // aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, int[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> Tensor + inline at::Tensor _convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) { + return at::_ops::_convolution_deprecated::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); + } + + // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _convolution_mode(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::_convolution_mode::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::_convolution_mode(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, str padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _convolution_mode_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::_convolution_mode::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _convolution_double_backward(c10::DispatchKeySet dispatchKeySet, const c10::optional & ggI, const c10::optional & ggW, const c10::optional & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask) { + return at::_ops::_convolution_double_backward::redispatch(dispatchKeySet, ggI, ggW, ggb, gO, weight, self, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask); + } + + // aten::_convolution_double_backward(Tensor? ggI, Tensor? ggW, Tensor? ggb, Tensor gO, Tensor weight, Tensor self, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _convolution_double_backward_symint(c10::DispatchKeySet dispatchKeySet, const c10::optional & ggI, const c10::optional & ggW, const c10::optional & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::_convolution_double_backward::redispatch(dispatchKeySet, ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + + // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv1d::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv1d::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv2d::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv2d::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv3d::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv3d::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv1d_padding::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv1d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, str padding="valid", SymInt[1] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv1d_padding::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv2d_padding::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv2d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, str padding="valid", SymInt[2] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv2d_padding::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, c10::string_view padding, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::conv3d_padding::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), padding, c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::conv3d.padding(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, str padding="valid", SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor conv3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::conv3d_padding::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, groups); + } + + // aten::conv_tbc(Tensor self, Tensor weight, Tensor bias, int pad=0) -> Tensor + inline at::Tensor conv_tbc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0) { + return at::_ops::conv_tbc::redispatch(dispatchKeySet, self, weight, bias, pad); + } + + // aten::conv_tbc_backward(Tensor self, Tensor input, Tensor weight, Tensor bias, int pad) -> (Tensor, Tensor, Tensor) + inline ::std::tuple conv_tbc_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { + return at::_ops::conv_tbc_backward::redispatch(dispatchKeySet, self, input, weight, bias, pad); + } + + // aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor + inline at::Tensor conv_transpose1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) { + return at::_ops::conv_transpose1d::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation)); + } + + // aten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor + inline at::Tensor conv_transpose1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::conv_transpose1d::redispatch(dispatchKeySet, input, weight, bias, stride, padding, output_padding, groups, dilation); + } + + // aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor + inline at::Tensor conv_transpose2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) { + return at::_ops::conv_transpose2d_input::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation)); + } + + // aten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt groups=1, SymInt[2] dilation=1) -> Tensor + inline at::Tensor conv_transpose2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::conv_transpose2d_input::redispatch(dispatchKeySet, input, weight, bias, stride, padding, output_padding, groups, dilation); + } + + // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor + inline at::Tensor conv_transpose3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, int64_t groups=1, at::IntArrayRef dilation=1) { + return at::_ops::conv_transpose3d_input::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), groups, c10::fromIntArrayRefSlow(dilation)); + } + + // aten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt groups=1, SymInt[3] dilation=1) -> Tensor + inline at::Tensor conv_transpose3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::conv_transpose3d_input::redispatch(dispatchKeySet, input, weight, bias, stride, padding, output_padding, groups, dilation); + } + + // aten::copy(Tensor self, Tensor src, bool non_blocking=False) -> Tensor + inline at::Tensor copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) + inline at::Tensor & copy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy_::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::_copy_from(Tensor self, Tensor dst, bool non_blocking=False) -> Tensor + inline at::Tensor _copy_from(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, bool non_blocking=false) { + return at::_ops::_copy_from::redispatch(dispatchKeySet, self, dst, non_blocking); + } + + // aten::_copy_from_and_resize(Tensor self, Tensor dst) -> Tensor + inline at::Tensor _copy_from_and_resize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst) { + return at::_ops::_copy_from_and_resize::redispatch(dispatchKeySet, self, dst); + } + + // aten::cos(Tensor self) -> Tensor + inline at::Tensor cos(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::cos::redispatch(dispatchKeySet, self); + } + + // aten::cos_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & cos_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::cos_::redispatch(dispatchKeySet, self); + } + + // aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cos_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::cos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cos_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::cos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::cosh(Tensor self) -> Tensor + inline at::Tensor cosh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::cosh::redispatch(dispatchKeySet, self); + } + + // aten::cosh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & cosh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::cosh_::redispatch(dispatchKeySet, self); + } + + // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cosh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::cosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cosh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::cosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::cosine_embedding_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor + inline at::Tensor cosine_embedding_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean) { + return at::_ops::cosine_embedding_loss::redispatch(dispatchKeySet, input1, input2, target, margin, reduction); + } + + // aten::count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor + inline at::Tensor count_nonzero(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::count_nonzero_dim_IntList::redispatch(dispatchKeySet, self, dim); + } + + // aten::count_nonzero(Tensor self, int? dim=None) -> Tensor + inline at::Tensor count_nonzero(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim=c10::nullopt) { + return at::_ops::count_nonzero::redispatch(dispatchKeySet, self, dim); + } + + // aten::cov(Tensor self, *, int correction=1, Tensor? fweights=None, Tensor? aweights=None) -> Tensor + inline at::Tensor cov(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t correction=1, const c10::optional & fweights={}, const c10::optional & aweights={}) { + return at::_ops::cov::redispatch(dispatchKeySet, self, correction, fweights, aweights); + } + + // aten::corrcoef(Tensor self) -> Tensor + inline at::Tensor corrcoef(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::corrcoef::redispatch(dispatchKeySet, self); + } + + // aten::cudnn_affine_grid_generator(Tensor theta, int N, int C, int H, int W) -> Tensor grid + inline at::Tensor cudnn_affine_grid_generator(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) { + return at::_ops::cudnn_affine_grid_generator::redispatch(dispatchKeySet, theta, N, C, H, W); + } + + // aten::cudnn_affine_grid_generator_backward(Tensor grad, int N, int C, int H, int W) -> Tensor grad_theta + inline at::Tensor cudnn_affine_grid_generator_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { + return at::_ops::cudnn_affine_grid_generator_backward::redispatch(dispatchKeySet, grad, N, C, H, W); + } + + // aten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple cudnn_batch_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + return at::_ops::cudnn_batch_norm::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); + } + + // aten::cudnn_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace) -> (Tensor, Tensor, Tensor) + inline ::std::tuple cudnn_batch_norm_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon, const at::Tensor & reserveSpace) { + return at::_ops::cudnn_batch_norm_backward::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace); + } + + // aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor + inline at::Tensor cudnn_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32); + } + + // aten::cudnn_convolution(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor + inline at::Tensor cudnn_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution::redispatch(dispatchKeySet, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + } + + // aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) { + return at::_ops::cudnn_convolution_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_out::redispatch(dispatchKeySet, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) { + return at::_ops::cudnn_convolution_out::redispatch(dispatchKeySet, self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor + inline at::Tensor cudnn_convolution_transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_transpose::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32); + } + + // aten::cudnn_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) -> Tensor + inline at::Tensor cudnn_convolution_transpose_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_transpose::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + } + + // aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _mps_convolution_transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::_mps_convolution_transpose::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::_mps_convolution_transpose(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _mps_convolution_transpose_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::_mps_convolution_transpose::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups); + } + + // aten::mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor) + inline ::std::tuple mps_convolution_transpose_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask) { + return at::_ops::mps_convolution_transpose_backward::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask); + } + + // aten::mps_convolution_transpose_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask) -> (Tensor, Tensor) + inline ::std::tuple mps_convolution_transpose_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::mps_convolution_transpose_backward::redispatch(dispatchKeySet, self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask); + } + + // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor cudnn_convolution_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::cudnn_convolution_relu::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::cudnn_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor cudnn_convolution_relu_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::cudnn_convolution_relu::redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups); + } + + // aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor cudnn_convolution_add_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::cudnn_convolution_add_relu::redispatch(dispatchKeySet, self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::cudnn_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor cudnn_convolution_add_relu_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::cudnn_convolution_add_relu::redispatch(dispatchKeySet, self, weight, z, alpha, bias, stride, padding, dilation, groups); + } + + // aten::cudnn_grid_sampler(Tensor self, Tensor grid) -> Tensor output + inline at::Tensor cudnn_grid_sampler(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid) { + return at::_ops::cudnn_grid_sampler::redispatch(dispatchKeySet, self, grid); + } + + // aten::cudnn_grid_sampler_backward(Tensor self, Tensor grid, Tensor grad_output) -> (Tensor grad_self, Tensor grad_grid) + inline ::std::tuple cudnn_grid_sampler_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output) { + return at::_ops::cudnn_grid_sampler_backward::redispatch(dispatchKeySet, self, grid, grad_output); + } + + // aten::cummax(Tensor self, int dim) -> (Tensor values, Tensor indices) + inline ::std::tuple cummax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::cummax::redispatch(dispatchKeySet, self, dim); + } + + // aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) { + return at::_ops::cummax_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummax.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::cummax_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummax.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) + inline ::std::tuple cummax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::cummax_dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) { + return at::_ops::cummax_dimname_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummax.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::cummax_dimname_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::_cummax_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () + inline void _cummax_helper(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { + return at::_ops::_cummax_helper::redispatch(dispatchKeySet, self, values, indices, dim); + } + + // aten::cummin(Tensor self, int dim) -> (Tensor values, Tensor indices) + inline ::std::tuple cummin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::cummin::redispatch(dispatchKeySet, self, dim); + } + + // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim) { + return at::_ops::cummin_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummin.out(Tensor self, int dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::cummin_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummin.dimname(Tensor self, Dimname dim) -> (Tensor values, Tensor indices) + inline ::std::tuple cummin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::cummin_dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim) { + return at::_ops::cummin_dimname_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::cummin.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple cummin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::cummin_dimname_out::redispatch(dispatchKeySet, self, dim, values, indices); + } + + // aten::_cummin_helper(Tensor self, Tensor(a!) values, Tensor(b!) indices, int dim) -> () + inline void _cummin_helper(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { + return at::_ops::_cummin_helper::redispatch(dispatchKeySet, self, values, indices, dim); + } + + // aten::cummaxmin_backward(Tensor grad, Tensor input, Tensor indices, int dim) -> Tensor + inline at::Tensor cummaxmin_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, const at::Tensor & indices, int64_t dim) { + return at::_ops::cummaxmin_backward::redispatch(dispatchKeySet, grad, input, indices, dim); + } + + // aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor cumprod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumprod_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) + inline at::Tensor & cumprod_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod_::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumprod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumprod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::cumprod_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumprod.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor cumprod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod_dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumprod_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) + inline at::Tensor & cumprod_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod__dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumprod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumprod_dimname_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumprod.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumprod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::cumprod_dimname_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumprod_backward(Tensor grad, Tensor input, int dim, Tensor output) -> Tensor + inline at::Tensor cumprod_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, int64_t dim, const at::Tensor & output) { + return at::_ops::cumprod_backward::redispatch(dispatchKeySet, grad, input, dim, output); + } + + // aten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor cumsum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumsum_(Tensor(a!) self, int dim, *, ScalarType? dtype=None) -> Tensor(a!) + inline at::Tensor & cumsum_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum_::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumsum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumsum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::cumsum_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumsum.dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor cumsum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum_dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumsum_.dimname(Tensor(a!) self, Dimname dim, *, ScalarType? dtype=None) -> Tensor(a!) + inline at::Tensor & cumsum_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum__dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumsum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::cumsum_dimname_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumsum.dimname_out(Tensor self, Dimname dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cumsum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::cumsum_dimname_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::cumulative_trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor + inline at::Tensor cumulative_trapezoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) { + return at::_ops::cumulative_trapezoid_x::redispatch(dispatchKeySet, y, x, dim); + } + + // aten::cumulative_trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor + inline at::Tensor cumulative_trapezoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) { + return at::_ops::cumulative_trapezoid_dx::redispatch(dispatchKeySet, y, dx, dim); + } + + // aten::ctc_loss.IntList(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor + inline at::Tensor ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false) { + return at::_ops::ctc_loss_IntList::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); + } + + // aten::ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, int reduction=Mean, bool zero_infinity=False) -> Tensor + inline at::Tensor ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, int64_t reduction=at::Reduction::Mean, bool zero_infinity=false) { + return at::_ops::ctc_loss_Tensor::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); + } + + // aten::_ctc_loss(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) + inline ::std::tuple _ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); + } + + // aten::_ctc_loss.Tensor(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False) -> (Tensor, Tensor) + inline ::std::tuple _ctc_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_Tensor::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); + } + + // aten::_ctc_loss_backward(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor + inline at::Tensor _ctc_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) { + return at::_ops::_ctc_loss_backward::redispatch(dispatchKeySet, grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); + } + + // aten::_ctc_loss_backward.Tensor(Tensor grad, Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False) -> Tensor + inline at::Tensor _ctc_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) { + return at::_ops::_ctc_loss_backward_Tensor::redispatch(dispatchKeySet, grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); + } + + // aten::diag_embed(Tensor self, int offset=0, int dim1=-2, int dim2=-1) -> Tensor + inline at::Tensor diag_embed(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) { + return at::_ops::diag_embed::redispatch(dispatchKeySet, self, offset, dim1, dim2); + } + + // aten::diagflat(Tensor self, int offset=0) -> Tensor + inline at::Tensor diagflat(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset=0) { + return at::_ops::diagflat::redispatch(dispatchKeySet, self, offset); + } + + // aten::diagonal(Tensor(a) self, int offset=0, int dim1=0, int dim2=1) -> Tensor(a) + inline at::Tensor diagonal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) { + return at::_ops::diagonal::redispatch(dispatchKeySet, self, offset, dim1, dim2); + } + + // aten::linalg_diagonal(Tensor(a) A, *, int offset=0, int dim1=-2, int dim2=-1) -> Tensor(a) + inline at::Tensor linalg_diagonal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) { + return at::_ops::linalg_diagonal::redispatch(dispatchKeySet, A, offset, dim1, dim2); + } + + // aten::diagonal.Dimname(Tensor(a) self, *, Dimname outdim, Dimname dim1, Dimname dim2, int offset=0) -> Tensor(a) + inline at::Tensor diagonal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset=0) { + return at::_ops::diagonal_Dimname::redispatch(dispatchKeySet, self, outdim, dim1, dim2, offset); + } + + // aten::diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor + inline at::Tensor diagonal_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) { + return at::_ops::diagonal_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2); + } + + // aten::diagonal_backward(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2) -> Tensor + inline at::Tensor diagonal_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) { + return at::_ops::diagonal_backward::redispatch(dispatchKeySet, grad_output, input_sizes, offset, dim1, dim2); + } + + // aten::fill_diagonal_(Tensor(a!) self, Scalar fill_value, bool wrap=False) -> Tensor(a!) + inline at::Tensor & fill_diagonal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & fill_value, bool wrap=false) { + return at::_ops::fill_diagonal_::redispatch(dispatchKeySet, self, fill_value, wrap); + } + + // aten::diff(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None) -> Tensor + inline at::Tensor diff(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n=1, int64_t dim=-1, const c10::optional & prepend={}, const c10::optional & append={}) { + return at::_ops::diff::redispatch(dispatchKeySet, self, n, dim, prepend, append); + } + + // aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diff_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t n=1, int64_t dim=-1, const c10::optional & prepend={}, const c10::optional & append={}) { + return at::_ops::diff_out::redispatch(dispatchKeySet, self, n, dim, prepend, append, out); + } + + // aten::diff.out(Tensor self, int n=1, int dim=-1, Tensor? prepend=None, Tensor? append=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diff_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n, int64_t dim, const c10::optional & prepend, const c10::optional & append, at::Tensor & out) { + return at::_ops::diff_out::redispatch(dispatchKeySet, self, n, dim, prepend, append, out); + } + + // aten::gradient.scalarint(Tensor self, *, Scalar? spacing=None, int? dim=None, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & spacing=c10::nullopt, c10::optional dim=c10::nullopt, int64_t edge_order=1) { + return at::_ops::gradient_scalarint::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::gradient.scalararray(Tensor self, *, Scalar spacing, int[] dim, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order=1) { + return at::_ops::gradient_scalararray::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::gradient.array(Tensor self, *, int[] dim, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order=1) { + return at::_ops::gradient_array::redispatch(dispatchKeySet, self, dim, edge_order); + } + + // aten::gradient.scalarrayint(Tensor self, *, Scalar[] spacing, int? dim=None, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ArrayRef spacing, c10::optional dim=c10::nullopt, int64_t edge_order=1) { + return at::_ops::gradient_scalarrayint::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::gradient.scalarrayarray(Tensor self, *, Scalar[] spacing, int[] dim, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ArrayRef spacing, at::IntArrayRef dim, int64_t edge_order=1) { + return at::_ops::gradient_scalarrayarray::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::gradient.tensorarrayint(Tensor self, *, Tensor[] spacing, int? dim=None, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList spacing, c10::optional dim=c10::nullopt, int64_t edge_order=1) { + return at::_ops::gradient_tensorarrayint::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::gradient.tensorarray(Tensor self, *, Tensor[] spacing, int[] dim, int edge_order=1) -> Tensor[] + inline ::std::vector gradient(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order=1) { + return at::_ops::gradient_tensorarray::redispatch(dispatchKeySet, self, spacing, dim, edge_order); + } + + // aten::div.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor div(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::div_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::div_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & div_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::div__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::div_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::div_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor + inline at::Tensor div(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::div_Tensor_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::div_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) + inline at::Tensor & div_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::div__Tensor_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::div_out_mode::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::div.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode, at::Tensor & out) { + return at::_ops::div_out_mode::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::div.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor div(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::div_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::div_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & div_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::div__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::div.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor + inline at::Tensor div(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + return at::_ops::div_Scalar_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::div_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) + inline at::Tensor & div_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + return at::_ops::div__Scalar_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::divide.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::divide_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::divide__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & divide_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & divide_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::divide.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::divide_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::divide__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::divide.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> Tensor + inline at::Tensor divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::divide_Tensor_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::divide_.Tensor_mode(Tensor(a!) self, Tensor other, *, str? rounding_mode) -> Tensor(a!) + inline at::Tensor & divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::divide__Tensor_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & divide_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + return at::_ops::divide_out_mode::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::divide.out_mode(Tensor self, Tensor other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & divide_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode, at::Tensor & out) { + return at::_ops::divide_out_mode::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::divide.Scalar_mode(Tensor self, Scalar other, *, str? rounding_mode) -> Tensor + inline at::Tensor divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + return at::_ops::divide_Scalar_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::divide_.Scalar_mode(Tensor(a!) self, Scalar other, *, str? rounding_mode) -> Tensor(a!) + inline at::Tensor & divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + return at::_ops::divide__Scalar_mode::redispatch(dispatchKeySet, self, other, rounding_mode); + } + + // aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor true_divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::true_divide_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::true_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & true_divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::true_divide__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & true_divide_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::true_divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & true_divide_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::true_divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor true_divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::true_divide_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::true_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & true_divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::true_divide__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::dot(Tensor self, Tensor tensor) -> Tensor + inline at::Tensor dot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor) { + return at::_ops::dot::redispatch(dispatchKeySet, self, tensor); + } + + // aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor) { + return at::_ops::dot_out::redispatch(dispatchKeySet, self, tensor, out); + } + + // aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dot_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor, at::Tensor & out) { + return at::_ops::dot_out::redispatch(dispatchKeySet, self, tensor, out); + } + + // aten::vdot(Tensor self, Tensor other) -> Tensor + inline at::Tensor vdot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::vdot::redispatch(dispatchKeySet, self, other); + } + + // aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & vdot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::vdot_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::vdot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & vdot_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::vdot_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::einsum(str equation, Tensor[] tensors, *, int[]? path=None) -> Tensor + inline at::Tensor einsum(c10::DispatchKeySet dispatchKeySet, c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path=c10::nullopt) { + return at::_ops::einsum::redispatch(dispatchKeySet, equation, tensors, path); + } + + // aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor + inline at::Tensor embedding(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) { + return at::_ops::embedding::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse); + } + + // aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> Tensor + inline at::Tensor embedding_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) { + return at::_ops::embedding::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse); + } + + // aten::embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor + inline at::Tensor embedding_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, bool sparse) { + return at::_ops::embedding_backward::redispatch(dispatchKeySet, grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse); + } + + // aten::embedding_backward(Tensor grad, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, bool sparse) -> Tensor + inline at::Tensor embedding_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) { + return at::_ops::embedding_backward::redispatch(dispatchKeySet, grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse); + } + + // aten::embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor + inline at::Tensor embedding_dense_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { + return at::_ops::embedding_dense_backward::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); + } + + // aten::embedding_dense_backward(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq) -> Tensor + inline at::Tensor embedding_dense_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) { + return at::_ops::embedding_dense_backward::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); + } + + // aten::embedding_renorm_(Tensor(a!) self, Tensor indices, float max_norm, float norm_type) -> Tensor(a!) + inline at::Tensor & embedding_renorm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { + return at::_ops::embedding_renorm_::redispatch(dispatchKeySet, self, indices, max_norm, norm_type); + } + + // aten::embedding_sparse_backward(Tensor grad, Tensor indices, int num_weights, int padding_idx, bool scale_grad_by_freq) -> Tensor + inline at::Tensor embedding_sparse_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { + return at::_ops::embedding_sparse_backward::redispatch(dispatchKeySet, grad, indices, num_weights, padding_idx, scale_grad_by_freq); + } + + // aten::_embedding_bag_forward_only(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _embedding_bag_forward_only(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_forward_only::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + + // aten::_rowwise_prune(Tensor weight, Tensor mask, ScalarType compressed_indices_dtype) -> (Tensor, Tensor) + inline ::std::tuple _rowwise_prune(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype) { + return at::_ops::_rowwise_prune::redispatch(dispatchKeySet, weight, mask, compressed_indices_dtype); + } + + // aten::row_stack(Tensor[] tensors) -> Tensor + inline at::Tensor row_stack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::row_stack::redispatch(dispatchKeySet, tensors); + } + + // aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & row_stack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::row_stack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::row_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & row_stack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::row_stack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple embedding_bag(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional & per_sample_weights={}, bool include_last_offset=false) { + return at::_ops::embedding_bag::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset); + } + + // aten::embedding_bag.padding_idx(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, bool include_last_offset, int? padding_idx) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple embedding_bag(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, c10::optional padding_idx) { + return at::_ops::embedding_bag_padding_idx::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + + // aten::_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _embedding_bag(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + + // aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_backward::redispatch(dispatchKeySet, grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, bool sparse, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_backward::redispatch(dispatchKeySet, grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_sparse_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_sparse_backward::redispatch(dispatchKeySet, grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_sparse_backward(Tensor grad, Tensor indices, Tensor offsets, Tensor offset2bag, Tensor bag_size, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_sparse_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_sparse_backward::redispatch(dispatchKeySet, grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_dense_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_dense_backward::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_dense_backward(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_dense_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_dense_backward::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + + // aten::_embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor + inline at::Tensor _embedding_bag_per_sample_weights_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_per_sample_weights_backward::redispatch(dispatchKeySet, grad, weight, indices, offsets, offset2bag, mode, padding_idx); + } + + // aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_names::redispatch(dispatchKeySet, size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::empty.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::empty_names::redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory, memory_format); + } + + // aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_memory_format::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::empty_memory_format::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, memory_format); + } + + // aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_memory_format::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::empty.memory_format(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::empty_memory_format::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory, memory_format); + } + + // aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_permuted(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) { + return at::_ops::empty_permuted::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_permuted(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef physical_layout, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::empty_permuted::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), physical_layout, dtype, layout, device, pin_memory); + } + + // aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_permuted_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::TensorOptions options={}) { + return at::_ops::empty_permuted::redispatch(dispatchKeySet, size, physical_layout, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::empty_permuted(SymInt[] size, int[] physical_layout, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_permuted_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::IntArrayRef physical_layout, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::empty_permuted::redispatch(dispatchKeySet, size, physical_layout, dtype, layout, device, pin_memory); + } + + // aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_empty::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_empty::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_empty::redispatch(dispatchKeySet, self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_empty(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_empty::redispatch(dispatchKeySet, self, size, dtype, layout, device, pin_memory); + } + + // aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_strided(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options={}) { + return at::_ops::new_empty_strided::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_strided(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_empty_strided::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory); + } + + // aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_strided_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options={}) { + return at::_ops::new_empty_strided::redispatch(dispatchKeySet, self, size, stride, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_empty_strided(Tensor self, SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_empty_strided_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_empty_strided::redispatch(dispatchKeySet, self, size, stride, dtype, layout, device, pin_memory); + } + + // aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_full(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) { + return at::_ops::new_full::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_full(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_full::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), fill_value, dtype, layout, device, pin_memory); + } + + // aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_full_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) { + return at::_ops::new_full::redispatch(dispatchKeySet, self, size, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_full(Tensor self, SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_full_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_full::redispatch(dispatchKeySet, self, size, fill_value, dtype, layout, device, pin_memory); + } + + // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_zeros(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_zeros::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_zeros(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_zeros::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_zeros_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_zeros::redispatch(dispatchKeySet, self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_zeros(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_zeros_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_zeros::redispatch(dispatchKeySet, self, size, dtype, layout, device, pin_memory); + } + + // aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_ones(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_ones::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_ones(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_ones::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_ones_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::new_ones::redispatch(dispatchKeySet, self, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::new_ones(Tensor self, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor new_ones_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::new_ones::redispatch(dispatchKeySet, self, size, dtype, layout, device, pin_memory); + } + + // aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_affine_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_affine_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, scale, zero_point, memory_format); + } + + // aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_affine_quantized_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), scale, zero_point, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_empty_affine_quantized(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_affine_quantized_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, double scale, int64_t zero_point, c10::optional memory_format) { + return at::_ops::_empty_affine_quantized::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory, scale, zero_point, memory_format); + } + + // aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_per_channel_affine_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_per_channel_affine_quantized::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_per_channel_affine_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::_empty_per_channel_affine_quantized::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format); + } + + // aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_per_channel_affine_quantized_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_per_channel_affine_quantized::redispatch(dispatchKeySet, size, scales, zero_points, axis, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_empty_per_channel_affine_quantized(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=contiguous_format) -> Tensor + inline at::Tensor _empty_per_channel_affine_quantized_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::_empty_per_channel_affine_quantized::redispatch(dispatchKeySet, size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format); + } + + // aten::resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!) + inline const at::Tensor & resize_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), memory_format); + } + + // aten::resize_(Tensor(a!) self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor(a!) + inline const at::Tensor & resize__symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_::redispatch(dispatchKeySet, self, size, memory_format); + } + + // aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!) + inline const at::Tensor & _resize_output_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device) { + return at::_ops::_resize_output_::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), device); + } + + // aten::_resize_output_(Tensor(a!) self, SymInt[] size, Device device) -> Tensor(a!) + inline const at::Tensor & _resize_output__symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) { + return at::_ops::_resize_output_::redispatch(dispatchKeySet, self, size, device); + } + + // aten::empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & qtensor, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_quantized::redispatch(dispatchKeySet, size, qtensor, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::empty_quantized(int[] size, Tensor qtensor, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_quantized(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & qtensor, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::empty_quantized::redispatch(dispatchKeySet, size, qtensor, dtype, layout, device, pin_memory, memory_format); + } + + // aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), memory_format, out); + } + + // aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional memory_format, at::Tensor & out) { + return at::_ops::empty_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), memory_format, out); + } + + // aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_out::redispatch(dispatchKeySet, size, memory_format, out); + } + + // aten::empty.out(SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional memory_format, at::Tensor & out) { + return at::_ops::empty_out::redispatch(dispatchKeySet, size, memory_format, out); + } + + // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_like::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::empty_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor empty_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::empty_like::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); + } + + // aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_strided(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef stride, at::TensorOptions options={}) { + return at::_ops::empty_strided::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_strided(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef stride, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::empty_strided::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), dtype, layout, device, pin_memory); + } + + // aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_strided_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::TensorOptions options={}) { + return at::_ops::empty_strided::redispatch(dispatchKeySet, size, stride, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::empty_strided(SymInt[] size, SymInt[] stride, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor empty_strided_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::empty_strided::redispatch(dispatchKeySet, size, stride, dtype, layout, device, pin_memory); + } + + // aten::erf(Tensor self) -> Tensor + inline at::Tensor erf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::erf::redispatch(dispatchKeySet, self); + } + + // aten::erf_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & erf_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::erf_::redispatch(dispatchKeySet, self); + } + + // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::erfc(Tensor self) -> Tensor + inline at::Tensor erfc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::erfc::redispatch(dispatchKeySet, self); + } + + // aten::erfc_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & erfc_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::erfc_::redispatch(dispatchKeySet, self); + } + + // aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erfc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erfc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::exp(Tensor self) -> Tensor + inline at::Tensor exp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::exp::redispatch(dispatchKeySet, self); + } + + // aten::exp_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & exp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::exp_::redispatch(dispatchKeySet, self); + } + + // aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::exp2(Tensor self) -> Tensor + inline at::Tensor exp2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::exp2::redispatch(dispatchKeySet, self); + } + + // aten::exp2_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & exp2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::exp2_::redispatch(dispatchKeySet, self); + } + + // aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exp2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::exp2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exp2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::exp2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::expm1(Tensor self) -> Tensor + inline at::Tensor expm1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::expm1::redispatch(dispatchKeySet, self); + } + + // aten::expm1_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & expm1_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::expm1_::redispatch(dispatchKeySet, self); + } + + // aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expm1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expm1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) + inline at::Tensor expand(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, bool implicit=false) { + return at::_ops::expand::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), implicit); + } + + // aten::expand(Tensor(a) self, SymInt[] size, *, bool implicit=False) -> Tensor(a) + inline at::Tensor expand_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) { + return at::_ops::expand::redispatch(dispatchKeySet, self, size, implicit); + } + + // aten::expand_as(Tensor(a) self, Tensor other) -> Tensor(a) + inline at::Tensor expand_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::expand_as::redispatch(dispatchKeySet, self, other); + } + + // aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye(c10::DispatchKeySet dispatchKeySet, int64_t n, at::TensorOptions options={}) { + return at::_ops::eye::redispatch(dispatchKeySet, n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::eye::redispatch(dispatchKeySet, n, dtype, layout, device, pin_memory); + } + + // aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, at::TensorOptions options={}) { + return at::_ops::eye::redispatch(dispatchKeySet, n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::eye(SymInt n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::eye::redispatch(dispatchKeySet, n, dtype, layout, device, pin_memory); + } + + // aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye(c10::DispatchKeySet dispatchKeySet, int64_t n, int64_t m, at::TensorOptions options={}) { + return at::_ops::eye_m::redispatch(dispatchKeySet, n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye(c10::DispatchKeySet dispatchKeySet, int64_t n, int64_t m, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::eye_m::redispatch(dispatchKeySet, n, m, dtype, layout, device, pin_memory); + } + + // aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::SymInt m, at::TensorOptions options={}) { + return at::_ops::eye_m::redispatch(dispatchKeySet, n, m, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::eye.m(SymInt n, SymInt m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor eye_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::SymInt m, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::eye_m::redispatch(dispatchKeySet, n, m, dtype, layout, device, pin_memory); + } + + // aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n) { + return at::_ops::eye_out::redispatch(dispatchKeySet, n, out); + } + + // aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, at::Tensor & out) { + return at::_ops::eye_out::redispatch(dispatchKeySet, n, out); + } + + // aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt n) { + return at::_ops::eye_out::redispatch(dispatchKeySet, n, out); + } + + // aten::eye.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, at::Tensor & out) { + return at::_ops::eye_out::redispatch(dispatchKeySet, n, out); + } + + // aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, int64_t m) { + return at::_ops::eye_m_out::redispatch(dispatchKeySet, n, m, out); + } + + // aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, int64_t m, at::Tensor & out) { + return at::_ops::eye_m_out::redispatch(dispatchKeySet, n, m, out); + } + + // aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt n, c10::SymInt m) { + return at::_ops::eye_m_out::redispatch(dispatchKeySet, n, m, out); + } + + // aten::eye.m_out(SymInt n, SymInt m, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eye_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::SymInt m, at::Tensor & out) { + return at::_ops::eye_m_out::redispatch(dispatchKeySet, n, m, out); + } + + // aten::flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a) + inline at::Tensor flatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim=0, int64_t end_dim=-1) { + return at::_ops::flatten_using_ints::redispatch(dispatchKeySet, self, start_dim, end_dim); + } + + // aten::flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a) + inline at::Tensor flatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) { + return at::_ops::flatten_named_out_dim::redispatch(dispatchKeySet, self, start_dim, end_dim, out_dim); + } + + // aten::flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a) + inline at::Tensor flatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) { + return at::_ops::flatten_using_names::redispatch(dispatchKeySet, self, start_dim, end_dim, out_dim); + } + + // aten::flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a) + inline at::Tensor flatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) { + return at::_ops::flatten_DimnameList::redispatch(dispatchKeySet, self, dims, out_dim); + } + + // aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a) + inline at::Tensor unflatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::IntArrayRef sizes) { + return at::_ops::unflatten_int::redispatch(dispatchKeySet, self, dim, c10::fromIntArrayRefSlow(sizes)); + } + + // aten::unflatten.int(Tensor(a) self, int dim, SymInt[] sizes) -> Tensor(a) + inline at::Tensor unflatten_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) { + return at::_ops::unflatten_int::redispatch(dispatchKeySet, self, dim, sizes); + } + + // aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a) + inline at::Tensor unflatten(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::IntArrayRef sizes, at::DimnameList names) { + return at::_ops::unflatten_Dimname::redispatch(dispatchKeySet, self, dim, c10::fromIntArrayRefSlow(sizes), names); + } + + // aten::unflatten.Dimname(Tensor(a) self, Dimname dim, SymInt[] sizes, Dimname[] names) -> Tensor(a) + inline at::Tensor unflatten_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) { + return at::_ops::unflatten_Dimname::redispatch(dispatchKeySet, self, dim, sizes, names); + } + + // aten::fill.Scalar(Tensor self, Scalar value) -> Tensor + inline at::Tensor fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & value) { + return at::_ops::fill_Scalar::redispatch(dispatchKeySet, self, value); + } + + // aten::fill.Tensor(Tensor self, Tensor value) -> Tensor + inline at::Tensor fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & value) { + return at::_ops::fill_Tensor::redispatch(dispatchKeySet, self, value); + } + + // aten::fill_.Scalar(Tensor(a!) self, Scalar value) -> Tensor(a!) + inline at::Tensor & fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & value) { + return at::_ops::fill__Scalar::redispatch(dispatchKeySet, self, value); + } + + // aten::fill_.Tensor(Tensor(a!) self, Tensor value) -> Tensor(a!) + inline at::Tensor & fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & value) { + return at::_ops::fill__Tensor::redispatch(dispatchKeySet, self, value); + } + + // aten::floor(Tensor self) -> Tensor + inline at::Tensor floor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::floor::redispatch(dispatchKeySet, self); + } + + // aten::floor_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & floor_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::floor_::redispatch(dispatchKeySet, self); + } + + // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::floor_out::redispatch(dispatchKeySet, self, out); + } + + // aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::floor_out::redispatch(dispatchKeySet, self, out); + } + + // aten::floor_divide(Tensor self, Tensor other) -> Tensor + inline at::Tensor floor_divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::floor_divide::redispatch(dispatchKeySet, self, other); + } + + // aten::floor_divide_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & floor_divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::floor_divide__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_divide_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::floor_divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::floor_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_divide_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::floor_divide_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::floor_divide.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor floor_divide(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::floor_divide_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::floor_divide_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & floor_divide_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::floor_divide__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::frac(Tensor self) -> Tensor + inline at::Tensor frac(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::frac::redispatch(dispatchKeySet, self); + } + + // aten::frac_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & frac_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::frac_::redispatch(dispatchKeySet, self); + } + + // aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & frac_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::frac_out::redispatch(dispatchKeySet, self, out); + } + + // aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & frac_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::frac_out::redispatch(dispatchKeySet, self, out); + } + + // aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional names, at::TensorOptions options={}) { + return at::_ops::full_names::redispatch(dispatchKeySet, size, fill_value, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::full.names(int[] size, Scalar fill_value, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::full_names::redispatch(dispatchKeySet, size, fill_value, names, dtype, layout, device, pin_memory); + } + + // aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) { + return at::_ops::full::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::full::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), fill_value, dtype, layout, device, pin_memory); + } + + // aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::TensorOptions options={}) { + return at::_ops::full::redispatch(dispatchKeySet, size, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor full_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::full::redispatch(dispatchKeySet, size, fill_value, dtype, layout, device, pin_memory); + } + + // aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, const at::Scalar & fill_value) { + return at::_ops::full_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), fill_value, out); + } + + // aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) { + return at::_ops::full_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), fill_value, out); + } + + // aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, const at::Scalar & fill_value) { + return at::_ops::full_out::redispatch(dispatchKeySet, size, fill_value, out); + } + + // aten::full.out(SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) { + return at::_ops::full_out::redispatch(dispatchKeySet, size, fill_value, out); + } + + // aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor full_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & fill_value, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::full_like::redispatch(dispatchKeySet, self, fill_value, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor full_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::full_like::redispatch(dispatchKeySet, self, fill_value, dtype, layout, device, pin_memory, memory_format); + } + + // aten::from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor from_file(c10::DispatchKeySet dispatchKeySet, c10::string_view filename, c10::optional shared=c10::nullopt, c10::optional size=0, at::TensorOptions options={}) { + return at::_ops::from_file::redispatch(dispatchKeySet, filename, shared, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::from_file(str filename, bool? shared=None, int? size=0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor from_file(c10::DispatchKeySet dispatchKeySet, c10::string_view filename, c10::optional shared, c10::optional size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::from_file::redispatch(dispatchKeySet, filename, shared, size, dtype, layout, device, pin_memory); + } + + // aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gcd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::gcd_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gcd.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gcd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::gcd_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gcd(Tensor self, Tensor other) -> Tensor + inline at::Tensor gcd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::gcd::redispatch(dispatchKeySet, self, other); + } + + // aten::gcd_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & gcd_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::gcd_::redispatch(dispatchKeySet, self, other); + } + + // aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lcm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::lcm_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lcm.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lcm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::lcm_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lcm(Tensor self, Tensor other) -> Tensor + inline at::Tensor lcm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::lcm::redispatch(dispatchKeySet, self, other); + } + + // aten::lcm_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & lcm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::lcm_::redispatch(dispatchKeySet, self, other); + } + + // aten::grid_sampler(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor + inline at::Tensor grid_sampler(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::grid_sampler::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); + } + + // aten::grid_sampler_2d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor + inline at::Tensor grid_sampler_2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::grid_sampler_2d::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); + } + + // aten::grid_sampler_2d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) + inline ::std::tuple grid_sampler_2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + return at::_ops::grid_sampler_2d_backward::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); + } + + // aten::_grid_sampler_2d_cpu_fallback(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor + inline at::Tensor _grid_sampler_2d_cpu_fallback(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::_grid_sampler_2d_cpu_fallback::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); + } + + // aten::_grid_sampler_2d_cpu_fallback_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> (Tensor, Tensor) + inline ::std::tuple _grid_sampler_2d_cpu_fallback_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::_grid_sampler_2d_cpu_fallback_backward::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners); + } + + // aten::grid_sampler_3d(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners) -> Tensor + inline at::Tensor grid_sampler_3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::grid_sampler_3d::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners); + } + + // aten::grid_sampler_3d_backward(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask) -> (Tensor, Tensor) + inline ::std::tuple grid_sampler_3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + return at::_ops::grid_sampler_3d_backward::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); + } + + // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hann_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::TensorOptions options={}) { + return at::_ops::hann_window::redispatch(dispatchKeySet, window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hann_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hann_window::redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); + } + + // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hann_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::TensorOptions options={}) { + return at::_ops::hann_window_periodic::redispatch(dispatchKeySet, window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hann_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hann_window_periodic::redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); + } + + // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::TensorOptions options={}) { + return at::_ops::hamming_window::redispatch(dispatchKeySet, window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hamming_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hamming_window::redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); + } + + // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::TensorOptions options={}) { + return at::_ops::hamming_window_periodic::redispatch(dispatchKeySet, window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hamming_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hamming_window_periodic::redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); + } + + // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, at::TensorOptions options={}) { + return at::_ops::hamming_window_periodic_alpha::redispatch(dispatchKeySet, window_length, periodic, alpha, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hamming_window.periodic_alpha(int window_length, bool periodic, float alpha, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hamming_window_periodic_alpha::redispatch(dispatchKeySet, window_length, periodic, alpha, dtype, layout, device, pin_memory); + } + + // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, double beta, at::TensorOptions options={}) { + return at::_ops::hamming_window_periodic_alpha_beta::redispatch(dispatchKeySet, window_length, periodic, alpha, beta, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::hamming_window.periodic_alpha_beta(int window_length, bool periodic, float alpha, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor hamming_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, double beta, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::hamming_window_periodic_alpha_beta::redispatch(dispatchKeySet, window_length, periodic, alpha, beta, dtype, layout, device, pin_memory); + } + + // aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::TensorOptions options={}) { + return at::_ops::kaiser_window::redispatch(dispatchKeySet, window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::kaiser_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::kaiser_window::redispatch(dispatchKeySet, window_length, dtype, layout, device, pin_memory); + } + + // aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::TensorOptions options={}) { + return at::_ops::kaiser_window_periodic::redispatch(dispatchKeySet, window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::kaiser_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::kaiser_window_periodic::redispatch(dispatchKeySet, window_length, periodic, dtype, layout, device, pin_memory); + } + + // aten::kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double beta, at::TensorOptions options={}) { + return at::_ops::kaiser_window_beta::redispatch(dispatchKeySet, window_length, periodic, beta, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::kaiser_window.beta(int window_length, bool periodic, float beta, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor kaiser_window(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double beta, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::kaiser_window_beta::redispatch(dispatchKeySet, window_length, periodic, beta, dtype, layout, device, pin_memory); + } + + // aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor + inline at::Tensor hinge_embedding_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, double margin=1.0, int64_t reduction=at::Reduction::Mean) { + return at::_ops::hinge_embedding_loss::redispatch(dispatchKeySet, self, target, margin, reduction); + } + + // aten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensor + inline at::Tensor group_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, int64_t num_groups, const c10::optional & weight={}, const c10::optional & bias={}, double eps=1e-05, bool cudnn_enabled=true) { + return at::_ops::group_norm::redispatch(dispatchKeySet, input, num_groups, weight, bias, eps, cudnn_enabled); + } + + // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_group_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) { + return at::_ops::native_group_norm::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps); + } + + // aten::native_group_norm(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_group_norm_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { + return at::_ops::native_group_norm::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps); + } + + // aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_group_norm_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array output_mask) { + return at::_ops::native_group_norm_backward::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); + } + + // aten::native_group_norm_backward(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_group_norm_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array output_mask) { + return at::_ops::native_group_norm_backward::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); + } + + // aten::_fft_r2c(Tensor self, int[] dim, int normalization, bool onesided) -> Tensor + inline at::Tensor _fft_r2c(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { + return at::_ops::_fft_r2c::redispatch(dispatchKeySet, self, dim, normalization, onesided); + } + + // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_r2c_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { + return at::_ops::_fft_r2c_out::redispatch(dispatchKeySet, self, dim, normalization, onesided, out); + } + + // aten::_fft_r2c.out(Tensor self, int[] dim, int normalization, bool onesided, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_r2c_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided, at::Tensor & out) { + return at::_ops::_fft_r2c_out::redispatch(dispatchKeySet, self, dim, normalization, onesided, out); + } + + // aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor + inline at::Tensor _fft_c2r(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) { + return at::_ops::_fft_c2r::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size); + } + + // aten::_fft_c2r(Tensor self, int[] dim, int normalization, SymInt last_dim_size) -> Tensor + inline at::Tensor _fft_c2r_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) { + return at::_ops::_fft_c2r::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size); + } + + // aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2r_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size) { + return at::_ops::_fft_c2r_out::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size, out); + } + + // aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2r_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, int64_t last_dim_size, at::Tensor & out) { + return at::_ops::_fft_c2r_out::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size, out); + } + + // aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2r_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) { + return at::_ops::_fft_c2r_out::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size, out); + } + + // aten::_fft_c2r.out(Tensor self, int[] dim, int normalization, SymInt last_dim_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2r_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size, at::Tensor & out) { + return at::_ops::_fft_c2r_out::redispatch(dispatchKeySet, self, dim, normalization, last_dim_size, out); + } + + // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor + inline at::Tensor _fft_c2c(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) { + return at::_ops::_fft_c2c::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(dim), normalization, forward); + } + + // aten::_fft_c2c(Tensor self, SymInt[] dim, int normalization, bool forward) -> Tensor + inline at::Tensor _fft_c2c_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { + return at::_ops::_fft_c2c::redispatch(dispatchKeySet, self, dim, normalization, forward); + } + + // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2c_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward) { + return at::_ops::_fft_c2c_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(dim), normalization, forward, out); + } + + // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2c_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { + return at::_ops::_fft_c2c_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(dim), normalization, forward, out); + } + + // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2c_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { + return at::_ops::_fft_c2c_out::redispatch(dispatchKeySet, self, dim, normalization, forward, out); + } + + // aten::_fft_c2c.out(Tensor self, SymInt[] dim, int normalization, bool forward, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fft_c2c_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward, at::Tensor & out) { + return at::_ops::_fft_c2c_out::redispatch(dispatchKeySet, self, dim, normalization, forward, out); + } + + // aten::_validate_compressed_sparse_indices(bool is_crow, Tensor compressed_idx, Tensor plain_idx, int cdim, int dim, int nnz) -> () + inline void _validate_compressed_sparse_indices(c10::DispatchKeySet dispatchKeySet, bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { + return at::_ops::_validate_compressed_sparse_indices::redispatch(dispatchKeySet, is_crow, compressed_idx, plain_idx, cdim, dim, nnz); + } + + // aten::_cufft_get_plan_cache_size(DeviceIndex device_index) -> int + inline int64_t _cufft_get_plan_cache_size(c10::DispatchKeySet dispatchKeySet, at::DeviceIndex device_index) { + return at::_ops::_cufft_get_plan_cache_size::redispatch(dispatchKeySet, device_index); + } + + // aten::_cufft_get_plan_cache_max_size(DeviceIndex device_index) -> int + inline int64_t _cufft_get_plan_cache_max_size(c10::DispatchKeySet dispatchKeySet, at::DeviceIndex device_index) { + return at::_ops::_cufft_get_plan_cache_max_size::redispatch(dispatchKeySet, device_index); + } + + // aten::_cufft_set_plan_cache_max_size(DeviceIndex device_index, int max_size) -> () + inline void _cufft_set_plan_cache_max_size(c10::DispatchKeySet dispatchKeySet, at::DeviceIndex device_index, int64_t max_size) { + return at::_ops::_cufft_set_plan_cache_max_size::redispatch(dispatchKeySet, device_index, max_size); + } + + // aten::_cufft_clear_plan_cache(DeviceIndex device_index) -> () + inline void _cufft_clear_plan_cache(c10::DispatchKeySet dispatchKeySet, at::DeviceIndex device_index) { + return at::_ops::_cufft_clear_plan_cache::redispatch(dispatchKeySet, device_index); + } + + // aten::index.Tensor(Tensor self, Tensor?[] indices) -> Tensor + inline at::Tensor index(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices) { + return at::_ops::index_Tensor::redispatch(dispatchKeySet, self, indices); + } + + // aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::List> & indices) { + return at::_ops::index_Tensor_out::redispatch(dispatchKeySet, self, indices, out); + } + + // aten::index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, at::Tensor & out) { + return at::_ops::index_Tensor_out::redispatch(dispatchKeySet, self, indices, out); + } + + // aten::_unsafe_index.Tensor(Tensor self, Tensor?[] indices) -> Tensor + inline at::Tensor _unsafe_index(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices) { + return at::_ops::_unsafe_index_Tensor::redispatch(dispatchKeySet, self, indices); + } + + // aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { + return at::_ops::index_copy_out::redispatch(dispatchKeySet, self, dim, index, source, out); + } + + // aten::index_copy.out(Tensor self, int dim, Tensor index, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out) { + return at::_ops::index_copy_out::redispatch(dispatchKeySet, self, dim, index, source, out); + } + + // aten::index_copy_(Tensor(a!) self, int dim, Tensor index, Tensor source) -> Tensor(a!) + inline at::Tensor & index_copy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { + return at::_ops::index_copy_::redispatch(dispatchKeySet, self, dim, index, source); + } + + // aten::index_copy(Tensor self, int dim, Tensor index, Tensor source) -> Tensor + inline at::Tensor index_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { + return at::_ops::index_copy::redispatch(dispatchKeySet, self, dim, index, source); + } + + // aten::index_copy_.dimname(Tensor(a!) self, Dimname dim, Tensor index, Tensor source) -> Tensor(a!) + inline at::Tensor & index_copy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) { + return at::_ops::index_copy__dimname::redispatch(dispatchKeySet, self, dim, index, source); + } + + // aten::index_copy.dimname(Tensor self, Dimname dim, Tensor index, Tensor source) -> Tensor + inline at::Tensor index_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) { + return at::_ops::index_copy_dimname::redispatch(dispatchKeySet, self, dim, index, source); + } + + // aten::index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!) + inline at::Tensor & index_put_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false) { + return at::_ops::index_put_::redispatch(dispatchKeySet, self, indices, values, accumulate); + } + + // aten::index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor + inline at::Tensor index_put(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false) { + return at::_ops::index_put::redispatch(dispatchKeySet, self, indices, values, accumulate); + } + + // aten::_unsafe_index_put(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor + inline at::Tensor _unsafe_index_put(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false) { + return at::_ops::_unsafe_index_put::redispatch(dispatchKeySet, self, indices, values, accumulate); + } + + // aten::_index_put_impl_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor(a!) + inline at::Tensor & _index_put_impl_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) { + return at::_ops::_index_put_impl_::redispatch(dispatchKeySet, self, indices, values, accumulate, unsafe); + } + + // aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensor + inline at::Tensor instance_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) { + return at::_ops::instance_norm::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled); + } + + // aten::isclose(Tensor self, Tensor other, float rtol=1e-05, float atol=1e-08, bool equal_nan=False) -> Tensor + inline at::Tensor isclose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, double rtol=1e-05, double atol=1e-08, bool equal_nan=false) { + return at::_ops::isclose::redispatch(dispatchKeySet, self, other, rtol, atol, equal_nan); + } + + // aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Tensor_out::redispatch(dispatchKeySet, elements, test_elements, assume_unique, invert, out); + } + + // aten::isin.Tensor_Tensor_out(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Tensor_Tensor_out::redispatch(dispatchKeySet, elements, test_elements, assume_unique, invert, out); + } + + // aten::isin.Tensor_Tensor(Tensor elements, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor + inline at::Tensor isin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Tensor::redispatch(dispatchKeySet, elements, test_elements, assume_unique, invert); + } + + // aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Scalar_out::redispatch(dispatchKeySet, elements, test_element, assume_unique, invert, out); + } + + // aten::isin.Tensor_Scalar_out(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Tensor_Scalar_out::redispatch(dispatchKeySet, elements, test_element, assume_unique, invert, out); + } + + // aten::isin.Tensor_Scalar(Tensor elements, Scalar test_element, *, bool assume_unique=False, bool invert=False) -> Tensor + inline at::Tensor isin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Tensor_Scalar::redispatch(dispatchKeySet, elements, test_element, assume_unique, invert); + } + + // aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Scalar_Tensor_out::redispatch(dispatchKeySet, element, test_elements, assume_unique, invert, out); + } + + // aten::isin.Scalar_Tensor_out(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isin_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert, at::Tensor & out) { + return at::_ops::isin_Scalar_Tensor_out::redispatch(dispatchKeySet, element, test_elements, assume_unique, invert, out); + } + + // aten::isin.Scalar_Tensor(Scalar element, Tensor test_elements, *, bool assume_unique=False, bool invert=False) -> Tensor + inline at::Tensor isin(c10::DispatchKeySet dispatchKeySet, const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique=false, bool invert=false) { + return at::_ops::isin_Scalar_Tensor::redispatch(dispatchKeySet, element, test_elements, assume_unique, invert); + } + + // aten::isnan(Tensor self) -> Tensor + inline at::Tensor isnan(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isnan::redispatch(dispatchKeySet, self); + } + + // aten::is_distributed(Tensor self) -> bool + inline bool is_distributed(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_distributed::redispatch(dispatchKeySet, self); + } + + // aten::is_floating_point(Tensor self) -> bool + inline bool __dispatch_is_floating_point(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_floating_point::redispatch(dispatchKeySet, self); + } + + // aten::is_complex(Tensor self) -> bool + inline bool __dispatch_is_complex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_complex::redispatch(dispatchKeySet, self); + } + + // aten::is_conj(Tensor self) -> bool + inline bool __dispatch_is_conj(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_conj::redispatch(dispatchKeySet, self); + } + + // aten::_is_zerotensor(Tensor self) -> bool + inline bool __dispatch__is_zerotensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_is_zerotensor::redispatch(dispatchKeySet, self); + } + + // aten::is_neg(Tensor self) -> bool + inline bool __dispatch_is_neg(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_neg::redispatch(dispatchKeySet, self); + } + + // aten::isreal(Tensor self) -> Tensor + inline at::Tensor isreal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isreal::redispatch(dispatchKeySet, self); + } + + // aten::is_nonzero(Tensor self) -> bool + inline bool is_nonzero(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_nonzero::redispatch(dispatchKeySet, self); + } + + // aten::is_same_size(Tensor self, Tensor other) -> bool + inline bool is_same_size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::is_same_size::redispatch(dispatchKeySet, self, other); + } + + // aten::is_signed(Tensor self) -> bool + inline bool __dispatch_is_signed(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_signed::redispatch(dispatchKeySet, self); + } + + // aten::is_inference(Tensor self) -> bool + inline bool __dispatch_is_inference(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_inference::redispatch(dispatchKeySet, self); + } + + // aten::kl_div(Tensor self, Tensor target, int reduction=Mean, *, bool log_target=False) -> Tensor + inline at::Tensor kl_div(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, bool log_target=false) { + return at::_ops::kl_div::redispatch(dispatchKeySet, self, target, reduction, log_target); + } + + // aten::kron(Tensor self, Tensor other) -> Tensor + inline at::Tensor kron(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::kron::redispatch(dispatchKeySet, self, other); + } + + // aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kron_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::kron_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::kron.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kron_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::kron_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::kthvalue(Tensor self, int k, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple kthvalue(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) { + return at::_ops::kthvalue::redispatch(dispatchKeySet, self, k, dim, keepdim); + } + + // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple kthvalue_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool keepdim=false) { + return at::_ops::kthvalue_values::redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); + } + + // aten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple kthvalue_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::kthvalue_values::redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); + } + + // aten::kthvalue.dimname(Tensor self, int k, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple kthvalue(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim=false) { + return at::_ops::kthvalue_dimname::redispatch(dispatchKeySet, self, k, dim, keepdim); + } + + // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple kthvalue_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim=false) { + return at::_ops::kthvalue_dimname_out::redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); + } + + // aten::kthvalue.dimname_out(Tensor self, int k, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple kthvalue_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::kthvalue_dimname_out::redispatch(dispatchKeySet, self, k, dim, keepdim, values, indices); + } + + // aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor + inline at::Tensor layer_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::IntArrayRef normalized_shape, const c10::optional & weight={}, const c10::optional & bias={}, double eps=1e-05, bool cudnn_enable=true) { + return at::_ops::layer_norm::redispatch(dispatchKeySet, input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, cudnn_enable); + } + + // aten::layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensor + inline at::Tensor layer_norm_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight={}, const c10::optional & bias={}, double eps=1e-05, bool cudnn_enable=true) { + return at::_ops::layer_norm::redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps, cudnn_enable); + } + + // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_layer_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::IntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps) { + return at::_ops::native_layer_norm::redispatch(dispatchKeySet, input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps); + } + + // aten::native_layer_norm(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_layer_norm_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps) { + return at::_ops::native_layer_norm::redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps); + } + + // aten::native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_layer_norm_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask) { + return at::_ops::native_layer_norm_backward::redispatch(dispatchKeySet, grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask); + } + + // aten::native_layer_norm_backward(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_layer_norm_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask) { + return at::_ops::native_layer_norm_backward::redispatch(dispatchKeySet, grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask); + } + + // aten::nan_to_num(Tensor self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor + inline at::Tensor nan_to_num(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional nan=c10::nullopt, c10::optional posinf=c10::nullopt, c10::optional neginf=c10::nullopt) { + return at::_ops::nan_to_num::redispatch(dispatchKeySet, self, nan, posinf, neginf); + } + + // aten::nan_to_num_(Tensor(a!) self, float? nan=None, float? posinf=None, float? neginf=None) -> Tensor(a!) + inline at::Tensor & nan_to_num_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional nan=c10::nullopt, c10::optional posinf=c10::nullopt, c10::optional neginf=c10::nullopt) { + return at::_ops::nan_to_num_::redispatch(dispatchKeySet, self, nan, posinf, neginf); + } + + // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nan_to_num_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional nan=c10::nullopt, c10::optional posinf=c10::nullopt, c10::optional neginf=c10::nullopt) { + return at::_ops::nan_to_num_out::redispatch(dispatchKeySet, self, nan, posinf, neginf, out); + } + + // aten::nan_to_num.out(Tensor self, float? nan=None, float? posinf=None, float? neginf=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nan_to_num_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional nan, c10::optional posinf, c10::optional neginf, at::Tensor & out) { + return at::_ops::nan_to_num_out::redispatch(dispatchKeySet, self, nan, posinf, neginf, out); + } + + // aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensor + inline at::Tensor linear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}) { + return at::_ops::linear::redispatch(dispatchKeySet, input, weight, bias); + } + + // aten::linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple linear_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + return at::_ops::linear_backward::redispatch(dispatchKeySet, self, grad_output, weight, output_mask); + } + + // aten::linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linear_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}) { + return at::_ops::linear_out::redispatch(dispatchKeySet, input, weight, bias, out); + } + + // aten::linear.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linear_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::Tensor & out) { + return at::_ops::linear_out::redispatch(dispatchKeySet, input, weight, bias, out); + } + + // aten::mkldnn_linear(Tensor self, Tensor weight, Tensor? bias=None) -> Tensor + inline at::Tensor mkldnn_linear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias={}) { + return at::_ops::mkldnn_linear::redispatch(dispatchKeySet, self, weight, bias); + } + + // aten::mkldnn_linear_backward_input(int[] input_size, Tensor grad_output, Tensor weight) -> Tensor + inline at::Tensor mkldnn_linear_backward_input(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) { + return at::_ops::mkldnn_linear_backward_input::redispatch(dispatchKeySet, input_size, grad_output, weight); + } + + // aten::mkldnn_linear_backward_weights(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined) -> (Tensor, Tensor) + inline ::std::tuple mkldnn_linear_backward_weights(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) { + return at::_ops::mkldnn_linear_backward_weights::redispatch(dispatchKeySet, grad_output, input, weight, bias_defined); + } + + // aten::mkldnn_linear_backward(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple mkldnn_linear_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + return at::_ops::mkldnn_linear_backward::redispatch(dispatchKeySet, self, grad_output, weight, output_mask); + } + + // aten::_cslt_compress(Tensor input) -> Tensor + inline at::Tensor _cslt_compress(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input) { + return at::_ops::_cslt_compress::redispatch(dispatchKeySet, input); + } + + // aten::_cslt_sparse_mm(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False, int alg_id=0) -> Tensor + inline at::Tensor _cslt_sparse_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_A, const at::Tensor & dense_B, const c10::optional & bias={}, const c10::optional & alpha={}, c10::optional out_dtype=c10::nullopt, bool transpose_result=false, int64_t alg_id=0) { + return at::_ops::_cslt_sparse_mm::redispatch(dispatchKeySet, compressed_A, dense_B, bias, alpha, out_dtype, transpose_result, alg_id); + } + + // aten::_cslt_sparse_mm_search(Tensor compressed_A, Tensor dense_B, Tensor? bias=None, Tensor? alpha=None, ScalarType? out_dtype=None, bool transpose_result=False) -> int + inline int64_t _cslt_sparse_mm_search(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_A, const at::Tensor & dense_B, const c10::optional & bias={}, const c10::optional & alpha={}, c10::optional out_dtype=c10::nullopt, bool transpose_result=false) { + return at::_ops::_cslt_sparse_mm_search::redispatch(dispatchKeySet, compressed_A, dense_B, bias, alpha, out_dtype, transpose_result); + } + + // aten::_sparse_semi_structured_linear(Tensor input, Tensor weight, Tensor meta, *, Tensor? bias=None, str? activation=None, ScalarType? out_dtype=None) -> Tensor + inline at::Tensor _sparse_semi_structured_linear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & meta, const c10::optional & bias={}, c10::optional activation=c10::nullopt, c10::optional out_dtype=c10::nullopt) { + return at::_ops::_sparse_semi_structured_linear::redispatch(dispatchKeySet, input, weight, meta, bias, activation, out_dtype); + } + + // aten::_mixed_dtypes_linear(Tensor input, Tensor weight, Tensor scale, *, Tensor? bias=None, str? activation=None) -> Tensor + inline at::Tensor _mixed_dtypes_linear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const c10::optional & bias={}, c10::optional activation=c10::nullopt) { + return at::_ops::_mixed_dtypes_linear::redispatch(dispatchKeySet, input, weight, scale, bias, activation); + } + + // aten::fbgemm_linear_int8_weight_fp32_activation(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor + inline at::Tensor fbgemm_linear_int8_weight_fp32_activation(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { + return at::_ops::fbgemm_linear_int8_weight_fp32_activation::redispatch(dispatchKeySet, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); + } + + // aten::fbgemm_linear_int8_weight(Tensor input, Tensor weight, Tensor packed, Tensor col_offsets, Scalar weight_scale, Scalar weight_zero_point, Tensor bias) -> Tensor + inline at::Tensor fbgemm_linear_int8_weight(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { + return at::_ops::fbgemm_linear_int8_weight::redispatch(dispatchKeySet, input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); + } + + // aten::fbgemm_linear_quantize_weight(Tensor input) -> (Tensor, Tensor, float, int) + inline ::std::tuple fbgemm_linear_quantize_weight(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input) { + return at::_ops::fbgemm_linear_quantize_weight::redispatch(dispatchKeySet, input); + } + + // aten::fbgemm_pack_gemm_matrix_fp16(Tensor input) -> Tensor + inline at::Tensor fbgemm_pack_gemm_matrix_fp16(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input) { + return at::_ops::fbgemm_pack_gemm_matrix_fp16::redispatch(dispatchKeySet, input); + } + + // aten::fbgemm_linear_fp16_weight_fp32_activation(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor + inline at::Tensor fbgemm_linear_fp16_weight_fp32_activation(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { + return at::_ops::fbgemm_linear_fp16_weight_fp32_activation::redispatch(dispatchKeySet, input, packed_weight, bias); + } + + // aten::fbgemm_linear_fp16_weight(Tensor input, Tensor packed_weight, Tensor bias) -> Tensor + inline at::Tensor fbgemm_linear_fp16_weight(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { + return at::_ops::fbgemm_linear_fp16_weight::redispatch(dispatchKeySet, input, packed_weight, bias); + } + + // aten::fbgemm_pack_quantized_matrix(Tensor input) -> Tensor + inline at::Tensor fbgemm_pack_quantized_matrix(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input) { + return at::_ops::fbgemm_pack_quantized_matrix::redispatch(dispatchKeySet, input); + } + + // aten::fbgemm_pack_quantized_matrix.KN(Tensor input, int K, int N) -> Tensor + inline at::Tensor fbgemm_pack_quantized_matrix(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, int64_t K, int64_t N) { + return at::_ops::fbgemm_pack_quantized_matrix_KN::redispatch(dispatchKeySet, input, K, N); + } + + // aten::ldexp.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor ldexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ldexp_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::ldexp_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & ldexp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::ldexp_::redispatch(dispatchKeySet, self, other); + } + + // aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ldexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ldexp_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ldexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ldexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::ldexp_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, at::TensorOptions options={}) { + return at::_ops::linspace::redispatch(dispatchKeySet, start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::linspace(Scalar start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::linspace::redispatch(dispatchKeySet, start, end, steps, dtype, layout, device, pin_memory); + } + + // aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, at::TensorOptions options={}) { + return at::_ops::linspace_Tensor_Tensor::redispatch(dispatchKeySet, start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::linspace.Tensor_Tensor(Tensor start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::linspace_Tensor_Tensor::redispatch(dispatchKeySet, start, end, steps, dtype, layout, device, pin_memory); + } + + // aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, at::TensorOptions options={}) { + return at::_ops::linspace_Tensor_Scalar::redispatch(dispatchKeySet, start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::linspace.Tensor_Scalar(Tensor start, Scalar end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::linspace_Tensor_Scalar::redispatch(dispatchKeySet, start, end, steps, dtype, layout, device, pin_memory); + } + + // aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, at::TensorOptions options={}) { + return at::_ops::linspace_Scalar_Tensor::redispatch(dispatchKeySet, start, end, steps, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::linspace.Scalar_Tensor(Scalar start, Tensor end, int steps, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor linspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::linspace_Scalar_Tensor::redispatch(dispatchKeySet, start, end, steps, dtype, layout, device, pin_memory); + } + + // aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps) { + return at::_ops::linspace_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.out(Scalar start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, at::Tensor & out) { + return at::_ops::linspace_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & start, const at::Tensor & end, int64_t steps) { + return at::_ops::linspace_Tensor_Tensor_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, at::Tensor & out) { + return at::_ops::linspace_Tensor_Tensor_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & start, const at::Scalar & end, int64_t steps) { + return at::_ops::linspace_Tensor_Scalar_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, at::Tensor & out) { + return at::_ops::linspace_Tensor_Scalar_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Tensor & end, int64_t steps) { + return at::_ops::linspace_Scalar_Tensor_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::linspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, at::Tensor & out) { + return at::_ops::linspace_Scalar_Tensor_out::redispatch(dispatchKeySet, start, end, steps, out); + } + + // aten::log(Tensor self) -> Tensor + inline at::Tensor log(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log::redispatch(dispatchKeySet, self); + } + + // aten::log_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & log_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::log_::redispatch(dispatchKeySet, self); + } + + // aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::log_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::log_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log10(Tensor self) -> Tensor + inline at::Tensor log10(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log10::redispatch(dispatchKeySet, self); + } + + // aten::log10_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & log10_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::log10_::redispatch(dispatchKeySet, self); + } + + // aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log10_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::log10_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log10_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::log10_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log1p(Tensor self) -> Tensor + inline at::Tensor log1p(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log1p::redispatch(dispatchKeySet, self); + } + + // aten::log1p_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & log1p_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::log1p_::redispatch(dispatchKeySet, self); + } + + // aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log1p_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log1p_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log2(Tensor self) -> Tensor + inline at::Tensor log2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log2::redispatch(dispatchKeySet, self); + } + + // aten::log2_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & log2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::log2_::redispatch(dispatchKeySet, self); + } + + // aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::log2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::log2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logaddexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logaddexp_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logaddexp.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logaddexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::logaddexp_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logaddexp(Tensor self, Tensor other) -> Tensor + inline at::Tensor logaddexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logaddexp::redispatch(dispatchKeySet, self, other); + } + + // aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logaddexp2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logaddexp2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logaddexp2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logaddexp2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::logaddexp2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logaddexp2(Tensor self, Tensor other) -> Tensor + inline at::Tensor logaddexp2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::logaddexp2::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor xlogy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::xlogy_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy.Scalar_Self(Scalar self, Tensor other) -> Tensor + inline at::Tensor xlogy(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::xlogy_Scalar_Self::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy.Scalar_Other(Tensor self, Scalar other) -> Tensor + inline at::Tensor xlogy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::xlogy_Scalar_Other::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & xlogy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::xlogy__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy_.Scalar_Other(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & xlogy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::xlogy__Scalar_Other::redispatch(dispatchKeySet, self, other); + } + + // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::xlogy_OutTensor::redispatch(dispatchKeySet, self, other, out); + } + + // aten::xlogy.OutTensor(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::xlogy_OutTensor::redispatch(dispatchKeySet, self, other, out); + } + + // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::xlogy_OutScalar_Self::redispatch(dispatchKeySet, self, other, out); + } + + // aten::xlogy.OutScalar_Self(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::xlogy_OutScalar_Self::redispatch(dispatchKeySet, self, other, out); + } + + // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::xlogy_OutScalar_Other::redispatch(dispatchKeySet, self, other, out); + } + + // aten::xlogy.OutScalar_Other(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::xlogy_OutScalar_Other::redispatch(dispatchKeySet, self, other, out); + } + + // aten::logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base=10.0, at::TensorOptions options={}) { + return at::_ops::logspace::redispatch(dispatchKeySet, start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::logspace(Scalar start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::logspace::redispatch(dispatchKeySet, start, end, steps, base, dtype, layout, device, pin_memory); + } + + // aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base=10.0, at::TensorOptions options={}) { + return at::_ops::logspace_Tensor_Tensor::redispatch(dispatchKeySet, start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::logspace.Tensor_Tensor(Tensor start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::logspace_Tensor_Tensor::redispatch(dispatchKeySet, start, end, steps, base, dtype, layout, device, pin_memory); + } + + // aten::logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base=10.0, at::TensorOptions options={}) { + return at::_ops::logspace_Tensor_Scalar::redispatch(dispatchKeySet, start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::logspace.Tensor_Scalar(Tensor start, Scalar end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::logspace_Tensor_Scalar::redispatch(dispatchKeySet, start, end, steps, base, dtype, layout, device, pin_memory); + } + + // aten::logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base=10.0, at::TensorOptions options={}) { + return at::_ops::logspace_Scalar_Tensor::redispatch(dispatchKeySet, start, end, steps, base, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::logspace.Scalar_Tensor(Scalar start, Tensor end, int steps, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor logspace(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::logspace_Scalar_Tensor::redispatch(dispatchKeySet, start, end, steps, base, dtype, layout, device, pin_memory); + } + + // aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base=10.0) { + return at::_ops::logspace_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.out(Scalar start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) { + return at::_ops::logspace_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base=10.0) { + return at::_ops::logspace_Tensor_Tensor_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Tensor_Tensor_out(Tensor start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out) { + return at::_ops::logspace_Tensor_Tensor_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base=10.0) { + return at::_ops::logspace_Tensor_Scalar_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Tensor_Scalar_out(Tensor start, Scalar end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, at::Tensor & out) { + return at::_ops::logspace_Tensor_Scalar_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base=10.0) { + return at::_ops::logspace_Scalar_Tensor_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::logspace.Scalar_Tensor_out(Scalar start, Tensor end, int steps, float base=10.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logspace_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, at::Tensor & out) { + return at::_ops::logspace_Scalar_Tensor_out::redispatch(dispatchKeySet, start, end, steps, base, out); + } + + // aten::log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor + inline at::Tensor log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::log_softmax_int::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::log_softmax_int_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::log_softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::log_softmax_int_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::log_softmax_Dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor + inline at::Tensor _log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_log_softmax::redispatch(dispatchKeySet, self, dim, half_to_float); + } + + // aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _log_softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_log_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _log_softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { + return at::_ops::_log_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor + inline at::Tensor _log_softmax_backward_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + return at::_ops::_log_softmax_backward_data::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype); + } + + // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _log_softmax_backward_data_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + return at::_ops::_log_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, out); + } + + // aten::_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _log_softmax_backward_data_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & out) { + return at::_ops::_log_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, out); + } + + // aten::_logcumsumexp(Tensor self, int dim) -> Tensor + inline at::Tensor _logcumsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::_logcumsumexp::redispatch(dispatchKeySet, self, dim); + } + + // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _logcumsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim) { + return at::_ops::_logcumsumexp_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::_logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _logcumsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { + return at::_ops::_logcumsumexp_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::logcumsumexp(Tensor self, int dim) -> Tensor + inline at::Tensor logcumsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::logcumsumexp::redispatch(dispatchKeySet, self, dim); + } + + // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logcumsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim) { + return at::_ops::logcumsumexp_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::logcumsumexp.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logcumsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { + return at::_ops::logcumsumexp_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::logcumsumexp.dimname(Tensor self, Dimname dim) -> Tensor + inline at::Tensor logcumsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::logcumsumexp_dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logcumsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim) { + return at::_ops::logcumsumexp_dimname_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::logcumsumexp.dimname_out(Tensor self, Dimname dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logcumsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, at::Tensor & out) { + return at::_ops::logcumsumexp_dimname_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor logsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::logsumexp::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::logsumexp_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::logsumexp_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::logsumexp.names(Tensor self, Dimname[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor logsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim=false) { + return at::_ops::logsumexp_names::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false) { + return at::_ops::logsumexp_names_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::logsumexp.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, at::Tensor & out) { + return at::_ops::logsumexp_names_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor + inline at::Tensor margin_ranking_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin=0.0, int64_t reduction=at::Reduction::Mean) { + return at::_ops::margin_ranking_loss::redispatch(dispatchKeySet, input1, input2, target, margin, reduction); + } + + // aten::matmul(Tensor self, Tensor other) -> Tensor + inline at::Tensor matmul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::matmul::redispatch(dispatchKeySet, self, other); + } + + // aten::matmul_backward(Tensor grad, Tensor self, Tensor other, bool[2] mask) -> (Tensor, Tensor) + inline ::std::tuple matmul_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array mask) { + return at::_ops::matmul_backward::redispatch(dispatchKeySet, grad, self, other, mask); + } + + // aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & matmul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & matmul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::matrix_power(Tensor self, int n) -> Tensor + inline at::Tensor matrix_power(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n) { + return at::_ops::matrix_power::redispatch(dispatchKeySet, self, n); + } + + // aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & matrix_power_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t n) { + return at::_ops::matrix_power_out::redispatch(dispatchKeySet, self, n, out); + } + + // aten::matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & matrix_power_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n, at::Tensor & out) { + return at::_ops::matrix_power_out::redispatch(dispatchKeySet, self, n, out); + } + + // aten::matrix_exp(Tensor self) -> Tensor + inline at::Tensor matrix_exp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::matrix_exp::redispatch(dispatchKeySet, self); + } + + // aten::matrix_exp_backward(Tensor self, Tensor grad) -> Tensor + inline at::Tensor matrix_exp_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad) { + return at::_ops::matrix_exp_backward::redispatch(dispatchKeySet, self, grad); + } + + // aten::_aminmax(Tensor self) -> (Tensor, Tensor) + inline ::std::tuple _aminmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_aminmax::redispatch(dispatchKeySet, self); + } + + // aten::_aminmax.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple _aminmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::_aminmax_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::aminmax(Tensor self, *, int? dim=None, bool keepdim=False) -> (Tensor min, Tensor max) + inline ::std::tuple aminmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::aminmax::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max) + inline ::std::tuple aminmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & min, at::Tensor & max, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) { + return at::_ops::aminmax_out::redispatch(dispatchKeySet, self, dim, keepdim, min, max); + } + + // aten::aminmax.out(Tensor self, *, int? dim=None, bool keepdim=False, Tensor(a!) min, Tensor(b!) max) -> (Tensor(a!) min, Tensor(b!) max) + inline ::std::tuple aminmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & min, at::Tensor & max) { + return at::_ops::aminmax_out::redispatch(dispatchKeySet, self, dim, keepdim, min, max); + } + + // aten::_compute_linear_combination(Tensor input, Tensor coefficients) -> Tensor + inline at::Tensor _compute_linear_combination(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & coefficients) { + return at::_ops::_compute_linear_combination::redispatch(dispatchKeySet, input, coefficients); + } + + // aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _compute_linear_combination_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & coefficients) { + return at::_ops::_compute_linear_combination_out::redispatch(dispatchKeySet, input, coefficients, out); + } + + // aten::_compute_linear_combination.out(Tensor input, Tensor coefficients, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _compute_linear_combination_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & coefficients, at::Tensor & out) { + return at::_ops::_compute_linear_combination_out::redispatch(dispatchKeySet, input, coefficients, out); + } + + // aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::max_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::max_dim_max::redispatch(dispatchKeySet, self, dim, keepdim, max, max_values); + } + + // aten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { + return at::_ops::max_dim_max::redispatch(dispatchKeySet, self, dim, keepdim, max, max_values); + } + + // aten::max.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::max_names_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & max, at::Tensor & max_values, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::max_names_dim_max::redispatch(dispatchKeySet, self, dim, keepdim, max, max_values); + } + + // aten::max.names_dim_max(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & max, at::Tensor & max_values) { + return at::_ops::max_names_dim_max::redispatch(dispatchKeySet, self, dim, keepdim, max, max_values); + } + + // aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor + inline at::Tensor value_selecting_reduction_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t dim, const at::Tensor & indices, at::IntArrayRef sizes, bool keepdim) { + return at::_ops::value_selecting_reduction_backward::redispatch(dispatchKeySet, grad, dim, indices, c10::fromIntArrayRefSlow(sizes), keepdim); + } + + // aten::value_selecting_reduction_backward(Tensor grad, int dim, Tensor indices, SymInt[] sizes, bool keepdim) -> Tensor + inline at::Tensor value_selecting_reduction_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) { + return at::_ops::value_selecting_reduction_backward::redispatch(dispatchKeySet, grad, dim, indices, sizes, keepdim); + } + + // aten::amax(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor + inline at::Tensor amax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) { + return at::_ops::amax::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & amax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) { + return at::_ops::amax_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::amax.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & amax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::amax_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::max_pool1d_with_indices(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) + inline ::std::tuple max_pool1d_with_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool1d_with_indices::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor max_pool1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool1d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor max_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool2d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor max_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool2d_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::mkldnn_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor mkldnn_max_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool2d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::mkldnn_max_pool2d_backward(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor mkldnn_max_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool2d_backward::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::mkldnn_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor mkldnn_max_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool3d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::mkldnn_max_pool3d_backward(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor mkldnn_max_pool3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool3d_backward::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::quantized_max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor quantized_max_pool1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool1d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::quantized_max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor quantized_max_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool2d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::quantized_max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor quantized_max_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool3d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> Tensor + inline at::Tensor max_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool3d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::mean::redispatch(dispatchKeySet, self, dtype); + } + + // aten::mean.dim(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::mean_dim::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mean_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::mean_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::mean.out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mean_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::mean_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::mean.names_dim(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::mean_names_dim::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mean_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::mean_names_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::mean.names_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mean_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::mean_names_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::nanmean(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor nanmean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::nanmean::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanmean_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::nanmean_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::nanmean.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanmean_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::nanmean_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::median(Tensor self) -> Tensor + inline at::Tensor median(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::median::redispatch(dispatchKeySet, self); + } + + // aten::median.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple median(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::median_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple median_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::median_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple median_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::median_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::median.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple median(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::median_names_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple median_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::median_names_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::median.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple median_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::median_names_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::nanmedian(Tensor self) -> Tensor + inline at::Tensor nanmedian(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::nanmedian::redispatch(dispatchKeySet, self); + } + + // aten::nanmedian.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple nanmedian(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::nanmedian_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple nanmedian_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::nanmedian_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::nanmedian.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple nanmedian_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::nanmedian_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::nanmedian.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple nanmedian(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::nanmedian_names_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple nanmedian_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::nanmedian_names_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::nanmedian.names_dim_values(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple nanmedian_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::nanmedian_names_dim_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::min_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::min_dim_min::redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); + } + + // aten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { + return at::_ops::min_dim_min::redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); + } + + // aten::min.names_dim(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::min_names_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & min, at::Tensor & min_indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::min_names_dim_min::redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); + } + + // aten::min.names_dim_min(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & min, at::Tensor & min_indices) { + return at::_ops::min_names_dim_min::redispatch(dispatchKeySet, self, dim, keepdim, min, min_indices); + } + + // aten::amin(Tensor self, int[1] dim=[], bool keepdim=False) -> Tensor + inline at::Tensor amin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) { + return at::_ops::amin::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & amin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim={}, bool keepdim=false) { + return at::_ops::amin_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::amin.out(Tensor self, int[1] dim=[], bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & amin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::amin_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _mps_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::_mps_convolution::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::_mps_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor _mps_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::_mps_convolution::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups); + } + + // aten::mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple mps_convolution_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask) { + return at::_ops::mps_convolution_backward::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask); + } + + // aten::mps_convolution_backward(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple mps_convolution_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::mps_convolution_backward::redispatch(dispatchKeySet, self, grad_output, weight, padding, stride, dilation, groups, output_mask); + } + + // aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor mkldnn_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::mkldnn_convolution::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor mkldnn_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::mkldnn_convolution::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups); + } + + // aten::mkldnn_rnn_layer(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple mkldnn_rnn_layer(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) { + return at::_ops::mkldnn_rnn_layer::redispatch(dispatchKeySet, input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); + } + + // aten::mkldnn_rnn_layer_backward(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple mkldnn_rnn_layer_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { + return at::_ops::mkldnn_rnn_layer_backward::redispatch(dispatchKeySet, input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); + } + + // aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor) + inline ::std::tuple miopen_batch_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + return at::_ops::miopen_batch_norm::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); + } + + // aten::miopen_batch_norm_backward(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon) -> (Tensor, Tensor, Tensor) + inline ::std::tuple miopen_batch_norm_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon) { + return at::_ops::miopen_batch_norm_backward::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon); + } + + // aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic); + } + + // aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); + } + + // aten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_convolution_transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_transpose::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic); + } + + // aten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_convolution_transpose_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_transpose::redispatch(dispatchKeySet, self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic); + } + + // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_depthwise_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_depthwise_convolution::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic); + } + + // aten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic) -> Tensor + inline at::Tensor miopen_depthwise_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_depthwise_convolution::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); + } + + // aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor miopen_convolution_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::miopen_convolution_relu::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::miopen_convolution_relu(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor miopen_convolution_relu_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::miopen_convolution_relu::redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups); + } + + // aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor miopen_convolution_add_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::miopen_convolution_add_relu::redispatch(dispatchKeySet, self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::miopen_convolution_add_relu(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups) -> Tensor + inline at::Tensor miopen_convolution_add_relu_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::miopen_convolution_add_relu::redispatch(dispatchKeySet, self, weight, z, alpha, bias, stride, padding, dilation, groups); + } + + // aten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple miopen_rnn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::miopen_rnn::redispatch(dispatchKeySet, input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); + } + + // aten::miopen_rnn_backward(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask) -> (Tensor, Tensor, Tensor, Tensor[]) + inline ::std::tuple> miopen_rnn_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::miopen_rnn_backward::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); + } + + // aten::mm(Tensor self, Tensor mat2) -> Tensor + inline at::Tensor mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::mm::redispatch(dispatchKeySet, self, mat2); + } + + // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::mm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { + return at::_ops::mm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::_int_mm(Tensor self, Tensor mat2) -> Tensor + inline at::Tensor _int_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::_int_mm::redispatch(dispatchKeySet, self, mat2); + } + + // aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _int_mm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::_int_mm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::_int_mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _int_mm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { + return at::_ops::_int_mm_out::redispatch(dispatchKeySet, self, mat2, out); + } + + // aten::_convert_weight_to_int4pack(Tensor self, int innerKTiles) -> Tensor + inline at::Tensor _convert_weight_to_int4pack(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t innerKTiles) { + return at::_ops::_convert_weight_to_int4pack::redispatch(dispatchKeySet, self, innerKTiles); + } + + // aten::_weight_int4pack_mm(Tensor self, Tensor mat2, int qGroupSize, Tensor qScaleAndZeros) -> Tensor + inline at::Tensor _weight_int4pack_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) { + return at::_ops::_weight_int4pack_mm::redispatch(dispatchKeySet, self, mat2, qGroupSize, qScaleAndZeros); + } + + // aten::_weight_int8pack_mm(Tensor self, Tensor mat2, Tensor scales) -> Tensor + inline at::Tensor _weight_int8pack_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scales) { + return at::_ops::_weight_int8pack_mm::redispatch(dispatchKeySet, self, mat2, scales); + } + + // aten::_sparse_mm(Tensor sparse, Tensor dense) -> Tensor + inline at::Tensor _sparse_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sparse, const at::Tensor & dense) { + return at::_ops::_sparse_mm::redispatch(dispatchKeySet, sparse, dense); + } + + // aten::_sparse_mm.reduce(Tensor sparse, Tensor dense, str reduce) -> Tensor + inline at::Tensor _sparse_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sparse, const at::Tensor & dense, c10::string_view reduce) { + return at::_ops::_sparse_mm_reduce::redispatch(dispatchKeySet, sparse, dense, reduce); + } + + // aten::_sparse_sparse_matmul(Tensor self, Tensor other) -> Tensor + inline at::Tensor _sparse_sparse_matmul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::_sparse_sparse_matmul::redispatch(dispatchKeySet, self, other); + } + + // aten::mode(Tensor self, int dim=-1, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple mode(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=-1, bool keepdim=false) { + return at::_ops::mode::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple mode_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim=-1, bool keepdim=false) { + return at::_ops::mode_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple mode_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::mode_values::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::mode.dimname(Tensor self, Dimname dim, bool keepdim=False) -> (Tensor values, Tensor indices) + inline ::std::tuple mode(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::mode_dimname::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple mode_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool keepdim=false) { + return at::_ops::mode_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::mode.dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple mode_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, at::Tensor & values, at::Tensor & indices) { + return at::_ops::mode_dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, values, indices); + } + + // aten::mul.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor mul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::mul_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & mul_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::mul__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::mul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::mul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::mul.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor mul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::mul_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::mul_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & mul_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::mul__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::multiply.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor multiply(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::multiply_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::multiply_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & multiply_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::multiply__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multiply_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::multiply_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::multiply.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multiply_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::multiply_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::multiply.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor multiply(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::multiply_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::multiply_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & multiply_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::multiply__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::mv(Tensor self, Tensor vec) -> Tensor + inline at::Tensor mv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec) { + return at::_ops::mv::redispatch(dispatchKeySet, self, vec); + } + + // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & vec) { + return at::_ops::mv_out::redispatch(dispatchKeySet, self, vec, out); + } + + // aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec, at::Tensor & out) { + return at::_ops::mv_out::redispatch(dispatchKeySet, self, vec, out); + } + + // aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mvlgamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t p) { + return at::_ops::mvlgamma_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::mvlgamma.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mvlgamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p, at::Tensor & out) { + return at::_ops::mvlgamma_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::mvlgamma(Tensor self, int p) -> Tensor + inline at::Tensor mvlgamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p) { + return at::_ops::mvlgamma::redispatch(dispatchKeySet, self, p); + } + + // aten::mvlgamma_(Tensor(a!) self, int p) -> Tensor(a!) + inline at::Tensor & mvlgamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t p) { + return at::_ops::mvlgamma_::redispatch(dispatchKeySet, self, p); + } + + // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor + inline at::Tensor narrow_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { + return at::_ops::narrow_copy::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::narrow_copy(Tensor self, int dim, SymInt start, SymInt length) -> Tensor + inline at::Tensor narrow_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { + return at::_ops::narrow_copy::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & narrow_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { + return at::_ops::narrow_copy_out::redispatch(dispatchKeySet, self, dim, start, length, out); + } + + // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & narrow_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t start, int64_t length, at::Tensor & out) { + return at::_ops::narrow_copy_out::redispatch(dispatchKeySet, self, dim, start, length, out); + } + + // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & narrow_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { + return at::_ops::narrow_copy_out::redispatch(dispatchKeySet, self, dim, start, length, out); + } + + // aten::narrow_copy.out(Tensor self, int dim, SymInt start, SymInt length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & narrow_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length, at::Tensor & out) { + return at::_ops::narrow_copy_out::redispatch(dispatchKeySet, self, dim, start, length, out); + } + + // aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a) + inline at::Tensor narrow(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t start, int64_t length) { + return at::_ops::narrow::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::narrow(Tensor(a) self, int dim, SymInt start, SymInt length) -> Tensor(a) + inline at::Tensor narrow_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { + return at::_ops::narrow::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a) + inline at::Tensor narrow(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & start, int64_t length) { + return at::_ops::narrow_Tensor::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, SymInt length) -> Tensor(a) + inline at::Tensor narrow_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length) { + return at::_ops::narrow_Tensor::redispatch(dispatchKeySet, self, dim, start, length); + } + + // aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_batch_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps) { + return at::_ops::native_batch_norm::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps); + } + + // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_batch_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps) { + return at::_ops::native_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::native_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_batch_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { + return at::_ops::native_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::_native_batch_norm_legit(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _native_batch_norm_legit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps); + } + + // aten::_native_batch_norm_legit_no_training(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _native_batch_norm_legit_no_training(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_no_training::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, momentum, eps); + } + + // aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!)) + inline ::std::tuple _native_batch_norm_legit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::_native_batch_norm_legit.out(Tensor input, Tensor? weight, Tensor? bias, Tensor(a!) running_mean, Tensor(b!) running_var, bool training, float momentum, float eps, *, Tensor(d!) out, Tensor(e!) save_mean, Tensor(f!) save_invstd) -> (Tensor(d!), Tensor(e!), Tensor(f!)) + inline ::std::tuple _native_batch_norm_legit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, at::Tensor & running_mean, at::Tensor & running_var, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { + return at::_ops::_native_batch_norm_legit_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::_native_batch_norm_legit.no_stats(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _native_batch_norm_legit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, bool training, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_no_stats::redispatch(dispatchKeySet, input, weight, bias, training, momentum, eps); + } + + // aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _native_batch_norm_legit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, bool training, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_no_stats_out::redispatch(dispatchKeySet, input, weight, bias, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::_native_batch_norm_legit.no_stats_out(Tensor input, Tensor? weight, Tensor? bias, bool training, float momentum, float eps, *, Tensor(a!) out, Tensor(b!) save_mean, Tensor(c!) save_invstd) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _native_batch_norm_legit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, bool training, double momentum, double eps, at::Tensor & out, at::Tensor & save_mean, at::Tensor & save_invstd) { + return at::_ops::_native_batch_norm_legit_no_stats_out::redispatch(dispatchKeySet, input, weight, bias, training, momentum, eps, out, save_mean, save_invstd); + } + + // aten::batch_norm_stats(Tensor input, float eps) -> (Tensor, Tensor) + inline ::std::tuple batch_norm_stats(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double eps) { + return at::_ops::batch_norm_stats::redispatch(dispatchKeySet, input, eps); + } + + // aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensor + inline at::Tensor batch_norm_elemt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) { + return at::_ops::batch_norm_elemt::redispatch(dispatchKeySet, input, weight, bias, mean, invstd, eps); + } + + // aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & batch_norm_elemt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) { + return at::_ops::batch_norm_elemt_out::redispatch(dispatchKeySet, input, weight, bias, mean, invstd, eps, out); + } + + // aten::batch_norm_elemt.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & batch_norm_elemt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps, at::Tensor & out) { + return at::_ops::batch_norm_elemt_out::redispatch(dispatchKeySet, input, weight, bias, mean, invstd, eps, out); + } + + // aten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor) + inline ::std::tuple batch_norm_gather_stats(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, int64_t count) { + return at::_ops::batch_norm_gather_stats::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, count); + } + + // aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts) -> (Tensor, Tensor) + inline ::std::tuple batch_norm_gather_stats_with_counts(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, const at::Tensor & counts) { + return at::_ops::batch_norm_gather_stats_with_counts::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, counts); + } + + // aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) + inline ::std::tuple native_batch_norm_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask) { + return at::_ops::native_batch_norm_backward::redispatch(dispatchKeySet, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); + } + + // aten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple batch_norm_backward_reduce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, bool input_g, bool weight_g, bool bias_g) { + return at::_ops::batch_norm_backward_reduce::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); + } + + // aten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count) -> Tensor + inline at::Tensor batch_norm_backward_elemt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) { + return at::_ops::batch_norm_backward_elemt::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count); + } + + // aten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor) + inline ::std::tuple batch_norm_update_stats(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum) { + return at::_ops::batch_norm_update_stats::redispatch(dispatchKeySet, input, running_mean, running_var, momentum); + } + + // aten::is_vulkan_available() -> bool + inline bool is_vulkan_available(c10::DispatchKeySet dispatchKeySet) { + return at::_ops::is_vulkan_available::redispatch(dispatchKeySet); + } + + // aten::_nnpack_available() -> bool + inline bool _nnpack_available(c10::DispatchKeySet dispatchKeySet) { + return at::_ops::_nnpack_available::redispatch(dispatchKeySet); + } + + // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor + inline at::Tensor _nnpack_spatial_convolution(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) { + return at::_ops::_nnpack_spatial_convolution::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride)); + } + + // aten::_nnpack_spatial_convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1) -> Tensor + inline at::Tensor _nnpack_spatial_convolution_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) { + return at::_ops::_nnpack_spatial_convolution::redispatch(dispatchKeySet, input, weight, bias, padding, stride); + } + + // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::ones_names::redispatch(dispatchKeySet, size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::ones.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::ones_names::redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory); + } + + // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::ones::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::ones::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::ones::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::ones(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor ones_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::ones::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::ones_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::ones_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size) { + return at::_ops::ones_out::redispatch(dispatchKeySet, size, out); + } + + // aten::ones.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::ones_out::redispatch(dispatchKeySet, size, out); + } + + // aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor ones_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::ones_like::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::ones_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor ones_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::ones_like::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); + } + + // aten::pairwise_distance(Tensor x1, Tensor x2, float p=2, float eps=1e-06, bool keepdim=False) -> Tensor + inline at::Tensor pairwise_distance(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p=2, double eps=1e-06, bool keepdim=false) { + return at::_ops::pairwise_distance::redispatch(dispatchKeySet, x1, x2, p, eps, keepdim); + } + + // aten::cdist(Tensor x1, Tensor x2, float p=2, int? compute_mode=None) -> Tensor + inline at::Tensor cdist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p=2, c10::optional compute_mode=c10::nullopt) { + return at::_ops::cdist::redispatch(dispatchKeySet, x1, x2, p, compute_mode); + } + + // aten::_euclidean_dist(Tensor x1, Tensor x2) -> Tensor + inline at::Tensor _euclidean_dist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2) { + return at::_ops::_euclidean_dist::redispatch(dispatchKeySet, x1, x2); + } + + // aten::_cdist_forward(Tensor x1, Tensor x2, float p, int? compute_mode) -> Tensor + inline at::Tensor _cdist_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional compute_mode) { + return at::_ops::_cdist_forward::redispatch(dispatchKeySet, x1, x2, p, compute_mode); + } + + // aten::_cdist_backward(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist) -> Tensor + inline at::Tensor _cdist_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) { + return at::_ops::_cdist_backward::redispatch(dispatchKeySet, grad, x1, x2, p, cdist); + } + + // aten::pdist(Tensor self, float p=2) -> Tensor + inline at::Tensor pdist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p=2) { + return at::_ops::pdist::redispatch(dispatchKeySet, self, p); + } + + // aten::_pdist_forward(Tensor self, float p=2) -> Tensor + inline at::Tensor _pdist_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p=2) { + return at::_ops::_pdist_forward::redispatch(dispatchKeySet, self, p); + } + + // aten::_pdist_backward(Tensor grad, Tensor self, float p, Tensor pdist) -> Tensor + inline at::Tensor _pdist_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { + return at::_ops::_pdist_backward::redispatch(dispatchKeySet, grad, self, p, pdist); + } + + // aten::cosine_similarity(Tensor x1, Tensor x2, int dim=1, float eps=1e-08) -> Tensor + inline at::Tensor cosine_similarity(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, int64_t dim=1, double eps=1e-08) { + return at::_ops::cosine_similarity::redispatch(dispatchKeySet, x1, x2, dim, eps); + } + + // aten::permute(Tensor(a) self, int[] dims) -> Tensor(a) + inline at::Tensor permute(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::permute::redispatch(dispatchKeySet, self, dims); + } + + // aten::movedim.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) + inline at::Tensor movedim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { + return at::_ops::movedim_intlist::redispatch(dispatchKeySet, self, source, destination); + } + + // aten::movedim.int(Tensor(a) self, int source, int destination) -> Tensor(a) + inline at::Tensor movedim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t source, int64_t destination) { + return at::_ops::movedim_int::redispatch(dispatchKeySet, self, source, destination); + } + + // aten::moveaxis.intlist(Tensor(a) self, int[] source, int[] destination) -> Tensor(a) + inline at::Tensor moveaxis(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { + return at::_ops::moveaxis_intlist::redispatch(dispatchKeySet, self, source, destination); + } + + // aten::moveaxis.int(Tensor(a) self, int source, int destination) -> Tensor(a) + inline at::Tensor moveaxis(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t source, int64_t destination) { + return at::_ops::moveaxis_int::redispatch(dispatchKeySet, self, source, destination); + } + + // aten::numpy_T(Tensor(a) self) -> Tensor(a) + inline at::Tensor numpy_T(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::numpy_T::redispatch(dispatchKeySet, self); + } + + // aten::matrix_H(Tensor(a) self) -> Tensor(a) + inline at::Tensor matrix_H(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::matrix_H::redispatch(dispatchKeySet, self); + } + + // aten::mT(Tensor(a) self) -> Tensor(a) + inline at::Tensor mT(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::mT::redispatch(dispatchKeySet, self); + } + + // aten::mH(Tensor(a) self) -> Tensor(a) + inline at::Tensor mH(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::mH::redispatch(dispatchKeySet, self); + } + + // aten::adjoint(Tensor(a) self) -> Tensor(a) + inline at::Tensor adjoint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::adjoint::redispatch(dispatchKeySet, self); + } + + // aten::pixel_shuffle(Tensor self, int upscale_factor) -> Tensor + inline at::Tensor pixel_shuffle(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t upscale_factor) { + return at::_ops::pixel_shuffle::redispatch(dispatchKeySet, self, upscale_factor); + } + + // aten::pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor + inline at::Tensor pixel_unshuffle(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t downscale_factor) { + return at::_ops::pixel_unshuffle::redispatch(dispatchKeySet, self, downscale_factor); + } + + // aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor + inline at::Tensor channel_shuffle(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t groups) { + return at::_ops::channel_shuffle::redispatch(dispatchKeySet, self, groups); + } + + // aten::channel_shuffle(Tensor self, SymInt groups) -> Tensor + inline at::Tensor channel_shuffle_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt groups) { + return at::_ops::channel_shuffle::redispatch(dispatchKeySet, self, groups); + } + + // aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor + inline at::Tensor native_channel_shuffle(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t groups) { + return at::_ops::native_channel_shuffle::redispatch(dispatchKeySet, self, groups); + } + + // aten::native_channel_shuffle(Tensor self, SymInt groups) -> Tensor + inline at::Tensor native_channel_shuffle_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt groups) { + return at::_ops::native_channel_shuffle::redispatch(dispatchKeySet, self, groups); + } + + // aten::is_pinned(Tensor self, Device? device=None) -> bool + inline bool is_pinned(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional device=c10::nullopt) { + return at::_ops::is_pinned::redispatch(dispatchKeySet, self, device); + } + + // aten::pin_memory(Tensor(a) self, Device? device=None) -> Tensor(a) + inline at::Tensor pin_memory(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional device=c10::nullopt) { + return at::_ops::pin_memory::redispatch(dispatchKeySet, self, device); + } + + // aten::_pin_memory(Tensor self, Device? device=None) -> Tensor + inline at::Tensor _pin_memory(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional device=c10::nullopt) { + return at::_ops::_pin_memory::redispatch(dispatchKeySet, self, device); + } + + // aten::pinverse(Tensor self, float rcond=1e-15) -> Tensor + inline at::Tensor pinverse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond=1e-15) { + return at::_ops::pinverse::redispatch(dispatchKeySet, self, rcond); + } + + // aten::poisson_nll_loss(Tensor input, Tensor target, bool log_input, bool full, float eps, int reduction) -> Tensor + inline at::Tensor poisson_nll_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction) { + return at::_ops::poisson_nll_loss::redispatch(dispatchKeySet, input, target, log_input, full, eps, reduction); + } + + // aten::rad2deg(Tensor self) -> Tensor + inline at::Tensor rad2deg(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::rad2deg::redispatch(dispatchKeySet, self); + } + + // aten::rad2deg_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & rad2deg_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::rad2deg_::redispatch(dispatchKeySet, self); + } + + // aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rad2deg_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::rad2deg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::rad2deg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rad2deg_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::rad2deg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::deg2rad(Tensor self) -> Tensor + inline at::Tensor deg2rad(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::deg2rad::redispatch(dispatchKeySet, self); + } + + // aten::deg2rad_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & deg2rad_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::deg2rad_::redispatch(dispatchKeySet, self); + } + + // aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & deg2rad_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::deg2rad_out::redispatch(dispatchKeySet, self, out); + } + + // aten::deg2rad.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & deg2rad_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::deg2rad_out::redispatch(dispatchKeySet, self, out); + } + + // aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor scalar_tensor(c10::DispatchKeySet dispatchKeySet, const at::Scalar & s, at::TensorOptions options={}) { + return at::_ops::scalar_tensor::redispatch(dispatchKeySet, s, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::scalar_tensor(Scalar s, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor scalar_tensor(c10::DispatchKeySet dispatchKeySet, const at::Scalar & s, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::scalar_tensor::redispatch(dispatchKeySet, s, dtype, layout, device, pin_memory); + } + + // aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::rand_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory); + } + + // aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::rand_names::redispatch(dispatchKeySet, size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_names::redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory); + } + + // aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, at::TensorOptions options={}) { + return at::_ops::rand_generator_with_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_generator_with_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory); + } + + // aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, at::TensorOptions options={}) { + return at::_ops::rand_generator_with_names::redispatch(dispatchKeySet, size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_generator_with_names::redispatch(dispatchKeySet, size, generator, names, dtype, layout, device, pin_memory); + } + + // aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::rand::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::rand::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, at::TensorOptions options={}) { + return at::_ops::rand_generator::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_generator::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory); + } + + // aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, at::TensorOptions options={}) { + return at::_ops::rand_generator::redispatch(dispatchKeySet, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::rand.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor rand_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::rand_generator::redispatch(dispatchKeySet, size, generator, dtype, layout, device, pin_memory); + } + + // aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::rand_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::rand_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size) { + return at::_ops::rand_out::redispatch(dispatchKeySet, size, out); + } + + // aten::rand.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::rand_out::redispatch(dispatchKeySet, size, out); + } + + // aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional generator) { + return at::_ops::rand_generator_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::rand_generator_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional generator) { + return at::_ops::rand_generator_out::redispatch(dispatchKeySet, size, generator, out); + } + + // aten::rand.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::rand_generator_out::redispatch(dispatchKeySet, size, generator, out); + } + + // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor rand_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::rand_like::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::rand_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor rand_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::rand_like::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) { + return at::_ops::randint::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) { + return at::_ops::randint::redispatch(dispatchKeySet, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint::redispatch(dispatchKeySet, high, size, dtype, layout, device, pin_memory); + } + + // aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randint_generator::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_generator::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory); + } + + // aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randint_generator::redispatch(dispatchKeySet, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_generator::redispatch(dispatchKeySet, high, size, generator, dtype, layout, device, pin_memory); + } + + // aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, at::TensorOptions options=at::kLong) { + return at::_ops::randint_low::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_low::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::TensorOptions options=at::kLong) { + return at::_ops::randint_low::redispatch(dispatchKeySet, low, high, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_low::redispatch(dispatchKeySet, low, high, size, dtype, layout, device, pin_memory); + } + + // aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randint_low_generator::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_low_generator::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory); + } + + // aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randint_low_generator::redispatch(dispatchKeySet, low, high, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randint_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randint_low_generator::redispatch(dispatchKeySet, low, high, size, generator, dtype, layout, device, pin_memory); + } + + // aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t high, at::IntArrayRef size) { + return at::_ops::randint_out::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_outf(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::randint_out::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size) { + return at::_ops::randint_out::redispatch(dispatchKeySet, high, size, out); + } + + // aten::randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::randint_out::redispatch(dispatchKeySet, high, size, out); + } + + // aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t high, at::IntArrayRef size, c10::optional generator) { + return at::_ops::randint_generator_out::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_outf(c10::DispatchKeySet dispatchKeySet, int64_t high, at::IntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randint_generator_out::redispatch(dispatchKeySet, high, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator) { + return at::_ops::randint_generator_out::redispatch(dispatchKeySet, high, size, generator, out); + } + + // aten::randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randint_generator_out::redispatch(dispatchKeySet, high, size, generator, out); + } + + // aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size) { + return at::_ops::randint_low_out::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_outf(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::randint_low_out::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size) { + return at::_ops::randint_low_out::redispatch(dispatchKeySet, low, high, size, out); + } + + // aten::randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::randint_low_out::redispatch(dispatchKeySet, low, high, size, out); + } + + // aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t low, int64_t high, at::IntArrayRef size, c10::optional generator) { + return at::_ops::randint_low_generator_out::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_outf(c10::DispatchKeySet dispatchKeySet, int64_t low, int64_t high, at::IntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randint_low_generator_out::redispatch(dispatchKeySet, low, high, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator) { + return at::_ops::randint_low_generator_out::redispatch(dispatchKeySet, low, high, size, generator, out); + } + + // aten::randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randint_low_generator_out::redispatch(dispatchKeySet, low, high, size, generator, out); + } + + // aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like::redispatch(dispatchKeySet, self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::randint_like::redispatch(dispatchKeySet, self, high, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt high, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like::redispatch(dispatchKeySet, self, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::randint_like(Tensor self, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::randint_like::redispatch(dispatchKeySet, self, high, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_low_dtype::redispatch(dispatchKeySet, self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::randint_like_low_dtype::redispatch(dispatchKeySet, self, low, high, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt low, c10::SymInt high, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_low_dtype::redispatch(dispatchKeySet, self, low, high, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::randint_like.low_dtype(Tensor self, SymInt low, SymInt high, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randint_like_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt low, c10::SymInt high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::randint_like_low_dtype::redispatch(dispatchKeySet, self, low, high, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::randn::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::randn::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, at::TensorOptions options={}) { + return at::_ops::randn_generator::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_generator::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory); + } + + // aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, at::TensorOptions options={}) { + return at::_ops::randn_generator::redispatch(dispatchKeySet, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.generator(SymInt[] size, *, Generator? generator, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_generator::redispatch(dispatchKeySet, size, generator, dtype, layout, device, pin_memory); + } + + // aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::randn_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, dtype, layout, device, pin_memory); + } + + // aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::randn_names::redispatch(dispatchKeySet, size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.names(SymInt[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_names::redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory); + } + + // aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, at::TensorOptions options={}) { + return at::_ops::randn_generator_with_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_generator_with_names::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, dtype, layout, device, pin_memory); + } + + // aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, at::TensorOptions options={}) { + return at::_ops::randn_generator_with_names::redispatch(dispatchKeySet, size, generator, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randn.generator_with_names(SymInt[] size, *, Generator? generator, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randn_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randn_generator_with_names::redispatch(dispatchKeySet, size, generator, names, dtype, layout, device, pin_memory); + } + + // aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::randn_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::randn_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size) { + return at::_ops::randn_out::redispatch(dispatchKeySet, size, out); + } + + // aten::randn.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::randn_out::redispatch(dispatchKeySet, size, out); + } + + // aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional generator) { + return at::_ops::randn_generator_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randn_generator_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional generator) { + return at::_ops::randn_generator_out::redispatch(dispatchKeySet, size, generator, out); + } + + // aten::randn.generator_out(SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::randn_generator_out::redispatch(dispatchKeySet, size, generator, out); + } + + // aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randn_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::randn_like::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::randn_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor randn_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::randn_like::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); + } + + // aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm(c10::DispatchKeySet dispatchKeySet, int64_t n, at::TensorOptions options=at::kLong) { + return at::_ops::randperm::redispatch(dispatchKeySet, n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randperm::redispatch(dispatchKeySet, n, dtype, layout, device, pin_memory); + } + + // aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, at::TensorOptions options=at::kLong) { + return at::_ops::randperm::redispatch(dispatchKeySet, n, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randperm(SymInt n, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randperm::redispatch(dispatchKeySet, n, dtype, layout, device, pin_memory); + } + + // aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randperm_generator::redispatch(dispatchKeySet, n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randperm_generator::redispatch(dispatchKeySet, n, generator, dtype, layout, device, pin_memory); + } + + // aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional generator, at::TensorOptions options=at::kLong) { + return at::_ops::randperm_generator::redispatch(dispatchKeySet, n, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::randperm.generator(SymInt n, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor randperm_symint(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::randperm_generator::redispatch(dispatchKeySet, n, generator, dtype, layout, device, pin_memory); + } + + // aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n) { + return at::_ops::randperm_out::redispatch(dispatchKeySet, n, out); + } + + // aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, at::Tensor & out) { + return at::_ops::randperm_out::redispatch(dispatchKeySet, n, out); + } + + // aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt n) { + return at::_ops::randperm_out::redispatch(dispatchKeySet, n, out); + } + + // aten::randperm.out(SymInt n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, at::Tensor & out) { + return at::_ops::randperm_out::redispatch(dispatchKeySet, n, out); + } + + // aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, c10::optional generator) { + return at::_ops::randperm_generator_out::redispatch(dispatchKeySet, n, generator, out); + } + + // aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, c10::optional generator, at::Tensor & out) { + return at::_ops::randperm_generator_out::redispatch(dispatchKeySet, n, generator, out); + } + + // aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymInt n, c10::optional generator) { + return at::_ops::randperm_generator_out::redispatch(dispatchKeySet, n, generator, out); + } + + // aten::randperm.generator_out(SymInt n, *, Generator? generator, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randperm_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymInt n, c10::optional generator, at::Tensor & out) { + return at::_ops::randperm_generator_out::redispatch(dispatchKeySet, n, generator, out); + } + + // aten::range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step=1, at::TensorOptions options={}) { + return at::_ops::range_step::redispatch(dispatchKeySet, start, end, step, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::range.step(Scalar start, Scalar end, Scalar step=1, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::range_step::redispatch(dispatchKeySet, start, end, step, dtype, layout, device, pin_memory); + } + + // aten::range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, at::TensorOptions options={}) { + return at::_ops::range::redispatch(dispatchKeySet, start, end, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::range(Scalar start, Scalar end, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor range(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::range::redispatch(dispatchKeySet, start, end, dtype, layout, device, pin_memory); + } + + // aten::range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & range_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Scalar & end) { + return at::_ops::range_out_::redispatch(dispatchKeySet, start, end, out); + } + + // aten::range.out_(Scalar start, Scalar end, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & range_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, at::Tensor & out) { + return at::_ops::range_out_::redispatch(dispatchKeySet, start, end, out); + } + + // aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & range_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step) { + return at::_ops::range_out::redispatch(dispatchKeySet, start, end, step, out); + } + + // aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & range_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & start, const at::Scalar & end, const at::Scalar & step, at::Tensor & out) { + return at::_ops::range_out::redispatch(dispatchKeySet, start, end, step, out); + } + + // aten::ravel(Tensor(a) self) -> Tensor(a) + inline at::Tensor ravel(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::ravel::redispatch(dispatchKeySet, self); + } + + // aten::reciprocal(Tensor self) -> Tensor + inline at::Tensor reciprocal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::reciprocal::redispatch(dispatchKeySet, self); + } + + // aten::reciprocal_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & reciprocal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::reciprocal_::redispatch(dispatchKeySet, self); + } + + // aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reciprocal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::reciprocal_out::redispatch(dispatchKeySet, self, out); + } + + // aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reciprocal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::reciprocal_out::redispatch(dispatchKeySet, self, out); + } + + // aten::neg(Tensor self) -> Tensor + inline at::Tensor neg(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::neg::redispatch(dispatchKeySet, self); + } + + // aten::neg_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & neg_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::neg_::redispatch(dispatchKeySet, self); + } + + // aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & neg_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::neg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & neg_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::neg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::negative(Tensor self) -> Tensor + inline at::Tensor negative(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::negative::redispatch(dispatchKeySet, self); + } + + // aten::negative_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & negative_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::negative_::redispatch(dispatchKeySet, self); + } + + // aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & negative_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::negative_out::redispatch(dispatchKeySet, self, out); + } + + // aten::negative.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & negative_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::negative_out::redispatch(dispatchKeySet, self, out); + } + + // aten::repeat(Tensor self, SymInt[] repeats) -> Tensor + inline at::Tensor repeat(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef repeats) { + return at::_ops::repeat::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(repeats)); + } + + // aten::repeat(Tensor self, SymInt[] repeats) -> Tensor + inline at::Tensor repeat_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef repeats) { + return at::_ops::repeat::redispatch(dispatchKeySet, self, repeats); + } + + // aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave(c10::DispatchKeySet dispatchKeySet, const at::Tensor & repeats, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_Tensor::redispatch(dispatchKeySet, repeats, output_size.has_value() ? c10::make_optional(c10::SymInt(*output_size)) : c10::nullopt); + } + + // aten::repeat_interleave.Tensor(Tensor repeats, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & repeats, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_Tensor::redispatch(dispatchKeySet, repeats, output_size); + } + + // aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & repeats, c10::optional dim=c10::nullopt, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_self_Tensor::redispatch(dispatchKeySet, self, repeats, dim, output_size.has_value() ? c10::make_optional(c10::SymInt(*output_size)) : c10::nullopt); + } + + // aten::repeat_interleave.self_Tensor(Tensor self, Tensor repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & repeats, c10::optional dim=c10::nullopt, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_self_Tensor::redispatch(dispatchKeySet, self, repeats, dim, output_size); + } + + // aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t repeats, c10::optional dim=c10::nullopt, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_self_int::redispatch(dispatchKeySet, self, repeats, dim, output_size.has_value() ? c10::make_optional(c10::SymInt(*output_size)) : c10::nullopt); + } + + // aten::repeat_interleave.self_int(Tensor self, SymInt repeats, int? dim=None, *, SymInt? output_size=None) -> Tensor + inline at::Tensor repeat_interleave_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt repeats, c10::optional dim=c10::nullopt, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_self_int::redispatch(dispatchKeySet, self, repeats, dim, output_size); + } + + // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) + inline at::Tensor reshape(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shape) { + return at::_ops::reshape::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(shape)); + } + + // aten::reshape(Tensor(a) self, SymInt[] shape) -> Tensor(a) + inline at::Tensor reshape_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef shape) { + return at::_ops::reshape::redispatch(dispatchKeySet, self, shape); + } + + // aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor _reshape_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_reshape_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::_reshape_copy(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor _reshape_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::_reshape_copy::redispatch(dispatchKeySet, self, size); + } + + // aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) + inline at::Tensor _reshape_alias(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { + return at::_ops::_reshape_alias::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); + } + + // aten::_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a) + inline at::Tensor _reshape_alias_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + return at::_ops::_reshape_alias::redispatch(dispatchKeySet, self, size, stride); + } + + // aten::_mkldnn_reshape(Tensor self, int[] shape) -> Tensor + inline at::Tensor _mkldnn_reshape(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shape) { + return at::_ops::_mkldnn_reshape::redispatch(dispatchKeySet, self, shape); + } + + // aten::reshape_as(Tensor(a) self, Tensor other) -> Tensor(a) + inline at::Tensor reshape_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::reshape_as::redispatch(dispatchKeySet, self, other); + } + + // aten::round(Tensor self) -> Tensor + inline at::Tensor round(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::round::redispatch(dispatchKeySet, self); + } + + // aten::round_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & round_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::round_::redispatch(dispatchKeySet, self); + } + + // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & round_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::round_out::redispatch(dispatchKeySet, self, out); + } + + // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & round_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::round_out::redispatch(dispatchKeySet, self, out); + } + + // aten::round.decimals(Tensor self, *, int decimals) -> Tensor + inline at::Tensor round(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals) { + return at::_ops::round_decimals::redispatch(dispatchKeySet, self, decimals); + } + + // aten::round_.decimals(Tensor(a!) self, *, int decimals) -> Tensor(a!) + inline at::Tensor & round_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t decimals) { + return at::_ops::round__decimals::redispatch(dispatchKeySet, self, decimals); + } + + // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & round_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t decimals) { + return at::_ops::round_decimals_out::redispatch(dispatchKeySet, self, decimals, out); + } + + // aten::round.decimals_out(Tensor self, *, int decimals, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & round_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals, at::Tensor & out) { + return at::_ops::round_decimals_out::redispatch(dispatchKeySet, self, decimals, out); + } + + // aten::rrelu(Tensor self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor + inline at::Tensor rrelu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt) { + return at::_ops::rrelu::redispatch(dispatchKeySet, self, lower, upper, training, generator); + } + + // aten::rrelu_(Tensor(a!) self, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & rrelu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt) { + return at::_ops::rrelu_::redispatch(dispatchKeySet, self, lower, upper, training, generator); + } + + // aten::relu(Tensor self) -> Tensor + inline at::Tensor relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::relu::redispatch(dispatchKeySet, self); + } + + // aten::relu_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & relu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::relu_::redispatch(dispatchKeySet, self); + } + + // aten::relu6(Tensor self) -> Tensor + inline at::Tensor relu6(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::relu6::redispatch(dispatchKeySet, self); + } + + // aten::relu6_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & relu6_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::relu6_::redispatch(dispatchKeySet, self); + } + + // aten::prelu(Tensor self, Tensor weight) -> Tensor + inline at::Tensor prelu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight) { + return at::_ops::prelu::redispatch(dispatchKeySet, self, weight); + } + + // aten::_prelu_kernel(Tensor self, Tensor weight) -> Tensor + inline at::Tensor _prelu_kernel(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight) { + return at::_ops::_prelu_kernel::redispatch(dispatchKeySet, self, weight); + } + + // aten::_prelu_kernel_backward(Tensor grad_output, Tensor self, Tensor weight) -> (Tensor, Tensor) + inline ::std::tuple _prelu_kernel_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) { + return at::_ops::_prelu_kernel_backward::redispatch(dispatchKeySet, grad_output, self, weight); + } + + // aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gelu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::string_view approximate="none") { + return at::_ops::gelu_out::redispatch(dispatchKeySet, self, approximate, out); + } + + // aten::gelu.out(Tensor self, *, str approximate='none', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gelu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate, at::Tensor & out) { + return at::_ops::gelu_out::redispatch(dispatchKeySet, self, approximate, out); + } + + // aten::gelu_(Tensor(a!) self, *, str approximate='none') -> Tensor(a!) + inline at::Tensor & gelu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::string_view approximate="none") { + return at::_ops::gelu_::redispatch(dispatchKeySet, self, approximate); + } + + // aten::gelu(Tensor self, *, str approximate='none') -> Tensor + inline at::Tensor gelu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view approximate="none") { + return at::_ops::gelu::redispatch(dispatchKeySet, self, approximate); + } + + // aten::gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & gelu_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate="none") { + return at::_ops::gelu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, approximate, grad_input); + } + + // aten::gelu_backward.grad_input(Tensor grad_output, Tensor self, *, str approximate='none', Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & gelu_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate, at::Tensor & grad_input) { + return at::_ops::gelu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, approximate, grad_input); + } + + // aten::gelu_backward(Tensor grad_output, Tensor self, *, str approximate='none') -> Tensor + inline at::Tensor gelu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate="none") { + return at::_ops::gelu_backward::redispatch(dispatchKeySet, grad_output, self, approximate); + } + + // aten::infinitely_differentiable_gelu_backward(Tensor grad, Tensor self) -> Tensor + inline at::Tensor infinitely_differentiable_gelu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self) { + return at::_ops::infinitely_differentiable_gelu_backward::redispatch(dispatchKeySet, grad, self); + } + + // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardshrink_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd=0.5) { + return at::_ops::hardshrink_out::redispatch(dispatchKeySet, self, lambd, out); + } + + // aten::hardshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardshrink_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { + return at::_ops::hardshrink_out::redispatch(dispatchKeySet, self, lambd, out); + } + + // aten::hardshrink(Tensor self, Scalar lambd=0.5) -> Tensor + inline at::Tensor hardshrink(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd=0.5) { + return at::_ops::hardshrink::redispatch(dispatchKeySet, self, lambd); + } + + // aten::hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardshrink_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { + return at::_ops::hardshrink_backward_grad_input::redispatch(dispatchKeySet, grad_out, self, lambd, grad_input); + } + + // aten::hardshrink_backward.grad_input(Tensor grad_out, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardshrink_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { + return at::_ops::hardshrink_backward_grad_input::redispatch(dispatchKeySet, grad_out, self, lambd, grad_input); + } + + // aten::hardshrink_backward(Tensor grad_out, Tensor self, Scalar lambd) -> Tensor + inline at::Tensor hardshrink_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { + return at::_ops::hardshrink_backward::redispatch(dispatchKeySet, grad_out, self, lambd); + } + + // aten::rsqrt(Tensor self) -> Tensor + inline at::Tensor rsqrt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::rsqrt::redispatch(dispatchKeySet, self); + } + + // aten::rsqrt_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & rsqrt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::rsqrt_::redispatch(dispatchKeySet, self); + } + + // aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsqrt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::rsqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsqrt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::rsqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::select.Dimname(Tensor(a) self, Dimname dim, int index) -> Tensor(a) + inline at::Tensor select(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, int64_t index) { + return at::_ops::select_Dimname::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) + inline at::Tensor select(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t index) { + return at::_ops::select_int::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::select.int(Tensor(a) self, int dim, SymInt index) -> Tensor(a) + inline at::Tensor select_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index) { + return at::_ops::select_int::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor + inline at::Tensor select_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) { + return at::_ops::select_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index); + } + + // aten::select_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index) -> Tensor + inline at::Tensor select_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { + return at::_ops::select_backward::redispatch(dispatchKeySet, grad_output, input_sizes, dim, index); + } + + // aten::_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor + inline at::Tensor _nested_select_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, int64_t index) { + return at::_ops::_nested_select_backward::redispatch(dispatchKeySet, grad_output, self, dim, index); + } + + // aten::_nested_select_backward(Tensor grad_output, Tensor self, int dim, SymInt index) -> Tensor + inline at::Tensor _nested_select_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, c10::SymInt index) { + return at::_ops::_nested_select_backward::redispatch(dispatchKeySet, grad_output, self, dim, index); + } + + // aten::selu(Tensor self) -> Tensor + inline at::Tensor selu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::selu::redispatch(dispatchKeySet, self); + } + + // aten::selu_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & selu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::selu_::redispatch(dispatchKeySet, self); + } + + // aten::celu(Tensor self, Scalar alpha=1.0) -> Tensor + inline at::Tensor celu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha=1.0) { + return at::_ops::celu::redispatch(dispatchKeySet, self, alpha); + } + + // aten::celu_(Tensor(a!) self, Scalar alpha=1.0) -> Tensor(a!) + inline at::Tensor & celu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & alpha=1.0) { + return at::_ops::celu_::redispatch(dispatchKeySet, self, alpha); + } + + // aten::silu(Tensor self) -> Tensor + inline at::Tensor silu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::silu::redispatch(dispatchKeySet, self); + } + + // aten::silu_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & silu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::silu_::redispatch(dispatchKeySet, self); + } + + // aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & silu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::silu_out::redispatch(dispatchKeySet, self, out); + } + + // aten::silu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & silu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::silu_out::redispatch(dispatchKeySet, self, out); + } + + // aten::silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & silu_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::silu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::silu_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & silu_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { + return at::_ops::silu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::silu_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor silu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::silu_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::mish(Tensor self) -> Tensor + inline at::Tensor mish(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::mish::redispatch(dispatchKeySet, self); + } + + // aten::mish_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & mish_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::mish_::redispatch(dispatchKeySet, self); + } + + // aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mish_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::mish_out::redispatch(dispatchKeySet, self, out); + } + + // aten::mish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mish_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::mish_out::redispatch(dispatchKeySet, self, out); + } + + // aten::mish_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor mish_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::mish_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::sigmoid(Tensor self) -> Tensor + inline at::Tensor sigmoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sigmoid::redispatch(dispatchKeySet, self); + } + + // aten::sigmoid_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sigmoid_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sigmoid_::redispatch(dispatchKeySet, self); + } + + // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sigmoid_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sigmoid_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::logit(Tensor self, float? eps=None) -> Tensor + inline at::Tensor logit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit::redispatch(dispatchKeySet, self, eps); + } + + // aten::logit_(Tensor(a!) self, float? eps=None) -> Tensor(a!) + inline at::Tensor & logit_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_::redispatch(dispatchKeySet, self, eps); + } + + // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_out::redispatch(dispatchKeySet, self, eps, out); + } + + // aten::logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & logit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional eps, at::Tensor & out) { + return at::_ops::logit_out::redispatch(dispatchKeySet, self, eps, out); + } + + // aten::sin(Tensor self) -> Tensor + inline at::Tensor sin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sin::redispatch(dispatchKeySet, self); + } + + // aten::sin_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sin_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sin_::redispatch(dispatchKeySet, self); + } + + // aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sinc(Tensor self) -> Tensor + inline at::Tensor sinc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sinc::redispatch(dispatchKeySet, self); + } + + // aten::sinc_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sinc_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sinc_::redispatch(dispatchKeySet, self); + } + + // aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sinc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sinc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sinc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sinc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sinh(Tensor self) -> Tensor + inline at::Tensor sinh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sinh::redispatch(dispatchKeySet, self); + } + + // aten::sinh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sinh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sinh_::redispatch(dispatchKeySet, self); + } + + // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sinh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sinh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::detach(Tensor(a) self) -> Tensor(a) + inline at::Tensor detach(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::detach::redispatch(dispatchKeySet, self); + } + + // aten::detach_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & detach_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::detach_::redispatch(dispatchKeySet, self); + } + + // aten::size.int(Tensor self, int dim) -> int + inline int64_t __dispatch_size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::size_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::size.Dimname(Tensor self, Dimname dim) -> int + inline int64_t size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::size_Dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::sym_size.int(Tensor self, int dim) -> SymInt + inline c10::SymInt __dispatch_sym_size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::sym_size_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::sym_numel(Tensor self) -> SymInt + inline c10::SymInt __dispatch_sym_numel(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sym_numel::redispatch(dispatchKeySet, self); + } + + // aten::sym_storage_offset(Tensor self) -> SymInt + inline c10::SymInt __dispatch_sym_storage_offset(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sym_storage_offset::redispatch(dispatchKeySet, self); + } + + // aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + inline at::Tensor slice(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_Tensor::redispatch(dispatchKeySet, self, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step); + } + + // aten::slice.Tensor(Tensor(a) self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + inline at::Tensor slice_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_Tensor::redispatch(dispatchKeySet, self, dim, start, end, step); + } + + // aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor + inline at::Tensor slice_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) { + return at::_ops::slice_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step); + } + + // aten::slice_backward(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step) -> Tensor + inline at::Tensor slice_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) { + return at::_ops::slice_backward::redispatch(dispatchKeySet, grad_output, input_sizes, dim, start, end, step); + } + + // aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + inline at::Tensor slice_inverse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_inverse::redispatch(dispatchKeySet, self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step); + } + + // aten::slice_inverse(Tensor(a) self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor(a) + inline at::Tensor slice_inverse_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_inverse::redispatch(dispatchKeySet, self, src, dim, start, end, step); + } + + // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor + inline at::Tensor slice_scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_scatter::redispatch(dispatchKeySet, self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step); + } + + // aten::slice_scatter(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor + inline at::Tensor slice_scatter_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_scatter::redispatch(dispatchKeySet, self, src, dim, start, end, step); + } + + // aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor + inline at::Tensor select_scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) { + return at::_ops::select_scatter::redispatch(dispatchKeySet, self, src, dim, index); + } + + // aten::select_scatter(Tensor self, Tensor src, int dim, SymInt index) -> Tensor + inline at::Tensor select_scatter_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) { + return at::_ops::select_scatter::redispatch(dispatchKeySet, self, src, dim, index); + } + + // aten::diagonal_scatter(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1) -> Tensor + inline at::Tensor diagonal_scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) { + return at::_ops::diagonal_scatter::redispatch(dispatchKeySet, self, src, offset, dim1, dim2); + } + + // aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor + inline at::Tensor as_strided_scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_scatter::redispatch(dispatchKeySet, self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); + } + + // aten::as_strided_scatter(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor + inline at::Tensor as_strided_scatter_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_scatter::redispatch(dispatchKeySet, self, src, size, stride, storage_offset); + } + + // aten::smm(Tensor self, Tensor mat2) -> Tensor + inline at::Tensor smm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::smm::redispatch(dispatchKeySet, self, mat2); + } + + // aten::softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor + inline at::Tensor softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::softmax_int::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::softmax_int_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::softmax.int_out(Tensor self, int dim, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype, at::Tensor & out) { + return at::_ops::softmax_int_out::redispatch(dispatchKeySet, self, dim, dtype, out); + } + + // aten::softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::softmax_Dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_softmax(Tensor self, int dim, bool half_to_float) -> Tensor + inline at::Tensor _softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_softmax::redispatch(dispatchKeySet, self, dim, half_to_float); + } + + // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { + return at::_ops::_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor + inline at::Tensor _softmax_backward_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + return at::_ops::_softmax_backward_data::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype); + } + + // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _softmax_backward_data_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + return at::_ops::_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, grad_input); + } + + // aten::_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _softmax_backward_data_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, at::Tensor & grad_input) { + return at::_ops::_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, input_dtype, grad_input); + } + + // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] + inline ::std::vector unsafe_split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t split_size, int64_t dim=0) { + return at::_ops::unsafe_split_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::unsafe_split.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] + inline ::std::vector unsafe_split_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) { + return at::_ops::unsafe_split_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[] + inline ::std::vector split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t split_size, int64_t dim=0) { + return at::_ops::split_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::split.Tensor(Tensor(a -> *) self, SymInt split_size, int dim=0) -> Tensor(a)[] + inline ::std::vector split_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) { + return at::_ops::split_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[] + inline ::std::vector split(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0) { + return at::_ops::split_sizes::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_size), dim); + } + + // aten::split.sizes(Tensor(a -> *) self, SymInt[] split_size, int dim=0) -> Tensor(a)[] + inline ::std::vector split_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0) { + return at::_ops::split_sizes::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] + inline ::std::vector unsafe_split_with_sizes(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::unsafe_split_with_sizes::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim); + } + + // aten::unsafe_split_with_sizes(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] + inline ::std::vector unsafe_split_with_sizes_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::unsafe_split_with_sizes::redispatch(dispatchKeySet, self, split_sizes, dim); + } + + // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] + inline ::std::vector split_with_sizes(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim); + } + + // aten::split_with_sizes(Tensor(a -> *) self, SymInt[] split_sizes, int dim=0) -> Tensor(a)[] + inline ::std::vector split_with_sizes_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes::redispatch(dispatchKeySet, self, split_sizes, dim); + } + + // aten::hsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] + inline ::std::vector hsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections) { + return at::_ops::hsplit_int::redispatch(dispatchKeySet, self, sections); + } + + // aten::hsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] + inline ::std::vector hsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices) { + return at::_ops::hsplit_array::redispatch(dispatchKeySet, self, indices); + } + + // aten::vsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] + inline ::std::vector vsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections) { + return at::_ops::vsplit_int::redispatch(dispatchKeySet, self, sections); + } + + // aten::vsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] + inline ::std::vector vsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices) { + return at::_ops::vsplit_array::redispatch(dispatchKeySet, self, indices); + } + + // aten::dsplit.int(Tensor(a -> *) self, int sections) -> Tensor(a)[] + inline ::std::vector dsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sections) { + return at::_ops::dsplit_int::redispatch(dispatchKeySet, self, sections); + } + + // aten::dsplit.array(Tensor(a -> *) self, int[] indices) -> Tensor(a)[] + inline ::std::vector dsplit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef indices) { + return at::_ops::dsplit_array::redispatch(dispatchKeySet, self, indices); + } + + // aten::squeeze(Tensor(a) self) -> Tensor(a) + inline at::Tensor squeeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::squeeze::redispatch(dispatchKeySet, self); + } + + // aten::squeeze.dim(Tensor(a) self, int dim) -> Tensor(a) + inline at::Tensor squeeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::squeeze_dim::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze.dimname(Tensor(a) self, Dimname dim) -> Tensor(a) + inline at::Tensor squeeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::squeeze_dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze.dims(Tensor(a) self, int[] dim) -> Tensor(a) + inline at::Tensor squeeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::squeeze_dims::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & squeeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::squeeze_::redispatch(dispatchKeySet, self); + } + + // aten::squeeze_.dim(Tensor(a!) self, int dim) -> Tensor(a!) + inline at::Tensor & squeeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim) { + return at::_ops::squeeze__dim::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze_.dims(Tensor(a!) self, int[] dim) -> Tensor(a!) + inline at::Tensor & squeeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::squeeze__dims::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze_.dimname(Tensor(a!) self, Dimname dim) -> Tensor(a!) + inline at::Tensor & squeeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim) { + return at::_ops::squeeze__dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::sspaddmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor sspaddmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::sspaddmm::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); + } + + // aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sspaddmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::sspaddmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sspaddmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::sspaddmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::_chunk_cat(Tensor[] tensors, int dim, int num_chunks) -> Tensor + inline at::Tensor _chunk_cat(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, int64_t num_chunks) { + return at::_ops::_chunk_cat::redispatch(dispatchKeySet, tensors, dim, num_chunks); + } + + // aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _chunk_cat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, int64_t dim, int64_t num_chunks) { + return at::_ops::_chunk_cat_out::redispatch(dispatchKeySet, tensors, dim, num_chunks, out); + } + + // aten::_chunk_cat.out(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _chunk_cat_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, int64_t num_chunks, at::Tensor & out) { + return at::_ops::_chunk_cat_out::redispatch(dispatchKeySet, tensors, dim, num_chunks, out); + } + + // aten::stack(Tensor[] tensors, int dim=0) -> Tensor + inline at::Tensor stack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim=0) { + return at::_ops::stack::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & stack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, int64_t dim=0) { + return at::_ops::stack_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & stack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { + return at::_ops::stack_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::_stack(Tensor[] tensors, int dim=0) -> Tensor + inline at::Tensor _stack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim=0) { + return at::_ops::_stack::redispatch(dispatchKeySet, tensors, dim); + } + + // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _stack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors, int64_t dim=0) { + return at::_ops::_stack_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::_stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _stack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, int64_t dim, at::Tensor & out) { + return at::_ops::_stack_out::redispatch(dispatchKeySet, tensors, dim, out); + } + + // aten::hstack(Tensor[] tensors) -> Tensor + inline at::Tensor hstack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::hstack::redispatch(dispatchKeySet, tensors); + } + + // aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hstack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::hstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::hstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hstack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::hstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::vstack(Tensor[] tensors) -> Tensor + inline at::Tensor vstack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::vstack::redispatch(dispatchKeySet, tensors); + } + + // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & vstack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::vstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::vstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & vstack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::vstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::dstack(Tensor[] tensors) -> Tensor + inline at::Tensor dstack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::dstack::redispatch(dispatchKeySet, tensors); + } + + // aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dstack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::dstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::dstack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dstack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::dstack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::stft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor + inline at::Tensor stft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const c10::optional & window, bool normalized, c10::optional onesided=c10::nullopt, c10::optional return_complex=c10::nullopt) { + return at::_ops::stft::redispatch(dispatchKeySet, self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex); + } + + // aten::stft.center(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, str pad_mode="reflect", bool normalized=False, bool? onesided=None, bool? return_complex=None) -> Tensor + inline at::Tensor stft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const c10::optional & window={}, bool center=true, c10::string_view pad_mode="reflect", bool normalized=false, c10::optional onesided=c10::nullopt, c10::optional return_complex=c10::nullopt) { + return at::_ops::stft_center::redispatch(dispatchKeySet, self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex); + } + + // aten::istft(Tensor self, int n_fft, int? hop_length=None, int? win_length=None, Tensor? window=None, bool center=True, bool normalized=False, bool? onesided=None, int? length=None, bool return_complex=False) -> Tensor + inline at::Tensor istft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const c10::optional & window={}, bool center=true, bool normalized=false, c10::optional onesided=c10::nullopt, c10::optional length=c10::nullopt, bool return_complex=false) { + return at::_ops::istft::redispatch(dispatchKeySet, self, n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex); + } + + // aten::stride.int(Tensor self, int dim) -> int + inline int64_t __dispatch_stride(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::stride_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::stride.Dimname(Tensor self, Dimname dim) -> int + inline int64_t stride(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::stride_Dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::sym_stride.int(Tensor self, int dim) -> SymInt + inline c10::SymInt __dispatch_sym_stride(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::sym_stride_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::sum::redispatch(dispatchKeySet, self, dtype); + } + + // aten::sum.dim_IntList(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::sum_dim_IntList::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::sum.dim_DimnameList(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::sum_dim_DimnameList::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::sum_IntList_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::sum.IntList_out(Tensor self, int[1]? dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::sum_IntList_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::sum_DimnameList_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::sum_DimnameList_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor + inline at::Tensor _nested_sum_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim=false) { + return at::_ops::_nested_sum_backward::redispatch(dispatchKeySet, grad, self, dim, keepdim); + } + + // aten::nansum(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor nansum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::nansum::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nansum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::nansum_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::nansum.out(Tensor self, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nansum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::nansum_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::sum_to_size(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor sum_to_size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::sum_to_size::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::sum_to_size(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor sum_to_size_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::sum_to_size::redispatch(dispatchKeySet, self, size); + } + + // aten::sqrt(Tensor self) -> Tensor + inline at::Tensor sqrt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sqrt::redispatch(dispatchKeySet, self); + } + + // aten::sqrt_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sqrt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sqrt_::redispatch(dispatchKeySet, self); + } + + // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sqrt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sqrt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::square(Tensor self) -> Tensor + inline at::Tensor square(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::square::redispatch(dispatchKeySet, self); + } + + // aten::square_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & square_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::square_::redispatch(dispatchKeySet, self); + } + + // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & square_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::square_out::redispatch(dispatchKeySet, self, out); + } + + // aten::square.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & square_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::square_out::redispatch(dispatchKeySet, self, out); + } + + // aten::std(Tensor self, bool unbiased=True) -> Tensor + inline at::Tensor std(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { + return at::_ops::std::redispatch(dispatchKeySet, self, unbiased); + } + + // aten::std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor + inline at::Tensor std(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor + inline at::Tensor std(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_correction::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::std_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) + inline ::std::tuple std_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { + return at::_ops::std_mean::redispatch(dispatchKeySet, self, unbiased); + } + + // aten::std_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple std_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_mean_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::std_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple std_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_mean_correction::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::std_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple std_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_mean_names_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::std_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple std_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_mean_correction_names::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::std.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { + return at::_ops::std_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::std.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out) { + return at::_ops::std_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::std.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor + inline at::Tensor std(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_names_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::std_names_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::std.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { + return at::_ops::std_names_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::std.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor + inline at::Tensor std(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_correction_names::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_correction_names_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::std.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & std_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim, at::Tensor & out) { + return at::_ops::std_correction_names_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor prod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::prod::redispatch(dispatchKeySet, self, dtype); + } + + // aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor prod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::prod_dim_int::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::prod_int_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::prod_int_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::prod.dim_Dimname(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor prod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::prod_dim_Dimname::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::prod_Dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::prod.Dimname_out(Tensor self, Dimname dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::prod_Dimname_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::t(Tensor(a) self) -> Tensor(a) + inline at::Tensor t(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::t::redispatch(dispatchKeySet, self); + } + + // aten::t_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & t_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::t_::redispatch(dispatchKeySet, self); + } + + // aten::tan(Tensor self) -> Tensor + inline at::Tensor tan(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::tan::redispatch(dispatchKeySet, self); + } + + // aten::tan_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & tan_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::tan_::redispatch(dispatchKeySet, self); + } + + // aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tan_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::tan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tan_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::tan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::tanh(Tensor self) -> Tensor + inline at::Tensor tanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::tanh::redispatch(dispatchKeySet, self); + } + + // aten::tanh_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & tanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::tanh_::redispatch(dispatchKeySet, self); + } + + // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tanh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::tanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tanh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::tanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::tensordot(Tensor self, Tensor other, int[] dims_self, int[] dims_other) -> Tensor + inline at::Tensor tensordot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { + return at::_ops::tensordot::redispatch(dispatchKeySet, self, other, dims_self, dims_other); + } + + // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tensordot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { + return at::_ops::tensordot_out::redispatch(dispatchKeySet, self, other, dims_self, dims_other, out); + } + + // aten::tensordot.out(Tensor self, Tensor other, int[] dims_self, int[] dims_other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tensordot_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other, at::Tensor & out) { + return at::_ops::tensordot_out::redispatch(dispatchKeySet, self, other, dims_self, dims_other, out); + } + + // aten::threshold(Tensor self, Scalar threshold, Scalar value) -> Tensor + inline at::Tensor threshold(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { + return at::_ops::threshold::redispatch(dispatchKeySet, self, threshold, value); + } + + // aten::threshold_(Tensor(a!) self, Scalar threshold, Scalar value) -> Tensor(a!) + inline at::Tensor & threshold_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { + return at::_ops::threshold_::redispatch(dispatchKeySet, self, threshold, value); + } + + // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & threshold_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { + return at::_ops::threshold_out::redispatch(dispatchKeySet, self, threshold, value, out); + } + + // aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & threshold_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value, at::Tensor & out) { + return at::_ops::threshold_out::redispatch(dispatchKeySet, self, threshold, value, out); + } + + // aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & threshold_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { + return at::_ops::threshold_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, threshold, grad_input); + } + + // aten::threshold_backward.grad_input(Tensor grad_output, Tensor self, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & threshold_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold, at::Tensor & grad_input) { + return at::_ops::threshold_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, threshold, grad_input); + } + + // aten::threshold_backward(Tensor grad_output, Tensor self, Scalar threshold) -> Tensor + inline at::Tensor threshold_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { + return at::_ops::threshold_backward::redispatch(dispatchKeySet, grad_output, self, threshold); + } + + // aten::tile(Tensor self, SymInt[] dims) -> Tensor + inline at::Tensor tile(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::tile::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(dims)); + } + + // aten::tile(Tensor self, SymInt[] dims) -> Tensor + inline at::Tensor tile_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef dims) { + return at::_ops::tile::redispatch(dispatchKeySet, self, dims); + } + + // aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> Tensor(a) + inline at::Tensor transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::transpose_int::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::transpose.Dimname(Tensor(a) self, Dimname dim0, Dimname dim1) -> Tensor(a) + inline at::Tensor transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) { + return at::_ops::transpose_Dimname::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::_mkldnn_transpose(Tensor self, int dim0, int dim1) -> Tensor + inline at::Tensor _mkldnn_transpose(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::_mkldnn_transpose::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) + inline at::Tensor & transpose_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::transpose_::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::_mkldnn_transpose_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) + inline at::Tensor & _mkldnn_transpose_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::_mkldnn_transpose_::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::one_hot(Tensor self, int num_classes=-1) -> Tensor + inline at::Tensor one_hot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t num_classes=-1) { + return at::_ops::one_hot::redispatch(dispatchKeySet, self, num_classes); + } + + // aten::flip(Tensor self, int[] dims) -> Tensor + inline at::Tensor flip(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::flip::redispatch(dispatchKeySet, self, dims); + } + + // aten::fliplr(Tensor self) -> Tensor + inline at::Tensor fliplr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::fliplr::redispatch(dispatchKeySet, self); + } + + // aten::flipud(Tensor self) -> Tensor + inline at::Tensor flipud(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::flipud::redispatch(dispatchKeySet, self); + } + + // aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor + inline at::Tensor roll(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) { + return at::_ops::roll::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(shifts), dims); + } + + // aten::roll(Tensor self, SymInt[1] shifts, int[1] dims=[]) -> Tensor + inline at::Tensor roll_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) { + return at::_ops::roll::redispatch(dispatchKeySet, self, shifts, dims); + } + + // aten::rot90(Tensor self, int k=1, int[] dims=[0,1]) -> Tensor + inline at::Tensor rot90(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k=1, at::IntArrayRef dims={0,1}) { + return at::_ops::rot90::redispatch(dispatchKeySet, self, k, dims); + } + + // aten::trapezoid.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor + inline at::Tensor trapezoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) { + return at::_ops::trapezoid_x::redispatch(dispatchKeySet, y, x, dim); + } + + // aten::trapezoid.dx(Tensor y, *, Scalar dx=1, int dim=-1) -> Tensor + inline at::Tensor trapezoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Scalar & dx=1, int64_t dim=-1) { + return at::_ops::trapezoid_dx::redispatch(dispatchKeySet, y, dx, dim); + } + + // aten::trapz.x(Tensor y, Tensor x, *, int dim=-1) -> Tensor + inline at::Tensor trapz(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, const at::Tensor & x, int64_t dim=-1) { + return at::_ops::trapz_x::redispatch(dispatchKeySet, y, x, dim); + } + + // aten::trapz.dx(Tensor y, *, float dx=1, int dim=-1) -> Tensor + inline at::Tensor trapz(c10::DispatchKeySet dispatchKeySet, const at::Tensor & y, double dx=1, int64_t dim=-1) { + return at::_ops::trapz_dx::redispatch(dispatchKeySet, y, dx, dim); + } + + // aten::_transform_bias_rescale_qkv(Tensor qkv, Tensor qkv_bias, int num_heads) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _transform_bias_rescale_qkv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) { + return at::_ops::_transform_bias_rescale_qkv::redispatch(dispatchKeySet, qkv, qkv_bias, num_heads); + } + + // aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor + inline at::Tensor _nested_tensor_from_mask(c10::DispatchKeySet dispatchKeySet, const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) { + return at::_ops::_nested_tensor_from_mask::redispatch(dispatchKeySet, t, mask, mask_check); + } + + // aten::_nested_tensor_from_mask_left_aligned(Tensor t, Tensor mask) -> bool + inline bool _nested_tensor_from_mask_left_aligned(c10::DispatchKeySet dispatchKeySet, const at::Tensor & t, const at::Tensor & mask) { + return at::_ops::_nested_tensor_from_mask_left_aligned::redispatch(dispatchKeySet, t, mask); + } + + // aten::_nested_from_padded(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False) -> Tensor + inline at::Tensor _nested_from_padded(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213=false) { + return at::_ops::_nested_from_padded::redispatch(dispatchKeySet, padded, cpu_nested_shape_example, fuse_transform_0213); + } + + // aten::_nested_tensor_size(Tensor self) -> Tensor + inline at::Tensor _nested_tensor_size(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_tensor_size::redispatch(dispatchKeySet, self); + } + + // aten::_nested_tensor_strides(Tensor self) -> Tensor + inline at::Tensor _nested_tensor_strides(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_tensor_strides::redispatch(dispatchKeySet, self); + } + + // aten::_nested_tensor_storage_offsets(Tensor self) -> Tensor + inline at::Tensor _nested_tensor_storage_offsets(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_tensor_storage_offsets::redispatch(dispatchKeySet, self); + } + + // aten::_nested_from_padded_and_nested_example(Tensor padded, Tensor nt_example) -> Tensor + inline at::Tensor _nested_from_padded_and_nested_example(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & nt_example) { + return at::_ops::_nested_from_padded_and_nested_example::redispatch(dispatchKeySet, padded, nt_example); + } + + // aten::_nested_view_from_buffer(Tensor(a) self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor(a) + inline at::Tensor _nested_view_from_buffer(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) { + return at::_ops::_nested_view_from_buffer::redispatch(dispatchKeySet, self, nested_size, nested_strides, offsets); + } + + // aten::_nested_view_from_buffer_copy(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets) -> Tensor + inline at::Tensor _nested_view_from_buffer_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) { + return at::_ops::_nested_view_from_buffer_copy::redispatch(dispatchKeySet, self, nested_size, nested_strides, offsets); + } + + // aten::_nested_view_from_jagged(Tensor(a) self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1) -> Tensor(a) + inline at::Tensor _nested_view_from_jagged(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths={}, int64_t ragged_idx=1) { + return at::_ops::_nested_view_from_jagged::redispatch(dispatchKeySet, self, offsets, dummy, lengths, ragged_idx); + } + + // aten::_nested_view_from_jagged_copy(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1) -> Tensor + inline at::Tensor _nested_view_from_jagged_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths={}, int64_t ragged_idx=1) { + return at::_ops::_nested_view_from_jagged_copy::redispatch(dispatchKeySet, self, offsets, dummy, lengths, ragged_idx); + } + + // aten::_nested_get_values(Tensor(a) self) -> Tensor(a) + inline at::Tensor _nested_get_values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_get_values::redispatch(dispatchKeySet, self); + } + + // aten::_nested_get_values_copy(Tensor self) -> Tensor + inline at::Tensor _nested_get_values_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_get_values_copy::redispatch(dispatchKeySet, self); + } + + // aten::_nested_get_offsets(Tensor self) -> Tensor + inline at::Tensor _nested_get_offsets(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_get_offsets::redispatch(dispatchKeySet, self); + } + + // aten::_nested_get_lengths(Tensor self) -> Tensor + inline at::Tensor _nested_get_lengths(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_get_lengths::redispatch(dispatchKeySet, self); + } + + // aten::_nested_get_ragged_idx(Tensor self) -> int + inline int64_t _nested_get_ragged_idx(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nested_get_ragged_idx::redispatch(dispatchKeySet, self); + } + + // aten::_nested_get_jagged_dummy(Tensor any) -> Tensor + inline at::Tensor _nested_get_jagged_dummy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & any) { + return at::_ops::_nested_get_jagged_dummy::redispatch(dispatchKeySet, any); + } + + // aten::_trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor + inline at::Tensor _trilinear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim=1) { + return at::_ops::_trilinear::redispatch(dispatchKeySet, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); + } + + // aten::triplet_margin_loss(Tensor anchor, Tensor positive, Tensor negative, float margin=1.0, float p=2, float eps=1e-06, bool swap=False, int reduction=Mean) -> Tensor + inline at::Tensor triplet_margin_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin=1.0, double p=2, double eps=1e-06, bool swap=false, int64_t reduction=at::Reduction::Mean) { + return at::_ops::triplet_margin_loss::redispatch(dispatchKeySet, anchor, positive, negative, margin, p, eps, swap, reduction); + } + + // aten::trunc(Tensor self) -> Tensor + inline at::Tensor trunc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::trunc::redispatch(dispatchKeySet, self); + } + + // aten::trunc_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & trunc_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::trunc_::redispatch(dispatchKeySet, self); + } + + // aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & trunc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::trunc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & trunc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::trunc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::fix(Tensor self) -> Tensor + inline at::Tensor fix(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::fix::redispatch(dispatchKeySet, self); + } + + // aten::fix_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & fix_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::fix_::redispatch(dispatchKeySet, self); + } + + // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fix_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::fix_out::redispatch(dispatchKeySet, self, out); + } + + // aten::fix.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fix_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::fix_out::redispatch(dispatchKeySet, self, out); + } + + // aten::type_as(Tensor self, Tensor other) -> Tensor + inline at::Tensor type_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::type_as::redispatch(dispatchKeySet, self, other); + } + + // aten::_has_compatible_shallow_copy_type(Tensor self, Tensor from) -> bool + inline bool _has_compatible_shallow_copy_type(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & from) { + return at::_ops::_has_compatible_shallow_copy_type::redispatch(dispatchKeySet, self, from); + } + + // aten::_unique(Tensor self, bool sorted=True, bool return_inverse=False) -> (Tensor, Tensor) + inline ::std::tuple _unique(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted=true, bool return_inverse=false) { + return at::_ops::_unique::redispatch(dispatchKeySet, self, sorted, return_inverse); + } + + // aten::unique_dim(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) + inline ::std::tuple unique_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) { + return at::_ops::unique_dim::redispatch(dispatchKeySet, self, dim, sorted, return_inverse, return_counts); + } + + // aten::unique_consecutive(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None) -> (Tensor, Tensor, Tensor) + inline ::std::tuple unique_consecutive(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse=false, bool return_counts=false, c10::optional dim=c10::nullopt) { + return at::_ops::unique_consecutive::redispatch(dispatchKeySet, self, return_inverse, return_counts, dim); + } + + // aten::unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) + inline ::std::tuple unique_dim_consecutive(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) { + return at::_ops::unique_dim_consecutive::redispatch(dispatchKeySet, self, dim, return_inverse, return_counts); + } + + // aten::_unique2(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _unique2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false) { + return at::_ops::_unique2::redispatch(dispatchKeySet, self, sorted, return_inverse, return_counts); + } + + // aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor _unsafe_view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_unsafe_view::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::_unsafe_view(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor _unsafe_view_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::_unsafe_view::redispatch(dispatchKeySet, self, size); + } + + // aten::unsqueeze(Tensor(a) self, int dim) -> Tensor(a) + inline at::Tensor unsqueeze(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::unsqueeze::redispatch(dispatchKeySet, self, dim); + } + + // aten::unsqueeze_(Tensor(a!) self, int dim) -> Tensor(a!) + inline at::Tensor & unsqueeze_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim) { + return at::_ops::unsqueeze_::redispatch(dispatchKeySet, self, dim); + } + + // aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor + inline at::Tensor vander(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, c10::optional N=c10::nullopt, bool increasing=false) { + return at::_ops::vander::redispatch(dispatchKeySet, x, N, increasing); + } + + // aten::var(Tensor self, bool unbiased=True) -> Tensor + inline at::Tensor var(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { + return at::_ops::var::redispatch(dispatchKeySet, self, unbiased); + } + + // aten::var.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor + inline at::Tensor var(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::var.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor + inline at::Tensor var(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_correction::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::var.out(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out) { + return at::_ops::var_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::var.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out) { + return at::_ops::var_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::var.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> Tensor + inline at::Tensor var(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_names_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_names_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::var.names_out(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out) { + return at::_ops::var_names_out::redispatch(dispatchKeySet, self, dim, unbiased, keepdim, out); + } + + // aten::var.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> Tensor + inline at::Tensor var(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_correction_names::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_correction_names_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::var.correction_names_out(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & var_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim, at::Tensor & out) { + return at::_ops::var_correction_names_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out); + } + + // aten::var_mean(Tensor self, bool unbiased=True) -> (Tensor, Tensor) + inline ::std::tuple var_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool unbiased) { + return at::_ops::var_mean::redispatch(dispatchKeySet, self, unbiased); + } + + // aten::var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple var_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_mean_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::var_mean.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple var_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_mean_correction::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::var_mean.names_dim(Tensor self, Dimname[1] dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple var_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false) { + return at::_ops::var_mean_names_dim::redispatch(dispatchKeySet, self, dim, unbiased, keepdim); + } + + // aten::var_mean.correction_names(Tensor self, Dimname[1] dim, *, Scalar? correction=None, bool keepdim=False) -> (Tensor, Tensor) + inline ::std::tuple var_mean(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_mean_correction_names::redispatch(dispatchKeySet, self, dim, correction, keepdim); + } + + // aten::view_as(Tensor(a) self, Tensor other) -> Tensor(a) + inline at::Tensor view_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::view_as::redispatch(dispatchKeySet, self, other); + } + + // aten::where.self(Tensor condition, Tensor self, Tensor other) -> Tensor + inline at::Tensor where(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::where_self::redispatch(dispatchKeySet, condition, self, other); + } + + // aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & where_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::where_self_out::redispatch(dispatchKeySet, condition, self, other, out); + } + + // aten::where.self_out(Tensor condition, Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & where_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::where_self_out::redispatch(dispatchKeySet, condition, self, other, out); + } + + // aten::where.ScalarSelf(Tensor condition, Scalar self, Tensor other) -> Tensor + inline at::Tensor where(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::where_ScalarSelf::redispatch(dispatchKeySet, condition, self, other); + } + + // aten::where.ScalarOther(Tensor condition, Tensor self, Scalar other) -> Tensor + inline at::Tensor where(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::where_ScalarOther::redispatch(dispatchKeySet, condition, self, other); + } + + // aten::where.Scalar(Tensor condition, Scalar self, Scalar other) -> Tensor + inline at::Tensor where(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) { + return at::_ops::where_Scalar::redispatch(dispatchKeySet, condition, self, other); + } + + // aten::where(Tensor condition) -> Tensor[] + inline ::std::vector where(c10::DispatchKeySet dispatchKeySet, const at::Tensor & condition) { + return at::_ops::where::redispatch(dispatchKeySet, condition); + } + + // aten::norm_except_dim(Tensor v, int pow=2, int dim=0) -> Tensor + inline at::Tensor norm_except_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & v, int64_t pow=2, int64_t dim=0) { + return at::_ops::norm_except_dim::redispatch(dispatchKeySet, v, pow, dim); + } + + // aten::_weight_norm(Tensor v, Tensor g, int dim=0) -> Tensor + inline at::Tensor _weight_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & v, const at::Tensor & g, int64_t dim=0) { + return at::_ops::_weight_norm::redispatch(dispatchKeySet, v, g, dim); + } + + // aten::_weight_norm_interface(Tensor v, Tensor g, int dim=0) -> (Tensor, Tensor) + inline ::std::tuple _weight_norm_interface(c10::DispatchKeySet dispatchKeySet, const at::Tensor & v, const at::Tensor & g, int64_t dim=0) { + return at::_ops::_weight_norm_interface::redispatch(dispatchKeySet, v, g, dim); + } + + // aten::_weight_norm_interface_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) + inline ::std::tuple _weight_norm_interface_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { + return at::_ops::_weight_norm_interface_backward::redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim); + } + + // aten::_weight_norm_differentiable_backward(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim) -> (Tensor, Tensor) + inline ::std::tuple _weight_norm_differentiable_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { + return at::_ops::_weight_norm_differentiable_backward::redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim); + } + + // aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::TensorOptions options={}) { + return at::_ops::zeros_names::redispatch(dispatchKeySet, size, names, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::zeros.names(int[] size, *, Dimname[]? names, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::zeros_names::redispatch(dispatchKeySet, size, names, dtype, layout, device, pin_memory); + } + + // aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _efficientzerotensor(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_efficientzerotensor::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _efficientzerotensor(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_efficientzerotensor::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _efficientzerotensor_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_efficientzerotensor::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_efficientzerotensor(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _efficientzerotensor_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_efficientzerotensor::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::zeros::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::zeros::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::zeros::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::zeros(SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor zeros_symint(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::zeros::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::zeros_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::zeros_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size) { + return at::_ops::zeros_out::redispatch(dispatchKeySet, size, out); + } + + // aten::zeros.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::zeros_out::redispatch(dispatchKeySet, size, out); + } + + // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor zeros_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, c10::optional memory_format=c10::nullopt) { + return at::_ops::zeros_like::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::zeros_like(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor zeros_like(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + return at::_ops::zeros_like::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, memory_format); + } + + // aten::_standard_gamma_grad(Tensor self, Tensor output) -> Tensor + inline at::Tensor _standard_gamma_grad(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output) { + return at::_ops::_standard_gamma_grad::redispatch(dispatchKeySet, self, output); + } + + // aten::_standard_gamma(Tensor self, Generator? generator=None) -> Tensor + inline at::Tensor _standard_gamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::_standard_gamma::redispatch(dispatchKeySet, self, generator); + } + + // aten::_dirichlet_grad(Tensor x, Tensor alpha, Tensor total) -> Tensor + inline at::Tensor _dirichlet_grad(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { + return at::_ops::_dirichlet_grad::redispatch(dispatchKeySet, x, alpha, total); + } + + // aten::_sample_dirichlet(Tensor self, Generator? generator=None) -> Tensor + inline at::Tensor _sample_dirichlet(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::_sample_dirichlet::redispatch(dispatchKeySet, self, generator); + } + + // aten::poisson(Tensor self, Generator? generator=None) -> Tensor + inline at::Tensor poisson(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::poisson::redispatch(dispatchKeySet, self, generator); + } + + // aten::binomial(Tensor count, Tensor prob, Generator? generator=None) -> Tensor + inline at::Tensor binomial(c10::DispatchKeySet dispatchKeySet, const at::Tensor & count, const at::Tensor & prob, c10::optional generator=c10::nullopt) { + return at::_ops::binomial::redispatch(dispatchKeySet, count, prob, generator); + } + + // aten::native_norm(Tensor self, Scalar p=2) -> Tensor + inline at::Tensor native_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p=2) { + return at::_ops::native_norm::redispatch(dispatchKeySet, self, p); + } + + // aten::native_norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype) -> Tensor + inline at::Tensor native_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + return at::_ops::native_norm_ScalarOpt_dim_dtype::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype); + } + + // aten::_sparse_sum(Tensor self) -> Tensor + inline at::Tensor _sparse_sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_sparse_sum::redispatch(dispatchKeySet, self); + } + + // aten::_sparse_sum.dtype(Tensor self, *, ScalarType dtype) -> Tensor + inline at::Tensor _sparse_sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype) { + return at::_ops::_sparse_sum_dtype::redispatch(dispatchKeySet, self, dtype); + } + + // aten::_sparse_sum.dim(Tensor self, int[1] dim) -> Tensor + inline at::Tensor _sparse_sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::_sparse_sum_dim::redispatch(dispatchKeySet, self, dim); + } + + // aten::_sparse_sum.dim_dtype(Tensor self, int[1] dim, *, ScalarType dtype) -> Tensor + inline at::Tensor _sparse_sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::ScalarType dtype) { + return at::_ops::_sparse_sum_dim_dtype::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_sparse_sum_backward(Tensor grad, Tensor self, int[] dim) -> Tensor + inline at::Tensor _sparse_sum_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::_sparse_sum_backward::redispatch(dispatchKeySet, grad, self, dim); + } + + // aten::_sparse_csr_sum.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_csr_sum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_csr_sum_dim_dtype::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::_sparse_csr_prod.dim_dtype(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_csr_prod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_csr_prod_dim_dtype::redispatch(dispatchKeySet, self, dim, keepdim, dtype); + } + + // aten::_sparse_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_softmax_int::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_sparse_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_softmax_Dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_sparse_softmax(Tensor self, int dim, bool half_to_float) -> Tensor + inline at::Tensor _sparse_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_sparse_softmax::redispatch(dispatchKeySet, self, dim, half_to_float); + } + + // aten::_sparse_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor + inline at::Tensor _sparse_softmax_backward_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + return at::_ops::_sparse_softmax_backward_data::redispatch(dispatchKeySet, grad_output, output, dim, self); + } + + // aten::_sparse_log_softmax.int(Tensor self, int dim, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_log_softmax_int::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_sparse_log_softmax.Dimname(Tensor self, Dimname dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor _sparse_log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_log_softmax_Dimname::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::_sparse_log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor + inline at::Tensor _sparse_log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_sparse_log_softmax::redispatch(dispatchKeySet, self, dim, half_to_float); + } + + // aten::_sparse_log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, Tensor self) -> Tensor + inline at::Tensor _sparse_log_softmax_backward_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + return at::_ops::_sparse_log_softmax_backward_data::redispatch(dispatchKeySet, grad_output, output, dim, self); + } + + // aten::_spdiags(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None) -> Tensor + inline at::Tensor _spdiags(c10::DispatchKeySet dispatchKeySet, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional layout=c10::nullopt) { + return at::_ops::_spdiags::redispatch(dispatchKeySet, diagonals, offsets, shape, layout); + } + + // aten::norm.ScalarOpt_dtype(Tensor self, Scalar? p, *, ScalarType dtype) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::ScalarType dtype) { + return at::_ops::norm_ScalarOpt_dtype::redispatch(dispatchKeySet, self, p, dtype); + } + + // aten::norm.Scalar(Tensor self, Scalar p=2) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p=2) { + return at::_ops::norm_Scalar::redispatch(dispatchKeySet, self, p); + } + + // aten::norm.ScalarOpt_dim_dtype(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { + return at::_ops::norm_ScalarOpt_dim_dtype::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype); + } + + // aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::norm_ScalarOpt_dim::redispatch(dispatchKeySet, self, p, dim, keepdim); + } + + // aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { + return at::_ops::norm_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::norm.dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::norm_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::norm_out::redispatch(dispatchKeySet, self, p, dim, keepdim, out); + } + + // aten::norm.out(Tensor self, Scalar? p, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::norm_out::redispatch(dispatchKeySet, self, p, dim, keepdim, out); + } + + // aten::norm.names_ScalarOpt_dim_dtype(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) { + return at::_ops::norm_names_ScalarOpt_dim_dtype::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype); + } + + // aten::norm.names_ScalarOpt_dim(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim=false) { + return at::_ops::norm_names_ScalarOpt_dim::redispatch(dispatchKeySet, self, p, dim, keepdim); + } + + // aten::norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) { + return at::_ops::norm_names_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::norm.names_dtype_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::norm_names_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim=false) { + return at::_ops::norm_names_out::redispatch(dispatchKeySet, self, p, dim, keepdim, out); + } + + // aten::norm.names_out(Tensor self, Scalar? p, Dimname[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim, at::Tensor & out) { + return at::_ops::norm_names_out::redispatch(dispatchKeySet, self, p, dim, keepdim, out); + } + + // aten::frexp.Tensor(Tensor self) -> (Tensor mantissa, Tensor exponent) + inline ::std::tuple frexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::frexp_Tensor::redispatch(dispatchKeySet, self); + } + + // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) + inline ::std::tuple frexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & mantissa, at::Tensor & exponent, const at::Tensor & self) { + return at::_ops::frexp_Tensor_out::redispatch(dispatchKeySet, self, mantissa, exponent); + } + + // aten::frexp.Tensor_out(Tensor self, *, Tensor(a!) mantissa, Tensor(b!) exponent) -> (Tensor(a!) mantissa, Tensor(b!) exponent) + inline ::std::tuple frexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & mantissa, at::Tensor & exponent) { + return at::_ops::frexp_Tensor_out::redispatch(dispatchKeySet, self, mantissa, exponent); + } + + // aten::frobenius_norm.dim(Tensor self, int[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor frobenius_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::frobenius_norm_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & frobenius_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::frobenius_norm_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & frobenius_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::frobenius_norm_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::nuclear_norm(Tensor self, bool keepdim=False) -> Tensor + inline at::Tensor nuclear_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool keepdim=false) { + return at::_ops::nuclear_norm::redispatch(dispatchKeySet, self, keepdim); + } + + // aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nuclear_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool keepdim=false) { + return at::_ops::nuclear_norm_out::redispatch(dispatchKeySet, self, keepdim, out); + } + + // aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nuclear_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool keepdim, at::Tensor & out) { + return at::_ops::nuclear_norm_out::redispatch(dispatchKeySet, self, keepdim, out); + } + + // aten::nuclear_norm.dim(Tensor self, int[2] dim, bool keepdim=False) -> Tensor + inline at::Tensor nuclear_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::nuclear_norm_dim::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nuclear_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::nuclear_norm_dim_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nuclear_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::nuclear_norm_dim_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::clone(Tensor self, *, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor clone(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::clone::redispatch(dispatchKeySet, self, memory_format); + } + + // aten::positive(Tensor(a) self) -> Tensor(a) + inline at::Tensor positive(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::positive::redispatch(dispatchKeySet, self); + } + + // aten::resize_as_(Tensor(a!) self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor(a!) + inline const at::Tensor & resize_as_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_as_::redispatch(dispatchKeySet, self, the_template, memory_format); + } + + // aten::resize_as_sparse_(Tensor(a!) self, Tensor the_template) -> Tensor(a!) + inline const at::Tensor & resize_as_sparse_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template) { + return at::_ops::resize_as_sparse_::redispatch(dispatchKeySet, self, the_template); + } + + // aten::zero_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & zero_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::zero_::redispatch(dispatchKeySet, self); + } + + // aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sub_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::sub_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sub_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::sub_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::sub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor + inline at::Tensor sub(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::sub_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & sub_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::sub__Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor + inline at::Tensor sub(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::sub_Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::sub_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & sub_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::sub__Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & subtract_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::subtract_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::subtract.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & subtract_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::subtract_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::subtract.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor + inline at::Tensor subtract(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::subtract_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::subtract_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & subtract_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::subtract__Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::subtract.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor + inline at::Tensor subtract(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::subtract_Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::subtract_.Scalar(Tensor(a!) self, Scalar other, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & subtract_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::subtract__Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::rsub.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor + inline at::Tensor rsub(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::rsub_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & heaviside_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & values) { + return at::_ops::heaviside_out::redispatch(dispatchKeySet, self, values, out); + } + + // aten::heaviside.out(Tensor self, Tensor values, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & heaviside_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & values, at::Tensor & out) { + return at::_ops::heaviside_out::redispatch(dispatchKeySet, self, values, out); + } + + // aten::heaviside(Tensor self, Tensor values) -> Tensor + inline at::Tensor heaviside(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & values) { + return at::_ops::heaviside::redispatch(dispatchKeySet, self, values); + } + + // aten::heaviside_(Tensor(a!) self, Tensor values) -> Tensor(a!) + inline at::Tensor & heaviside_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & values) { + return at::_ops::heaviside_::redispatch(dispatchKeySet, self, values); + } + + // aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor + inline at::Tensor rsub(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::rsub_Scalar::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_sparse_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor _sparse_addmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::_sparse_addmm::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); + } + + // aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_sampled_addmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::sparse_sampled_addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::sparse_sampled_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_sampled_addmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::sparse_sampled_addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::sparse_sampled_addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor sparse_sampled_addmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::sparse_sampled_addmm::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); + } + + // aten::_sparse_mm_reduce_impl(Tensor self, Tensor other, str reduce) -> (Tensor, Tensor) + inline ::std::tuple _sparse_mm_reduce_impl(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::string_view reduce) { + return at::_ops::_sparse_mm_reduce_impl::redispatch(dispatchKeySet, self, other, reduce); + } + + // aten::_sparse_mm_reduce_impl_backward(Tensor self, Tensor grad_out, Tensor weight, str reduce, Tensor arg_out, bool[2] output_mask) -> (Tensor, Tensor) + inline ::std::tuple _sparse_mm_reduce_impl_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_out, const at::Tensor & weight, c10::string_view reduce, const at::Tensor & arg_out, ::std::array output_mask) { + return at::_ops::_sparse_mm_reduce_impl_backward::redispatch(dispatchKeySet, self, grad_out, weight, reduce, arg_out, output_mask); + } + + // aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor addmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmm::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); + } + + // aten::addmm_(Tensor(a!) self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & addmm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addmm_::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha); + } + + // aten::_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _addmm_activation_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1, bool use_gelu=false) { + return at::_ops::_addmm_activation_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, use_gelu, out); + } + + // aten::_addmm_activation.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _addmm_activation_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu, at::Tensor & out) { + return at::_ops::_addmm_activation_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, use_gelu, out); + } + + // aten::_addmm_activation(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, bool use_gelu=False) -> Tensor + inline at::Tensor _addmm_activation(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1, bool use_gelu=false) { + return at::_ops::_addmm_activation::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, use_gelu); + } + + // aten::_scaled_mm(Tensor self, Tensor mat2, *, Tensor? bias=None, ScalarType? out_dtype=None, Tensor? scale_a=None, Tensor? scale_b=None, Tensor? scale_result=None, bool use_fast_accum=False) -> (Tensor, Tensor) + inline ::std::tuple _scaled_mm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias={}, c10::optional out_dtype=c10::nullopt, const c10::optional & scale_a={}, const c10::optional & scale_b={}, const c10::optional & scale_result={}, bool use_fast_accum=false) { + return at::_ops::_scaled_mm::redispatch(dispatchKeySet, self, mat2, bias, out_dtype, scale_a, scale_b, scale_result, use_fast_accum); + } + + // aten::_scaled_mm.out(Tensor self, Tensor mat2, *, Tensor? bias=None, ScalarType? out_dtype=None, Tensor? scale_a=None, Tensor? scale_b=None, Tensor? scale_result=None, bool use_fast_accum=False, Tensor(a!) out, Tensor(b!) out_amax) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _scaled_mm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & out_amax, const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias={}, c10::optional out_dtype=c10::nullopt, const c10::optional & scale_a={}, const c10::optional & scale_b={}, const c10::optional & scale_result={}, bool use_fast_accum=false) { + return at::_ops::_scaled_mm_out::redispatch(dispatchKeySet, self, mat2, bias, out_dtype, scale_a, scale_b, scale_result, use_fast_accum, out, out_amax); + } + + // aten::_scaled_mm.out(Tensor self, Tensor mat2, *, Tensor? bias=None, ScalarType? out_dtype=None, Tensor? scale_a=None, Tensor? scale_b=None, Tensor? scale_result=None, bool use_fast_accum=False, Tensor(a!) out, Tensor(b!) out_amax) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _scaled_mm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias, c10::optional out_dtype, const c10::optional & scale_a, const c10::optional & scale_b, const c10::optional & scale_result, bool use_fast_accum, at::Tensor & out, at::Tensor & out_amax) { + return at::_ops::_scaled_mm_out::redispatch(dispatchKeySet, self, mat2, bias, out_dtype, scale_a, scale_b, scale_result, use_fast_accum, out, out_amax); + } + + // aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_compressed_tensor_comp_plain_value_size::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_compressed_tensor_comp_plain_value_size::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_compressed_tensor_comp_plain_value_size::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_compressed_tensor.comp_plain_value_size(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_compressed_tensor_comp_plain_value_size::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_csr_tensor_crow_col_value_size::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_csr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_csr_tensor_crow_col_value_size::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_csc_tensor_ccol_row_value_size::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_csc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_csc_tensor_ccol_row_value_size::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_bsr_tensor_crow_col_value_size::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_bsr_tensor.crow_col_value_size(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_bsr_tensor_crow_col_value_size::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_bsc_tensor_ccol_row_value_size::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_bsc_tensor.ccol_row_value_size(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_bsc_tensor_ccol_row_value_size::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::TensorOptions options) { + return at::_ops::sparse_compressed_tensor_comp_plain_value::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_compressed_tensor.comp_plain_value(Tensor compressed_indices, Tensor plain_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_compressed_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_compressed_tensor_comp_plain_value::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, dtype, layout, device, pin_memory); + } + + // aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::TensorOptions options) { + return at::_ops::sparse_csr_tensor_crow_col_value::redispatch(dispatchKeySet, crow_indices, col_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_csr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_csr_tensor_crow_col_value::redispatch(dispatchKeySet, crow_indices, col_indices, values, dtype, layout, device, pin_memory); + } + + // aten::sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::TensorOptions options) { + return at::_ops::sparse_csc_tensor_ccol_row_value::redispatch(dispatchKeySet, ccol_indices, row_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_csc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_csc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_csc_tensor_ccol_row_value::redispatch(dispatchKeySet, ccol_indices, row_indices, values, dtype, layout, device, pin_memory); + } + + // aten::sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::TensorOptions options) { + return at::_ops::sparse_bsr_tensor_crow_col_value::redispatch(dispatchKeySet, crow_indices, col_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_bsr_tensor.crow_col_value(Tensor crow_indices, Tensor col_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsr_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_bsr_tensor_crow_col_value::redispatch(dispatchKeySet, crow_indices, col_indices, values, dtype, layout, device, pin_memory); + } + + // aten::sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::TensorOptions options) { + return at::_ops::sparse_bsc_tensor_ccol_row_value::redispatch(dispatchKeySet, ccol_indices, row_indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_bsc_tensor.ccol_row_value(Tensor ccol_indices, Tensor row_indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_bsc_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_bsc_tensor_ccol_row_value::redispatch(dispatchKeySet, ccol_indices, row_indices, values, dtype, layout, device, pin_memory); + } + + // aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_compressed_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_compressed_tensor_unsafe::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_compressed_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_compressed_tensor_unsafe::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory); + } + + // aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_compressed_tensor_unsafe_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_compressed_tensor_unsafe::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_compressed_tensor_unsafe(Tensor compressed_indices, Tensor plain_indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_compressed_tensor_unsafe_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_compressed_tensor_unsafe::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::_sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_csr_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_csr_tensor_unsafe::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_csr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_csr_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_csr_tensor_unsafe::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::_sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_csc_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_csc_tensor_unsafe::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_csc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_csc_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_csc_tensor_unsafe::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_bsr_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_bsr_tensor_unsafe::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_bsr_tensor_unsafe(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_bsr_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_bsr_tensor_unsafe::redispatch(dispatchKeySet, crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_bsc_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}) { + return at::_ops::_sparse_bsc_tensor_unsafe::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_bsc_tensor_unsafe(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _sparse_bsc_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_bsc_tensor_unsafe::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::sparse_coo_tensor_size::redispatch(dispatchKeySet, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::sparse_coo_tensor_size::redispatch(dispatchKeySet, size, dtype, layout, device, pin_memory); + } + + // aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::sparse_coo_tensor_indices::redispatch(dispatchKeySet, indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::sparse_coo_tensor_indices::redispatch(dispatchKeySet, indices, values, dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::sparse_coo_tensor_indices_size::redispatch(dispatchKeySet, indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor sparse_coo_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::sparse_coo_tensor_indices_size::redispatch(dispatchKeySet, indices, values, size, dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_unsafe::redispatch(dispatchKeySet, indices, values, c10::fromIntArrayRefSlow(size), c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_unsafe(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::_sparse_coo_tensor_unsafe::redispatch(dispatchKeySet, indices, values, c10::fromIntArrayRefSlow(size), dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_unsafe_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_unsafe::redispatch(dispatchKeySet, indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::_sparse_coo_tensor_unsafe(Tensor indices, Tensor values, SymInt[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_unsafe_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::_sparse_coo_tensor_unsafe::redispatch(dispatchKeySet, indices, values, size, dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::_validate_sparse_coo_tensor_args(Tensor indices, Tensor values, int[] size, bool? is_coalesced=None) -> () + inline void _validate_sparse_coo_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_validate_sparse_coo_tensor_args::redispatch(dispatchKeySet, indices, values, size, is_coalesced); + } + + // aten::_validate_sparse_compressed_tensor_args(Tensor compressed_indices, Tensor plain_indices, Tensor values, int[] size, Layout layout) -> () + inline void _validate_sparse_compressed_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout) { + return at::_ops::_validate_sparse_compressed_tensor_args::redispatch(dispatchKeySet, compressed_indices, plain_indices, values, size, layout); + } + + // aten::_validate_sparse_csr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> () + inline void _validate_sparse_csr_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) { + return at::_ops::_validate_sparse_csr_tensor_args::redispatch(dispatchKeySet, crow_indices, col_indices, values, size); + } + + // aten::_validate_sparse_csc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () + inline void _validate_sparse_csc_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { + return at::_ops::_validate_sparse_csc_tensor_args::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size); + } + + // aten::_validate_sparse_bsr_tensor_args(Tensor crow_indices, Tensor col_indices, Tensor values, int[] size) -> () + inline void _validate_sparse_bsr_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) { + return at::_ops::_validate_sparse_bsr_tensor_args::redispatch(dispatchKeySet, crow_indices, col_indices, values, size); + } + + // aten::_validate_sparse_bsc_tensor_args(Tensor ccol_indices, Tensor row_indices, Tensor values, int[] size) -> () + inline void _validate_sparse_bsc_tensor_args(c10::DispatchKeySet dispatchKeySet, const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { + return at::_ops::_validate_sparse_bsc_tensor_args::redispatch(dispatchKeySet, ccol_indices, row_indices, values, size); + } + + // aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::TensorOptions options) { + return at::_ops::_sparse_coo_tensor_with_dims::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::_sparse_coo_tensor_with_dims(int sparse_dim, int dense_dim, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_sparse_coo_tensor_with_dims::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, dtype, layout, device, pin_memory); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::redispatch(dispatchKeySet, sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::redispatch(dispatchKeySet, sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors_symint(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False, bool? is_coalesced=None) -> Tensor + inline at::Tensor _sparse_coo_tensor_with_dims_and_tensors_symint(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, indices, values, dtype, layout, device, pin_memory, is_coalesced); + } + + // aten::sparse_resize_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!) + inline const at::Tensor & sparse_resize_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize_::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim); + } + + // aten::sparse_resize_and_clear_(Tensor(a!) self, int[] size, int sparse_dim, int dense_dim) -> Tensor(a!) + inline const at::Tensor & sparse_resize_and_clear_(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize_and_clear_::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim); + } + + // aten::sparse_mask(Tensor self, Tensor mask) -> Tensor + inline at::Tensor sparse_mask(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask) { + return at::_ops::sparse_mask::redispatch(dispatchKeySet, self, mask); + } + + // aten::_sparse_mask_projection(Tensor self, Tensor mask, bool accumulate_matches=False) -> Tensor + inline at::Tensor _sparse_mask_projection(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches=false) { + return at::_ops::_sparse_mask_projection::redispatch(dispatchKeySet, self, mask, accumulate_matches); + } + + // aten::_to_cpu(Tensor[] tensors) -> Tensor[] + inline ::std::vector _to_cpu(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::_to_cpu::redispatch(dispatchKeySet, tensors); + } + + // aten::to_dense(Tensor self, ScalarType? dtype=None, *, bool? masked_grad=None) -> Tensor + inline at::Tensor to_dense(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt, c10::optional masked_grad=c10::nullopt) { + return at::_ops::to_dense::redispatch(dispatchKeySet, self, dtype, masked_grad); + } + + // aten::_to_dense(Tensor self, ScalarType? dtype=None, bool? masked_grad=None) -> Tensor + inline at::Tensor _to_dense(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt, c10::optional masked_grad=c10::nullopt) { + return at::_ops::_to_dense::redispatch(dispatchKeySet, self, dtype, masked_grad); + } + + // aten::to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor + inline at::Tensor to_dense_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, c10::optional masked_grad=c10::nullopt) { + return at::_ops::to_dense_backward::redispatch(dispatchKeySet, grad, input, masked_grad); + } + + // aten::sparse_dim(Tensor self) -> int + inline int64_t sparse_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sparse_dim::redispatch(dispatchKeySet, self); + } + + // aten::_dimI(Tensor self) -> int + inline int64_t _dimI(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_dimI::redispatch(dispatchKeySet, self); + } + + // aten::dense_dim(Tensor self) -> int + inline int64_t dense_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::dense_dim::redispatch(dispatchKeySet, self); + } + + // aten::_dimV(Tensor self) -> int + inline int64_t _dimV(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_dimV::redispatch(dispatchKeySet, self); + } + + // aten::_nnz(Tensor self) -> int + inline int64_t _nnz(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_nnz::redispatch(dispatchKeySet, self); + } + + // aten::coalesce(Tensor(a) self) -> Tensor(a) + inline at::Tensor coalesce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::coalesce::redispatch(dispatchKeySet, self); + } + + // aten::_coalesce(Tensor self) -> Tensor + inline at::Tensor _coalesce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_coalesce::redispatch(dispatchKeySet, self); + } + + // aten::is_coalesced(Tensor self) -> bool + inline bool is_coalesced(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::is_coalesced::redispatch(dispatchKeySet, self); + } + + // aten::_indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor _indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_indices::redispatch(dispatchKeySet, self); + } + + // aten::_values(Tensor(a) self) -> Tensor(a) + inline at::Tensor _values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_values::redispatch(dispatchKeySet, self); + } + + // aten::_coalesced_(Tensor(a!) self, bool coalesced) -> Tensor(a!) + inline at::Tensor & _coalesced_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, bool coalesced) { + return at::_ops::_coalesced_::redispatch(dispatchKeySet, self, coalesced); + } + + // aten::indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::indices::redispatch(dispatchKeySet, self); + } + + // aten::values(Tensor(a) self) -> Tensor(a) + inline at::Tensor values(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::values::redispatch(dispatchKeySet, self); + } + + // aten::crow_indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor crow_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::crow_indices::redispatch(dispatchKeySet, self); + } + + // aten::col_indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor col_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::col_indices::redispatch(dispatchKeySet, self); + } + + // aten::ccol_indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor ccol_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::ccol_indices::redispatch(dispatchKeySet, self); + } + + // aten::row_indices(Tensor(a) self) -> Tensor(a) + inline at::Tensor row_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::row_indices::redispatch(dispatchKeySet, self); + } + + // aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hspmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & mat1, const at::Tensor & mat2) { + return at::_ops::hspmm_out::redispatch(dispatchKeySet, mat1, mat2, out); + } + + // aten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hspmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mat1, const at::Tensor & mat2, at::Tensor & out) { + return at::_ops::hspmm_out::redispatch(dispatchKeySet, mat1, mat2, out); + } + + // aten::hspmm(Tensor mat1, Tensor mat2) -> Tensor + inline at::Tensor hspmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mat1, const at::Tensor & mat2) { + return at::_ops::hspmm::redispatch(dispatchKeySet, mat1, mat2); + } + + // aten::copy_sparse_to_sparse_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> Tensor(a!) + inline at::Tensor & copy_sparse_to_sparse_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy_sparse_to_sparse_::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[] + inline ::std::vector unbind(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0) { + return at::_ops::unbind_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::unbind.Dimname(Tensor(a -> *) self, Dimname dim) -> Tensor(a)[] + inline ::std::vector unbind(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim) { + return at::_ops::unbind_Dimname::redispatch(dispatchKeySet, self, dim); + } + + // aten::to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor + inline at::Tensor to_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sparse_dim) { + return at::_ops::to_sparse_sparse_dim::redispatch(dispatchKeySet, self, sparse_dim); + } + + // aten::_to_sparse.sparse_dim(Tensor self, int sparse_dim) -> Tensor + inline at::Tensor _to_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sparse_dim) { + return at::_ops::_to_sparse_sparse_dim::redispatch(dispatchKeySet, self, sparse_dim); + } + + // aten::to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor + inline at::Tensor to_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional layout=c10::nullopt, at::OptionalIntArrayRef blocksize=c10::nullopt, c10::optional dense_dim=c10::nullopt) { + return at::_ops::to_sparse::redispatch(dispatchKeySet, self, layout, blocksize, dense_dim); + } + + // aten::_to_sparse(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None) -> Tensor + inline at::Tensor _to_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional layout=c10::nullopt, at::OptionalIntArrayRef blocksize=c10::nullopt, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse::redispatch(dispatchKeySet, self, layout, blocksize, dense_dim); + } + + // aten::to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor + inline at::Tensor to_sparse_csr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::to_sparse_csr::redispatch(dispatchKeySet, self, dense_dim); + } + + // aten::_to_sparse_csr(Tensor self, int? dense_dim=None) -> Tensor + inline at::Tensor _to_sparse_csr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_csr::redispatch(dispatchKeySet, self, dense_dim); + } + + // aten::to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor + inline at::Tensor to_sparse_csc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::to_sparse_csc::redispatch(dispatchKeySet, self, dense_dim); + } + + // aten::_to_sparse_csc(Tensor self, int? dense_dim=None) -> Tensor + inline at::Tensor _to_sparse_csc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_csc::redispatch(dispatchKeySet, self, dense_dim); + } + + // aten::to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor + inline at::Tensor to_sparse_bsr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::to_sparse_bsr::redispatch(dispatchKeySet, self, blocksize, dense_dim); + } + + // aten::_to_sparse_bsr(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor + inline at::Tensor _to_sparse_bsr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_bsr::redispatch(dispatchKeySet, self, blocksize, dense_dim); + } + + // aten::to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor + inline at::Tensor to_sparse_bsc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::to_sparse_bsc::redispatch(dispatchKeySet, self, blocksize, dense_dim); + } + + // aten::_to_sparse_bsc(Tensor self, int[2] blocksize, int? dense_dim=None) -> Tensor + inline at::Tensor _to_sparse_bsc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_bsc::redispatch(dispatchKeySet, self, blocksize, dense_dim); + } + + // aten::_to_sparse_semi_structured(Tensor dense) -> (Tensor, Tensor) + inline ::std::tuple _to_sparse_semi_structured(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dense) { + return at::_ops::_to_sparse_semi_structured::redispatch(dispatchKeySet, dense); + } + + // aten::to_mkldnn(Tensor self, ScalarType? dtype=None) -> Tensor + inline at::Tensor to_mkldnn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::to_mkldnn::redispatch(dispatchKeySet, self, dtype); + } + + // aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor + inline at::Tensor mkldnn_reorder_conv2d_weight(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=c10::nullopt) { + return at::_ops::mkldnn_reorder_conv2d_weight::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*input_size)) : c10::nullopt); + } + + // aten::mkldnn_reorder_conv2d_weight(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None) -> Tensor + inline at::Tensor mkldnn_reorder_conv2d_weight_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=c10::nullopt) { + return at::_ops::mkldnn_reorder_conv2d_weight::redispatch(dispatchKeySet, self, padding, stride, dilation, groups, input_size); + } + + // aten::mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor mkldnn_reorder_conv3d_weight(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::mkldnn_reorder_conv3d_weight::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups); + } + + // aten::mkldnn_reorder_conv3d_weight(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1) -> Tensor + inline at::Tensor mkldnn_reorder_conv3d_weight_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::mkldnn_reorder_conv3d_weight::redispatch(dispatchKeySet, self, padding, stride, dilation, groups); + } + + // aten::to_mkldnn_backward(Tensor grad, Tensor input) -> Tensor + inline at::Tensor to_mkldnn_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input) { + return at::_ops::to_mkldnn_backward::redispatch(dispatchKeySet, grad, input); + } + + // aten::quantize_per_tensor_dynamic(Tensor self, ScalarType dtype, bool reduce_range) -> Tensor + inline at::Tensor quantize_per_tensor_dynamic(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool reduce_range) { + return at::_ops::quantize_per_tensor_dynamic::redispatch(dispatchKeySet, self, dtype, reduce_range); + } + + // aten::quantize_per_tensor(Tensor self, float scale, int zero_point, ScalarType dtype) -> Tensor + inline at::Tensor quantize_per_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor::redispatch(dispatchKeySet, self, scale, zero_point, dtype); + } + + // aten::quantize_per_tensor.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype) -> Tensor + inline at::Tensor quantize_per_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor_tensor_qparams::redispatch(dispatchKeySet, self, scale, zero_point, dtype); + } + + // aten::quantize_per_tensor.tensors(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype) -> Tensor[] + inline ::std::vector quantize_per_tensor(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor_tensors::redispatch(dispatchKeySet, tensors, scales, zero_points, dtype); + } + + // aten::quantize_per_channel(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype) -> Tensor + inline at::Tensor quantize_per_channel(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { + return at::_ops::quantize_per_channel::redispatch(dispatchKeySet, self, scales, zero_points, axis, dtype); + } + + // aten::dequantize.self(Tensor self) -> Tensor + inline at::Tensor dequantize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::dequantize_self::redispatch(dispatchKeySet, self); + } + + // aten::dequantize.tensors(Tensor[] tensors) -> Tensor[] + inline ::std::vector dequantize(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::dequantize_tensors::redispatch(dispatchKeySet, tensors); + } + + // aten::q_scale(Tensor self) -> float + inline double q_scale(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::q_scale::redispatch(dispatchKeySet, self); + } + + // aten::q_zero_point(Tensor self) -> int + inline int64_t q_zero_point(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::q_zero_point::redispatch(dispatchKeySet, self); + } + + // aten::q_per_channel_scales(Tensor self) -> Tensor + inline at::Tensor q_per_channel_scales(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::q_per_channel_scales::redispatch(dispatchKeySet, self); + } + + // aten::q_per_channel_zero_points(Tensor self) -> Tensor + inline at::Tensor q_per_channel_zero_points(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::q_per_channel_zero_points::redispatch(dispatchKeySet, self); + } + + // aten::q_per_channel_axis(Tensor self) -> int + inline int64_t q_per_channel_axis(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::q_per_channel_axis::redispatch(dispatchKeySet, self); + } + + // aten::int_repr(Tensor self) -> Tensor + inline at::Tensor int_repr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::int_repr::redispatch(dispatchKeySet, self); + } + + // aten::_make_per_tensor_quantized_tensor(Tensor self, float scale, int zero_point) -> Tensor + inline at::Tensor _make_per_tensor_quantized_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point) { + return at::_ops::_make_per_tensor_quantized_tensor::redispatch(dispatchKeySet, self, scale, zero_point); + } + + // aten::_make_per_channel_quantized_tensor(Tensor self, Tensor scale, Tensor zero_point, int axis) -> Tensor + inline at::Tensor _make_per_channel_quantized_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) { + return at::_ops::_make_per_channel_quantized_tensor::redispatch(dispatchKeySet, self, scale, zero_point, axis); + } + + // aten::qscheme(Tensor self) -> QScheme + inline at::QScheme qscheme(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::qscheme::redispatch(dispatchKeySet, self); + } + + // aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> Tensor + inline at::Tensor fake_quantize_per_tensor_affine(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_tensor_affine::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max); + } + + // aten::fake_quantize_per_tensor_affine.tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max) -> Tensor + inline at::Tensor fake_quantize_per_tensor_affine(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_tensor_affine_tensor_qparams::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max); + } + + // aten::fake_quantize_per_tensor_affine_cachemask(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor output, Tensor mask) + inline ::std::tuple fake_quantize_per_tensor_affine_cachemask(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max); + } + + // aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max) -> (Tensor output, Tensor mask) + inline ::std::tuple _fake_quantize_per_tensor_affine_cachemask_tensor_qparams(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) { + return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams::redispatch(dispatchKeySet, self, scale, zero_point, fake_quant_enabled, quant_min, quant_max); + } + + // aten::fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor + inline at::Tensor fake_quantize_per_tensor_affine_cachemask_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & mask) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask_backward::redispatch(dispatchKeySet, grad, mask); + } + + // aten::_fake_quantize_learnable_per_tensor_affine(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor + inline at::Tensor _fake_quantize_learnable_per_tensor_affine(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max, grad_factor); + } + + // aten::_fake_quantize_learnable_per_tensor_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _fake_quantize_learnable_per_tensor_affine_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine_backward::redispatch(dispatchKeySet, grad, self, scale, zero_point, quant_min, quant_max, grad_factor); + } + + // aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensor + inline at::Tensor fake_quantize_per_channel_affine(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_channel_affine::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max); + } + + // aten::fake_quantize_per_channel_affine_cachemask(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor output, Tensor mask) + inline ::std::tuple fake_quantize_per_channel_affine_cachemask(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_channel_affine_cachemask::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max); + } + + // aten::fake_quantize_per_channel_affine_cachemask_backward(Tensor grad, Tensor mask) -> Tensor + inline at::Tensor fake_quantize_per_channel_affine_cachemask_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & mask) { + return at::_ops::fake_quantize_per_channel_affine_cachemask_backward::redispatch(dispatchKeySet, grad, mask); + } + + // aten::_fake_quantize_learnable_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> Tensor + inline at::Tensor _fake_quantize_learnable_per_channel_affine(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_channel_affine::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); + } + + // aten::_fake_quantize_learnable_per_channel_affine_backward(Tensor grad, Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _fake_quantize_learnable_per_channel_affine_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_channel_affine_backward::redispatch(dispatchKeySet, grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); + } + + // aten::fused_moving_avg_obs_fake_quant(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> Tensor + inline at::Tensor fused_moving_avg_obs_fake_quant(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) { + return at::_ops::fused_moving_avg_obs_fake_quant::redispatch(dispatchKeySet, self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); + } + + // aten::_fused_moving_avg_obs_fq_helper(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask) + inline ::std::tuple _fused_moving_avg_obs_fq_helper(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) { + return at::_ops::_fused_moving_avg_obs_fq_helper::redispatch(dispatchKeySet, self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); + } + + // aten::_choose_qparams_per_tensor(Tensor self, bool reduce_range=False) -> (float, int) + inline ::std::tuple _choose_qparams_per_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool reduce_range=false) { + return at::_ops::_choose_qparams_per_tensor::redispatch(dispatchKeySet, self, reduce_range); + } + + // aten::_saturate_weight_to_fp16(Tensor weight) -> Tensor + inline at::Tensor _saturate_weight_to_fp16(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight) { + return at::_ops::_saturate_weight_to_fp16::redispatch(dispatchKeySet, weight); + } + + // aten::choose_qparams_optimized(Tensor input, int numel, int n_bins, float ratio, int bit_width) -> (Tensor, Tensor) + inline ::std::tuple choose_qparams_optimized(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width) { + return at::_ops::choose_qparams_optimized::redispatch(dispatchKeySet, input, numel, n_bins, ratio, bit_width); + } + + // aten::_autocast_to_reduced_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled, ScalarType cuda_dtype, ScalarType cpu_dtype) -> Tensor(a) + inline at::Tensor _autocast_to_reduced_precision(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool cuda_enabled, bool cpu_enabled, at::ScalarType cuda_dtype, at::ScalarType cpu_dtype) { + return at::_ops::_autocast_to_reduced_precision::redispatch(dispatchKeySet, self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype); + } + + // aten::_autocast_to_full_precision(Tensor(a) self, bool cuda_enabled, bool cpu_enabled) -> Tensor(a) + inline at::Tensor _autocast_to_full_precision(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool cuda_enabled, bool cpu_enabled) { + return at::_ops::_autocast_to_full_precision::redispatch(dispatchKeySet, self, cuda_enabled, cpu_enabled); + } + + // aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor _to_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, bool non_blocking=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::_to_copy::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), non_blocking, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::_to_copy(Tensor self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor _to_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, bool non_blocking, c10::optional memory_format) { + return at::_ops::_to_copy::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, non_blocking, memory_format); + } + + // aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) + inline at::Tensor to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorOptions options={}, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::to_dtype_layout::redispatch(dispatchKeySet, self, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), non_blocking, copy, c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format)); + } + + // aten::to.dtype_layout(Tensor(a) self, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) + inline at::Tensor to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, bool non_blocking, bool copy, c10::optional memory_format) { + return at::_ops::to_dtype_layout::redispatch(dispatchKeySet, self, dtype, layout, device, pin_memory, non_blocking, copy, memory_format); + } + + // aten::to.device(Tensor(a) self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) + inline at::Tensor to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::to_device::redispatch(dispatchKeySet, self, device, dtype, non_blocking, copy, memory_format); + } + + // aten::to.dtype(Tensor(a) self, ScalarType dtype, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) + inline at::Tensor to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::to_dtype::redispatch(dispatchKeySet, self, dtype, non_blocking, copy, memory_format); + } + + // aten::to.other(Tensor(a) self, Tensor other, bool non_blocking=False, bool copy=False, MemoryFormat? memory_format=None) -> Tensor(a) + inline at::Tensor to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, bool non_blocking=false, bool copy=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::to_other::redispatch(dispatchKeySet, self, other, non_blocking, copy, memory_format); + } + + // aten::meshgrid(Tensor[] tensors) -> Tensor[] + inline ::std::vector meshgrid(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::meshgrid::redispatch(dispatchKeySet, tensors); + } + + // aten::meshgrid.indexing(Tensor[] tensors, *, str indexing) -> Tensor[] + inline ::std::vector meshgrid(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, c10::string_view indexing) { + return at::_ops::meshgrid_indexing::redispatch(dispatchKeySet, tensors, indexing); + } + + // aten::cartesian_prod(Tensor[] tensors) -> Tensor + inline at::Tensor cartesian_prod(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::cartesian_prod::redispatch(dispatchKeySet, tensors); + } + + // aten::combinations(Tensor self, int r=2, bool with_replacement=False) -> Tensor + inline at::Tensor combinations(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t r=2, bool with_replacement=false) { + return at::_ops::combinations::redispatch(dispatchKeySet, self, r, with_replacement); + } + + // aten::item(Tensor self) -> Scalar + inline at::Scalar item(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::item::redispatch(dispatchKeySet, self); + } + + // aten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarType + inline at::ScalarType result_type(c10::DispatchKeySet dispatchKeySet, const at::Tensor & tensor, const at::Tensor & other) { + return at::_ops::result_type_Tensor::redispatch(dispatchKeySet, tensor, other); + } + + // aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarType + inline at::ScalarType result_type(c10::DispatchKeySet dispatchKeySet, const at::Tensor & tensor, const at::Scalar & other) { + return at::_ops::result_type_Scalar::redispatch(dispatchKeySet, tensor, other); + } + + // aten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarType + inline at::ScalarType result_type(c10::DispatchKeySet dispatchKeySet, const at::Scalar & scalar, const at::Tensor & tensor) { + return at::_ops::result_type_Scalar_Tensor::redispatch(dispatchKeySet, scalar, tensor); + } + + // aten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarType + inline at::ScalarType result_type(c10::DispatchKeySet dispatchKeySet, const at::Scalar & scalar1, const at::Scalar & scalar2) { + return at::_ops::result_type_Scalar_Scalar::redispatch(dispatchKeySet, scalar1, scalar2); + } + + // aten::can_cast(ScalarType from, ScalarType to) -> bool + inline bool can_cast(c10::DispatchKeySet dispatchKeySet, at::ScalarType from, at::ScalarType to) { + return at::_ops::can_cast::redispatch(dispatchKeySet, from, to); + } + + // aten::promote_types(ScalarType type1, ScalarType type2) -> ScalarType + inline at::ScalarType promote_types(c10::DispatchKeySet dispatchKeySet, at::ScalarType type1, at::ScalarType type2) { + return at::_ops::promote_types::redispatch(dispatchKeySet, type1, type2); + } + + // aten::_local_scalar_dense(Tensor self) -> Scalar + inline at::Scalar _local_scalar_dense(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_local_scalar_dense::redispatch(dispatchKeySet, self); + } + + // aten::_lstm_mps(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _lstm_mps(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::_lstm_mps::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::lstm_mps_backward(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor[], Tensor[]) + inline ::std::tuple,::std::vector> lstm_mps_backward(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_y, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::lstm_mps_backward::redispatch(dispatchKeySet, grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::_thnn_fused_lstm_cell(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_fused_lstm_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias={}, const c10::optional & hidden_bias={}) { + return at::_ops::_thnn_fused_lstm_cell::redispatch(dispatchKeySet, input_gates, hidden_gates, cx, input_bias, hidden_bias); + } + + // aten::_thnn_fused_lstm_cell_backward_impl(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_fused_lstm_cell_backward_impl(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { + return at::_ops::_thnn_fused_lstm_cell_backward_impl::redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias); + } + + // aten::_thnn_fused_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_fused_lstm_cell_backward(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { + return at::_ops::_thnn_fused_lstm_cell_backward::redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias); + } + + // aten::_thnn_differentiable_lstm_cell_backward(Tensor? grad_hy, Tensor? grad_cy, Tensor input_gates, Tensor hidden_gates, Tensor? input_bias, Tensor? hidden_bias, Tensor cx, Tensor cy) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_differentiable_lstm_cell_backward(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const c10::optional & input_bias, const c10::optional & hidden_bias, const at::Tensor & cx, const at::Tensor & cy) { + return at::_ops::_thnn_differentiable_lstm_cell_backward::redispatch(dispatchKeySet, grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy); + } + + // aten::_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor) + inline ::std::tuple _thnn_fused_gru_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias={}, const c10::optional & hidden_bias={}) { + return at::_ops::_thnn_fused_gru_cell::redispatch(dispatchKeySet, input_gates, hidden_gates, hx, input_bias, hidden_bias); + } + + // aten::_thnn_fused_gru_cell_backward(Tensor grad_hy, Tensor workspace, bool has_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_fused_gru_cell_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) { + return at::_ops::_thnn_fused_gru_cell_backward::redispatch(dispatchKeySet, grad_hy, workspace, has_bias); + } + + // aten::_thnn_differentiable_gru_cell_backward(Tensor grad_hy, Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias, Tensor? hidden_bias) -> (Tensor, Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _thnn_differentiable_gru_cell_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_hy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias) { + return at::_ops::_thnn_differentiable_gru_cell_backward::redispatch(dispatchKeySet, grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias); + } + + // aten::lstm.input(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor, Tensor) + inline ::std::tuple lstm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::lstm_input::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::lstm.data(Tensor data, Tensor batch_sizes, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor, Tensor) + inline ::std::tuple lstm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + return at::_ops::lstm_data::redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + + // aten::gru.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) + inline ::std::tuple gru(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::gru_input::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::gru.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) + inline ::std::tuple gru(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + return at::_ops::gru_data::redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + + // aten::rnn_tanh.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) + inline ::std::tuple rnn_tanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::rnn_tanh_input::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::rnn_tanh.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) + inline ::std::tuple rnn_tanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + return at::_ops::rnn_tanh_data::redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + + // aten::rnn_relu.input(Tensor input, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first) -> (Tensor, Tensor) + inline ::std::tuple rnn_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::rnn_relu_input::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + + // aten::rnn_relu.data(Tensor data, Tensor batch_sizes, Tensor hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional) -> (Tensor, Tensor) + inline ::std::tuple rnn_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + return at::_ops::rnn_relu_data::redispatch(dispatchKeySet, data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + + // aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor) + inline ::std::tuple lstm_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih={}, const c10::optional & b_hh={}) { + return at::_ops::lstm_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh); + } + + // aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor + inline at::Tensor gru_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih={}, const c10::optional & b_hh={}) { + return at::_ops::gru_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh); + } + + // aten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor + inline at::Tensor rnn_tanh_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih={}, const c10::optional & b_hh={}) { + return at::_ops::rnn_tanh_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh); + } + + // aten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensor + inline at::Tensor rnn_relu_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih={}, const c10::optional & b_hh={}) { + return at::_ops::rnn_relu_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh); + } + + // aten::quantized_lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> (Tensor, Tensor) + inline ::std::tuple quantized_lstm_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + return at::_ops::quantized_lstm_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + + // aten::quantized_gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor + inline at::Tensor quantized_gru_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + return at::_ops::quantized_gru_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + + // aten::quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor + inline at::Tensor quantized_rnn_relu_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + return at::_ops::quantized_rnn_relu_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + + // aten::quantized_rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor + inline at::Tensor quantized_rnn_tanh_cell(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + return at::_ops::quantized_rnn_tanh_cell::redispatch(dispatchKeySet, input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + + // aten::_pack_padded_sequence(Tensor input, Tensor lengths, bool batch_first) -> (Tensor, Tensor) + inline ::std::tuple _pack_padded_sequence(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & lengths, bool batch_first) { + return at::_ops::_pack_padded_sequence::redispatch(dispatchKeySet, input, lengths, batch_first); + } + + // aten::_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor + inline at::Tensor _pack_padded_sequence_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, at::IntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) { + return at::_ops::_pack_padded_sequence_backward::redispatch(dispatchKeySet, grad, c10::fromIntArrayRefSlow(input_size), batch_sizes, batch_first); + } + + // aten::_pack_padded_sequence_backward(Tensor grad, SymInt[] input_size, Tensor batch_sizes, bool batch_first) -> Tensor + inline at::Tensor _pack_padded_sequence_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, c10::SymIntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) { + return at::_ops::_pack_padded_sequence_backward::redispatch(dispatchKeySet, grad, input_size, batch_sizes, batch_first); + } + + // aten::_pad_packed_sequence(Tensor data, Tensor batch_sizes, bool batch_first, Scalar padding_value, int total_length) -> (Tensor, Tensor) + inline ::std::tuple _pad_packed_sequence(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) { + return at::_ops::_pad_packed_sequence::redispatch(dispatchKeySet, data, batch_sizes, batch_first, padding_value, total_length); + } + + // aten::set_.source_Storage(Tensor(a!) self, Storage source) -> Tensor(a!) + inline at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Storage source) { + return at::_ops::set__source_Storage::redispatch(dispatchKeySet, self, source); + } + + // aten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) + inline at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) { + return at::_ops::set__source_Storage_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); + } + + // aten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) + inline at::Tensor & set__symint(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) { + return at::_ops::set__source_Storage_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, size, stride); + } + + // aten::set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) + inline at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) { + return at::_ops::set__source_Tensor_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); + } + + // aten::set_.source_Tensor_storage_offset(Tensor(a!) self, Tensor source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor(a!) + inline at::Tensor & set__symint(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) { + return at::_ops::set__source_Tensor_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, size, stride); + } + + // aten::set_.source_Tensor(Tensor(a!) self, Tensor source) -> Tensor(a!) + inline at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & source) { + return at::_ops::set__source_Tensor::redispatch(dispatchKeySet, self, source); + } + + // aten::set_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & set_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::set_::redispatch(dispatchKeySet, self); + } + + // aten::lift(Tensor self) -> Tensor + inline at::Tensor lift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::lift::redispatch(dispatchKeySet, self); + } + + // aten::lift_fresh(Tensor(a) self) -> Tensor(a) + inline at::Tensor lift_fresh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::lift_fresh::redispatch(dispatchKeySet, self); + } + + // aten::lift_fresh_copy(Tensor self) -> Tensor + inline at::Tensor lift_fresh_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::lift_fresh_copy::redispatch(dispatchKeySet, self); + } + + // aten::is_set_to(Tensor self, Tensor tensor) -> bool + inline bool is_set_to(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor) { + return at::_ops::is_set_to::redispatch(dispatchKeySet, self, tensor); + } + + // aten::masked_fill_.Scalar(Tensor(a!) self, Tensor mask, Scalar value) -> Tensor(a!) + inline at::Tensor & masked_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { + return at::_ops::masked_fill__Scalar::redispatch(dispatchKeySet, self, mask, value); + } + + // aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> Tensor + inline at::Tensor masked_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { + return at::_ops::masked_fill_Scalar::redispatch(dispatchKeySet, self, mask, value); + } + + // aten::masked_fill_.Tensor(Tensor(a!) self, Tensor mask, Tensor value) -> Tensor(a!) + inline at::Tensor & masked_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { + return at::_ops::masked_fill__Tensor::redispatch(dispatchKeySet, self, mask, value); + } + + // aten::masked_fill.Tensor(Tensor self, Tensor mask, Tensor value) -> Tensor + inline at::Tensor masked_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { + return at::_ops::masked_fill_Tensor::redispatch(dispatchKeySet, self, mask, value); + } + + // aten::masked_scatter_(Tensor(a!) self, Tensor mask, Tensor source) -> Tensor(a!) + inline at::Tensor & masked_scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { + return at::_ops::masked_scatter_::redispatch(dispatchKeySet, self, mask, source); + } + + // aten::masked_scatter(Tensor self, Tensor mask, Tensor source) -> Tensor + inline at::Tensor masked_scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { + return at::_ops::masked_scatter::redispatch(dispatchKeySet, self, mask, source); + } + + // aten::masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor + inline at::Tensor masked_scatter_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, at::IntArrayRef sizes) { + return at::_ops::masked_scatter_backward::redispatch(dispatchKeySet, grad_output, mask, c10::fromIntArrayRefSlow(sizes)); + } + + // aten::masked_scatter_backward(Tensor grad_output, Tensor mask, SymInt[] sizes) -> Tensor + inline at::Tensor masked_scatter_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, c10::SymIntArrayRef sizes) { + return at::_ops::masked_scatter_backward::redispatch(dispatchKeySet, grad_output, mask, sizes); + } + + // aten::_masked_softmax(Tensor self, Tensor mask, int? dim=None, int? mask_type=None) -> Tensor + inline at::Tensor _masked_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, c10::optional dim=c10::nullopt, c10::optional mask_type=c10::nullopt) { + return at::_ops::_masked_softmax::redispatch(dispatchKeySet, self, mask, dim, mask_type); + } + + // aten::_masked_softmax_backward(Tensor grad_output, Tensor output, Tensor mask, int? dim=None) -> Tensor + inline at::Tensor _masked_softmax_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional dim=c10::nullopt) { + return at::_ops::_masked_softmax_backward::redispatch(dispatchKeySet, grad_output, output, mask, dim); + } + + // aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a) + inline at::Tensor view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::view::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::view(Tensor(a) self, SymInt[] size) -> Tensor(a) + inline at::Tensor view_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::view::redispatch(dispatchKeySet, self, size); + } + + // aten::view.dtype(Tensor(a) self, ScalarType dtype) -> Tensor(a) + inline at::Tensor view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype) { + return at::_ops::view_dtype::redispatch(dispatchKeySet, self, dtype); + } + + // aten::put_(Tensor(a!) self, Tensor index, Tensor source, bool accumulate=False) -> Tensor(a!) + inline at::Tensor & put_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate=false) { + return at::_ops::put_::redispatch(dispatchKeySet, self, index, source, accumulate); + } + + // aten::put(Tensor self, Tensor index, Tensor source, bool accumulate=False) -> Tensor + inline at::Tensor put(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate=false) { + return at::_ops::put::redispatch(dispatchKeySet, self, index, source, accumulate); + } + + // aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_add_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) { + return at::_ops::index_add_out::redispatch(dispatchKeySet, self, dim, index, source, alpha, out); + } + + // aten::index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_add_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::index_add_out::redispatch(dispatchKeySet, self, dim, index, source, alpha, out); + } + + // aten::index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & index_add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) { + return at::_ops::index_add_::redispatch(dispatchKeySet, self, dim, index, source, alpha); + } + + // aten::index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor + inline at::Tensor index_add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) { + return at::_ops::index_add::redispatch(dispatchKeySet, self, dim, index, source, alpha); + } + + // aten::index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor + inline at::Tensor index_add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha=1) { + return at::_ops::index_add_dimname::redispatch(dispatchKeySet, self, dim, index, source, alpha); + } + + // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_reduce_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self=true) { + return at::_ops::index_reduce_out::redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self, out); + } + + // aten::index_reduce.out(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_reduce_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self, at::Tensor & out) { + return at::_ops::index_reduce_out::redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self, out); + } + + // aten::index_reduce_(Tensor(a!) self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor(a!) + inline at::Tensor & index_reduce_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self=true) { + return at::_ops::index_reduce_::redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self); + } + + // aten::index_reduce(Tensor self, int dim, Tensor index, Tensor source, str reduce, *, bool include_self=True) -> Tensor + inline at::Tensor index_reduce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self=true) { + return at::_ops::index_reduce::redispatch(dispatchKeySet, self, dim, index, source, reduce, include_self); + } + + // aten::index_fill_.int_Scalar(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) + inline at::Tensor & index_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::index_fill__int_Scalar::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill.int_Scalar(Tensor self, int dim, Tensor index, Scalar value) -> Tensor + inline at::Tensor index_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::index_fill_int_Scalar::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill_.int_Tensor(Tensor(a!) self, int dim, Tensor index, Tensor value) -> Tensor(a!) + inline at::Tensor & index_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { + return at::_ops::index_fill__int_Tensor::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill.int_Tensor(Tensor self, int dim, Tensor index, Tensor value) -> Tensor + inline at::Tensor index_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { + return at::_ops::index_fill_int_Tensor::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill_.Dimname_Scalar(Tensor(a!) self, Dimname dim, Tensor index, Scalar value) -> Tensor(a!) + inline at::Tensor & index_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::index_fill__Dimname_Scalar::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill_.Dimname_Tensor(Tensor(a!) self, Dimname dim, Tensor index, Tensor value) -> Tensor(a!) + inline at::Tensor & index_fill_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { + return at::_ops::index_fill__Dimname_Tensor::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill.Dimname_Scalar(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor + inline at::Tensor index_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::index_fill_Dimname_Scalar::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::index_fill.Dimname_Tensor(Tensor self, Dimname dim, Tensor index, Tensor value) -> Tensor + inline at::Tensor index_fill(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { + return at::_ops::index_fill_Dimname_Tensor::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_src::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter_.src(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) + inline at::Tensor & scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter__src::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_src_out::redispatch(dispatchKeySet, self, dim, index, src, out); + } + + // aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { + return at::_ops::scatter_src_out::redispatch(dispatchKeySet, self, dim, index, src, out); + } + + // aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::scatter_value::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::scatter_.value(Tensor(a!) self, int dim, Tensor index, Scalar value) -> Tensor(a!) + inline at::Tensor & scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::scatter__value::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::scatter_value_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { + return at::_ops::scatter_value_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { + return at::_ops::scatter_reduce::redispatch(dispatchKeySet, self, dim, index, src, reduce); + } + + // aten::scatter_.reduce(Tensor(a!) self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor(a!) + inline at::Tensor & scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { + return at::_ops::scatter__reduce::redispatch(dispatchKeySet, self, dim, index, src, reduce); + } + + // aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { + return at::_ops::scatter_reduce_out::redispatch(dispatchKeySet, self, dim, index, src, reduce, out); + } + + // aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) { + return at::_ops::scatter_reduce_out::redispatch(dispatchKeySet, self, dim, index, src, reduce, out); + } + + // aten::scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { + return at::_ops::scatter_value_reduce::redispatch(dispatchKeySet, self, dim, index, value, reduce); + } + + // aten::scatter_.value_reduce(Tensor(a!) self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor(a!) + inline at::Tensor & scatter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { + return at::_ops::scatter__value_reduce::redispatch(dispatchKeySet, self, dim, index, value, reduce); + } + + // aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { + return at::_ops::scatter_value_reduce_out::redispatch(dispatchKeySet, self, dim, index, value, reduce, out); + } + + // aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out) { + return at::_ops::scatter_value_reduce_out::redispatch(dispatchKeySet, self, dim, index, value, reduce, out); + } + + // aten::scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_dimname_src::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor + inline at::Tensor scatter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::scatter_dimname_value::redispatch(dispatchKeySet, self, dim, index, value); + } + + // aten::scatter_add(Tensor self, int dim, Tensor index, Tensor src) -> Tensor + inline at::Tensor scatter_add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_add::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter_add_(Tensor(a!) self, int dim, Tensor index, Tensor src) -> Tensor(a!) + inline at::Tensor & scatter_add_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_add_::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_add_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_add_out::redispatch(dispatchKeySet, self, dim, index, src, out); + } + + // aten::scatter_add.out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_add_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) { + return at::_ops::scatter_add_out::redispatch(dispatchKeySet, self, dim, index, src, out); + } + + // aten::scatter_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor + inline at::Tensor scatter_add(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { + return at::_ops::scatter_add_dimname::redispatch(dispatchKeySet, self, dim, index, src); + } + + // aten::scatter_reduce.two(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor + inline at::Tensor scatter_reduce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true) { + return at::_ops::scatter_reduce_two::redispatch(dispatchKeySet, self, dim, index, src, reduce, include_self); + } + + // aten::scatter_reduce_.two(Tensor(a!) self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True) -> Tensor(a!) + inline at::Tensor & scatter_reduce_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true) { + return at::_ops::scatter_reduce__two::redispatch(dispatchKeySet, self, dim, index, src, reduce, include_self); + } + + // aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_reduce_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self=true) { + return at::_ops::scatter_reduce_two_out::redispatch(dispatchKeySet, self, dim, index, src, reduce, include_self, out); + } + + // aten::scatter_reduce.two_out(Tensor self, int dim, Tensor index, Tensor src, str reduce, *, bool include_self=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scatter_reduce_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self, at::Tensor & out) { + return at::_ops::scatter_reduce_two_out::redispatch(dispatchKeySet, self, dim, index, src, reduce, include_self, out); + } + + // aten::eq_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & eq_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::eq__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::eq_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & eq_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::eq__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_and_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_and.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_and_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_and_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_and.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::bitwise_and_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_and.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor bitwise_and(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_and_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_and.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor bitwise_and(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_and_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_and.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor bitwise_and(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_and_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_and_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & bitwise_and_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_and__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_and_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & bitwise_and_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_and__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor __and__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__and___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor __and__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__and___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__iand__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & __iand__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::__iand___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__iand__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & __iand__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::__iand___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_or_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_or_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_or_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::bitwise_or_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor bitwise_or(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_or_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_or.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor bitwise_or(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_or_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_or.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor bitwise_or(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_or_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_or_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & bitwise_or_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_or__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_or_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & bitwise_or_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_or__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__or__.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor __or__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__or___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__or__.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor __or__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__or___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__ior__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & __ior__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::__ior___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__ior__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & __ior__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::__ior___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_xor_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_xor_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_xor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::bitwise_xor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor bitwise_xor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_xor_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_xor.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor bitwise_xor(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_xor_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_xor.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor bitwise_xor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_xor_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_xor_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & bitwise_xor_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_xor__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_xor_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & bitwise_xor_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_xor__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__xor__.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor __xor__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__xor___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__xor__.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor __xor__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__xor___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__ixor__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & __ixor__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::__ixor___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__ixor__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & __ixor__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::__ixor___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor __lshift__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__lshift___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__lshift__.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor __lshift__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__lshift___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__ilshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & __ilshift__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::__ilshift___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__ilshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & __ilshift__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::__ilshift___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_left_shift.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor bitwise_left_shift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_left_shift_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_left_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_left_shift__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_left_shift_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_left_shift_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor bitwise_left_shift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_left_shift_Tensor_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_left_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_left_shift__Tensor_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_left_shift_Tensor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::bitwise_left_shift_Tensor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor bitwise_left_shift(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_left_shift_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__rshift__.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor __rshift__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__rshift___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__rshift__.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor __rshift__(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__rshift___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::__irshift__.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & __irshift__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::__irshift___Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::__irshift__.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & __irshift__(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::__irshift___Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_right_shift.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor bitwise_right_shift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_right_shift_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_right_shift_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_right_shift__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::bitwise_right_shift_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_right_shift_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Tensor_Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor bitwise_right_shift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_right_shift_Tensor_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_right_shift_.Tensor_Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_right_shift__Tensor_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::bitwise_right_shift_Tensor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Tensor_Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::bitwise_right_shift_Tensor_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor bitwise_right_shift(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_right_shift_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::tril_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) + inline at::Tensor & tril_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal=0) { + return at::_ops::tril_::redispatch(dispatchKeySet, self, diagonal); + } + + // aten::triu_(Tensor(a!) self, int diagonal=0) -> Tensor(a!) + inline at::Tensor & triu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t diagonal=0) { + return at::_ops::triu_::redispatch(dispatchKeySet, self, diagonal); + } + + // aten::digamma_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & digamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::digamma_::redispatch(dispatchKeySet, self); + } + + // aten::lerp_.Scalar(Tensor(a!) self, Tensor end, Scalar weight) -> Tensor(a!) + inline at::Tensor & lerp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { + return at::_ops::lerp__Scalar::redispatch(dispatchKeySet, self, end, weight); + } + + // aten::lerp_.Tensor(Tensor(a!) self, Tensor end, Tensor weight) -> Tensor(a!) + inline at::Tensor & lerp_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { + return at::_ops::lerp__Tensor::redispatch(dispatchKeySet, self, end, weight); + } + + // aten::addbmm_(Tensor(a!) self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor(a!) + inline at::Tensor & addbmm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addbmm_::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); + } + + // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addbmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addbmm_out::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha, out); + } + + // aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addbmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::addbmm_out::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha, out); + } + + // aten::addbmm(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1) -> Tensor + inline at::Tensor addbmm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::addbmm::redispatch(dispatchKeySet, self, batch1, batch2, beta, alpha); + } + + // aten::random_.from(Tensor(a!) self, int from, int? to, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & random_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt) { + return at::_ops::random__from::redispatch(dispatchKeySet, self, from, to, generator); + } + + // aten::random_.to(Tensor(a!) self, int to, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & random_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt) { + return at::_ops::random__to::redispatch(dispatchKeySet, self, to, generator); + } + + // aten::random_(Tensor(a!) self, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & random_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::random_::redispatch(dispatchKeySet, self, generator); + } + + // aten::uniform_(Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & uniform_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double from=0, double to=1, c10::optional generator=c10::nullopt) { + return at::_ops::uniform_::redispatch(dispatchKeySet, self, from, to, generator); + } + + // aten::cauchy_(Tensor(a!) self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & cauchy_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double median=0, double sigma=1, c10::optional generator=c10::nullopt) { + return at::_ops::cauchy_::redispatch(dispatchKeySet, self, median, sigma, generator); + } + + // aten::log_normal_(Tensor(a!) self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & log_normal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double mean=1, double std=2, c10::optional generator=c10::nullopt) { + return at::_ops::log_normal_::redispatch(dispatchKeySet, self, mean, std, generator); + } + + // aten::exponential_(Tensor(a!) self, float lambd=1, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & exponential_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double lambd=1, c10::optional generator=c10::nullopt) { + return at::_ops::exponential_::redispatch(dispatchKeySet, self, lambd, generator); + } + + // aten::geometric_(Tensor(a!) self, float p, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & geometric_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::geometric_::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diag_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::diag_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diag_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { + return at::_ops::diag_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::diag(Tensor self, int diagonal=0) -> Tensor + inline at::Tensor diag(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::diag::redispatch(dispatchKeySet, self, diagonal); + } + + // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cross_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, c10::optional dim=c10::nullopt) { + return at::_ops::cross_out::redispatch(dispatchKeySet, self, other, dim, out); + } + + // aten::cross.out(Tensor self, Tensor other, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cross_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional dim, at::Tensor & out) { + return at::_ops::cross_out::redispatch(dispatchKeySet, self, other, dim, out); + } + + // aten::cross(Tensor self, Tensor other, int? dim=None) -> Tensor + inline at::Tensor cross(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, c10::optional dim=c10::nullopt) { + return at::_ops::cross::redispatch(dispatchKeySet, self, other, dim); + } + + // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & triu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::triu_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & triu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { + return at::_ops::triu_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::triu(Tensor self, int diagonal=0) -> Tensor + inline at::Tensor triu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::triu::redispatch(dispatchKeySet, self, diagonal); + } + + // aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tril_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::tril_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tril_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal, at::Tensor & out) { + return at::_ops::tril_out::redispatch(dispatchKeySet, self, diagonal, out); + } + + // aten::tril(Tensor self, int diagonal=0) -> Tensor + inline at::Tensor tril(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t diagonal=0) { + return at::_ops::tril::redispatch(dispatchKeySet, self, diagonal); + } + + // aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor tril_indices(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) { + return at::_ops::tril_indices::redispatch(dispatchKeySet, row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::tril_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor tril_indices(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::tril_indices::redispatch(dispatchKeySet, row, col, offset, dtype, layout, device, pin_memory); + } + + // aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor triu_indices(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset=0, at::TensorOptions options=at::kLong) { + return at::_ops::triu_indices::redispatch(dispatchKeySet, row, col, offset, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::triu_indices(int row, int col, int offset=0, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor triu_indices(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::triu_indices::redispatch(dispatchKeySet, row, col, offset, dtype, layout, device, pin_memory); + } + + // aten::trace(Tensor self) -> Tensor + inline at::Tensor trace(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::trace::redispatch(dispatchKeySet, self); + } + + // aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor + inline at::Tensor trace_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, at::IntArrayRef sizes) { + return at::_ops::trace_backward::redispatch(dispatchKeySet, grad, c10::fromIntArrayRefSlow(sizes)); + } + + // aten::trace_backward(Tensor grad, SymInt[] sizes) -> Tensor + inline at::Tensor trace_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, c10::SymIntArrayRef sizes) { + return at::_ops::trace_backward::redispatch(dispatchKeySet, grad, sizes); + } + + // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ne_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::ne_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ne_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::ne_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ne.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor ne(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::ne_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ne_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ne_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ne_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::ne_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ne.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor ne(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ne_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::ne_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & ne_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::ne__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::ne_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & ne_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::ne__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & not_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::not_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::not_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & not_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::not_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::not_equal.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor not_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::not_equal_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & not_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::not_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::not_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & not_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::not_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::not_equal.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor not_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::not_equal_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::not_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & not_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::not_equal__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::not_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & not_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::not_equal__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eq_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::eq_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eq_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::eq_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::eq.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor eq(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::eq_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eq_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::eq_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & eq_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::eq_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::eq.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor eq(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::eq_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ge_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::ge_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ge_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::ge_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ge.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor ge(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::ge_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ge_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ge_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ge_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::ge_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::ge.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor ge(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::ge_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::ge_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & ge_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::ge__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::ge_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & ge_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::ge__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::greater_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater_equal.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor greater_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater_equal_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::greater_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater_equal.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor greater_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater_equal_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & greater_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater_equal__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & greater_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater_equal__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & le_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::le_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & le_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::le_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::le.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor le(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::le_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & le_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::le_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & le_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::le_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::le.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor le(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::le_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::le_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & le_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::le__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::le_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & le_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::le__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::less_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less_equal.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::less_equal_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less_equal.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor less_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::less_equal_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_equal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::less_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less_equal.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_equal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::less_equal_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less_equal.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor less_equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::less_equal_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::less_equal_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & less_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::less_equal__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::less_equal_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & less_equal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::less_equal__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::gt_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::gt_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gt.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor gt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::gt_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::gt_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::gt_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::gt.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor gt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::gt_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::gt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & gt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::gt__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::gt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & gt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::gt__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::greater_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor greater(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & greater_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::greater_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::greater.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor greater(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & greater_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::greater__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::greater_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & greater_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::greater__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::lt_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::lt_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lt.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor lt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::lt_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::lt_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::lt_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::lt.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor lt(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::lt_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::lt_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & lt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::lt__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::lt_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & lt_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::lt__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::less_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::less_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor less(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::less_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::less_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & less_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::less_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::less.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor less(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::less_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::less_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & less_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::less__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::less_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & less_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::less__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & take_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & index) { + return at::_ops::take_out::redispatch(dispatchKeySet, self, index, out); + } + + // aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & take_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, at::Tensor & out) { + return at::_ops::take_out::redispatch(dispatchKeySet, self, index, out); + } + + // aten::take(Tensor self, Tensor index) -> Tensor + inline at::Tensor take(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index) { + return at::_ops::take::redispatch(dispatchKeySet, self, index); + } + + // aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & take_along_dim_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::optional dim=c10::nullopt) { + return at::_ops::take_along_dim_out::redispatch(dispatchKeySet, self, indices, dim, out); + } + + // aten::take_along_dim.out(Tensor self, Tensor indices, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & take_along_dim_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::optional dim, at::Tensor & out) { + return at::_ops::take_along_dim_out::redispatch(dispatchKeySet, self, indices, dim, out); + } + + // aten::take_along_dim(Tensor self, Tensor indices, int? dim=None) -> Tensor + inline at::Tensor take_along_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::optional dim=c10::nullopt) { + return at::_ops::take_along_dim::redispatch(dispatchKeySet, self, indices, dim); + } + + // aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_select_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index) { + return at::_ops::index_select_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_select_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, at::Tensor & out) { + return at::_ops::index_select_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::index_select(Tensor self, int dim, Tensor index) -> Tensor + inline at::Tensor index_select(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index) { + return at::_ops::index_select::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_select_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index) { + return at::_ops::index_select_dimname_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::index_select.dimname_out(Tensor self, Dimname dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_select_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, at::Tensor & out) { + return at::_ops::index_select_dimname_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::index_select.dimname(Tensor self, Dimname dim, Tensor index) -> Tensor + inline at::Tensor index_select(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index) { + return at::_ops::index_select_dimname::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor + inline at::Tensor index_select_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, at::IntArrayRef self_sizes, int64_t dim, const at::Tensor & index) { + return at::_ops::index_select_backward::redispatch(dispatchKeySet, grad, c10::fromIntArrayRefSlow(self_sizes), dim, index); + } + + // aten::index_select_backward(Tensor grad, SymInt[] self_sizes, int dim, Tensor index) -> Tensor + inline at::Tensor index_select_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index) { + return at::_ops::index_select_backward::redispatch(dispatchKeySet, grad, self_sizes, dim, index); + } + + // aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_select_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask) { + return at::_ops::masked_select_out::redispatch(dispatchKeySet, self, mask, out); + } + + // aten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_select_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { + return at::_ops::masked_select_out::redispatch(dispatchKeySet, self, mask, out); + } + + // aten::masked_select(Tensor self, Tensor mask) -> Tensor + inline at::Tensor masked_select(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask) { + return at::_ops::masked_select::redispatch(dispatchKeySet, self, mask); + } + + // aten::masked_select_backward(Tensor grad, Tensor input, Tensor mask) -> Tensor + inline at::Tensor masked_select_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & input, const at::Tensor & mask) { + return at::_ops::masked_select_backward::redispatch(dispatchKeySet, grad, input, mask); + } + + // aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nonzero_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::nonzero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nonzero_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::nonzero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::nonzero(Tensor self) -> Tensor + inline at::Tensor nonzero(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::nonzero::redispatch(dispatchKeySet, self); + } + + // aten::nonzero_static.out(Tensor self, *, int size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nonzero_static_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t size, int64_t fill_value=-1) { + return at::_ops::nonzero_static_out::redispatch(dispatchKeySet, self, size, fill_value, out); + } + + // aten::nonzero_static.out(Tensor self, *, int size, int fill_value=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nonzero_static_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t size, int64_t fill_value, at::Tensor & out) { + return at::_ops::nonzero_static_out::redispatch(dispatchKeySet, self, size, fill_value, out); + } + + // aten::nonzero_static(Tensor self, *, int size, int fill_value=-1) -> Tensor + inline at::Tensor nonzero_static(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t size, int64_t fill_value=-1) { + return at::_ops::nonzero_static::redispatch(dispatchKeySet, self, size, fill_value); + } + + // aten::nonzero_numpy(Tensor self) -> Tensor[] + inline ::std::vector nonzero_numpy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::nonzero_numpy::redispatch(dispatchKeySet, self); + } + + // aten::argwhere(Tensor self) -> Tensor + inline at::Tensor argwhere(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::argwhere::redispatch(dispatchKeySet, self); + } + + // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gather_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad=false) { + return at::_ops::gather_out::redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); + } + + // aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gather_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { + return at::_ops::gather_out::redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); + } + + // aten::gather(Tensor self, int dim, Tensor index, *, bool sparse_grad=False) -> Tensor + inline at::Tensor gather(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad=false) { + return at::_ops::gather::redispatch(dispatchKeySet, self, dim, index, sparse_grad); + } + + // aten::gather_backward(Tensor grad, Tensor self, int dim, Tensor index, bool sparse_grad) -> Tensor + inline at::Tensor gather_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { + return at::_ops::gather_backward::redispatch(dispatchKeySet, grad, self, dim, index, sparse_grad); + } + + // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gather_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad=false) { + return at::_ops::gather_dimname_out::redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); + } + + // aten::gather.dimname_out(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & gather_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad, at::Tensor & out) { + return at::_ops::gather_dimname_out::redispatch(dispatchKeySet, self, dim, index, sparse_grad, out); + } + + // aten::gather.dimname(Tensor self, Dimname dim, Tensor index, *, bool sparse_grad=False) -> Tensor + inline at::Tensor gather(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad=false) { + return at::_ops::gather_dimname::redispatch(dispatchKeySet, self, dim, index, sparse_grad); + } + + // aten::_gather_sparse_backward(Tensor self, int dim, Tensor index, Tensor grad) -> Tensor + inline at::Tensor _gather_sparse_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad) { + return at::_ops::_gather_sparse_backward::redispatch(dispatchKeySet, self, dim, index, grad); + } + + // aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addcmul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcmul_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addcmul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { + return at::_ops::addcmul_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::addcmul(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor + inline at::Tensor addcmul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcmul::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::addcmul_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!) + inline at::Tensor & addcmul_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcmul_::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addcdiv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcdiv_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & addcdiv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value, at::Tensor & out) { + return at::_ops::addcdiv_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::addcdiv(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor + inline at::Tensor addcdiv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcdiv::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::addcdiv_(Tensor(a!) self, Tensor tensor1, Tensor tensor2, *, Scalar value=1) -> Tensor(a!) + inline at::Tensor & addcdiv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value=1) { + return at::_ops::addcdiv_::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor + inline at::Tensor cross_entropy_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100, double label_smoothing=0.0) { + return at::_ops::cross_entropy_loss::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, label_smoothing); + } + + // aten::cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor + inline at::Tensor cross_entropy_loss_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100, double label_smoothing=0.0) { + return at::_ops::cross_entropy_loss::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, label_smoothing); + } + + // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) + inline ::std::tuple triangular_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & X, at::Tensor & M, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) { + return at::_ops::triangular_solve_X::redispatch(dispatchKeySet, self, A, upper, transpose, unitriangular, X, M); + } + + // aten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient) + inline ::std::tuple triangular_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular, at::Tensor & X, at::Tensor & M) { + return at::_ops::triangular_solve_X::redispatch(dispatchKeySet, self, A, upper, transpose, unitriangular, X, M); + } + + // aten::triangular_solve(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False) -> (Tensor solution, Tensor cloned_coefficient) + inline ::std::tuple triangular_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper=true, bool transpose=false, bool unitriangular=false) { + return at::_ops::triangular_solve::redispatch(dispatchKeySet, self, A, upper, transpose, unitriangular); + } + + // aten::_linalg_check_errors(Tensor info, str api_name, *, bool is_matrix) -> () + inline void _linalg_check_errors(c10::DispatchKeySet dispatchKeySet, const at::Tensor & info, c10::string_view api_name, bool is_matrix) { + return at::_ops::_linalg_check_errors::redispatch(dispatchKeySet, info, api_name, is_matrix); + } + + // aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_solve_triangular_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false) { + return at::_ops::linalg_solve_triangular_out::redispatch(dispatchKeySet, self, B, upper, left, unitriangular, out); + } + + // aten::linalg_solve_triangular.out(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_solve_triangular_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out) { + return at::_ops::linalg_solve_triangular_out::redispatch(dispatchKeySet, self, B, upper, left, unitriangular, out); + } + + // aten::linalg_solve_triangular(Tensor self, Tensor B, *, bool upper, bool left=True, bool unitriangular=False) -> Tensor + inline at::Tensor linalg_solve_triangular(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false) { + return at::_ops::linalg_solve_triangular::redispatch(dispatchKeySet, self, B, upper, left, unitriangular); + } + + // aten::linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor + inline at::Tensor linalg_vander(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, c10::optional N=c10::nullopt) { + return at::_ops::linalg_vander::redispatch(dispatchKeySet, x, N.has_value() ? c10::make_optional(c10::SymInt(*N)) : c10::nullopt); + } + + // aten::linalg_vander(Tensor x, *, SymInt? N=None) -> Tensor + inline at::Tensor linalg_vander_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, c10::optional N=c10::nullopt) { + return at::_ops::linalg_vander::redispatch(dispatchKeySet, x, N); + } + + // aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) + inline ::std::tuple svd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & U, at::Tensor & S, at::Tensor & V, const at::Tensor & self, bool some=true, bool compute_uv=true) { + return at::_ops::svd_U::redispatch(dispatchKeySet, self, some, compute_uv, U, S, V); + } + + // aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) + inline ::std::tuple svd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, bool compute_uv, at::Tensor & U, at::Tensor & S, at::Tensor & V) { + return at::_ops::svd_U::redispatch(dispatchKeySet, self, some, compute_uv, U, S, V); + } + + // aten::svd(Tensor self, bool some=True, bool compute_uv=True) -> (Tensor U, Tensor S, Tensor V) + inline ::std::tuple svd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some=true, bool compute_uv=true) { + return at::_ops::svd::redispatch(dispatchKeySet, self, some, compute_uv); + } + + // aten::swapaxes(Tensor(a) self, int axis0, int axis1) -> Tensor(a) + inline at::Tensor swapaxes(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t axis0, int64_t axis1) { + return at::_ops::swapaxes::redispatch(dispatchKeySet, self, axis0, axis1); + } + + // aten::swapaxes_(Tensor(a!) self, int axis0, int axis1) -> Tensor(a!) + inline at::Tensor & swapaxes_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t axis0, int64_t axis1) { + return at::_ops::swapaxes_::redispatch(dispatchKeySet, self, axis0, axis1); + } + + // aten::swapdims(Tensor(a) self, int dim0, int dim1) -> Tensor(a) + inline at::Tensor swapdims(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::swapdims::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::swapdims_(Tensor(a!) self, int dim0, int dim1) -> Tensor(a!) + inline at::Tensor & swapdims_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::swapdims_::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool upper=false) { + return at::_ops::cholesky_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { + return at::_ops::cholesky_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::cholesky(Tensor self, bool upper=False) -> Tensor + inline at::Tensor cholesky(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper=false) { + return at::_ops::cholesky::redispatch(dispatchKeySet, self, upper); + } + + // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & input2, bool upper=false) { + return at::_ops::cholesky_solve_out::redispatch(dispatchKeySet, self, input2, upper, out); + } + + // aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, bool upper, at::Tensor & out) { + return at::_ops::cholesky_solve_out::redispatch(dispatchKeySet, self, input2, upper, out); + } + + // aten::cholesky_solve(Tensor self, Tensor input2, bool upper=False) -> Tensor + inline at::Tensor cholesky_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, bool upper=false) { + return at::_ops::cholesky_solve::redispatch(dispatchKeySet, self, input2, upper); + } + + // aten::_cholesky_solve_helper(Tensor self, Tensor A, bool upper) -> Tensor + inline at::Tensor _cholesky_solve_helper(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper) { + return at::_ops::_cholesky_solve_helper::redispatch(dispatchKeySet, self, A, upper); + } + + // aten::cholesky_inverse(Tensor self, bool upper=False) -> Tensor + inline at::Tensor cholesky_inverse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper=false) { + return at::_ops::cholesky_inverse::redispatch(dispatchKeySet, self, upper); + } + + // aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_inverse_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool upper=false) { + return at::_ops::cholesky_inverse_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cholesky_inverse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { + return at::_ops::cholesky_inverse_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) + inline ::std::tuple qr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & Q, at::Tensor & R, const at::Tensor & self, bool some=true) { + return at::_ops::qr_Q::redispatch(dispatchKeySet, self, some, Q, R); + } + + // aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) + inline ::std::tuple qr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some, at::Tensor & Q, at::Tensor & R) { + return at::_ops::qr_Q::redispatch(dispatchKeySet, self, some, Q, R); + } + + // aten::qr(Tensor self, bool some=True) -> (Tensor Q, Tensor R) + inline ::std::tuple qr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool some=true) { + return at::_ops::qr::redispatch(dispatchKeySet, self, some); + } + + // aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau) + inline ::std::tuple geqrf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & a, at::Tensor & tau, const at::Tensor & self) { + return at::_ops::geqrf_a::redispatch(dispatchKeySet, self, a, tau); + } + + // aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau) + inline ::std::tuple geqrf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & a, at::Tensor & tau) { + return at::_ops::geqrf_a::redispatch(dispatchKeySet, self, a, tau); + } + + // aten::geqrf(Tensor self) -> (Tensor a, Tensor tau) + inline ::std::tuple geqrf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::geqrf::redispatch(dispatchKeySet, self); + } + + // aten::orgqr(Tensor self, Tensor input2) -> Tensor + inline at::Tensor orgqr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2) { + return at::_ops::orgqr::redispatch(dispatchKeySet, self, input2); + } + + // aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & orgqr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & input2) { + return at::_ops::orgqr_out::redispatch(dispatchKeySet, self, input2, out); + } + + // aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & orgqr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, at::Tensor & out) { + return at::_ops::orgqr_out::redispatch(dispatchKeySet, self, input2, out); + } + + // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ormqr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left=true, bool transpose=false) { + return at::_ops::ormqr_out::redispatch(dispatchKeySet, self, input2, input3, left, transpose, out); + } + + // aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ormqr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose, at::Tensor & out) { + return at::_ops::ormqr_out::redispatch(dispatchKeySet, self, input2, input3, left, transpose, out); + } + + // aten::ormqr(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False) -> Tensor + inline at::Tensor ormqr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left=true, bool transpose=false) { + return at::_ops::ormqr::redispatch(dispatchKeySet, self, input2, input3, left, transpose); + } + + // aten::_lu_with_info(Tensor self, bool pivot=True, bool check_errors=True) -> (Tensor LU, Tensor pivots, Tensor info) + inline ::std::tuple _lu_with_info(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool pivot=true, bool check_errors=true) { + return at::_ops::_lu_with_info::redispatch(dispatchKeySet, self, pivot, check_errors); + } + + // aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lu_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) { + return at::_ops::lu_solve_out::redispatch(dispatchKeySet, self, LU_data, LU_pivots, out); + } + + // aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lu_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots, at::Tensor & out) { + return at::_ops::lu_solve_out::redispatch(dispatchKeySet, self, LU_data, LU_pivots, out); + } + + // aten::lu_solve(Tensor self, Tensor LU_data, Tensor LU_pivots) -> Tensor + inline at::Tensor lu_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) { + return at::_ops::lu_solve::redispatch(dispatchKeySet, self, LU_data, LU_pivots); + } + + // aten::lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U) + inline ::std::tuple lu_unpack(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) { + return at::_ops::lu_unpack::redispatch(dispatchKeySet, LU_data, LU_pivots, unpack_data, unpack_pivots); + } + + // aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) + inline ::std::tuple lu_unpack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) { + return at::_ops::lu_unpack_out::redispatch(dispatchKeySet, LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U); + } + + // aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) + inline ::std::tuple lu_unpack_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U) { + return at::_ops::lu_unpack_out::redispatch(dispatchKeySet, LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U); + } + + // aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multinomial_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t num_samples, bool replacement=false, c10::optional generator=c10::nullopt) { + return at::_ops::multinomial_out::redispatch(dispatchKeySet, self, num_samples, replacement, generator, out); + } + + // aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multinomial_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional generator, at::Tensor & out) { + return at::_ops::multinomial_out::redispatch(dispatchKeySet, self, num_samples, replacement, generator, out); + } + + // aten::multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor + inline at::Tensor multinomial(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t num_samples, bool replacement=false, c10::optional generator=c10::nullopt) { + return at::_ops::multinomial::redispatch(dispatchKeySet, self, num_samples, replacement, generator); + } + + // aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lgamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::lgamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lgamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::lgamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::lgamma_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & lgamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::lgamma_::redispatch(dispatchKeySet, self); + } + + // aten::lgamma(Tensor self) -> Tensor + inline at::Tensor lgamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::lgamma::redispatch(dispatchKeySet, self); + } + + // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & digamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::digamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & digamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::digamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::digamma(Tensor self) -> Tensor + inline at::Tensor digamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::digamma::redispatch(dispatchKeySet, self); + } + + // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & polygamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, const at::Tensor & self) { + return at::_ops::polygamma_out::redispatch(dispatchKeySet, n, self, out); + } + + // aten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & polygamma_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { + return at::_ops::polygamma_out::redispatch(dispatchKeySet, n, self, out); + } + + // aten::polygamma(int n, Tensor self) -> Tensor + inline at::Tensor polygamma(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self) { + return at::_ops::polygamma::redispatch(dispatchKeySet, n, self); + } + + // aten::polygamma_(Tensor(a!) self, int n) -> Tensor(a!) + inline at::Tensor & polygamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t n) { + return at::_ops::polygamma_::redispatch(dispatchKeySet, self, n); + } + + // aten::erfinv(Tensor self) -> Tensor + inline at::Tensor erfinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::erfinv::redispatch(dispatchKeySet, self); + } + + // aten::erfinv_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & erfinv_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::erfinv_::redispatch(dispatchKeySet, self); + } + + // aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erfinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::erfinv_out::redispatch(dispatchKeySet, self, out); + } + + // aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & erfinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::erfinv_out::redispatch(dispatchKeySet, self, out); + } + + // aten::i0(Tensor self) -> Tensor + inline at::Tensor i0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::i0::redispatch(dispatchKeySet, self); + } + + // aten::i0_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & i0_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::i0_::redispatch(dispatchKeySet, self); + } + + // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & i0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & i0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sign(Tensor self) -> Tensor + inline at::Tensor sign(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::sign::redispatch(dispatchKeySet, self); + } + + // aten::sign_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & sign_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::sign_::redispatch(dispatchKeySet, self); + } + + // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sign_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::sign_out::redispatch(dispatchKeySet, self, out); + } + + // aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sign_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::sign_out::redispatch(dispatchKeySet, self, out); + } + + // aten::signbit(Tensor self) -> Tensor + inline at::Tensor signbit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::signbit::redispatch(dispatchKeySet, self); + } + + // aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & signbit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::signbit_out::redispatch(dispatchKeySet, self, out); + } + + // aten::signbit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & signbit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::signbit_out::redispatch(dispatchKeySet, self, out); + } + + // aten::dist(Tensor self, Tensor other, Scalar p=2) -> Tensor + inline at::Tensor dist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & p=2) { + return at::_ops::dist::redispatch(dispatchKeySet, self, other, p); + } + + // aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atan2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::atan2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & atan2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::atan2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::atan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & atan2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::atan2_::redispatch(dispatchKeySet, self, other); + } + + // aten::atan2(Tensor self, Tensor other) -> Tensor + inline at::Tensor atan2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::atan2::redispatch(dispatchKeySet, self, other); + } + + // aten::arctan2(Tensor self, Tensor other) -> Tensor + inline at::Tensor arctan2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::arctan2::redispatch(dispatchKeySet, self, other); + } + + // aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctan2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::arctan2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::arctan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & arctan2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::arctan2_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::arctan2_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & arctan2_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::arctan2_::redispatch(dispatchKeySet, self, other); + } + + // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lerp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { + return at::_ops::lerp_Scalar_out::redispatch(dispatchKeySet, self, end, weight, out); + } + + // aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lerp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight, at::Tensor & out) { + return at::_ops::lerp_Scalar_out::redispatch(dispatchKeySet, self, end, weight, out); + } + + // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lerp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { + return at::_ops::lerp_Tensor_out::redispatch(dispatchKeySet, self, end, weight, out); + } + + // aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lerp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight, at::Tensor & out) { + return at::_ops::lerp_Tensor_out::redispatch(dispatchKeySet, self, end, weight, out); + } + + // aten::lerp.Scalar(Tensor self, Tensor end, Scalar weight) -> Tensor + inline at::Tensor lerp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { + return at::_ops::lerp_Scalar::redispatch(dispatchKeySet, self, end, weight); + } + + // aten::lerp.Tensor(Tensor self, Tensor end, Tensor weight) -> Tensor + inline at::Tensor lerp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { + return at::_ops::lerp_Tensor::redispatch(dispatchKeySet, self, end, weight); + } + + // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & histc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t bins=100, const at::Scalar & min=0, const at::Scalar & max=0) { + return at::_ops::histc_out::redispatch(dispatchKeySet, self, bins, min, max, out); + } + + // aten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & histc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max, at::Tensor & out) { + return at::_ops::histc_out::redispatch(dispatchKeySet, self, bins, min, max, out); + } + + // aten::histc(Tensor self, int bins=100, Scalar min=0, Scalar max=0) -> Tensor + inline at::Tensor histc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins=100, const at::Scalar & min=0, const at::Scalar & max=0) { + return at::_ops::histc::redispatch(dispatchKeySet, self, bins, min, max); + } + + // aten::histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) + inline ::std::tuple histogram_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, const at::Tensor & bins, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogram_bins_tensor_out::redispatch(dispatchKeySet, self, bins, weight, density, hist, bin_edges); + } + + // aten::histogram.bins_tensor_out(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) + inline ::std::tuple histogram_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & bins, const c10::optional & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { + return at::_ops::histogram_bins_tensor_out::redispatch(dispatchKeySet, self, bins, weight, density, hist, bin_edges); + } + + // aten::histogram.bins_tensor(Tensor self, Tensor bins, *, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) + inline ::std::tuple histogram(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & bins, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogram_bins_tensor::redispatch(dispatchKeySet, self, bins, weight, density); + } + + // aten::histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) + inline ::std::tuple histogram_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & hist, at::Tensor & bin_edges, const at::Tensor & self, int64_t bins=100, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogram_bin_ct_out::redispatch(dispatchKeySet, self, bins, range, weight, density, hist, bin_edges); + } + + // aten::histogram.bin_ct_out(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) hist, Tensor(b!) bin_edges) -> (Tensor(a!) hist, Tensor(b!) bin_edges) + inline ::std::tuple histogram_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, c10::optional> range, const c10::optional & weight, bool density, at::Tensor & hist, at::Tensor & bin_edges) { + return at::_ops::histogram_bin_ct_out::redispatch(dispatchKeySet, self, bins, range, weight, density, hist, bin_edges); + } + + // aten::histogram.bin_ct(Tensor self, int bins=100, *, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor bin_edges) + inline ::std::tuple histogram(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins=100, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogram_bin_ct::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::_histogramdd_bin_edges(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor[] + inline ::std::vector _histogramdd_bin_edges(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_bin_edges::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::_histogramdd_from_bin_cts(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False) -> Tensor + inline at::Tensor _histogramdd_from_bin_cts(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_from_bin_cts::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::_histogramdd_from_bin_tensors(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False) -> Tensor + inline at::Tensor _histogramdd_from_bin_tensors(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_from_bin_tensors::redispatch(dispatchKeySet, self, bins, weight, density); + } + + // aten::histogramdd(Tensor self, int[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) + inline ::std::tuple> histogramdd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogramdd::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::histogramdd.int_bins(Tensor self, int bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) + inline ::std::tuple> histogramdd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogramdd_int_bins::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::histogramdd.TensorList_bins(Tensor self, Tensor[] bins, float[]? range=None, Tensor? weight=None, bool density=False) -> (Tensor hist, Tensor[] bin_edges) + inline ::std::tuple> histogramdd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::histogramdd_TensorList_bins::redispatch(dispatchKeySet, self, bins, range, weight, density); + } + + // aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::fmod_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::fmod_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmod.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor fmod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::fmod_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::fmod_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & fmod_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::fmod__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmod_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::fmod_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmod.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor fmod(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmod_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::fmod_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & fmod_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmod__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hypot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::hypot_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::hypot.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hypot_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::hypot_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::hypot(Tensor self, Tensor other) -> Tensor + inline at::Tensor hypot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::hypot::redispatch(dispatchKeySet, self, other); + } + + // aten::hypot_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & hypot_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::hypot_::redispatch(dispatchKeySet, self, other); + } + + // aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & igamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::igamma_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::igamma.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & igamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::igamma_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::igamma(Tensor self, Tensor other) -> Tensor + inline at::Tensor igamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::igamma::redispatch(dispatchKeySet, self, other); + } + + // aten::igamma_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & igamma_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::igamma_::redispatch(dispatchKeySet, self, other); + } + + // aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & igammac_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::igammac_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::igammac.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & igammac_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::igammac_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::igammac(Tensor self, Tensor other) -> Tensor + inline at::Tensor igammac(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::igammac::redispatch(dispatchKeySet, self, other); + } + + // aten::igammac_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & igammac_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::igammac_::redispatch(dispatchKeySet, self, other); + } + + // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nextafter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::nextafter_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::nextafter.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nextafter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::nextafter_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::nextafter(Tensor self, Tensor other) -> Tensor + inline at::Tensor nextafter(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::nextafter::redispatch(dispatchKeySet, self, other); + } + + // aten::nextafter_(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & nextafter_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::nextafter_::redispatch(dispatchKeySet, self, other); + } + + // aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::remainder_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::remainder_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::remainder.Scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor remainder(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::remainder_Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::remainder_.Scalar(Tensor(a!) self, Scalar other) -> Tensor(a!) + inline at::Tensor & remainder_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & other) { + return at::_ops::remainder__Scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::remainder_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::remainder_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::remainder.Tensor(Tensor self, Tensor other) -> Tensor + inline at::Tensor remainder(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::remainder_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::remainder_.Tensor(Tensor(a!) self, Tensor other) -> Tensor(a!) + inline at::Tensor & remainder_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & other) { + return at::_ops::remainder__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::remainder.Scalar_Tensor(Scalar self, Tensor other) -> Tensor + inline at::Tensor remainder(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::remainder_Scalar_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::min(Tensor self) -> Tensor + inline at::Tensor min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::min::redispatch(dispatchKeySet, self); + } + + // aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::min_unary_out::redispatch(dispatchKeySet, self, out); + } + + // aten::min.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::min_unary_out::redispatch(dispatchKeySet, self, out); + } + + // aten::fmin(Tensor self, Tensor other) -> Tensor + inline at::Tensor fmin(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmin::redispatch(dispatchKeySet, self, other); + } + + // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmin_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmin_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmin_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::fmin_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::max(Tensor self) -> Tensor + inline at::Tensor max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::max::redispatch(dispatchKeySet, self); + } + + // aten::fmax(Tensor self, Tensor other) -> Tensor + inline at::Tensor fmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmax::redispatch(dispatchKeySet, self, other); + } + + // aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::fmax_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::fmax.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::fmax_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::maximum(Tensor self, Tensor other) -> Tensor + inline at::Tensor maximum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::maximum::redispatch(dispatchKeySet, self, other); + } + + // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & maximum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::maximum_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::maximum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & maximum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::maximum_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::max.other(Tensor self, Tensor other) -> Tensor + inline at::Tensor max(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::max_other::redispatch(dispatchKeySet, self, other); + } + + // aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::max_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::max_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::max_unary_out::redispatch(dispatchKeySet, self, out); + } + + // aten::max.unary_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::max_unary_out::redispatch(dispatchKeySet, self, out); + } + + // aten::minimum(Tensor self, Tensor other) -> Tensor + inline at::Tensor minimum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::minimum::redispatch(dispatchKeySet, self, other); + } + + // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & minimum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::minimum_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::minimum.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & minimum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::minimum_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & min_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::min_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & min_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::min_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::min.other(Tensor self, Tensor other) -> Tensor + inline at::Tensor min(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::min_other::redispatch(dispatchKeySet, self, other); + } + + // aten::quantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor + inline at::Tensor quantile(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::quantile::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); + } + + // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantile_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::quantile_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::quantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantile_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { + return at::_ops::quantile_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::quantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor + inline at::Tensor quantile(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::quantile_scalar::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); + } + + // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantile_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::quantile_scalar_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::quantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantile_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { + return at::_ops::quantile_scalar_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::nanquantile(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor + inline at::Tensor nanquantile(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::nanquantile::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); + } + + // aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanquantile_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::nanquantile_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::nanquantile.out(Tensor self, Tensor q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanquantile_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & q, c10::optional dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { + return at::_ops::nanquantile_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::nanquantile.scalar(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear') -> Tensor + inline at::Tensor nanquantile(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::nanquantile_scalar::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation); + } + + // aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanquantile_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double q, c10::optional dim=c10::nullopt, bool keepdim=false, c10::string_view interpolation="linear") { + return at::_ops::nanquantile_scalar_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::nanquantile.scalar_out(Tensor self, float q, int? dim=None, bool keepdim=False, *, str interpolation='linear', Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanquantile_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double q, c10::optional dim, bool keepdim, c10::string_view interpolation, at::Tensor & out) { + return at::_ops::nanquantile_scalar_out::redispatch(dispatchKeySet, self, q, dim, keepdim, interpolation, out); + } + + // aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim=-1, bool descending=false) { + return at::_ops::sort_values::redispatch(dispatchKeySet, self, dim, descending, values, indices); + } + + // aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { + return at::_ops::sort_values::redispatch(dispatchKeySet, self, dim, descending, values, indices); + } + + // aten::sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::optional stable, int64_t dim=-1, bool descending=false) { + return at::_ops::sort_values_stable::redispatch(dispatchKeySet, self, stable, dim, descending, values, indices); + } + + // aten::sort.values_stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional stable, int64_t dim, bool descending, at::Tensor & values, at::Tensor & indices) { + return at::_ops::sort_values_stable::redispatch(dispatchKeySet, self, stable, dim, descending, values, indices); + } + + // aten::sort(Tensor self, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) + inline ::std::tuple sort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=-1, bool descending=false) { + return at::_ops::sort::redispatch(dispatchKeySet, self, dim, descending); + } + + // aten::sort.stable(Tensor self, *, bool? stable, int dim=-1, bool descending=False) -> (Tensor values, Tensor indices) + inline ::std::tuple sort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional stable, int64_t dim=-1, bool descending=false) { + return at::_ops::sort_stable::redispatch(dispatchKeySet, self, stable, dim, descending); + } + + // aten::sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, at::Dimname dim, bool descending=false) { + return at::_ops::sort_dimname_values::redispatch(dispatchKeySet, self, dim, descending, values, indices); + } + + // aten::sort.dimname_values(Tensor self, Dimname dim, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) { + return at::_ops::sort_dimname_values::redispatch(dispatchKeySet, self, dim, descending, values, indices); + } + + // aten::sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::optional stable, at::Dimname dim, bool descending=false) { + return at::_ops::sort_dimname_values_stable::redispatch(dispatchKeySet, self, stable, dim, descending, values, indices); + } + + // aten::sort.dimname_values_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple sort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional stable, at::Dimname dim, bool descending, at::Tensor & values, at::Tensor & indices) { + return at::_ops::sort_dimname_values_stable::redispatch(dispatchKeySet, self, stable, dim, descending, values, indices); + } + + // aten::sort.dimname(Tensor self, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) + inline ::std::tuple sort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool descending=false) { + return at::_ops::sort_dimname::redispatch(dispatchKeySet, self, dim, descending); + } + + // aten::sort.dimname_stable(Tensor self, *, bool? stable, Dimname dim, bool descending=False) -> (Tensor values, Tensor indices) + inline ::std::tuple sort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional stable, at::Dimname dim, bool descending=false) { + return at::_ops::sort_dimname_stable::redispatch(dispatchKeySet, self, stable, dim, descending); + } + + // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & msort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::msort_out::redispatch(dispatchKeySet, self, out); + } + + // aten::msort.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & msort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::msort_out::redispatch(dispatchKeySet, self, out); + } + + // aten::msort(Tensor self) -> Tensor + inline at::Tensor msort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::msort::redispatch(dispatchKeySet, self); + } + + // aten::argsort(Tensor self, int dim=-1, bool descending=False) -> Tensor + inline at::Tensor argsort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=-1, bool descending=false) { + return at::_ops::argsort::redispatch(dispatchKeySet, self, dim, descending); + } + + // aten::argsort.stable(Tensor self, *, bool stable, int dim=-1, bool descending=False) -> Tensor + inline at::Tensor argsort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool stable, int64_t dim=-1, bool descending=false) { + return at::_ops::argsort_stable::redispatch(dispatchKeySet, self, stable, dim, descending); + } + + // aten::argsort.dimname(Tensor self, Dimname dim, bool descending=False) -> Tensor + inline at::Tensor argsort(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, bool descending=false) { + return at::_ops::argsort_dimname::redispatch(dispatchKeySet, self, dim, descending); + } + + // aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple topk_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) { + return at::_ops::topk_values::redispatch(dispatchKeySet, self, k, dim, largest, sorted, values, indices); + } + + // aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple topk_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { + return at::_ops::topk_values::redispatch(dispatchKeySet, self, k, dim, largest, sorted, values, indices); + } + + // aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple topk_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & values, at::Tensor & indices, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) { + return at::_ops::topk_values::redispatch(dispatchKeySet, self, k, dim, largest, sorted, values, indices); + } + + // aten::topk.values(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices) + inline ::std::tuple topk_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted, at::Tensor & values, at::Tensor & indices) { + return at::_ops::topk_values::redispatch(dispatchKeySet, self, k, dim, largest, sorted, values, indices); + } + + // aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) + inline ::std::tuple topk(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, int64_t dim=-1, bool largest=true, bool sorted=true) { + return at::_ops::topk::redispatch(dispatchKeySet, self, k, dim, largest, sorted); + } + + // aten::topk(Tensor self, SymInt k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices) + inline ::std::tuple topk_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt k, int64_t dim=-1, bool largest=true, bool sorted=true) { + return at::_ops::topk::redispatch(dispatchKeySet, self, k, dim, largest, sorted); + } + + // aten::all(Tensor self) -> Tensor + inline at::Tensor all(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::all::redispatch(dispatchKeySet, self); + } + + // aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::all_all_out::redispatch(dispatchKeySet, self, out); + } + + // aten::all.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & all_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::all_all_out::redispatch(dispatchKeySet, self, out); + } + + // aten::any(Tensor self) -> Tensor + inline at::Tensor any(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::any::redispatch(dispatchKeySet, self); + } + + // aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::any_all_out::redispatch(dispatchKeySet, self, out); + } + + // aten::any.all_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & any_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::any_all_out::redispatch(dispatchKeySet, self, out); + } + + // aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & renorm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { + return at::_ops::renorm_out::redispatch(dispatchKeySet, self, p, dim, maxnorm, out); + } + + // aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & renorm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm, at::Tensor & out) { + return at::_ops::renorm_out::redispatch(dispatchKeySet, self, p, dim, maxnorm, out); + } + + // aten::renorm(Tensor self, Scalar p, int dim, Scalar maxnorm) -> Tensor + inline at::Tensor renorm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { + return at::_ops::renorm::redispatch(dispatchKeySet, self, p, dim, maxnorm); + } + + // aten::renorm_(Tensor(a!) self, Scalar p, int dim, Scalar maxnorm) -> Tensor(a!) + inline at::Tensor & renorm_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { + return at::_ops::renorm_::redispatch(dispatchKeySet, self, p, dim, maxnorm); + } + + // aten::unfold(Tensor(a) self, int dimension, int size, int step) -> Tensor(a) + inline at::Tensor unfold(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { + return at::_ops::unfold::redispatch(dispatchKeySet, self, dimension, size, step); + } + + // aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor + inline at::Tensor unfold_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { + return at::_ops::unfold_backward::redispatch(dispatchKeySet, grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step); + } + + // aten::unfold_backward(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step) -> Tensor + inline at::Tensor unfold_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { + return at::_ops::unfold_backward::redispatch(dispatchKeySet, grad_in, input_sizes, dim, size, step); + } + + // aten::equal(Tensor self, Tensor other) -> bool + inline bool equal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::equal::redispatch(dispatchKeySet, self, other); + } + + // aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::pow_Tensor_Tensor_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { + return at::_ops::pow_Tensor_Tensor_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor + inline at::Tensor pow(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::pow_Tensor_Tensor::redispatch(dispatchKeySet, self, exponent); + } + + // aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & exponent) { + return at::_ops::pow_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { + return at::_ops::pow_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Scalar(Scalar self, Tensor exponent) -> Tensor + inline at::Tensor pow(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent) { + return at::_ops::pow_Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::pow_Tensor_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pow_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { + return at::_ops::pow_Tensor_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor + inline at::Tensor pow(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::pow_Tensor_Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::pow_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) + inline at::Tensor & pow_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::pow__Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::pow_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) + inline at::Tensor & pow_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::pow__Tensor::redispatch(dispatchKeySet, self, exponent); + } + + // aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::float_power_Tensor_Tensor_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent, at::Tensor & out) { + return at::_ops::float_power_Tensor_Tensor_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Tensor_Tensor(Tensor self, Tensor exponent) -> Tensor + inline at::Tensor float_power(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::float_power_Tensor_Tensor::redispatch(dispatchKeySet, self, exponent); + } + + // aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & exponent) { + return at::_ops::float_power_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent, at::Tensor & out) { + return at::_ops::float_power_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Scalar(Scalar self, Tensor exponent) -> Tensor + inline at::Tensor float_power(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & exponent) { + return at::_ops::float_power_Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::float_power_Tensor_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & float_power_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent, at::Tensor & out) { + return at::_ops::float_power_Tensor_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::float_power.Tensor_Scalar(Tensor self, Scalar exponent) -> Tensor + inline at::Tensor float_power(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::float_power_Tensor_Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::float_power_.Scalar(Tensor(a!) self, Scalar exponent) -> Tensor(a!) + inline at::Tensor & float_power_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & exponent) { + return at::_ops::float_power__Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::float_power_.Tensor(Tensor(a!) self, Tensor exponent) -> Tensor(a!) + inline at::Tensor & float_power_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & exponent) { + return at::_ops::float_power__Tensor::redispatch(dispatchKeySet, self, exponent); + } + + // aten::normal_(Tensor(a!) self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & normal_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double mean=0, double std=1, c10::optional generator=c10::nullopt) { + return at::_ops::normal_::redispatch(dispatchKeySet, self, mean, std, generator); + } + + // aten::normal_functional(Tensor self, float mean=0, float std=1, *, Generator? generator=None) -> Tensor + inline at::Tensor normal_functional(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean=0, double std=1, c10::optional generator=c10::nullopt) { + return at::_ops::normal_functional::redispatch(dispatchKeySet, self, mean, std, generator); + } + + // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & mean, double std=1, c10::optional generator=c10::nullopt) { + return at::_ops::normal_Tensor_float_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.Tensor_float_out(Tensor mean, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, double std, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_Tensor_float_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.Tensor_float(Tensor mean, float std=1, *, Generator? generator=None) -> Tensor + inline at::Tensor normal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, double std=1, c10::optional generator=c10::nullopt) { + return at::_ops::normal_Tensor_float::redispatch(dispatchKeySet, mean, std, generator); + } + + // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, double mean, const at::Tensor & std, c10::optional generator=c10::nullopt) { + return at::_ops::normal_float_Tensor_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.float_Tensor_out(float mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_outf(c10::DispatchKeySet dispatchKeySet, double mean, const at::Tensor & std, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_float_Tensor_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.float_Tensor(float mean, Tensor std, *, Generator? generator=None) -> Tensor + inline at::Tensor normal(c10::DispatchKeySet dispatchKeySet, double mean, const at::Tensor & std, c10::optional generator=c10::nullopt) { + return at::_ops::normal_float_Tensor::redispatch(dispatchKeySet, mean, std, generator); + } + + // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & mean, const at::Tensor & std, c10::optional generator=c10::nullopt) { + return at::_ops::normal_Tensor_Tensor_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.Tensor_Tensor_out(Tensor mean, Tensor std, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, const at::Tensor & std, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_Tensor_Tensor_out::redispatch(dispatchKeySet, mean, std, generator, out); + } + + // aten::normal.Tensor_Tensor(Tensor mean, Tensor std, *, Generator? generator=None) -> Tensor + inline at::Tensor normal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & mean, const at::Tensor & std, c10::optional generator=c10::nullopt) { + return at::_ops::normal_Tensor_Tensor::redispatch(dispatchKeySet, mean, std, generator); + } + + // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor normal(c10::DispatchKeySet dispatchKeySet, double mean, double std, at::IntArrayRef size, c10::optional generator=c10::nullopt, at::TensorOptions options={}) { + return at::_ops::normal_float_float::redispatch(dispatchKeySet, mean, std, c10::fromIntArrayRefSlow(size), generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor normal(c10::DispatchKeySet dispatchKeySet, double mean, double std, at::IntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::normal_float_float::redispatch(dispatchKeySet, mean, std, c10::fromIntArrayRefSlow(size), generator, dtype, layout, device, pin_memory); + } + + // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor normal_symint(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional generator=c10::nullopt, at::TensorOptions options={}) { + return at::_ops::normal_float_float::redispatch(dispatchKeySet, mean, std, size, generator, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::normal.float_float(float mean, float std, SymInt[] size, *, Generator? generator=None, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor normal_symint(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::normal_float_float::redispatch(dispatchKeySet, mean, std, size, generator, dtype, layout, device, pin_memory); + } + + // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, double mean, double std, at::IntArrayRef size, c10::optional generator=c10::nullopt) { + return at::_ops::normal_float_float_out::redispatch(dispatchKeySet, mean, std, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_outf(c10::DispatchKeySet dispatchKeySet, double mean, double std, at::IntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_float_float_out::redispatch(dispatchKeySet, mean, std, c10::fromIntArrayRefSlow(size), generator, out); + } + + // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, double mean, double std, c10::SymIntArrayRef size, c10::optional generator=c10::nullopt) { + return at::_ops::normal_float_float_out::redispatch(dispatchKeySet, mean, std, size, generator, out); + } + + // aten::normal.float_float_out(float mean, float std, SymInt[] size, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_symint_outf(c10::DispatchKeySet dispatchKeySet, double mean, double std, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_float_float_out::redispatch(dispatchKeySet, mean, std, size, generator, out); + } + + // aten::alias(Tensor(a) self) -> Tensor(a) + inline at::Tensor alias(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::alias::redispatch(dispatchKeySet, self); + } + + // aten::_amp_foreach_non_finite_check_and_unscale_(Tensor(a!)[] self, Tensor(b!) found_inf, Tensor inv_scale) -> () + inline void _amp_foreach_non_finite_check_and_unscale_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale_::redispatch(dispatchKeySet, self, found_inf, inv_scale); + } + + // aten::_amp_update_scale_(Tensor(a!) self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> Tensor(a!) + inline at::Tensor & _amp_update_scale_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { + return at::_ops::_amp_update_scale_::redispatch(dispatchKeySet, self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval); + } + + // aten::_foreach_add.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_add(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_add_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_add_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_add_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_add__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_add.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] + inline ::std::vector _foreach_add(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add_List::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_add_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () + inline void _foreach_add_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add__List::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_add.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_add(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_add_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_add_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_add_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_add__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_add.Tensor(Tensor[] self, Tensor other, *, Scalar alpha=1) -> Tensor[] + inline ::std::vector _foreach_add(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add_Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_add_.Tensor(Tensor(a!)[] self, Tensor other, *, Scalar alpha=1) -> () + inline void _foreach_add_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add__Tensor::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_sub.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_sub(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_sub_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_sub_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_sub_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_sub__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_sub.List(Tensor[] self, Tensor[] other, *, Scalar alpha=1) -> Tensor[] + inline ::std::vector _foreach_sub(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_sub_List::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_sub_.List(Tensor(a!)[] self, Tensor[] other, *, Scalar alpha=1) -> () + inline void _foreach_sub_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_sub__List::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_foreach_sub.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_sub(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_sub_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_sub_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_sub_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_sub__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_mul.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_mul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_mul_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_mul_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_mul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_mul__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_mul.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_mul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_mul_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_mul_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_mul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_mul__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_mul.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_mul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_mul_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_mul_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_mul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_mul__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_mul.Tensor(Tensor[] self, Tensor other) -> Tensor[] + inline ::std::vector _foreach_mul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_mul_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_mul_.Tensor(Tensor(a!)[] self, Tensor other) -> () + inline void _foreach_mul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_mul__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_div.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_div(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_div_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_div_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_div_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_div__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_div.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_div(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_div_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_div_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_div_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_div__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_div.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_div(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_div_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_div_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_div_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_div__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_div.Tensor(Tensor[] self, Tensor other) -> Tensor[] + inline ::std::vector _foreach_div(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_div_Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_div_.Tensor(Tensor(a!)[] self, Tensor other) -> () + inline void _foreach_div_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_div__Tensor::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_clamp_max.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_clamp_max(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_max_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_clamp_max_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_clamp_max_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_max__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_clamp_max.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_clamp_max(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_max_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_clamp_max_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_clamp_max_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_max__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_clamp_max.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_clamp_max(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_max_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_clamp_max_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_clamp_max_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_max__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_clamp_min.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_clamp_min(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_min_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_clamp_min_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_clamp_min_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_min__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_clamp_min.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_clamp_min(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_min_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_clamp_min_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_clamp_min_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_min__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_clamp_min.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_clamp_min(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_min_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_clamp_min_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_clamp_min_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_min__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_maximum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_maximum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_maximum_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_maximum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_maximum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_maximum__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_maximum.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_maximum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_maximum_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_maximum_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_maximum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_maximum__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_maximum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_maximum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_maximum_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_maximum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_maximum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_maximum__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_minimum.Scalar(Tensor[] self, Scalar scalar) -> Tensor[] + inline ::std::vector _foreach_minimum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_minimum_Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_minimum_.Scalar(Tensor(a!)[] self, Scalar scalar) -> () + inline void _foreach_minimum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_minimum__Scalar::redispatch(dispatchKeySet, self, scalar); + } + + // aten::_foreach_minimum.List(Tensor[] self, Tensor[] other) -> Tensor[] + inline ::std::vector _foreach_minimum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_minimum_List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_minimum_.List(Tensor(a!)[] self, Tensor[] other) -> () + inline void _foreach_minimum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_minimum__List::redispatch(dispatchKeySet, self, other); + } + + // aten::_foreach_minimum.ScalarList(Tensor[] self, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_minimum(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_minimum_ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_minimum_.ScalarList(Tensor(a!)[] self, Scalar[] scalars) -> () + inline void _foreach_minimum_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_minimum__ScalarList::redispatch(dispatchKeySet, self, scalars); + } + + // aten::_foreach_addcdiv.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] + inline ::std::vector _foreach_addcdiv(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcdiv_Scalar::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::_foreach_addcdiv.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_addcdiv(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcdiv_ScalarList::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcdiv.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] + inline ::std::vector _foreach_addcdiv(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcdiv_Tensor::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcdiv_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () + inline void _foreach_addcdiv_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcdiv__Scalar::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::_foreach_addcdiv_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () + inline void _foreach_addcdiv_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcdiv__ScalarList::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcdiv_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () + inline void _foreach_addcdiv_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcdiv__Tensor::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcmul.Scalar(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> Tensor[] + inline ::std::vector _foreach_addcmul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcmul_Scalar::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::_foreach_addcmul.ScalarList(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> Tensor[] + inline ::std::vector _foreach_addcmul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcmul_ScalarList::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcmul.Tensor(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> Tensor[] + inline ::std::vector _foreach_addcmul(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcmul_Tensor::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcmul_.Scalar(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1) -> () + inline void _foreach_addcmul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcmul__Scalar::redispatch(dispatchKeySet, self, tensor1, tensor2, value); + } + + // aten::_foreach_addcmul_.ScalarList(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars) -> () + inline void _foreach_addcmul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcmul__ScalarList::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_addcmul_.Tensor(Tensor(a!)[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars) -> () + inline void _foreach_addcmul_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcmul__Tensor::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars); + } + + // aten::_foreach_abs(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_abs(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_abs::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_abs_(Tensor(a!)[] self) -> () + inline void _foreach_abs_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_abs_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_acos(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_acos(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_acos::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_acos_(Tensor(a!)[] self) -> () + inline void _foreach_acos_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_acos_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_asin(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_asin(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_asin::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_asin_(Tensor(a!)[] self) -> () + inline void _foreach_asin_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_asin_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_atan(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_atan(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_atan::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_atan_(Tensor(a!)[] self) -> () + inline void _foreach_atan_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_atan_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_ceil(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_ceil(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_ceil::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_ceil_(Tensor(a!)[] self) -> () + inline void _foreach_ceil_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_ceil_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_cos(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_cos(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_cos::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_cos_(Tensor(a!)[] self) -> () + inline void _foreach_cos_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_cos_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_cosh(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_cosh(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_cosh::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_cosh_(Tensor(a!)[] self) -> () + inline void _foreach_cosh_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_cosh_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_erf(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_erf(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_erf::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_erf_(Tensor(a!)[] self) -> () + inline void _foreach_erf_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_erf_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_erfc(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_erfc(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_erfc::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_erfc_(Tensor(a!)[] self) -> () + inline void _foreach_erfc_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_erfc_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_exp(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_exp(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_exp::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_exp_(Tensor(a!)[] self) -> () + inline void _foreach_exp_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_exp_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_expm1(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_expm1(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_expm1::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_expm1_(Tensor(a!)[] self) -> () + inline void _foreach_expm1_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_expm1_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_floor(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_floor(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_floor::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_floor_(Tensor(a!)[] self) -> () + inline void _foreach_floor_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_floor_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_frac(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_frac(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_frac::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_frac_(Tensor(a!)[] self) -> () + inline void _foreach_frac_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_frac_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_lerp.List(Tensor[] self, Tensor[] tensors1, Tensor[] weights) -> Tensor[] + inline ::std::vector _foreach_lerp(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights) { + return at::_ops::_foreach_lerp_List::redispatch(dispatchKeySet, self, tensors1, weights); + } + + // aten::_foreach_lerp_.List(Tensor(a!)[] self, Tensor[] tensors1, Tensor[] weights) -> () + inline void _foreach_lerp_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights) { + return at::_ops::_foreach_lerp__List::redispatch(dispatchKeySet, self, tensors1, weights); + } + + // aten::_foreach_lerp.Scalar(Tensor[] self, Tensor[] tensors1, Scalar weight) -> Tensor[] + inline ::std::vector _foreach_lerp(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { + return at::_ops::_foreach_lerp_Scalar::redispatch(dispatchKeySet, self, tensors1, weight); + } + + // aten::_foreach_lerp_.Scalar(Tensor(a!)[] self, Tensor[] tensors1, Scalar weight) -> () + inline void _foreach_lerp_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { + return at::_ops::_foreach_lerp__Scalar::redispatch(dispatchKeySet, self, tensors1, weight); + } + + // aten::_foreach_lgamma(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_lgamma(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_lgamma::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_lgamma_(Tensor(a!)[] self) -> () + inline void _foreach_lgamma_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_lgamma_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_log(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log_(Tensor(a!)[] self) -> () + inline void _foreach_log_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log10(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_log10(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log10::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log10_(Tensor(a!)[] self) -> () + inline void _foreach_log10_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log10_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log1p(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_log1p(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log1p::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log1p_(Tensor(a!)[] self) -> () + inline void _foreach_log1p_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log1p_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log2(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_log2(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log2::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_log2_(Tensor(a!)[] self) -> () + inline void _foreach_log2_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_log2_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_neg(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_neg(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_neg::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_neg_(Tensor(a!)[] self) -> () + inline void _foreach_neg_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_neg_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_norm.Scalar(Tensor[] self, Scalar ord=2) -> Tensor[] + inline ::std::vector _foreach_norm(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & ord=2) { + return at::_ops::_foreach_norm_Scalar::redispatch(dispatchKeySet, self, ord); + } + + // aten::_foreach_pow.List(Tensor[] self, Tensor[] exponent) -> Tensor[] + inline ::std::vector _foreach_pow(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList exponent) { + return at::_ops::_foreach_pow_List::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow.Scalar(Tensor[] self, Scalar exponent) -> Tensor[] + inline ::std::vector _foreach_pow(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & exponent) { + return at::_ops::_foreach_pow_Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow.ScalarList(Tensor[] self, Scalar[] exponent) -> Tensor[] + inline ::std::vector _foreach_pow(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef exponent) { + return at::_ops::_foreach_pow_ScalarList::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow.ScalarAndTensor(Scalar self, Tensor[] exponent) -> Tensor[] + inline ::std::vector _foreach_pow(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, at::TensorList exponent) { + return at::_ops::_foreach_pow_ScalarAndTensor::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow_.List(Tensor(a!)[] self, Tensor[] exponent) -> () + inline void _foreach_pow_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList exponent) { + return at::_ops::_foreach_pow__List::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow_.Scalar(Tensor(a!)[] self, Scalar exponent) -> () + inline void _foreach_pow_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & exponent) { + return at::_ops::_foreach_pow__Scalar::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_pow_.ScalarList(Tensor(a!)[] self, Scalar[] exponent) -> () + inline void _foreach_pow_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef exponent) { + return at::_ops::_foreach_pow__ScalarList::redispatch(dispatchKeySet, self, exponent); + } + + // aten::_foreach_reciprocal(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_reciprocal(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_reciprocal::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_reciprocal_(Tensor(a!)[] self) -> () + inline void _foreach_reciprocal_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_reciprocal_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_round(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_round(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_round::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_round_(Tensor(a!)[] self) -> () + inline void _foreach_round_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_round_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sigmoid(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_sigmoid(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sigmoid::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sigmoid_(Tensor(a!)[] self) -> () + inline void _foreach_sigmoid_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sigmoid_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sign(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_sign(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sign::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sign_(Tensor(a!)[] self) -> () + inline void _foreach_sign_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sign_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sin(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_sin(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sin::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sin_(Tensor(a!)[] self) -> () + inline void _foreach_sin_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sin_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sinh(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_sinh(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sinh::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sinh_(Tensor(a!)[] self) -> () + inline void _foreach_sinh_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sinh_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sqrt(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_sqrt(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sqrt::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_sqrt_(Tensor(a!)[] self) -> () + inline void _foreach_sqrt_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_sqrt_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_tan(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_tan(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_tan::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_tan_(Tensor(a!)[] self) -> () + inline void _foreach_tan_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_tan_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_tanh(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_tanh(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_tanh::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_tanh_(Tensor(a!)[] self) -> () + inline void _foreach_tanh_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_tanh_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_trunc(Tensor[] self) -> Tensor[] + inline ::std::vector _foreach_trunc(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_trunc::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_trunc_(Tensor(a!)[] self) -> () + inline void _foreach_trunc_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_trunc_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_zero_(Tensor(a!)[] self) -> () + inline void _foreach_zero_(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_zero_::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_copy_(Tensor(a!)[] self, Tensor[] src, bool non_blocking=False) -> () + inline void _foreach_copy_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList src, bool non_blocking=false) { + return at::_ops::_foreach_copy_::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::bucketize.Tensor(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor + inline at::Tensor bucketize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) { + return at::_ops::bucketize_Tensor::redispatch(dispatchKeySet, self, boundaries, out_int32, right); + } + + // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bucketize_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) { + return at::_ops::bucketize_Tensor_out::redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); + } + + // aten::bucketize.Tensor_out(Tensor self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bucketize_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { + return at::_ops::bucketize_Tensor_out::redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); + } + + // aten::bucketize.Scalar(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False) -> Tensor + inline at::Tensor bucketize(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) { + return at::_ops::bucketize_Scalar::redispatch(dispatchKeySet, self, boundaries, out_int32, right); + } + + // aten::searchsorted.Tensor(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor + inline at::Tensor searchsorted(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32=false, bool right=false, c10::optional side=c10::nullopt, const c10::optional & sorter={}) { + return at::_ops::searchsorted_Tensor::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter); + } + + // aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & searchsorted_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32=false, bool right=false, c10::optional side=c10::nullopt, const c10::optional & sorter={}) { + return at::_ops::searchsorted_Tensor_out::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter, out); + } + + // aten::searchsorted.Tensor_out(Tensor sorted_sequence, Tensor self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & searchsorted_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional side, const c10::optional & sorter, at::Tensor & out) { + return at::_ops::searchsorted_Tensor_out::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter, out); + } + + // aten::searchsorted.Scalar(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None) -> Tensor + inline at::Tensor searchsorted(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32=false, bool right=false, c10::optional side=c10::nullopt, const c10::optional & sorter={}) { + return at::_ops::searchsorted_Scalar::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter); + } + + // aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & searchsorted_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32=false, bool right=false, c10::optional side=c10::nullopt, const c10::optional & sorter={}) { + return at::_ops::searchsorted_Scalar_out::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter, out); + } + + // aten::searchsorted.Scalar_out(Tensor sorted_sequence, Scalar self, *, bool out_int32=False, bool right=False, str? side=None, Tensor? sorter=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & searchsorted_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, c10::optional side, const c10::optional & sorter, at::Tensor & out) { + return at::_ops::searchsorted_Scalar_out::redispatch(dispatchKeySet, sorted_sequence, self, out_int32, right, side, sorter, out); + } + + // aten::_convert_indices_from_coo_to_csr(Tensor self, int size, *, bool out_int32=False) -> Tensor + inline at::Tensor _convert_indices_from_coo_to_csr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t size, bool out_int32=false) { + return at::_ops::_convert_indices_from_coo_to_csr::redispatch(dispatchKeySet, self, size, out_int32); + } + + // aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convert_indices_from_coo_to_csr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t size, bool out_int32=false) { + return at::_ops::_convert_indices_from_coo_to_csr_out::redispatch(dispatchKeySet, self, size, out_int32, out); + } + + // aten::_convert_indices_from_coo_to_csr.out(Tensor self, int size, *, bool out_int32=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convert_indices_from_coo_to_csr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t size, bool out_int32, at::Tensor & out) { + return at::_ops::_convert_indices_from_coo_to_csr_out::redispatch(dispatchKeySet, self, size, out_int32, out); + } + + // aten::_convert_indices_from_csr_to_coo(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False) -> Tensor + inline at::Tensor _convert_indices_from_csr_to_coo(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32=false, bool transpose=false) { + return at::_ops::_convert_indices_from_csr_to_coo::redispatch(dispatchKeySet, crow_indices, col_indices, out_int32, transpose); + } + + // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convert_indices_from_csr_to_coo_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32=false, bool transpose=false) { + return at::_ops::_convert_indices_from_csr_to_coo_out::redispatch(dispatchKeySet, crow_indices, col_indices, out_int32, transpose, out); + } + + // aten::_convert_indices_from_csr_to_coo.out(Tensor crow_indices, Tensor col_indices, *, bool out_int32=False, bool transpose=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convert_indices_from_csr_to_coo_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose, at::Tensor & out) { + return at::_ops::_convert_indices_from_csr_to_coo_out::redispatch(dispatchKeySet, crow_indices, col_indices, out_int32, transpose, out); + } + + // aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mse_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::mse_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mse_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { + return at::_ops::mse_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::mse_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor + inline at::Tensor mse_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::mse_loss::redispatch(dispatchKeySet, self, target, reduction); + } + + // aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & mse_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::mse_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, grad_input); + } + + // aten::mse_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & mse_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) { + return at::_ops::mse_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, grad_input); + } + + // aten::mse_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor + inline at::Tensor mse_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::mse_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, reduction); + } + + // aten::l1_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor + inline at::Tensor l1_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::l1_loss::redispatch(dispatchKeySet, self, target, reduction); + } + + // aten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multi_margin_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p=1, const at::Scalar & margin=1, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multi_margin_loss_out::redispatch(dispatchKeySet, self, target, p, margin, weight, reduction, out); + } + + // aten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multi_margin_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight, int64_t reduction, at::Tensor & out) { + return at::_ops::multi_margin_loss_out::redispatch(dispatchKeySet, self, target, p, margin, weight, reduction, out); + } + + // aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensor + inline at::Tensor multi_margin_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p=1, const at::Scalar & margin=1, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multi_margin_loss::redispatch(dispatchKeySet, self, target, p, margin, weight, reduction); + } + + // aten::multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & multi_margin_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multi_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, p, margin, weight, reduction, grad_input); + } + + // aten::multi_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & multi_margin_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight, int64_t reduction, at::Tensor & grad_input) { + return at::_ops::multi_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, p, margin, weight, reduction, grad_input); + } + + // aten::multi_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, Scalar p, Scalar margin, Tensor? weight=None, int reduction=Mean) -> Tensor + inline at::Tensor multi_margin_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multi_margin_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, p, margin, weight, reduction); + } + + // aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multilabel_margin_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multilabel_margin_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & multilabel_margin_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { + return at::_ops::multilabel_margin_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::multilabel_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor + inline at::Tensor multilabel_margin_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::multilabel_margin_loss::redispatch(dispatchKeySet, self, target, reduction); + } + + // aten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple multilabel_margin_loss_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & is_target, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::multilabel_margin_loss_forward_output::redispatch(dispatchKeySet, self, target, reduction, output, is_target); + } + + // aten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple multilabel_margin_loss_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & output, at::Tensor & is_target) { + return at::_ops::multilabel_margin_loss_forward_output::redispatch(dispatchKeySet, self, target, reduction, output, is_target); + } + + // aten::multilabel_margin_loss_forward(Tensor self, Tensor target, int reduction) -> (Tensor output, Tensor is_target) + inline ::std::tuple multilabel_margin_loss_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::multilabel_margin_loss_forward::redispatch(dispatchKeySet, self, target, reduction); + } + + // aten::multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & multilabel_margin_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { + return at::_ops::multilabel_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, is_target, grad_input); + } + + // aten::multilabel_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & multilabel_margin_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input) { + return at::_ops::multilabel_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, is_target, grad_input); + } + + // aten::multilabel_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, Tensor is_target) -> Tensor + inline at::Tensor multilabel_margin_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { + return at::_ops::multilabel_margin_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, reduction, is_target); + } + + // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) { + return at::_ops::nll_loss_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) { + return at::_ops::nll_loss_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) { + return at::_ops::nll_loss_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { + return at::_ops::nll_loss_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss_nd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) { + return at::_ops::nll_loss_nd::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss_nd(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss_nd_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) { + return at::_ops::nll_loss_nd::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) { + return at::_ops::nll_loss::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) { + return at::_ops::nll_loss::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index) { + return at::_ops::nll_loss_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) { + return at::_ops::nll_loss_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss_forward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + return at::_ops::nll_loss_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss_forward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { + return at::_ops::nll_loss_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) + inline ::std::tuple nll_loss_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index) { + return at::_ops::nll_loss_forward::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) + inline ::std::tuple nll_loss_forward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + return at::_ops::nll_loss_forward::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { + return at::_ops::nll_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { + return at::_ops::nll_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor + inline at::Tensor nll_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + + // aten::nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor + inline at::Tensor nll_loss_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + + // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) { + return at::_ops::nll_loss2d_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, at::Tensor & out) { + return at::_ops::nll_loss2d_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) { + return at::_ops::nll_loss2d_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nll_loss2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & out) { + return at::_ops::nll_loss2d_out::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, out); + } + + // aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, int64_t ignore_index=-100) { + return at::_ops::nll_loss2d::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100) -> Tensor + inline at::Tensor nll_loss2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, int64_t reduction=at::Reduction::Mean, c10::SymInt ignore_index=-100) { + return at::_ops::nll_loss2d::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss2d_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index) { + return at::_ops::nll_loss2d_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss2d_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, at::Tensor & output, at::Tensor & total_weight) { + return at::_ops::nll_loss2d_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss2d_forward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & total_weight, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + return at::_ops::nll_loss2d_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple nll_loss2d_forward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, at::Tensor & output, at::Tensor & total_weight) { + return at::_ops::nll_loss2d_forward_output::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index, output, total_weight); + } + + // aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) + inline ::std::tuple nll_loss2d_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index) { + return at::_ops::nll_loss2d_forward::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index) -> (Tensor output, Tensor total_weight) + inline ::std::tuple nll_loss2d_forward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + return at::_ops::nll_loss2d_forward::redispatch(dispatchKeySet, self, target, weight, reduction, ignore_index); + } + + // aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { + return at::_ops::nll_loss2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & nll_loss2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input) { + return at::_ops::nll_loss2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight, grad_input); + } + + // aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor + inline at::Tensor nll_loss2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, int64_t ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss2d_backward::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + + // aten::nll_loss2d_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor + inline at::Tensor nll_loss2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + return at::_ops::nll_loss2d_backward::redispatch(dispatchKeySet, grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + + // aten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & smooth_l1_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double beta=1.0) { + return at::_ops::smooth_l1_loss_out::redispatch(dispatchKeySet, self, target, reduction, beta, out); + } + + // aten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, float beta=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & smooth_l1_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & out) { + return at::_ops::smooth_l1_loss_out::redispatch(dispatchKeySet, self, target, reduction, beta, out); + } + + // aten::smooth_l1_loss(Tensor self, Tensor target, int reduction=Mean, float beta=1.0) -> Tensor + inline at::Tensor smooth_l1_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double beta=1.0) { + return at::_ops::smooth_l1_loss::redispatch(dispatchKeySet, self, target, reduction, beta); + } + + // aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & smooth_l1_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { + return at::_ops::smooth_l1_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, beta, grad_input); + } + + // aten::smooth_l1_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & smooth_l1_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta, at::Tensor & grad_input) { + return at::_ops::smooth_l1_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, beta, grad_input); + } + + // aten::smooth_l1_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float beta) -> Tensor + inline at::Tensor smooth_l1_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { + return at::_ops::smooth_l1_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, reduction, beta); + } + + // aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & huber_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double delta=1.0) { + return at::_ops::huber_loss_out::redispatch(dispatchKeySet, self, target, reduction, delta, out); + } + + // aten::huber_loss.out(Tensor self, Tensor target, int reduction=Mean, float delta=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & huber_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & out) { + return at::_ops::huber_loss_out::redispatch(dispatchKeySet, self, target, reduction, delta, out); + } + + // aten::huber_loss(Tensor self, Tensor target, int reduction=Mean, float delta=1.0) -> Tensor + inline at::Tensor huber_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean, double delta=1.0) { + return at::_ops::huber_loss::redispatch(dispatchKeySet, self, target, reduction, delta); + } + + // aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & huber_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { + return at::_ops::huber_loss_backward_out::redispatch(dispatchKeySet, grad_output, self, target, reduction, delta, grad_input); + } + + // aten::huber_loss_backward.out(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & huber_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta, at::Tensor & grad_input) { + return at::_ops::huber_loss_backward_out::redispatch(dispatchKeySet, grad_output, self, target, reduction, delta, grad_input); + } + + // aten::huber_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction, float delta) -> Tensor + inline at::Tensor huber_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { + return at::_ops::huber_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, reduction, delta); + } + + // aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & soft_margin_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::soft_margin_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & soft_margin_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & out) { + return at::_ops::soft_margin_loss_out::redispatch(dispatchKeySet, self, target, reduction, out); + } + + // aten::soft_margin_loss(Tensor self, Tensor target, int reduction=Mean) -> Tensor + inline at::Tensor soft_margin_loss(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, int64_t reduction=at::Reduction::Mean) { + return at::_ops::soft_margin_loss::redispatch(dispatchKeySet, self, target, reduction); + } + + // aten::soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & soft_margin_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::soft_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, grad_input); + } + + // aten::soft_margin_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, int reduction, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & soft_margin_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, at::Tensor & grad_input) { + return at::_ops::soft_margin_loss_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, target, reduction, grad_input); + } + + // aten::soft_margin_loss_backward(Tensor grad_output, Tensor self, Tensor target, int reduction) -> Tensor + inline at::Tensor soft_margin_loss_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + return at::_ops::soft_margin_loss_backward::redispatch(dispatchKeySet, grad_output, self, target, reduction); + } + + // aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & elu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) { + return at::_ops::elu_out::redispatch(dispatchKeySet, self, alpha, scale, input_scale, out); + } + + // aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & elu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, at::Tensor & out) { + return at::_ops::elu_out::redispatch(dispatchKeySet, self, alpha, scale, input_scale, out); + } + + // aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor + inline at::Tensor elu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) { + return at::_ops::elu::redispatch(dispatchKeySet, self, alpha, scale, input_scale); + } + + // aten::elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & elu_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) { + return at::_ops::elu_backward_grad_input::redispatch(dispatchKeySet, grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input); + } + + // aten::elu_backward.grad_input(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & elu_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result, at::Tensor & grad_input) { + return at::_ops::elu_backward_grad_input::redispatch(dispatchKeySet, grad_output, alpha, scale, input_scale, is_result, self_or_result, grad_input); + } + + // aten::elu_backward(Tensor grad_output, Scalar alpha, Scalar scale, Scalar input_scale, bool is_result, Tensor self_or_result) -> Tensor + inline at::Tensor elu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) { + return at::_ops::elu_backward::redispatch(dispatchKeySet, grad_output, alpha, scale, input_scale, is_result, self_or_result); + } + + // aten::elu_(Tensor(a!) self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> Tensor(a!) + inline at::Tensor & elu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & alpha=1, const at::Scalar & scale=1, const at::Scalar & input_scale=1) { + return at::_ops::elu_::redispatch(dispatchKeySet, self, alpha, scale, input_scale); + } + + // aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim=-1) { + return at::_ops::glu_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { + return at::_ops::glu_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::glu(Tensor self, int dim=-1) -> Tensor + inline at::Tensor glu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=-1) { + return at::_ops::glu::redispatch(dispatchKeySet, self, dim); + } + + // aten::glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & glu_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { + return at::_ops::glu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, dim, grad_input); + } + + // aten::glu_backward.grad_input(Tensor grad_output, Tensor self, int dim, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & glu_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, at::Tensor & grad_input) { + return at::_ops::glu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, dim, grad_input); + } + + // aten::glu_backward(Tensor grad_output, Tensor self, int dim) -> Tensor + inline at::Tensor glu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { + return at::_ops::glu_backward::redispatch(dispatchKeySet, grad_output, self, dim); + } + + // aten::glu_jvp(Tensor glu, Tensor x, Tensor dx, int dim) -> Tensor + inline at::Tensor glu_jvp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) { + return at::_ops::glu_jvp::redispatch(dispatchKeySet, glu, x, dx, dim); + } + + // aten::glu_backward_jvp(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim) -> Tensor + inline at::Tensor glu_backward_jvp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { + return at::_ops::glu_backward_jvp::redispatch(dispatchKeySet, grad_x, grad_glu, x, dgrad_glu, dx, dim); + } + + // aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardsigmoid_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::hardsigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::hardsigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardsigmoid_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::hardsigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::hardsigmoid(Tensor self) -> Tensor + inline at::Tensor hardsigmoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::hardsigmoid::redispatch(dispatchKeySet, self); + } + + // aten::hardsigmoid_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & hardsigmoid_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::hardsigmoid_::redispatch(dispatchKeySet, self); + } + + // aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardsigmoid_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardsigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::hardsigmoid_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardsigmoid_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { + return at::_ops::hardsigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::hardsigmoid_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor hardsigmoid_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardsigmoid_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardtanh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) { + return at::_ops::hardtanh_out::redispatch(dispatchKeySet, self, min_val, max_val, out); + } + + // aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardtanh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & out) { + return at::_ops::hardtanh_out::redispatch(dispatchKeySet, self, min_val, max_val, out); + } + + // aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> Tensor + inline at::Tensor hardtanh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) { + return at::_ops::hardtanh::redispatch(dispatchKeySet, self, min_val, max_val); + } + + // aten::hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardtanh_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { + return at::_ops::hardtanh_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, min_val, max_val, grad_input); + } + + // aten::hardtanh_backward.grad_input(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & hardtanh_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val, at::Tensor & grad_input) { + return at::_ops::hardtanh_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, min_val, max_val, grad_input); + } + + // aten::hardtanh_backward(Tensor grad_output, Tensor self, Scalar min_val, Scalar max_val) -> Tensor + inline at::Tensor hardtanh_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { + return at::_ops::hardtanh_backward::redispatch(dispatchKeySet, grad_output, self, min_val, max_val); + } + + // aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> Tensor(a!) + inline at::Tensor & hardtanh_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & min_val=-1, const at::Scalar & max_val=1) { + return at::_ops::hardtanh_::redispatch(dispatchKeySet, self, min_val, max_val); + } + + // aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardswish_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::hardswish_out::redispatch(dispatchKeySet, self, out); + } + + // aten::hardswish.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardswish_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::hardswish_out::redispatch(dispatchKeySet, self, out); + } + + // aten::hardswish(Tensor self) -> Tensor + inline at::Tensor hardswish(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::hardswish::redispatch(dispatchKeySet, self); + } + + // aten::hardswish_(Tensor(a!) self) -> Tensor(a!) + inline at::Tensor & hardswish_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self) { + return at::_ops::hardswish_::redispatch(dispatchKeySet, self); + } + + // aten::hardswish_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor hardswish_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardswish_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & leaky_relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & negative_slope=0.01) { + return at::_ops::leaky_relu_out::redispatch(dispatchKeySet, self, negative_slope, out); + } + + // aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & leaky_relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & negative_slope, at::Tensor & out) { + return at::_ops::leaky_relu_out::redispatch(dispatchKeySet, self, negative_slope, out); + } + + // aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor + inline at::Tensor leaky_relu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & negative_slope=0.01) { + return at::_ops::leaky_relu::redispatch(dispatchKeySet, self, negative_slope); + } + + // aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & leaky_relu_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { + return at::_ops::leaky_relu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, negative_slope, self_is_result, grad_input); + } + + // aten::leaky_relu_backward.grad_input(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & leaky_relu_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result, at::Tensor & grad_input) { + return at::_ops::leaky_relu_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, negative_slope, self_is_result, grad_input); + } + + // aten::leaky_relu_backward(Tensor grad_output, Tensor self, Scalar negative_slope, bool self_is_result) -> Tensor + inline at::Tensor leaky_relu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { + return at::_ops::leaky_relu_backward::redispatch(dispatchKeySet, grad_output, self, negative_slope, self_is_result); + } + + // aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> Tensor(a!) + inline at::Tensor & leaky_relu_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Scalar & negative_slope=0.01) { + return at::_ops::leaky_relu_::redispatch(dispatchKeySet, self, negative_slope); + } + + // aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_sigmoid_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::log_sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_sigmoid_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::log_sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::log_sigmoid(Tensor self) -> Tensor + inline at::Tensor log_sigmoid(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log_sigmoid::redispatch(dispatchKeySet, self); + } + + // aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple log_sigmoid_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & buffer, const at::Tensor & self) { + return at::_ops::log_sigmoid_forward_output::redispatch(dispatchKeySet, self, output, buffer); + } + + // aten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple log_sigmoid_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & output, at::Tensor & buffer) { + return at::_ops::log_sigmoid_forward_output::redispatch(dispatchKeySet, self, output, buffer); + } + + // aten::log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer) + inline ::std::tuple log_sigmoid_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::log_sigmoid_forward::redispatch(dispatchKeySet, self); + } + + // aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & log_sigmoid_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { + return at::_ops::log_sigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, buffer, grad_input); + } + + // aten::log_sigmoid_backward.grad_input(Tensor grad_output, Tensor self, Tensor buffer, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & log_sigmoid_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer, at::Tensor & grad_input) { + return at::_ops::log_sigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, buffer, grad_input); + } + + // aten::log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor + inline at::Tensor log_sigmoid_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { + return at::_ops::log_sigmoid_backward::redispatch(dispatchKeySet, grad_output, self, buffer); + } + + // aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rrelu_with_noise_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt) { + return at::_ops::rrelu_with_noise_out::redispatch(dispatchKeySet, self, noise, lower, upper, training, generator, out); + } + + // aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rrelu_with_noise_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional generator, at::Tensor & out) { + return at::_ops::rrelu_with_noise_out::redispatch(dispatchKeySet, self, noise, lower, upper, training, generator, out); + } + + // aten::rrelu_with_noise(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor + inline at::Tensor rrelu_with_noise(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt) { + return at::_ops::rrelu_with_noise::redispatch(dispatchKeySet, self, noise, lower, upper, training, generator); + } + + // aten::rrelu_with_noise_backward(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result) -> Tensor + inline at::Tensor rrelu_with_noise_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result) { + return at::_ops::rrelu_with_noise_backward::redispatch(dispatchKeySet, grad_output, self, noise, lower, upper, training, self_is_result); + } + + // aten::rrelu_with_noise_(Tensor(a!) self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None) -> Tensor(a!) + inline at::Tensor & rrelu_with_noise_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt) { + return at::_ops::rrelu_with_noise_::redispatch(dispatchKeySet, self, noise, lower, upper, training, generator); + } + + // aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softplus_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) { + return at::_ops::softplus_out::redispatch(dispatchKeySet, self, beta, threshold, out); + } + + // aten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softplus_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & out) { + return at::_ops::softplus_out::redispatch(dispatchKeySet, self, beta, threshold, out); + } + + // aten::softplus(Tensor self, Scalar beta=1, Scalar threshold=20) -> Tensor + inline at::Tensor softplus(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20) { + return at::_ops::softplus::redispatch(dispatchKeySet, self, beta, threshold); + } + + // aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & softplus_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + return at::_ops::softplus_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, beta, threshold, grad_input); + } + + // aten::softplus_backward.grad_input(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & softplus_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold, at::Tensor & grad_input) { + return at::_ops::softplus_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, beta, threshold, grad_input); + } + + // aten::softplus_backward(Tensor grad_output, Tensor self, Scalar beta, Scalar threshold) -> Tensor + inline at::Tensor softplus_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + return at::_ops::softplus_backward::redispatch(dispatchKeySet, grad_output, self, beta, threshold); + } + + // aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softshrink_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & lambd=0.5) { + return at::_ops::softshrink_out::redispatch(dispatchKeySet, self, lambd, out); + } + + // aten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & softshrink_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & out) { + return at::_ops::softshrink_out::redispatch(dispatchKeySet, self, lambd, out); + } + + // aten::softshrink(Tensor self, Scalar lambd=0.5) -> Tensor + inline at::Tensor softshrink(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & lambd=0.5) { + return at::_ops::softshrink::redispatch(dispatchKeySet, self, lambd); + } + + // aten::softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & softshrink_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { + return at::_ops::softshrink_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, lambd, grad_input); + } + + // aten::softshrink_backward.grad_input(Tensor grad_output, Tensor self, Scalar lambd, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & softshrink_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd, at::Tensor & grad_input) { + return at::_ops::softshrink_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, lambd, grad_input); + } + + // aten::softshrink_backward(Tensor grad_output, Tensor self, Scalar lambd) -> Tensor + inline at::Tensor softshrink_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { + return at::_ops::softshrink_backward::redispatch(dispatchKeySet, grad_output, self, lambd); + } + + // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor + inline at::Tensor adaptive_avg_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_avg_pool2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size)); + } + + // aten::adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor + inline at::Tensor adaptive_avg_pool2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::adaptive_avg_pool2d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::mkldnn_adaptive_avg_pool2d(Tensor self, int[2] output_size) -> Tensor + inline at::Tensor mkldnn_adaptive_avg_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::mkldnn_adaptive_avg_pool2d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_adaptive_avg_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::mkldnn_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::mkldnn_adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_adaptive_avg_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::mkldnn_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor mkldnn_adaptive_avg_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::mkldnn_adaptive_avg_pool2d_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor + inline at::Tensor _adaptive_avg_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size)); + } + + // aten::_adaptive_avg_pool2d(Tensor self, SymInt[2] output_size) -> Tensor + inline at::Tensor _adaptive_avg_pool2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool2d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::_adaptive_avg_pool2d_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor _adaptive_avg_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::_adaptive_avg_pool2d_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor + inline at::Tensor adaptive_avg_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_avg_pool3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size)); + } + + // aten::adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor + inline at::Tensor adaptive_avg_pool3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::adaptive_avg_pool3d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor + inline at::Tensor _adaptive_avg_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size)); + } + + // aten::_adaptive_avg_pool3d(Tensor self, SymInt[3] output_size) -> Tensor + inline at::Tensor _adaptive_avg_pool3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool3d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::adaptive_avg_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::adaptive_avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_avg_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & grad_input) { + return at::_ops::adaptive_avg_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, grad_input); + } + + // aten::_adaptive_avg_pool3d_backward(Tensor grad_output, Tensor self) -> Tensor + inline at::Tensor _adaptive_avg_pool3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::_adaptive_avg_pool3d_backward::redispatch(dispatchKeySet, grad_output, self); + } + + // aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple adaptive_max_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_max_pool2d_out::redispatch(dispatchKeySet, self, output_size, out, indices); + } + + // aten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple adaptive_max_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { + return at::_ops::adaptive_max_pool2d_out::redispatch(dispatchKeySet, self, output_size, out, indices); + } + + // aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor) + inline ::std::tuple adaptive_max_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_max_pool2d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_max_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + return at::_ops::adaptive_max_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, indices, grad_input); + } + + // aten::adaptive_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_max_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::adaptive_max_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, indices, grad_input); + } + + // aten::adaptive_max_pool2d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor + inline at::Tensor adaptive_max_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + return at::_ops::adaptive_max_pool2d_backward::redispatch(dispatchKeySet, grad_output, self, indices); + } + + // aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple adaptive_max_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_max_pool3d_out::redispatch(dispatchKeySet, self, output_size, out, indices); + } + + // aten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple adaptive_max_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices) { + return at::_ops::adaptive_max_pool3d_out::redispatch(dispatchKeySet, self, output_size, out, indices); + } + + // aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor) + inline ::std::tuple adaptive_max_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::adaptive_max_pool3d::redispatch(dispatchKeySet, self, output_size); + } + + // aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_max_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + return at::_ops::adaptive_max_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, indices, grad_input); + } + + // aten::adaptive_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & adaptive_max_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::adaptive_max_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, indices, grad_input); + } + + // aten::adaptive_max_pool3d_backward(Tensor grad_output, Tensor self, Tensor indices) -> Tensor + inline at::Tensor adaptive_max_pool3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + return at::_ops::adaptive_max_pool3d_backward::redispatch(dispatchKeySet, grad_output, self, indices); + } + + // aten::avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & avg_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt) { + return at::_ops::avg_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); + } + + // aten::avg_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & avg_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override, at::Tensor & out) { + return at::_ops::avg_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); + } + + // aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor + inline at::Tensor avg_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt) { + return at::_ops::avg_pool2d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + + // aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & avg_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + return at::_ops::avg_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); + } + + // aten::avg_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & avg_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override, at::Tensor & grad_input) { + return at::_ops::avg_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); + } + + // aten::avg_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor + inline at::Tensor avg_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + return at::_ops::avg_pool2d_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + + // aten::avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & avg_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt) { + return at::_ops::avg_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); + } + + // aten::avg_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & avg_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override, at::Tensor & out) { + return at::_ops::avg_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, out); + } + + // aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> Tensor + inline at::Tensor avg_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, bool ceil_mode=false, bool count_include_pad=true, c10::optional divisor_override=c10::nullopt) { + return at::_ops::avg_pool3d::redispatch(dispatchKeySet, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + + // aten::avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & avg_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + return at::_ops::avg_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); + } + + // aten::avg_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & avg_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override, at::Tensor & grad_input) { + return at::_ops::avg_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override, grad_input); + } + + // aten::avg_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, bool ceil_mode, bool count_include_pad, int? divisor_override) -> Tensor + inline at::Tensor avg_pool3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + return at::_ops::avg_pool3d_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + + // aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fractional_max_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + return at::_ops::fractional_max_pool2d_output::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); + } + + // aten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fractional_max_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { + return at::_ops::fractional_max_pool2d_output::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); + } + + // aten::fractional_max_pool2d(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples) -> (Tensor, Tensor) + inline ::std::tuple fractional_max_pool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + return at::_ops::fractional_max_pool2d::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples); + } + + // aten::fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & fractional_max_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + return at::_ops::fractional_max_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices, grad_input); + } + + // aten::fractional_max_pool2d_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & fractional_max_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::fractional_max_pool2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices, grad_input); + } + + // aten::fractional_max_pool2d_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] output_size, Tensor indices) -> Tensor + inline at::Tensor fractional_max_pool2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + return at::_ops::fractional_max_pool2d_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices); + } + + // aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fractional_max_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + return at::_ops::fractional_max_pool3d_output::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); + } + + // aten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fractional_max_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples, at::Tensor & output, at::Tensor & indices) { + return at::_ops::fractional_max_pool3d_output::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples, output, indices); + } + + // aten::fractional_max_pool3d(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples) -> (Tensor, Tensor) + inline ::std::tuple fractional_max_pool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + return at::_ops::fractional_max_pool3d::redispatch(dispatchKeySet, self, kernel_size, output_size, random_samples); + } + + // aten::fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & fractional_max_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + return at::_ops::fractional_max_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices, grad_input); + } + + // aten::fractional_max_pool3d_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & fractional_max_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::fractional_max_pool3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices, grad_input); + } + + // aten::fractional_max_pool3d_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] output_size, Tensor indices) -> Tensor + inline at::Tensor fractional_max_pool3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + return at::_ops::fractional_max_pool3d_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, output_size, indices); + } + + // aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple max_pool2d_with_indices_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool2d_with_indices_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); + } + + // aten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple max_pool2d_with_indices_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { + return at::_ops::max_pool2d_with_indices_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); + } + + // aten::max_pool2d_with_indices(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) + inline ::std::tuple max_pool2d_with_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool2d_with_indices::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & max_pool2d_with_indices_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + return at::_ops::max_pool2d_with_indices_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); + } + + // aten::max_pool2d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & max_pool2d_with_indices_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::max_pool2d_with_indices_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); + } + + // aten::max_pool2d_with_indices_backward(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride, int[2] padding, int[2] dilation, bool ceil_mode, Tensor indices) -> Tensor + inline at::Tensor max_pool2d_with_indices_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + return at::_ops::max_pool2d_with_indices_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + } + + // aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple max_pool3d_with_indices_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool3d_with_indices_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); + } + + // aten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple max_pool3d_with_indices_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out, at::Tensor & indices) { + return at::_ops::max_pool3d_with_indices_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out, indices); + } + + // aten::max_pool3d_with_indices(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False) -> (Tensor, Tensor) + inline ::std::tuple max_pool3d_with_indices(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool3d_with_indices::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode); + } + + // aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & max_pool3d_with_indices_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + return at::_ops::max_pool3d_with_indices_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); + } + + // aten::max_pool3d_with_indices_backward.grad_input(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & max_pool3d_with_indices_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices, at::Tensor & grad_input) { + return at::_ops::max_pool3d_with_indices_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices, grad_input); + } + + // aten::max_pool3d_with_indices_backward(Tensor grad_output, Tensor self, int[3] kernel_size, int[3] stride, int[3] padding, int[3] dilation, bool ceil_mode, Tensor indices) -> Tensor + inline at::Tensor max_pool3d_with_indices_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + return at::_ops::max_pool3d_with_indices_backward::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + } + + // aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { + return at::_ops::max_unpool2d_out::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::max_unpool2d_out::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) { + return at::_ops::max_unpool2d_out::redispatch(dispatchKeySet, self, indices, output_size, out); + } + + // aten::max_unpool2d.out(Tensor self, Tensor indices, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::max_unpool2d_out::redispatch(dispatchKeySet, self, indices, output_size, out); + } + + // aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor + inline at::Tensor max_unpool2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size) { + return at::_ops::max_unpool2d::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size)); + } + + // aten::max_unpool2d(Tensor self, Tensor indices, SymInt[2] output_size) -> Tensor + inline at::Tensor max_unpool2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) { + return at::_ops::max_unpool2d::redispatch(dispatchKeySet, self, indices, output_size); + } + + // aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::max_unpool3d_out::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out); + } + + // aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::max_unpool3d_out::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding, out); + } + + // aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::max_unpool3d_out::redispatch(dispatchKeySet, self, indices, output_size, stride, padding, out); + } + + // aten::max_unpool3d.out(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_unpool3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::max_unpool3d_out::redispatch(dispatchKeySet, self, indices, output_size, stride, padding, out); + } + + // aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor + inline at::Tensor max_unpool3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, at::IntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::max_unpool3d::redispatch(dispatchKeySet, self, indices, c10::fromIntArrayRefSlow(output_size), stride, padding); + } + + // aten::max_unpool3d(Tensor self, Tensor indices, SymInt[3] output_size, int[3] stride, int[3] padding) -> Tensor + inline at::Tensor max_unpool3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::max_unpool3d::redispatch(dispatchKeySet, self, indices, output_size, stride, padding); + } + + // aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad1d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad1d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor reflection_pad1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad1d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad1d(Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor reflection_pad1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad1d::redispatch(dispatchKeySet, self, padding); + } + + // aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad1d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor reflection_pad1d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad1d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor reflection_pad1d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad1d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad2d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad2d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor reflection_pad2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor reflection_pad2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad2d::redispatch(dispatchKeySet, self, padding); + } + + // aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor reflection_pad2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad2d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor reflection_pad2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad2d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad3d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::reflection_pad3d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor reflection_pad3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad3d(Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor reflection_pad3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad3d::redispatch(dispatchKeySet, self, padding); + } + + // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & reflection_pad3d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::reflection_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor reflection_pad3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::reflection_pad3d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::reflection_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor reflection_pad3d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::reflection_pad3d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad1d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad1d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad1d.out(Tensor self, SymInt[2] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad1d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad1d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor replication_pad1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad1d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad1d(Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor replication_pad1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad1d::redispatch(dispatchKeySet, self, padding); + } + + // aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad1d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad1d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad1d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad1d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[2] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad1d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor replication_pad1d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad1d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad1d_backward(Tensor grad_output, Tensor self, SymInt[2] padding) -> Tensor + inline at::Tensor replication_pad1d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad1d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad2d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad2d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor replication_pad2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad2d(Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor replication_pad2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad2d::redispatch(dispatchKeySet, self, padding); + } + + // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad2d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[4] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor replication_pad2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad2d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad2d_backward(Tensor grad_output, Tensor self, SymInt[4] padding) -> Tensor + inline at::Tensor replication_pad2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad2d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), out); + } + + // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad3d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad3d.out(Tensor self, SymInt[6] padding, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & replication_pad3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::replication_pad3d_out::redispatch(dispatchKeySet, self, padding, out); + } + + // aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor replication_pad3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad3d(Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor replication_pad3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad3d::redispatch(dispatchKeySet, self, padding); + } + + // aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding), grad_input); + } + + // aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad3d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad3d_backward.grad_input(Tensor grad_output, Tensor self, SymInt[6] padding, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & replication_pad3d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input) { + return at::_ops::replication_pad3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, padding, grad_input); + } + + // aten::replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor replication_pad3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding) { + return at::_ops::replication_pad3d_backward::redispatch(dispatchKeySet, grad_output, self, c10::fromIntArrayRefSlow(padding)); + } + + // aten::replication_pad3d_backward(Tensor grad_output, Tensor self, SymInt[6] padding) -> Tensor + inline at::Tensor replication_pad3d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + return at::_ops::replication_pad3d_backward::redispatch(dispatchKeySet, grad_output, self, padding); + } + + // aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor + inline at::Tensor _pad_circular(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef pad) { + return at::_ops::_pad_circular::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad)); + } + + // aten::_pad_circular(Tensor self, SymInt[] pad) -> Tensor + inline at::Tensor _pad_circular_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad) { + return at::_ops::_pad_circular::redispatch(dispatchKeySet, self, pad); + } + + // aten::_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor + inline at::Tensor _pad_enum(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef pad, int64_t mode, c10::optional value=c10::nullopt) { + return at::_ops::_pad_enum::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad), mode, value); + } + + // aten::_pad_enum(Tensor self, SymInt[] pad, int mode, float? value=None) -> Tensor + inline at::Tensor _pad_enum_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, c10::optional value=c10::nullopt) { + return at::_ops::_pad_enum::redispatch(dispatchKeySet, self, pad, mode, value); + } + + // aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor + inline at::Tensor pad(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef pad, c10::string_view mode="constant", c10::optional value=c10::nullopt) { + return at::_ops::pad::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad), mode, value); + } + + // aten::pad(Tensor self, SymInt[] pad, str mode="constant", float? value=None) -> Tensor + inline at::Tensor pad_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode="constant", c10::optional value=c10::nullopt) { + return at::_ops::pad::redispatch(dispatchKeySet, self, pad, mode, value); + } + + // aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_linear1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_linear1d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::upsample_linear1d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_linear1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_linear1d_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_bilinear2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_bilinear2d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::upsample_bilinear2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_bilinear2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_bilinear2d_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::_upsample_bilinear2d_aa_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::_upsample_bilinear2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::_upsample_bilinear2d_aa_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_trilinear3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_trilinear3d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::upsample_trilinear3d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_trilinear3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_trilinear3d_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_bicubic2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_bicubic2d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::upsample_bicubic2d.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_bicubic2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::upsample_bicubic2d_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::_upsample_bicubic2d_aa_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, align_corners, scale_factors); + } + + // aten::_upsample_bicubic2d_aa.vec(Tensor input, SymInt[]? output_size, bool align_corners, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + return at::_ops::_upsample_bicubic2d_aa_vec::redispatch(dispatchKeySet, input, output_size, align_corners, scale_factors); + } + + // aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest1d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::upsample_nearest1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest1d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact1d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::_upsample_nearest_exact1d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact1d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest2d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::upsample_nearest2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest2d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact2d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::_upsample_nearest_exact2d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact2d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest3d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::upsample_nearest3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor upsample_nearest3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::upsample_nearest3d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact3d_vec::redispatch(dispatchKeySet, input, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, scale_factors); + } + + // aten::_upsample_nearest_exact3d.vec(Tensor input, SymInt[]? output_size, float[]? scale_factors) -> Tensor + inline at::Tensor _upsample_nearest_exact3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + return at::_ops::_upsample_nearest_exact3d_vec::redispatch(dispatchKeySet, input, output_size, scale_factors); + } + + // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out); + } + + // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales, at::Tensor & out) { + return at::_ops::upsample_linear1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales, out); + } + + // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales, out); + } + + // aten::upsample_linear1d.out(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales, at::Tensor & out) { + return at::_ops::upsample_linear1d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales, out); + } + + // aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor + inline at::Tensor upsample_linear1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales); + } + + // aten::upsample_linear1d(Tensor self, SymInt[1] output_size, bool align_corners, float? scales=None) -> Tensor + inline at::Tensor upsample_linear1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d::redispatch(dispatchKeySet, self, output_size, align_corners, scales); + } + + // aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input); + } + + // aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::upsample_linear1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales, grad_input); + } + + // aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales, grad_input); + } + + // aten::upsample_linear1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_linear1d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::upsample_linear1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales, grad_input); + } + + // aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor + inline at::Tensor upsample_linear1d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales); + } + + // aten::upsample_linear1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, bool align_corners, float? scales=None) -> Tensor + inline at::Tensor upsample_linear1d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_linear1d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales); + } + + // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_bilinear2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bilinear2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_bilinear2d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bilinear2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w); + } + + // aten::upsample_bilinear2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bilinear2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); + } + + // aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_bilinear2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bilinear2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bilinear2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_bilinear2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bilinear2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w); + } + + // aten::upsample_bilinear2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bilinear2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bilinear2d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + + // aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_bilinear2d_aa_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bilinear2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_bilinear2d_aa_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w); + } + + // aten::_upsample_bilinear2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); + } + + // aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bilinear2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bilinear2d_aa_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_bilinear2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w); + } + + // aten::_upsample_bilinear2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bilinear2d_aa_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bilinear2d_aa_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + + // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_bicubic2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bicubic2d.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_bicubic2d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bicubic2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w); + } + + // aten::upsample_bicubic2d(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bicubic2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); + } + + // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_bicubic2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bicubic2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_bicubic2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_bicubic2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bicubic2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w); + } + + // aten::upsample_bicubic2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_bicubic2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_bicubic2d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + + // aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_bicubic2d_aa_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bicubic2d_aa.out(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_bicubic2d_aa_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w, out); + } + + // aten::_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_h, scales_w); + } + + // aten::_upsample_bicubic2d_aa(Tensor self, SymInt[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa::redispatch(dispatchKeySet, self, output_size, align_corners, scales_h, scales_w); + } + + // aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bicubic2d_aa_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_bicubic2d_aa_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_bicubic2d_aa_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w, grad_input); + } + + // aten::_upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_h, scales_w); + } + + // aten::_upsample_bicubic2d_aa_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_bicubic2d_aa_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_bicubic2d_aa_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + + // aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_trilinear3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_trilinear3d.out(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_trilinear3d_out::redispatch(dispatchKeySet, self, output_size, align_corners, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_trilinear3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), align_corners, scales_d, scales_h, scales_w); + } + + // aten::upsample_trilinear3d(Tensor self, SymInt[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_trilinear3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d::redispatch(dispatchKeySet, self, output_size, align_corners, scales_d, scales_h, scales_w); + } + + // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_trilinear3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_trilinear3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_trilinear3d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_trilinear3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_trilinear3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), align_corners, scales_d, scales_h, scales_w); + } + + // aten::upsample_trilinear3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_trilinear3d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_trilinear3d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); + } + + // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales, out); + } + + // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales, at::Tensor & out) { + return at::_ops::upsample_nearest1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales, out); + } + + // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_out::redispatch(dispatchKeySet, self, output_size, scales, out); + } + + // aten::upsample_nearest1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales, at::Tensor & out) { + return at::_ops::upsample_nearest1d_out::redispatch(dispatchKeySet, self, output_size, scales, out); + } + + // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales, out); + } + + // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact1d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales, out); + } + + // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_out::redispatch(dispatchKeySet, self, output_size, scales, out); + } + + // aten::_upsample_nearest_exact1d.out(Tensor self, SymInt[1] output_size, float? scales=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact1d_out::redispatch(dispatchKeySet, self, output_size, scales, out); + } + + // aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor + inline at::Tensor upsample_nearest1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales); + } + + // aten::upsample_nearest1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor + inline at::Tensor upsample_nearest1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d::redispatch(dispatchKeySet, self, output_size, scales); + } + + // aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor + inline at::Tensor _upsample_nearest_exact1d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales); + } + + // aten::_upsample_nearest_exact1d(Tensor self, SymInt[1] output_size, float? scales=None) -> Tensor + inline at::Tensor _upsample_nearest_exact1d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d::redispatch(dispatchKeySet, self, output_size, scales); + } + + // aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input); + } + + // aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::upsample_nearest1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input); + } + + // aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales, grad_input); + } + + // aten::upsample_nearest1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest1d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::upsample_nearest1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales, grad_input); + } + + // aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input); + } + + // aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales, grad_input); + } + + // aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales, grad_input); + } + + // aten::_upsample_nearest_exact1d_backward.grad_input(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact1d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact1d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales, grad_input); + } + + // aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor + inline at::Tensor upsample_nearest1d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales); + } + + // aten::upsample_nearest1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor + inline at::Tensor upsample_nearest1d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::upsample_nearest1d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales); + } + + // aten::_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor + inline at::Tensor _upsample_nearest_exact1d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales); + } + + // aten::_upsample_nearest_exact1d_backward(Tensor grad_output, SymInt[1] output_size, SymInt[3] input_size, float? scales=None) -> Tensor + inline at::Tensor _upsample_nearest_exact1d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales=c10::nullopt) { + return at::_ops::_upsample_nearest_exact1d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales); + } + + // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out); + } + + // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_nearest2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out); + } + + // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_out::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w, out); + } + + // aten::upsample_nearest2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_nearest2d_out::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_out::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact2d.out(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact2d_out::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w, out); + } + + // aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w); + } + + // aten::upsample_nearest2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_h, scales_w); + } + + // aten::_upsample_nearest_exact2d(Tensor self, SymInt[2] output_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d::redispatch(dispatchKeySet, self, output_size, scales_h, scales_w); + } + + // aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_nearest2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_nearest2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact2d_backward.grad_input(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w); + } + + // aten::upsample_nearest2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest2d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_h, scales_w); + } + + // aten::_upsample_nearest_exact2d_backward(Tensor grad_output, SymInt[2] output_size, SymInt[4] input_size, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact2d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_h, scales_w); + } + + // aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out); + } + + // aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_nearest3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out); + } + + // aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_out::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_nearest3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::upsample_nearest3d_out::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_out::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w, out); + } + + // aten::_upsample_nearest_exact3d.out(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & out) { + return at::_ops::_upsample_nearest_exact3d_out::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w, out); + } + + // aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w); + } + + // aten::upsample_nearest3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), scales_d, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact3d(Tensor self, SymInt[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d::redispatch(dispatchKeySet, self, output_size, scales_d, scales_h, scales_w); + } + + // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_nearest3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & upsample_nearest3d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::upsample_nearest3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); + } + + // aten::_upsample_nearest_exact3d_backward.grad_input(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & _upsample_nearest_exact3d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, at::Tensor & grad_input) { + return at::_ops::_upsample_nearest_exact3d_backward_grad_input::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w, grad_input); + } + + // aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w); + } + + // aten::upsample_nearest3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor upsample_nearest3d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::upsample_nearest3d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact3d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef output_size, at::IntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_backward::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(output_size), c10::fromIntArrayRefSlow(input_size), scales_d, scales_h, scales_w); + } + + // aten::_upsample_nearest_exact3d_backward(Tensor grad_output, SymInt[3] output_size, SymInt[5] input_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> Tensor + inline at::Tensor _upsample_nearest_exact3d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d=c10::nullopt, c10::optional scales_h=c10::nullopt, c10::optional scales_w=c10::nullopt) { + return at::_ops::_upsample_nearest_exact3d_backward::redispatch(dispatchKeySet, grad_output, output_size, input_size, scales_d, scales_h, scales_w); + } + + // aten::sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & sigmoid_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) { + return at::_ops::sigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, output, grad_input); + } + + // aten::sigmoid_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & sigmoid_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { + return at::_ops::sigmoid_backward_grad_input::redispatch(dispatchKeySet, grad_output, output, grad_input); + } + + // aten::sigmoid_backward(Tensor grad_output, Tensor output) -> Tensor + inline at::Tensor sigmoid_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output) { + return at::_ops::sigmoid_backward::redispatch(dispatchKeySet, grad_output, output); + } + + // aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & logit_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, eps, grad_input); + } + + // aten::logit_backward.grad_input(Tensor grad_output, Tensor self, float? eps=None, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & logit_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps, at::Tensor & grad_input) { + return at::_ops::logit_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, eps, grad_input); + } + + // aten::logit_backward(Tensor grad_output, Tensor self, float? eps=None) -> Tensor + inline at::Tensor logit_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::logit_backward::redispatch(dispatchKeySet, grad_output, self, eps); + } + + // aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & tanh_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & output) { + return at::_ops::tanh_backward_grad_input::redispatch(dispatchKeySet, grad_output, output, grad_input); + } + + // aten::tanh_backward.grad_input(Tensor grad_output, Tensor output, *, Tensor(a!) grad_input) -> Tensor(a!) + inline at::Tensor & tanh_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, at::Tensor & grad_input) { + return at::_ops::tanh_backward_grad_input::redispatch(dispatchKeySet, grad_output, output, grad_input); + } + + // aten::tanh_backward(Tensor grad_output, Tensor output) -> Tensor + inline at::Tensor tanh_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output) { + return at::_ops::tanh_backward::redispatch(dispatchKeySet, grad_output, output); + } + + // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_transpose2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_transpose2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_transpose2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); + } + + // aten::slow_conv_transpose2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_transpose2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); + } + + // aten::slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor + inline at::Tensor slow_conv_transpose2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_transpose2d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::slow_conv_transpose2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] output_padding=0, SymInt[2] dilation=1) -> Tensor + inline at::Tensor slow_conv_transpose2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_transpose2d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + } + + // aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_transpose3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_transpose3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_transpose3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); + } + + // aten::slow_conv_transpose3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_transpose3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_transpose3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation, out); + } + + // aten::slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor + inline at::Tensor slow_conv_transpose3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef output_padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_transpose3d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::slow_conv_transpose3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] output_padding=0, SymInt[3] dilation=1) -> Tensor + inline at::Tensor slow_conv_transpose3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_transpose3d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + } + + // aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & thnn_conv2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) { + return at::_ops::thnn_conv2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out); + } + + // aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & thnn_conv2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::thnn_conv2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out); + } + + // aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & thnn_conv2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) { + return at::_ops::thnn_conv2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, out); + } + + // aten::thnn_conv2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & thnn_conv2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::thnn_conv2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, out); + } + + // aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor + inline at::Tensor thnn_conv2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) { + return at::_ops::thnn_conv2d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding)); + } + + // aten::thnn_conv2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0) -> Tensor + inline at::Tensor thnn_conv2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) { + return at::_ops::thnn_conv2d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding); + } + + // aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & _slow_conv2d_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::_slow_conv2d_forward_output::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output); + } + + // aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & _slow_conv2d_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) { + return at::_ops::_slow_conv2d_forward_output::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output); + } + + // aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & _slow_conv2d_forward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + return at::_ops::_slow_conv2d_forward_output::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output); + } + + // aten::_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & _slow_conv2d_forward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) { + return at::_ops::_slow_conv2d_forward_output::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output); + } + + // aten::_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor + inline at::Tensor _slow_conv2d_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::_slow_conv2d_forward::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding)); + } + + // aten::_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor + inline at::Tensor _slow_conv2d_forward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + return at::_ops::_slow_conv2d_forward::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding); + } + + // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::_slow_conv2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias); + } + + // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { + return at::_ops::_slow_conv2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), grad_input, grad_weight, grad_bias); + } + + // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + return at::_ops::_slow_conv2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); + } + + // aten::_slow_conv2d_backward.grad_input(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) grad_input, Tensor(b!) grad_weight, Tensor(c!) grad_bias) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & grad_input, at::Tensor & grad_weight, at::Tensor & grad_bias) { + return at::_ops::_slow_conv2d_backward_grad_input::redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, grad_input, grad_weight, grad_bias); + } + + // aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) + inline ::std::tuple _slow_conv2d_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array output_mask) { + return at::_ops::_slow_conv2d_backward_output_mask::redispatch(dispatchKeySet, grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask); + } + + // aten::_slow_conv2d_backward.output_mask(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask) -> (Tensor grad_input, Tensor grad_weight, Tensor grad_bias) + inline ::std::tuple _slow_conv2d_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask) { + return at::_ops::_slow_conv2d_backward_output_mask::redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, output_mask); + } + + // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _conv_depthwise2d_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::_conv_depthwise2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _conv_depthwise2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, const at::Tensor & out) { + return at::_ops::_conv_depthwise2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _conv_depthwise2d_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::_conv_depthwise2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::_conv_depthwise2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _conv_depthwise2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, const at::Tensor & out) { + return at::_ops::_conv_depthwise2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::_conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor + inline at::Tensor _conv_depthwise2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::_conv_depthwise2d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::_conv_depthwise2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation) -> Tensor + inline at::Tensor _conv_depthwise2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::_conv_depthwise2d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); + } + + // aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor + inline at::Tensor conv_depthwise3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::conv_depthwise3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation) -> Tensor + inline at::Tensor conv_depthwise3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); + } + + // aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) { + return at::_ops::slow_conv3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out); + } + + // aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & out) { + return at::_ops::slow_conv3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), out); + } + + // aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) { + return at::_ops::slow_conv3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, out); + } + + // aten::slow_conv3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & out) { + return at::_ops::slow_conv3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, out); + } + + // aten::slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor + inline at::Tensor slow_conv3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0) { + return at::_ops::slow_conv3d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding)); + } + + // aten::slow_conv3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0) -> Tensor + inline at::Tensor slow_conv3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0)) { + return at::_ops::slow_conv3d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding); + } + + // aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & slow_conv3d_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::slow_conv3d_forward_output::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output); + } + + // aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & slow_conv3d_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::Tensor & output) { + return at::_ops::slow_conv3d_forward_output::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output); + } + + // aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & slow_conv3d_forward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + return at::_ops::slow_conv3d_forward_output::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output); + } + + // aten::slow_conv3d_forward.output(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, *, Tensor(a!) output) -> Tensor(a!) + inline at::Tensor & slow_conv3d_forward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output) { + return at::_ops::slow_conv3d_forward_output::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, output); + } + + // aten::slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor + inline at::Tensor slow_conv3d_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding) { + return at::_ops::slow_conv3d_forward::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding)); + } + + // aten::slow_conv3d_forward(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding) -> Tensor + inline at::Tensor slow_conv3d_forward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + return at::_ops::slow_conv3d_forward::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding); + } + + // aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor + inline at::Tensor slow_conv_dilated2d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::slow_conv_dilated2d(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1) -> Tensor + inline at::Tensor slow_conv_dilated2d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); + } + + // aten::slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor + inline at::Tensor slow_conv_dilated3d(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated3d::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation)); + } + + // aten::slow_conv_dilated3d(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1) -> Tensor + inline at::Tensor slow_conv_dilated3d_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated3d::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation); + } + + // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col2im_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::col2im_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out); + } + + // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col2im_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::col2im_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride, out); + } + + // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col2im_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::col2im_out::redispatch(dispatchKeySet, self, output_size, kernel_size, dilation, padding, stride, out); + } + + // aten::col2im.out(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col2im_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::col2im_out::redispatch(dispatchKeySet, self, output_size, kernel_size, dilation, padding, stride, out); + } + + // aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor + inline at::Tensor col2im(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::col2im::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), kernel_size, dilation, padding, stride); + } + + // aten::col2im(Tensor self, SymInt[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor + inline at::Tensor col2im_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::col2im::redispatch(dispatchKeySet, self, output_size, kernel_size, dilation, padding, stride); + } + + // aten::column_stack(Tensor[] tensors) -> Tensor + inline at::Tensor column_stack(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::column_stack::redispatch(dispatchKeySet, tensors); + } + + // aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & column_stack_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::column_stack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::column_stack.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & column_stack_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::column_stack_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & im2col_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::im2col_out::redispatch(dispatchKeySet, self, kernel_size, dilation, padding, stride, out); + } + + // aten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & im2col_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::im2col_out::redispatch(dispatchKeySet, self, kernel_size, dilation, padding, stride, out); + } + + // aten::im2col(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride) -> Tensor + inline at::Tensor im2col(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + return at::_ops::im2col::redispatch(dispatchKeySet, self, kernel_size, dilation, padding, stride); + } + + // aten::isfinite(Tensor self) -> Tensor + inline at::Tensor isfinite(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isfinite::redispatch(dispatchKeySet, self); + } + + // aten::isinf(Tensor self) -> Tensor + inline at::Tensor isinf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isinf::redispatch(dispatchKeySet, self); + } + + // aten::record_stream(Tensor(a!) self, Stream s) -> () + inline void record_stream(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, at::Stream s) { + return at::_ops::record_stream::redispatch(dispatchKeySet, self, s); + } + + // aten::isposinf(Tensor self) -> Tensor + inline at::Tensor isposinf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isposinf::redispatch(dispatchKeySet, self); + } + + // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isposinf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::isposinf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::isposinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isposinf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::isposinf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::isneginf(Tensor self) -> Tensor + inline at::Tensor isneginf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::isneginf::redispatch(dispatchKeySet, self); + } + + // aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isneginf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::isneginf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::isneginf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isneginf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::isneginf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_add_batch_dim(Tensor self, int batch_dim, int level) -> Tensor + inline at::Tensor _add_batch_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t batch_dim, int64_t level) { + return at::_ops::_add_batch_dim::redispatch(dispatchKeySet, self, batch_dim, level); + } + + // aten::_remove_batch_dim(Tensor self, int level, int batch_size, int out_dim) -> Tensor + inline at::Tensor _remove_batch_dim(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) { + return at::_ops::_remove_batch_dim::redispatch(dispatchKeySet, self, level, batch_size, out_dim); + } + + // aten::special_entr(Tensor self) -> Tensor + inline at::Tensor special_entr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_entr::redispatch(dispatchKeySet, self); + } + + // aten::special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_entr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_entr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_entr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_entr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_entr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_ndtri(Tensor self) -> Tensor + inline at::Tensor special_ndtri(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_ndtri::redispatch(dispatchKeySet, self); + } + + // aten::special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_ndtri_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_ndtri_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_ndtri.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_ndtri_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_ndtri_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_log_ndtr(Tensor self) -> Tensor + inline at::Tensor special_log_ndtr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_log_ndtr::redispatch(dispatchKeySet, self); + } + + // aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_log_ndtr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_log_ndtr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_log_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_log_ndtr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_log_ndtr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_expm1(Tensor self) -> Tensor + inline at::Tensor special_expm1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_expm1::redispatch(dispatchKeySet, self); + } + + // aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_expm1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_expm1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_exp2(Tensor self) -> Tensor + inline at::Tensor special_exp2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_exp2::redispatch(dispatchKeySet, self); + } + + // aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_exp2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_exp2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_exp2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_exp2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_exp2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_psi(Tensor self) -> Tensor + inline at::Tensor special_psi(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_psi::redispatch(dispatchKeySet, self); + } + + // aten::special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_psi_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_psi_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_psi.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_psi_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_psi_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_digamma(Tensor self) -> Tensor + inline at::Tensor special_digamma(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_digamma::redispatch(dispatchKeySet, self); + } + + // aten::special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_digamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_digamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_digamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_digamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_gammaln(Tensor self) -> Tensor + inline at::Tensor special_gammaln(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_gammaln::redispatch(dispatchKeySet, self); + } + + // aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammaln_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_gammaln_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_gammaln.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammaln_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_gammaln_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erf(Tensor self) -> Tensor + inline at::Tensor special_erf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_erf::redispatch(dispatchKeySet, self); + } + + // aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfc(Tensor self) -> Tensor + inline at::Tensor special_erfc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_erfc::redispatch(dispatchKeySet, self); + } + + // aten::special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfcx(Tensor self) -> Tensor + inline at::Tensor special_erfcx(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_erfcx::redispatch(dispatchKeySet, self); + } + + // aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfcx_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_erfcx_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfcx.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfcx_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_erfcx_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfinv(Tensor self) -> Tensor + inline at::Tensor special_erfinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_erfinv::redispatch(dispatchKeySet, self); + } + + // aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_erfinv_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_erfinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_erfinv_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_ndtr(Tensor self) -> Tensor + inline at::Tensor special_ndtr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_ndtr::redispatch(dispatchKeySet, self); + } + + // aten::special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_ndtr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_ndtr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_ndtr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_ndtr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_ndtr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_xlog1py(Tensor self, Tensor other) -> Tensor + inline at::Tensor special_xlog1py(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_xlog1py::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlog1py.self_scalar(Scalar self, Tensor other) -> Tensor + inline at::Tensor special_xlog1py(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_xlog1py_self_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlog1py.other_scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor special_xlog1py(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_xlog1py_other_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_xlog1py_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlog1py.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_xlog1py_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_xlog1py_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlog1py.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_xlog1py_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_xlog1py_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlog1py.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlog1py_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::special_xlog1py_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy(Tensor self, Tensor other) -> Tensor + inline at::Tensor special_xlogy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_xlogy::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlogy.self_scalar(Scalar self, Tensor other) -> Tensor + inline at::Tensor special_xlogy(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_xlogy_self_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlogy.other_scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor special_xlogy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_xlogy_other_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_xlogy_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_xlogy_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_xlogy_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_xlogy_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_xlogy_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_xlogy.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_xlogy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::special_xlogy_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta(Tensor self, Tensor other) -> Tensor + inline at::Tensor special_zeta(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_zeta::redispatch(dispatchKeySet, self, other); + } + + // aten::special_zeta.self_scalar(Scalar self, Tensor other) -> Tensor + inline at::Tensor special_zeta(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_zeta_self_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_zeta.other_scalar(Tensor self, Scalar other) -> Tensor + inline at::Tensor special_zeta(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_zeta_other_scalar::redispatch(dispatchKeySet, self, other); + } + + // aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_zeta_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_zeta_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::special_zeta_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta.self_scalar_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_zeta_self_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::special_zeta_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_zeta.other_scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_zeta_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::special_zeta_other_scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_i0(Tensor self) -> Tensor + inline at::Tensor special_i0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_i0::redispatch(dispatchKeySet, self); + } + + // aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i0e(Tensor self) -> Tensor + inline at::Tensor special_i0e(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_i0e::redispatch(dispatchKeySet, self); + } + + // aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i0e_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_i0e_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i0e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i0e_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_i0e_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i1(Tensor self) -> Tensor + inline at::Tensor special_i1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_i1::redispatch(dispatchKeySet, self); + } + + // aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_i1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_i1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i1e(Tensor self) -> Tensor + inline at::Tensor special_i1e(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_i1e::redispatch(dispatchKeySet, self); + } + + // aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i1e_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_i1e_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_i1e.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_i1e_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_i1e_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_logit(Tensor self, float? eps=None) -> Tensor + inline at::Tensor special_logit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::special_logit::redispatch(dispatchKeySet, self, eps); + } + + // aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_logit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional eps=c10::nullopt) { + return at::_ops::special_logit_out::redispatch(dispatchKeySet, self, eps, out); + } + + // aten::special_logit.out(Tensor self, float? eps=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_logit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional eps, at::Tensor & out) { + return at::_ops::special_logit_out::redispatch(dispatchKeySet, self, eps, out); + } + + // aten::special_polygamma(int n, Tensor self) -> Tensor + inline at::Tensor special_polygamma(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self) { + return at::_ops::special_polygamma::redispatch(dispatchKeySet, n, self); + } + + // aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_polygamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, const at::Tensor & self) { + return at::_ops::special_polygamma_out::redispatch(dispatchKeySet, n, self, out); + } + + // aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_polygamma_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_polygamma_out::redispatch(dispatchKeySet, n, self, out); + } + + // aten::special_logsumexp(Tensor self, int[1] dim, bool keepdim=False) -> Tensor + inline at::Tensor special_logsumexp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::special_logsumexp::redispatch(dispatchKeySet, self, dim, keepdim); + } + + // aten::special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_logsumexp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false) { + return at::_ops::special_logsumexp_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::special_logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_logsumexp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out) { + return at::_ops::special_logsumexp_out::redispatch(dispatchKeySet, self, dim, keepdim, out); + } + + // aten::special_expit(Tensor self) -> Tensor + inline at::Tensor special_expit(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_expit::redispatch(dispatchKeySet, self); + } + + // aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_expit_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_expit_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_expit.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_expit_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_expit_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_sinc(Tensor self) -> Tensor + inline at::Tensor special_sinc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_sinc::redispatch(dispatchKeySet, self); + } + + // aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_sinc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_sinc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_sinc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_sinc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_sinc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_round(Tensor self, *, int decimals=0) -> Tensor + inline at::Tensor special_round(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals=0) { + return at::_ops::special_round::redispatch(dispatchKeySet, self, decimals); + } + + // aten::special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_round_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t decimals=0) { + return at::_ops::special_round_out::redispatch(dispatchKeySet, self, decimals, out); + } + + // aten::special_round.out(Tensor self, *, int decimals=0, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_round_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t decimals, at::Tensor & out) { + return at::_ops::special_round_out::redispatch(dispatchKeySet, self, decimals, out); + } + + // aten::special_log1p(Tensor self) -> Tensor + inline at::Tensor special_log1p(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_log1p::redispatch(dispatchKeySet, self); + } + + // aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_log1p_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_log1p_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_log_softmax(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor special_log_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::special_log_softmax::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammainc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_gammainc_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_gammainc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammainc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_gammainc_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_gammainc(Tensor self, Tensor other) -> Tensor + inline at::Tensor special_gammainc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_gammainc::redispatch(dispatchKeySet, self, other); + } + + // aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammaincc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_gammaincc_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_gammaincc.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_gammaincc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::special_gammaincc_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::special_gammaincc(Tensor self, Tensor other) -> Tensor + inline at::Tensor special_gammaincc(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::special_gammaincc::redispatch(dispatchKeySet, self, other); + } + + // aten::special_multigammaln(Tensor self, int p) -> Tensor + inline at::Tensor special_multigammaln(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p) { + return at::_ops::special_multigammaln::redispatch(dispatchKeySet, self, p); + } + + // aten::special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_multigammaln_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t p) { + return at::_ops::special_multigammaln_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::special_multigammaln.out(Tensor self, int p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_multigammaln_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t p, at::Tensor & out) { + return at::_ops::special_multigammaln_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::special_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensor + inline at::Tensor special_softmax(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt) { + return at::_ops::special_softmax::redispatch(dispatchKeySet, self, dim, dtype); + } + + // aten::fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_fft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_fft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_ifft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_ifft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_ifft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_ifft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_rfft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_rfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_rfft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_rfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_irfft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_irfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_irfft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_irfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_hfft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_hfft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_hfft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_hfft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_hfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_hfft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_hfft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_hfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_ihfft(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm); + } + + // aten::fft_ihfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor + inline at::Tensor fft_ihfft_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft::redispatch(dispatchKeySet, self, n, dim, norm); + } + + // aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ihfft_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ihfft_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ihfft_out::redispatch(dispatchKeySet, self, n.has_value() ? c10::make_optional(c10::SymInt(*n)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ihfft_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional n=c10::nullopt, int64_t dim=-1, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_ihfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ihfft_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ihfft_out::redispatch(dispatchKeySet, self, n, dim, norm, out); + } + + // aten::fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_fft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_fft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_fft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft2_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_fft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_ifft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_ifft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_ifft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft2_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ifft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_rfft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_rfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_rfft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft2_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_rfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_irfft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_irfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_irfft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft2_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_irfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_hfft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_hfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_hfft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfft2_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_hfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfft2_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_hfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_hfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_ihfft2(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft2::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_ihfft2(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None) -> Tensor + inline at::Tensor fft_ihfft2_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft2::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfft2_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfft2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::IntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_ihfft2_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfft2_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::IntArrayRef dim={-2,-1}, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ihfft2.out(Tensor self, SymInt[1]? s=None, int[1] dim=[-2,-1], str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfft2_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_ihfft2_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_fftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_fftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_fftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_fftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_fftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_fftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_ifftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_ifftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_ifftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifftn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifftn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ifftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ifftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_ifftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_ifftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_rfftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_rfftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_rfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_rfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_irfftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_irfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_irfftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfftn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfftn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_irfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_irfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_irfftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) { + return at::_ops::fft_irfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_hfftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_hfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_hfftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfftn_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_hfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfftn_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_hfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_hfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_hfftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_hfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_ihfftn(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfftn::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm); + } + + // aten::fft_ihfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor + inline at::Tensor fft_ihfftn_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfftn::redispatch(dispatchKeySet, self, s, dim, norm); + } + + // aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfftn_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfftn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_ihfftn_out::redispatch(dispatchKeySet, self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out); + } + + // aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfftn_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) { + return at::_ops::fft_ihfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_ihfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & fft_ihfftn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, const at::Tensor & out) { + return at::_ops::fft_ihfftn_out::redispatch(dispatchKeySet, self, s, dim, norm, out); + } + + // aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor fft_fftfreq(c10::DispatchKeySet dispatchKeySet, int64_t n, double d=1.0, at::TensorOptions options={}) { + return at::_ops::fft_fftfreq::redispatch(dispatchKeySet, n, d, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::fft_fftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor fft_fftfreq(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::fft_fftfreq::redispatch(dispatchKeySet, n, d, dtype, layout, device, pin_memory); + } + + // aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftfreq_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, double d=1.0) { + return at::_ops::fft_fftfreq_out::redispatch(dispatchKeySet, n, d, out); + } + + // aten::fft_fftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_fftfreq_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, at::Tensor & out) { + return at::_ops::fft_fftfreq_out::redispatch(dispatchKeySet, n, d, out); + } + + // aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor fft_rfftfreq(c10::DispatchKeySet dispatchKeySet, int64_t n, double d=1.0, at::TensorOptions options={}) { + return at::_ops::fft_rfftfreq::redispatch(dispatchKeySet, n, d, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); + } + + // aten::fft_rfftfreq(int n, float d=1.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor fft_rfftfreq(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::fft_rfftfreq::redispatch(dispatchKeySet, n, d, dtype, layout, device, pin_memory); + } + + // aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftfreq_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t n, double d=1.0) { + return at::_ops::fft_rfftfreq_out::redispatch(dispatchKeySet, n, d, out); + } + + // aten::fft_rfftfreq.out(int n, float d=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fft_rfftfreq_outf(c10::DispatchKeySet dispatchKeySet, int64_t n, double d, at::Tensor & out) { + return at::_ops::fft_rfftfreq_out::redispatch(dispatchKeySet, n, d, out); + } + + // aten::fft_fftshift(Tensor self, int[1]? dim=None) -> Tensor + inline at::Tensor fft_fftshift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt) { + return at::_ops::fft_fftshift::redispatch(dispatchKeySet, self, dim); + } + + // aten::fft_ifftshift(Tensor self, int[1]? dim=None) -> Tensor + inline at::Tensor fft_ifftshift(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt) { + return at::_ops::fft_ifftshift::redispatch(dispatchKeySet, self, dim); + } + + // aten::linalg_cholesky_ex(Tensor self, *, bool upper=False, bool check_errors=False) -> (Tensor L, Tensor info) + inline ::std::tuple linalg_cholesky_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper=false, bool check_errors=false) { + return at::_ops::linalg_cholesky_ex::redispatch(dispatchKeySet, self, upper, check_errors); + } + + // aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) + inline ::std::tuple linalg_cholesky_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & L, at::Tensor & info, const at::Tensor & self, bool upper=false, bool check_errors=false) { + return at::_ops::linalg_cholesky_ex_L::redispatch(dispatchKeySet, self, upper, check_errors, L, info); + } + + // aten::linalg_cholesky_ex.L(Tensor self, *, bool upper=False, bool check_errors=False, Tensor(a!) L, Tensor(b!) info) -> (Tensor(a!) L, Tensor(b!) info) + inline ::std::tuple linalg_cholesky_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, bool check_errors, at::Tensor & L, at::Tensor & info) { + return at::_ops::linalg_cholesky_ex_L::redispatch(dispatchKeySet, self, upper, check_errors, L, info); + } + + // aten::linalg_cholesky(Tensor self, *, bool upper=False) -> Tensor + inline at::Tensor linalg_cholesky(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper=false) { + return at::_ops::linalg_cholesky::redispatch(dispatchKeySet, self, upper); + } + + // aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cholesky_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool upper=false) { + return at::_ops::linalg_cholesky_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::linalg_cholesky.out(Tensor self, *, bool upper=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cholesky_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool upper, at::Tensor & out) { + return at::_ops::linalg_cholesky_out::redispatch(dispatchKeySet, self, upper, out); + } + + // aten::linalg_cross(Tensor self, Tensor other, *, int dim=-1) -> Tensor + inline at::Tensor linalg_cross(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim=-1) { + return at::_ops::linalg_cross::redispatch(dispatchKeySet, self, other, dim); + } + + // aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cross_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, int64_t dim=-1) { + return at::_ops::linalg_cross_out::redispatch(dispatchKeySet, self, other, dim, out); + } + + // aten::linalg_cross.out(Tensor self, Tensor other, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cross_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t dim, at::Tensor & out) { + return at::_ops::linalg_cross_out::redispatch(dispatchKeySet, self, other, dim, out); + } + + // aten::linalg_lu_factor(Tensor A, *, bool pivot=True) -> (Tensor LU, Tensor pivots) + inline ::std::tuple linalg_lu_factor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot=true) { + return at::_ops::linalg_lu_factor::redispatch(dispatchKeySet, A, pivot); + } + + // aten::linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots) + inline ::std::tuple linalg_lu_factor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A, bool pivot=true) { + return at::_ops::linalg_lu_factor_out::redispatch(dispatchKeySet, A, pivot, LU, pivots); + } + + // aten::linalg_lu_factor.out(Tensor A, *, bool pivot=True, Tensor(a!) LU, Tensor(b!) pivots) -> (Tensor(a!) LU, Tensor(b!) pivots) + inline ::std::tuple linalg_lu_factor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, at::Tensor & LU, at::Tensor & pivots) { + return at::_ops::linalg_lu_factor_out::redispatch(dispatchKeySet, A, pivot, LU, pivots); + } + + // aten::linalg_lu_factor_ex(Tensor A, *, bool pivot=True, bool check_errors=False) -> (Tensor LU, Tensor pivots, Tensor info) + inline ::std::tuple linalg_lu_factor_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot=true, bool check_errors=false) { + return at::_ops::linalg_lu_factor_ex::redispatch(dispatchKeySet, A, pivot, check_errors); + } + + // aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) + inline ::std::tuple linalg_lu_factor_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, bool pivot=true, bool check_errors=false) { + return at::_ops::linalg_lu_factor_ex_out::redispatch(dispatchKeySet, A, pivot, check_errors, LU, pivots, info); + } + + // aten::linalg_lu_factor_ex.out(Tensor A, *, bool pivot=True, bool check_errors=False, Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LU, Tensor(b!) pivots, Tensor(c!) info) + inline ::std::tuple linalg_lu_factor_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, bool check_errors, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { + return at::_ops::linalg_lu_factor_ex_out::redispatch(dispatchKeySet, A, pivot, check_errors, LU, pivots, info); + } + + // aten::linalg_lu(Tensor A, *, bool pivot=True) -> (Tensor P, Tensor L, Tensor U) + inline ::std::tuple linalg_lu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot=true) { + return at::_ops::linalg_lu::redispatch(dispatchKeySet, A, pivot); + } + + // aten::linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) + inline ::std::tuple linalg_lu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & A, bool pivot=true) { + return at::_ops::linalg_lu_out::redispatch(dispatchKeySet, A, pivot, P, L, U); + } + + // aten::linalg_lu.out(Tensor A, *, bool pivot=True, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) + inline ::std::tuple linalg_lu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool pivot, at::Tensor & P, at::Tensor & L, at::Tensor & U) { + return at::_ops::linalg_lu_out::redispatch(dispatchKeySet, A, pivot, P, L, U); + } + + // aten::linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor + inline at::Tensor linalg_lu_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) { + return at::_ops::linalg_lu_solve::redispatch(dispatchKeySet, LU, pivots, B, left, adjoint); + } + + // aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_lu_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) { + return at::_ops::linalg_lu_solve_out::redispatch(dispatchKeySet, LU, pivots, B, left, adjoint, out); + } + + // aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_lu_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) { + return at::_ops::linalg_lu_solve_out::redispatch(dispatchKeySet, LU, pivots, B, left, adjoint, out); + } + + // aten::_linalg_det(Tensor A) -> (Tensor result, Tensor LU, Tensor pivots) + inline ::std::tuple _linalg_det(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { + return at::_ops::_linalg_det::redispatch(dispatchKeySet, A); + } + + // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) + inline ::std::tuple _linalg_det_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) { + return at::_ops::_linalg_det_result::redispatch(dispatchKeySet, A, result, LU, pivots); + } + + // aten::_linalg_det.result(Tensor A, *, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots) + inline ::std::tuple _linalg_det_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots) { + return at::_ops::_linalg_det_result::redispatch(dispatchKeySet, A, result, LU, pivots); + } + + // aten::linalg_det(Tensor A) -> Tensor + inline at::Tensor linalg_det(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { + return at::_ops::linalg_det::redispatch(dispatchKeySet, A); + } + + // aten::linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_det_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & A) { + return at::_ops::linalg_det_out::redispatch(dispatchKeySet, A, out); + } + + // aten::linalg_det.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_det_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & out) { + return at::_ops::linalg_det_out::redispatch(dispatchKeySet, A, out); + } + + // aten::det(Tensor self) -> Tensor + inline at::Tensor det(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::det::redispatch(dispatchKeySet, self); + } + + // aten::linalg_ldl_factor_ex(Tensor self, *, bool hermitian=False, bool check_errors=False) -> (Tensor LD, Tensor pivots, Tensor info) + inline ::std::tuple linalg_ldl_factor_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian=false, bool check_errors=false) { + return at::_ops::linalg_ldl_factor_ex::redispatch(dispatchKeySet, self, hermitian, check_errors); + } + + // aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) + inline ::std::tuple linalg_ldl_factor_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info, const at::Tensor & self, bool hermitian=false, bool check_errors=false) { + return at::_ops::linalg_ldl_factor_ex_out::redispatch(dispatchKeySet, self, hermitian, check_errors, LD, pivots, info); + } + + // aten::linalg_ldl_factor_ex.out(Tensor self, *, bool hermitian=False, bool check_errors=False, Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) -> (Tensor(a!) LD, Tensor(b!) pivots, Tensor(c!) info) + inline ::std::tuple linalg_ldl_factor_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian, bool check_errors, at::Tensor & LD, at::Tensor & pivots, at::Tensor & info) { + return at::_ops::linalg_ldl_factor_ex_out::redispatch(dispatchKeySet, self, hermitian, check_errors, LD, pivots, info); + } + + // aten::linalg_ldl_factor(Tensor self, *, bool hermitian=False) -> (Tensor LD, Tensor pivots) + inline ::std::tuple linalg_ldl_factor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian=false) { + return at::_ops::linalg_ldl_factor::redispatch(dispatchKeySet, self, hermitian); + } + + // aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) + inline ::std::tuple linalg_ldl_factor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & LD, at::Tensor & pivots, const at::Tensor & self, bool hermitian=false) { + return at::_ops::linalg_ldl_factor_out::redispatch(dispatchKeySet, self, hermitian, LD, pivots); + } + + // aten::linalg_ldl_factor.out(Tensor self, *, bool hermitian=False, Tensor(a!) LD, Tensor(b!) pivots) -> (Tensor(a!) LD, Tensor(b!) pivots) + inline ::std::tuple linalg_ldl_factor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool hermitian, at::Tensor & LD, at::Tensor & pivots) { + return at::_ops::linalg_ldl_factor_out::redispatch(dispatchKeySet, self, hermitian, LD, pivots); + } + + // aten::linalg_ldl_solve(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False) -> Tensor + inline at::Tensor linalg_ldl_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false) { + return at::_ops::linalg_ldl_solve::redispatch(dispatchKeySet, LD, pivots, B, hermitian); + } + + // aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_ldl_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false) { + return at::_ops::linalg_ldl_solve_out::redispatch(dispatchKeySet, LD, pivots, B, hermitian, out); + } + + // aten::linalg_ldl_solve.out(Tensor LD, Tensor pivots, Tensor B, *, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_ldl_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_ldl_solve_out::redispatch(dispatchKeySet, LD, pivots, B, hermitian, out); + } + + // aten::linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values) + inline ::std::tuple linalg_lstsq(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & b, c10::optional rcond=c10::nullopt, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_lstsq::redispatch(dispatchKeySet, self, b, rcond, driver); + } + + // aten::linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) + inline ::std::tuple linalg_lstsq_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values, const at::Tensor & self, const at::Tensor & b, c10::optional rcond=c10::nullopt, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_lstsq_out::redispatch(dispatchKeySet, self, b, rcond, driver, solution, residuals, rank, singular_values); + } + + // aten::linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) + inline ::std::tuple linalg_lstsq_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values) { + return at::_ops::linalg_lstsq_out::redispatch(dispatchKeySet, self, b, rcond, driver, solution, residuals, rank, singular_values); + } + + // aten::linalg_matmul(Tensor self, Tensor other) -> Tensor + inline at::Tensor linalg_matmul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::linalg_matmul::redispatch(dispatchKeySet, self, other); + } + + // aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matmul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::linalg_matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::linalg_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matmul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::linalg_matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::linalg_vecdot(Tensor x, Tensor y, *, int dim=-1) -> Tensor + inline at::Tensor linalg_vecdot(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & y, int64_t dim=-1) { + return at::_ops::linalg_vecdot::redispatch(dispatchKeySet, x, y, dim); + } + + // aten::linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_vecdot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & y, int64_t dim=-1) { + return at::_ops::linalg_vecdot_out::redispatch(dispatchKeySet, x, y, dim, out); + } + + // aten::linalg_vecdot.out(Tensor x, Tensor y, *, int dim=-1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_vecdot_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & y, int64_t dim, at::Tensor & out) { + return at::_ops::linalg_vecdot_out::redispatch(dispatchKeySet, x, y, dim, out); + } + + // aten::linalg_matrix_exp(Tensor self) -> Tensor + inline at::Tensor linalg_matrix_exp(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::linalg_matrix_exp::redispatch(dispatchKeySet, self); + } + + // aten::_linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet, Tensor LU, Tensor pivots) + inline ::std::tuple _linalg_slogdet(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { + return at::_ops::_linalg_slogdet::redispatch(dispatchKeySet, A); + } + + // aten::_linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) + inline ::std::tuple _linalg_slogdet_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots, const at::Tensor & A) { + return at::_ops::_linalg_slogdet_sign::redispatch(dispatchKeySet, A, sign, logabsdet, LU, pivots); + } + + // aten::_linalg_slogdet.sign(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) -> (Tensor(a!) sign, Tensor(b!) logabsdet, Tensor(c!) LU, Tensor(d!) pivots) + inline ::std::tuple _linalg_slogdet_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet, at::Tensor & LU, at::Tensor & pivots) { + return at::_ops::_linalg_slogdet_sign::redispatch(dispatchKeySet, A, sign, logabsdet, LU, pivots); + } + + // aten::linalg_slogdet(Tensor A) -> (Tensor sign, Tensor logabsdet) + inline ::std::tuple linalg_slogdet(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { + return at::_ops::linalg_slogdet::redispatch(dispatchKeySet, A); + } + + // aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) + inline ::std::tuple linalg_slogdet_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & sign, at::Tensor & logabsdet, const at::Tensor & A) { + return at::_ops::linalg_slogdet_out::redispatch(dispatchKeySet, A, sign, logabsdet); + } + + // aten::linalg_slogdet.out(Tensor A, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) + inline ::std::tuple linalg_slogdet_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & sign, at::Tensor & logabsdet) { + return at::_ops::linalg_slogdet_out::redispatch(dispatchKeySet, A, sign, logabsdet); + } + + // aten::slogdet(Tensor self) -> (Tensor sign, Tensor logabsdet) + inline ::std::tuple slogdet(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::slogdet::redispatch(dispatchKeySet, self); + } + + // aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) + inline ::std::tuple slogdet_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & sign, at::Tensor & logabsdet, const at::Tensor & self) { + return at::_ops::slogdet_out::redispatch(dispatchKeySet, self, sign, logabsdet); + } + + // aten::slogdet.out(Tensor self, *, Tensor(a!) sign, Tensor(b!) logabsdet) -> (Tensor(a!) sign, Tensor(b!) logabsdet) + inline ::std::tuple slogdet_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & sign, at::Tensor & logabsdet) { + return at::_ops::slogdet_out::redispatch(dispatchKeySet, self, sign, logabsdet); + } + + // aten::logdet(Tensor self) -> Tensor + inline at::Tensor logdet(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::logdet::redispatch(dispatchKeySet, self); + } + + // aten::linalg_eig(Tensor self) -> (Tensor eigenvalues, Tensor eigenvectors) + inline ::std::tuple linalg_eig(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::linalg_eig::redispatch(dispatchKeySet, self); + } + + // aten::linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple linalg_eig_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & self) { + return at::_ops::linalg_eig_out::redispatch(dispatchKeySet, self, eigenvalues, eigenvectors); + } + + // aten::linalg_eig.out(Tensor self, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple linalg_eig_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { + return at::_ops::linalg_eig_out::redispatch(dispatchKeySet, self, eigenvalues, eigenvectors); + } + + // aten::_linalg_eigvals(Tensor self) -> Tensor + inline at::Tensor _linalg_eigvals(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_linalg_eigvals::redispatch(dispatchKeySet, self); + } + + // aten::linalg_eigvals(Tensor self) -> Tensor + inline at::Tensor linalg_eigvals(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::linalg_eigvals::redispatch(dispatchKeySet, self); + } + + // aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_eigvals_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::linalg_eigvals_out::redispatch(dispatchKeySet, self, out); + } + + // aten::linalg_eigvals.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_eigvals_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::linalg_eigvals_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_linalg_eigh(Tensor A, str UPLO="L", bool compute_v=True) -> (Tensor eigenvalues, Tensor eigenvectors) + inline ::std::tuple _linalg_eigh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true) { + return at::_ops::_linalg_eigh::redispatch(dispatchKeySet, A, UPLO, compute_v); + } + + // aten::_linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple _linalg_eigh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & eigenvalues, at::Tensor & eigenvectors, const at::Tensor & A, c10::string_view UPLO="L", bool compute_v=true) { + return at::_ops::_linalg_eigh_eigenvalues::redispatch(dispatchKeySet, A, UPLO, compute_v, eigenvalues, eigenvectors); + } + + // aten::_linalg_eigh.eigenvalues(Tensor A, str UPLO="L", bool compute_v=True, *, Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple _linalg_eigh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::string_view UPLO, bool compute_v, at::Tensor & eigenvalues, at::Tensor & eigenvectors) { + return at::_ops::_linalg_eigh_eigenvalues::redispatch(dispatchKeySet, A, UPLO, compute_v, eigenvalues, eigenvectors); + } + + // aten::linalg_eigh(Tensor self, str UPLO="L") -> (Tensor eigenvalues, Tensor eigenvectors) + inline ::std::tuple linalg_eigh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO="L") { + return at::_ops::linalg_eigh::redispatch(dispatchKeySet, self, UPLO); + } + + // aten::linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple linalg_eigh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & eigvals, at::Tensor & eigvecs, const at::Tensor & self, c10::string_view UPLO="L") { + return at::_ops::linalg_eigh_eigvals::redispatch(dispatchKeySet, self, UPLO, eigvals, eigvecs); + } + + // aten::linalg_eigh.eigvals(Tensor self, str UPLO="L", *, Tensor(a!) eigvals, Tensor(b!) eigvecs) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors) + inline ::std::tuple linalg_eigh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO, at::Tensor & eigvals, at::Tensor & eigvecs) { + return at::_ops::linalg_eigh_eigvals::redispatch(dispatchKeySet, self, UPLO, eigvals, eigvecs); + } + + // aten::linalg_eigvalsh(Tensor self, str UPLO="L") -> Tensor + inline at::Tensor linalg_eigvalsh(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO="L") { + return at::_ops::linalg_eigvalsh::redispatch(dispatchKeySet, self, UPLO); + } + + // aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_eigvalsh_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::string_view UPLO="L") { + return at::_ops::linalg_eigvalsh_out::redispatch(dispatchKeySet, self, UPLO, out); + } + + // aten::linalg_eigvalsh.out(Tensor self, str UPLO="L", *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_eigvalsh_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view UPLO, at::Tensor & out) { + return at::_ops::linalg_eigvalsh_out::redispatch(dispatchKeySet, self, UPLO, out); + } + + // aten::linalg_householder_product(Tensor input, Tensor tau) -> Tensor + inline at::Tensor linalg_householder_product(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tau) { + return at::_ops::linalg_householder_product::redispatch(dispatchKeySet, input, tau); + } + + // aten::linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_householder_product_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & tau) { + return at::_ops::linalg_householder_product_out::redispatch(dispatchKeySet, input, tau, out); + } + + // aten::linalg_householder_product.out(Tensor input, Tensor tau, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_householder_product_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tau, at::Tensor & out) { + return at::_ops::linalg_householder_product_out::redispatch(dispatchKeySet, input, tau, out); + } + + // aten::linalg_inv_ex(Tensor A, *, bool check_errors=False) -> (Tensor inverse, Tensor info) + inline ::std::tuple linalg_inv_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool check_errors=false) { + return at::_ops::linalg_inv_ex::redispatch(dispatchKeySet, A, check_errors); + } + + // aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) + inline ::std::tuple linalg_inv_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & inverse, at::Tensor & info, const at::Tensor & A, bool check_errors=false) { + return at::_ops::linalg_inv_ex_inverse::redispatch(dispatchKeySet, A, check_errors, inverse, info); + } + + // aten::linalg_inv_ex.inverse(Tensor A, *, bool check_errors=False, Tensor(a!) inverse, Tensor(b!) info) -> (Tensor(a!) inverse, Tensor(b!) info) + inline ::std::tuple linalg_inv_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool check_errors, at::Tensor & inverse, at::Tensor & info) { + return at::_ops::linalg_inv_ex_inverse::redispatch(dispatchKeySet, A, check_errors, inverse, info); + } + + // aten::linalg_inv(Tensor A) -> Tensor + inline at::Tensor linalg_inv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A) { + return at::_ops::linalg_inv::redispatch(dispatchKeySet, A); + } + + // aten::linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_inv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & A) { + return at::_ops::linalg_inv_out::redispatch(dispatchKeySet, A, out); + } + + // aten::linalg_inv.out(Tensor A, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_inv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, at::Tensor & out) { + return at::_ops::linalg_inv_out::redispatch(dispatchKeySet, A, out); + } + + // aten::inverse(Tensor self) -> Tensor + inline at::Tensor inverse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::inverse::redispatch(dispatchKeySet, self); + } + + // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & inverse_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::inverse_out::redispatch(dispatchKeySet, self, out); + } + + // aten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & inverse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::inverse_out::redispatch(dispatchKeySet, self, out); + } + + // aten::inner(Tensor self, Tensor other) -> Tensor + inline at::Tensor inner(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::inner::redispatch(dispatchKeySet, self, other); + } + + // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & inner_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::inner_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::inner.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & inner_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::inner_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::outer(Tensor self, Tensor vec2) -> Tensor + inline at::Tensor outer(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::outer::redispatch(dispatchKeySet, self, vec2); + } + + // aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & outer_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::outer_out::redispatch(dispatchKeySet, self, vec2, out); + } + + // aten::outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & outer_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) { + return at::_ops::outer_out::redispatch(dispatchKeySet, self, vec2, out); + } + + // aten::ger(Tensor self, Tensor vec2) -> Tensor + inline at::Tensor ger(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::ger::redispatch(dispatchKeySet, self, vec2); + } + + // aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ger_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::ger_out::redispatch(dispatchKeySet, self, vec2, out); + } + + // aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ger_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) { + return at::_ops::ger_out::redispatch(dispatchKeySet, self, vec2, out); + } + + // aten::linalg_norm(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor linalg_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & ord=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_norm::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); + } + + // aten::linalg_norm.ord_str(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor linalg_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_norm_ord_str::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); + } + + // aten::linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & ord=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_norm.out(Tensor self, Scalar? ord=None, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::linalg_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_norm_ord_str_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_norm.ord_str_out(Tensor self, str ord, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::linalg_norm_ord_str_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_vector_norm(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor linalg_vector_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord=2, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_vector_norm::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); + } + + // aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_vector_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & ord=2, at::OptionalIntArrayRef dim=c10::nullopt, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_vector_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_vector_norm.out(Tensor self, Scalar ord=2, int[1]? dim=None, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_vector_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::linalg_vector_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_matrix_norm(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor linalg_matrix_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim={-2,-1}, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_matrix_norm::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); + } + + // aten::linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim={-2,-1}, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_matrix_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_matrix_norm.out(Tensor self, Scalar ord, int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::linalg_matrix_norm_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_matrix_norm.str_ord(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor + inline at::Tensor linalg_matrix_norm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view ord="fro", at::IntArrayRef dim={-2,-1}, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_matrix_norm_str_ord::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype); + } + + // aten::linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::string_view ord="fro", at::IntArrayRef dim={-2,-1}, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::linalg_matrix_norm_str_ord_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::linalg_matrix_norm.str_ord_out(Tensor self, str ord='fro', int[] dim=[-2,-1], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view ord, at::IntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::linalg_matrix_norm_str_ord_out::redispatch(dispatchKeySet, self, ord, dim, keepdim, dtype, out); + } + + // aten::_linalg_svd(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh) + inline ::std::tuple _linalg_svd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, c10::optional driver=c10::nullopt) { + return at::_ops::_linalg_svd::redispatch(dispatchKeySet, A, full_matrices, compute_uv, driver); + } + + // aten::_linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) + inline ::std::tuple _linalg_svd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices=false, bool compute_uv=true, c10::optional driver=c10::nullopt) { + return at::_ops::_linalg_svd_U::redispatch(dispatchKeySet, A, full_matrices, compute_uv, driver, U, S, Vh); + } + + // aten::_linalg_svd.U(Tensor A, bool full_matrices=False, bool compute_uv=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) + inline ::std::tuple _linalg_svd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { + return at::_ops::_linalg_svd_U::redispatch(dispatchKeySet, A, full_matrices, compute_uv, driver, U, S, Vh); + } + + // aten::linalg_svd(Tensor A, bool full_matrices=True, *, str? driver=None) -> (Tensor U, Tensor S, Tensor Vh) + inline ::std::tuple linalg_svd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices=true, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_svd::redispatch(dispatchKeySet, A, full_matrices, driver); + } + + // aten::linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) + inline ::std::tuple linalg_svd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & U, at::Tensor & S, at::Tensor & Vh, const at::Tensor & A, bool full_matrices=true, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_svd_U::redispatch(dispatchKeySet, A, full_matrices, driver, U, S, Vh); + } + + // aten::linalg_svd.U(Tensor A, bool full_matrices=True, *, str? driver=None, Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) Vh) + inline ::std::tuple linalg_svd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, bool full_matrices, c10::optional driver, at::Tensor & U, at::Tensor & S, at::Tensor & Vh) { + return at::_ops::linalg_svd_U::redispatch(dispatchKeySet, A, full_matrices, driver, U, S, Vh); + } + + // aten::linalg_svdvals(Tensor A, *, str? driver=None) -> Tensor + inline at::Tensor linalg_svdvals(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_svdvals::redispatch(dispatchKeySet, A, driver); + } + + // aten::linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_svdvals_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & A, c10::optional driver=c10::nullopt) { + return at::_ops::linalg_svdvals_out::redispatch(dispatchKeySet, A, driver, out); + } + + // aten::linalg_svdvals.out(Tensor A, *, str? driver=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_svdvals_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::optional driver, at::Tensor & out) { + return at::_ops::linalg_svdvals_out::redispatch(dispatchKeySet, A, driver, out); + } + + // aten::linalg_cond(Tensor self, Scalar? p=None) -> Tensor + inline at::Tensor linalg_cond(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p=c10::nullopt) { + return at::_ops::linalg_cond::redispatch(dispatchKeySet, self, p); + } + + // aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cond_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p=c10::nullopt) { + return at::_ops::linalg_cond_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::linalg_cond.out(Tensor self, Scalar? p=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cond_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::Tensor & out) { + return at::_ops::linalg_cond_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::linalg_cond.p_str(Tensor self, str p) -> Tensor + inline at::Tensor linalg_cond(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view p) { + return at::_ops::linalg_cond_p_str::redispatch(dispatchKeySet, self, p); + } + + // aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cond_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::string_view p) { + return at::_ops::linalg_cond_p_str_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::linalg_cond.p_str_out(Tensor self, str p, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_cond_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::string_view p, at::Tensor & out) { + return at::_ops::linalg_cond_p_str_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::linalg_pinv.atol_rtol_tensor(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor + inline at::Tensor linalg_pinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) { + return at::_ops::linalg_pinv_atol_rtol_tensor::redispatch(dispatchKeySet, self, atol, rtol, hermitian); + } + + // aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) { + return at::_ops::linalg_pinv_atol_rtol_tensor_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_pinv.atol_rtol_tensor_out(Tensor self, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & atol, const c10::optional & rtol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_pinv_atol_rtol_tensor_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_pinv.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor + inline at::Tensor linalg_pinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) { + return at::_ops::linalg_pinv_atol_rtol_float::redispatch(dispatchKeySet, self, atol, rtol, hermitian); + } + + // aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) { + return at::_ops::linalg_pinv_atol_rtol_float_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_pinv.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_pinv_atol_rtol_float_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_pinv(Tensor self, float rcond, bool hermitian=False) -> Tensor + inline at::Tensor linalg_pinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond, bool hermitian=false) { + return at::_ops::linalg_pinv::redispatch(dispatchKeySet, self, rcond, hermitian); + } + + // aten::linalg_pinv.rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False) -> Tensor + inline at::Tensor linalg_pinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & rcond, bool hermitian=false) { + return at::_ops::linalg_pinv_rcond_tensor::redispatch(dispatchKeySet, self, rcond, hermitian); + } + + // aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double rcond, bool hermitian=false) { + return at::_ops::linalg_pinv_out::redispatch(dispatchKeySet, self, rcond, hermitian, out); + } + + // aten::linalg_pinv.out(Tensor self, float rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double rcond, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_pinv_out::redispatch(dispatchKeySet, self, rcond, hermitian, out); + } + + // aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & rcond, bool hermitian=false) { + return at::_ops::linalg_pinv_out_rcond_tensor::redispatch(dispatchKeySet, self, rcond, hermitian, out); + } + + // aten::linalg_pinv.out_rcond_tensor(Tensor self, Tensor rcond, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_pinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & rcond, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_pinv_out_rcond_tensor::redispatch(dispatchKeySet, self, rcond, hermitian, out); + } + + // aten::_linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor LU, Tensor pivots, Tensor info) + inline ::std::tuple _linalg_solve_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) { + return at::_ops::_linalg_solve_ex::redispatch(dispatchKeySet, A, B, left, check_errors); + } + + // aten::_linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) + inline ::std::tuple _linalg_solve_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) { + return at::_ops::_linalg_solve_ex_result::redispatch(dispatchKeySet, A, B, left, check_errors, result, LU, pivots, info); + } + + // aten::_linalg_solve_ex.result(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) -> (Tensor(a!) result, Tensor(b!) LU, Tensor(c!) pivots, Tensor(d!) info) + inline ::std::tuple _linalg_solve_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & LU, at::Tensor & pivots, at::Tensor & info) { + return at::_ops::_linalg_solve_ex_result::redispatch(dispatchKeySet, A, B, left, check_errors, result, LU, pivots, info); + } + + // aten::linalg_solve_ex(Tensor A, Tensor B, *, bool left=True, bool check_errors=False) -> (Tensor result, Tensor info) + inline ::std::tuple linalg_solve_ex(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) { + return at::_ops::linalg_solve_ex::redispatch(dispatchKeySet, A, B, left, check_errors); + } + + // aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) + inline ::std::tuple linalg_solve_ex_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & result, at::Tensor & info, const at::Tensor & A, const at::Tensor & B, bool left=true, bool check_errors=false) { + return at::_ops::linalg_solve_ex_out::redispatch(dispatchKeySet, A, B, left, check_errors, result, info); + } + + // aten::linalg_solve_ex.out(Tensor A, Tensor B, *, bool left=True, bool check_errors=False, Tensor(a!) result, Tensor(b!) info) -> (Tensor(a!) result, Tensor(b!) info) + inline ::std::tuple linalg_solve_ex_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors, at::Tensor & result, at::Tensor & info) { + return at::_ops::linalg_solve_ex_out::redispatch(dispatchKeySet, A, B, left, check_errors, result, info); + } + + // aten::linalg_solve(Tensor A, Tensor B, *, bool left=True) -> Tensor + inline at::Tensor linalg_solve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left=true) { + return at::_ops::linalg_solve::redispatch(dispatchKeySet, A, B, left); + } + + // aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_solve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & A, const at::Tensor & B, bool left=true) { + return at::_ops::linalg_solve_out::redispatch(dispatchKeySet, A, B, left, out); + } + + // aten::linalg_solve.out(Tensor A, Tensor B, *, bool left=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_solve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out) { + return at::_ops::linalg_solve_out::redispatch(dispatchKeySet, A, B, left, out); + } + + // aten::linalg_tensorinv(Tensor self, int ind=2) -> Tensor + inline at::Tensor linalg_tensorinv(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t ind=2) { + return at::_ops::linalg_tensorinv::redispatch(dispatchKeySet, self, ind); + } + + // aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_tensorinv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t ind=2) { + return at::_ops::linalg_tensorinv_out::redispatch(dispatchKeySet, self, ind, out); + } + + // aten::linalg_tensorinv.out(Tensor self, int ind=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_tensorinv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t ind, at::Tensor & out) { + return at::_ops::linalg_tensorinv_out::redispatch(dispatchKeySet, self, ind, out); + } + + // aten::linalg_tensorsolve(Tensor self, Tensor other, int[]? dims=None) -> Tensor + inline at::Tensor linalg_tensorsolve(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims=c10::nullopt) { + return at::_ops::linalg_tensorsolve::redispatch(dispatchKeySet, self, other, dims); + } + + // aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_tensorsolve_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims=c10::nullopt) { + return at::_ops::linalg_tensorsolve_out::redispatch(dispatchKeySet, self, other, dims, out); + } + + // aten::linalg_tensorsolve.out(Tensor self, Tensor other, int[]? dims=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_tensorsolve_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims, at::Tensor & out) { + return at::_ops::linalg_tensorsolve_out::redispatch(dispatchKeySet, self, other, dims, out); + } + + // aten::linalg_qr(Tensor A, str mode='reduced') -> (Tensor Q, Tensor R) + inline ::std::tuple linalg_qr(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::string_view mode="reduced") { + return at::_ops::linalg_qr::redispatch(dispatchKeySet, A, mode); + } + + // aten::linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) + inline ::std::tuple linalg_qr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & Q, at::Tensor & R, const at::Tensor & A, c10::string_view mode="reduced") { + return at::_ops::linalg_qr_out::redispatch(dispatchKeySet, A, mode, Q, R); + } + + // aten::linalg_qr.out(Tensor A, str mode='reduced', *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R) + inline ::std::tuple linalg_qr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & A, c10::string_view mode, at::Tensor & Q, at::Tensor & R) { + return at::_ops::linalg_qr_out::redispatch(dispatchKeySet, A, mode, Q, R); + } + + // aten::linalg_matrix_power(Tensor self, int n) -> Tensor + inline at::Tensor linalg_matrix_power(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n) { + return at::_ops::linalg_matrix_power::redispatch(dispatchKeySet, self, n); + } + + // aten::linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_power_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t n) { + return at::_ops::linalg_matrix_power_out::redispatch(dispatchKeySet, self, n, out); + } + + // aten::linalg_matrix_power.out(Tensor self, int n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_power_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t n, at::Tensor & out) { + return at::_ops::linalg_matrix_power_out::redispatch(dispatchKeySet, self, n, out); + } + + // aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor + inline at::Tensor linalg_matrix_rank(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_atol_rtol_tensor::redispatch(dispatchKeySet, input, atol, rtol, hermitian); + } + + // aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::redispatch(dispatchKeySet, input, atol, rtol, hermitian, out); + } + + // aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & atol, const c10::optional & rtol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::redispatch(dispatchKeySet, input, atol, rtol, hermitian, out); + } + + // aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor + inline at::Tensor linalg_matrix_rank(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_atol_rtol_float::redispatch(dispatchKeySet, self, atol, rtol, hermitian); + } + + // aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_atol_rtol_float_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_matrix_rank_atol_rtol_float_out::redispatch(dispatchKeySet, self, atol, rtol, hermitian, out); + } + + // aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor + inline at::Tensor linalg_matrix_rank(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double tol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank::redispatch(dispatchKeySet, self, tol, hermitian); + } + + // aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double tol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_out::redispatch(dispatchKeySet, self, tol, hermitian, out); + } + + // aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_matrix_rank_out::redispatch(dispatchKeySet, self, tol, hermitian, out); + } + + // aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor + inline at::Tensor linalg_matrix_rank(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_tol_tensor::redispatch(dispatchKeySet, input, tol, hermitian); + } + + // aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) { + return at::_ops::linalg_matrix_rank_out_tol_tensor::redispatch(dispatchKeySet, input, tol, hermitian, out); + } + + // aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_rank_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) { + return at::_ops::linalg_matrix_rank_out_tol_tensor::redispatch(dispatchKeySet, input, tol, hermitian, out); + } + + // aten::linalg_multi_dot(Tensor[] tensors) -> Tensor + inline at::Tensor linalg_multi_dot(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::linalg_multi_dot::redispatch(dispatchKeySet, tensors); + } + + // aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_multi_dot_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::linalg_multi_dot_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::linalg_multi_dot.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_multi_dot_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::linalg_multi_dot_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::nested_to_padded_tensor(Tensor self, float padding, int[]? output_size=None) -> Tensor + inline at::Tensor nested_to_padded_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=c10::nullopt) { + return at::_ops::nested_to_padded_tensor::redispatch(dispatchKeySet, self, padding, output_size); + } + + // aten::_test_serialization_subcmul(Tensor self, Tensor other, Scalar alpha=1) -> Tensor + inline at::Tensor _test_serialization_subcmul(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_test_serialization_subcmul::redispatch(dispatchKeySet, self, other, alpha); + } + + // aten::_test_parallel_materialize(Tensor self, int num_parallel, bool skip_first=False) -> Tensor + inline at::Tensor _test_parallel_materialize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t num_parallel, bool skip_first=false) { + return at::_ops::_test_parallel_materialize::redispatch(dispatchKeySet, self, num_parallel, skip_first); + } + + // aten::_test_optional_intlist(Tensor values, int[]? addends) -> Tensor + inline at::Tensor _test_optional_intlist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends) { + return at::_ops::_test_optional_intlist::redispatch(dispatchKeySet, values, addends); + } + + // aten::_test_optional_filled_intlist(Tensor values, int[2]? addends) -> Tensor + inline at::Tensor _test_optional_filled_intlist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends) { + return at::_ops::_test_optional_filled_intlist::redispatch(dispatchKeySet, values, addends); + } + + // aten::_test_optional_floatlist(Tensor values, float[]? addends) -> Tensor + inline at::Tensor _test_optional_floatlist(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, c10::optional> addends) { + return at::_ops::_test_optional_floatlist::redispatch(dispatchKeySet, values, addends); + } + + // aten::_test_string_default(Tensor dummy, str a="\"'\\", str b='"\'\\') -> Tensor + inline at::Tensor _test_string_default(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dummy, c10::string_view a="\"'\\", c10::string_view b="\"'\\") { + return at::_ops::_test_string_default::redispatch(dispatchKeySet, dummy, a, b); + } + + // aten::_test_ambiguous_defaults.a(Tensor dummy, int a=1, int b=1) -> Tensor + inline at::Tensor _test_ambiguous_defaults(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dummy, int64_t a=1, int64_t b=1) { + return at::_ops::_test_ambiguous_defaults_a::redispatch(dispatchKeySet, dummy, a, b); + } + + // aten::_test_ambiguous_defaults.b(Tensor dummy, int a=2, str b="2") -> Tensor + inline at::Tensor _test_ambiguous_defaults(c10::DispatchKeySet dispatchKeySet, const at::Tensor & dummy, int64_t a, c10::string_view b) { + return at::_ops::_test_ambiguous_defaults_b::redispatch(dispatchKeySet, dummy, a, b); + } + + // aten::_test_warn_in_autograd(Tensor self) -> Tensor + inline at::Tensor _test_warn_in_autograd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_test_warn_in_autograd::redispatch(dispatchKeySet, self); + } + + // aten::_test_autograd_multiple_dispatch.fullcoverage(Tensor self) -> Tensor + inline at::Tensor _test_autograd_multiple_dispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_test_autograd_multiple_dispatch_fullcoverage::redispatch(dispatchKeySet, self); + } + + // aten::_test_autograd_multiple_dispatch.ntonly(Tensor self, bool b) -> Tensor + inline at::Tensor _test_autograd_multiple_dispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool b) { + return at::_ops::_test_autograd_multiple_dispatch_ntonly::redispatch(dispatchKeySet, self, b); + } + + // aten::_test_autograd_multiple_dispatch_view(Tensor(a) self) -> Tensor(a) + inline at::Tensor _test_autograd_multiple_dispatch_view(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_test_autograd_multiple_dispatch_view::redispatch(dispatchKeySet, self); + } + + // aten::_test_autograd_multiple_dispatch_view_copy(Tensor self) -> Tensor + inline at::Tensor _test_autograd_multiple_dispatch_view_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_test_autograd_multiple_dispatch_view_copy::redispatch(dispatchKeySet, self); + } + + // aten::segment_reduce(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None) -> Tensor + inline at::Tensor segment_reduce(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths={}, const c10::optional & indices={}, const c10::optional & offsets={}, int64_t axis=0, bool unsafe=false, const c10::optional & initial=c10::nullopt) { + return at::_ops::segment_reduce::redispatch(dispatchKeySet, data, reduce, lengths, indices, offsets, axis, unsafe, initial); + } + + // aten::_segment_reduce_backward(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None) -> Tensor + inline at::Tensor _segment_reduce_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths={}, const c10::optional & offsets={}, int64_t axis=0, const c10::optional & initial=c10::nullopt) { + return at::_ops::_segment_reduce_backward::redispatch(dispatchKeySet, grad, output, data, reduce, lengths, offsets, axis, initial); + } + + // aten::pad_sequence(Tensor[] sequences, bool batch_first=False, float padding_value=0.0) -> Tensor + inline at::Tensor pad_sequence(c10::DispatchKeySet dispatchKeySet, at::TensorList sequences, bool batch_first=false, double padding_value=0.0) { + return at::_ops::pad_sequence::redispatch(dispatchKeySet, sequences, batch_first, padding_value); + } + + // aten::flatten_dense_tensors(Tensor[] tensors) -> Tensor + inline at::Tensor flatten_dense_tensors(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors) { + return at::_ops::flatten_dense_tensors::redispatch(dispatchKeySet, tensors); + } + + // aten::unflatten_dense_tensors(Tensor flat, Tensor[] tensors) -> Tensor[] + inline ::std::vector unflatten_dense_tensors(c10::DispatchKeySet dispatchKeySet, const at::Tensor & flat, at::TensorList tensors) { + return at::_ops::unflatten_dense_tensors::redispatch(dispatchKeySet, flat, tensors); + } + + // aten::_nested_tensor_from_tensor_list(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor + inline at::Tensor _nested_tensor_from_tensor_list(c10::DispatchKeySet dispatchKeySet, at::TensorList list, c10::optional dtype=c10::nullopt, c10::optional layout=c10::nullopt, c10::optional device=c10::nullopt, c10::optional pin_memory=c10::nullopt) { + return at::_ops::_nested_tensor_from_tensor_list::redispatch(dispatchKeySet, list, dtype, layout, device, pin_memory); + } + + // aten::_fw_primal_copy(Tensor self, int level) -> Tensor + inline at::Tensor _fw_primal_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level) { + return at::_ops::_fw_primal_copy::redispatch(dispatchKeySet, self, level); + } + + // aten::_make_dual_copy(Tensor primal, Tensor tangent, int level) -> Tensor + inline at::Tensor _make_dual_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { + return at::_ops::_make_dual_copy::redispatch(dispatchKeySet, primal, tangent, level); + } + + // aten::view_as_real_copy(Tensor self) -> Tensor + inline at::Tensor view_as_real_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::view_as_real_copy::redispatch(dispatchKeySet, self); + } + + // aten::view_as_complex_copy(Tensor self) -> Tensor + inline at::Tensor view_as_complex_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::view_as_complex_copy::redispatch(dispatchKeySet, self); + } + + // aten::_conj_copy(Tensor self) -> Tensor + inline at::Tensor _conj_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_conj_copy::redispatch(dispatchKeySet, self); + } + + // aten::_neg_view_copy(Tensor self) -> Tensor + inline at::Tensor _neg_view_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_neg_view_copy::redispatch(dispatchKeySet, self); + } + + // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor + inline at::Tensor as_strided_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt); + } + + // aten::as_strided_copy(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None) -> Tensor + inline at::Tensor as_strided_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_copy::redispatch(dispatchKeySet, self, size, stride, storage_offset); + } + + // aten::_sparse_broadcast_to_copy(Tensor self, int[] size) -> Tensor + inline at::Tensor _sparse_broadcast_to_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_sparse_broadcast_to_copy::redispatch(dispatchKeySet, self, size); + } + + // aten::diagonal_copy(Tensor self, int offset=0, int dim1=0, int dim2=1) -> Tensor + inline at::Tensor diagonal_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) { + return at::_ops::diagonal_copy::redispatch(dispatchKeySet, self, offset, dim1, dim2); + } + + // aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor + inline at::Tensor expand_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, bool implicit=false) { + return at::_ops::expand_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), implicit); + } + + // aten::expand_copy(Tensor self, SymInt[] size, *, bool implicit=False) -> Tensor + inline at::Tensor expand_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) { + return at::_ops::expand_copy::redispatch(dispatchKeySet, self, size, implicit); + } + + // aten::permute_copy(Tensor self, int[] dims) -> Tensor + inline at::Tensor permute_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::permute_copy::redispatch(dispatchKeySet, self, dims); + } + + // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor + inline at::Tensor _reshape_alias_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { + return at::_ops::_reshape_alias_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); + } + + // aten::_reshape_alias_copy(Tensor self, SymInt[] size, SymInt[] stride) -> Tensor + inline at::Tensor _reshape_alias_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + return at::_ops::_reshape_alias_copy::redispatch(dispatchKeySet, self, size, stride); + } + + // aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor + inline at::Tensor select_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t index) { + return at::_ops::select_copy_int::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::select_copy.int(Tensor self, int dim, SymInt index) -> Tensor + inline at::Tensor select_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index) { + return at::_ops::select_copy_int::redispatch(dispatchKeySet, self, dim, index); + } + + // aten::detach_copy(Tensor self) -> Tensor + inline at::Tensor detach_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::detach_copy::redispatch(dispatchKeySet, self); + } + + // aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor + inline at::Tensor slice_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_copy_Tensor::redispatch(dispatchKeySet, self, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step); + } + + // aten::slice_copy.Tensor(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1) -> Tensor + inline at::Tensor slice_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_copy_Tensor::redispatch(dispatchKeySet, self, dim, start, end, step); + } + + // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] + inline ::std::vector split_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t split_size, int64_t dim=0) { + return at::_ops::split_copy_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::split_copy.Tensor(Tensor self, SymInt split_size, int dim=0) -> Tensor[] + inline ::std::vector split_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) { + return at::_ops::split_copy_Tensor::redispatch(dispatchKeySet, self, split_size, dim); + } + + // aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] + inline ::std::vector split_with_sizes_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim); + } + + // aten::split_with_sizes_copy(Tensor self, SymInt[] split_sizes, int dim=0) -> Tensor[] + inline ::std::vector split_with_sizes_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes_copy::redispatch(dispatchKeySet, self, split_sizes, dim); + } + + // aten::squeeze_copy(Tensor self) -> Tensor + inline at::Tensor squeeze_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::squeeze_copy::redispatch(dispatchKeySet, self); + } + + // aten::squeeze_copy.dim(Tensor self, int dim) -> Tensor + inline at::Tensor squeeze_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::squeeze_copy_dim::redispatch(dispatchKeySet, self, dim); + } + + // aten::squeeze_copy.dims(Tensor self, int[] dim) -> Tensor + inline at::Tensor squeeze_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::squeeze_copy_dims::redispatch(dispatchKeySet, self, dim); + } + + // aten::t_copy(Tensor self) -> Tensor + inline at::Tensor t_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::t_copy::redispatch(dispatchKeySet, self); + } + + // aten::transpose_copy.int(Tensor self, int dim0, int dim1) -> Tensor + inline at::Tensor transpose_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::transpose_copy_int::redispatch(dispatchKeySet, self, dim0, dim1); + } + + // aten::unsqueeze_copy(Tensor self, int dim) -> Tensor + inline at::Tensor unsqueeze_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim) { + return at::_ops::unsqueeze_copy::redispatch(dispatchKeySet, self, dim); + } + + // aten::_indices_copy(Tensor self) -> Tensor + inline at::Tensor _indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::_values_copy(Tensor self) -> Tensor + inline at::Tensor _values_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::_values_copy::redispatch(dispatchKeySet, self); + } + + // aten::indices_copy(Tensor self) -> Tensor + inline at::Tensor indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::values_copy(Tensor self) -> Tensor + inline at::Tensor values_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::values_copy::redispatch(dispatchKeySet, self); + } + + // aten::crow_indices_copy(Tensor self) -> Tensor + inline at::Tensor crow_indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::crow_indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::col_indices_copy(Tensor self) -> Tensor + inline at::Tensor col_indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::col_indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::ccol_indices_copy(Tensor self) -> Tensor + inline at::Tensor ccol_indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::ccol_indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::row_indices_copy(Tensor self) -> Tensor + inline at::Tensor row_indices_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::row_indices_copy::redispatch(dispatchKeySet, self); + } + + // aten::unbind_copy.int(Tensor self, int dim=0) -> Tensor[] + inline ::std::vector unbind_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim=0) { + return at::_ops::unbind_copy_int::redispatch(dispatchKeySet, self, dim); + } + + // aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> () + inline void unbind_copy_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, int64_t dim=0) { + return at::_ops::unbind_copy_int_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::unbind_copy.int_out(Tensor self, int dim=0, *, Tensor(a!)[] out) -> () + inline void unbind_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::TensorList out) { + return at::_ops::unbind_copy_int_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_copy_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) { + return at::_ops::split_copy_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) { + return at::_ops::split_copy_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) { + return at::_ops::split_copy_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::split_copy.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { + return at::_ops::split_copy_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_with_sizes_copy_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim, out); + } + + // aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_with_sizes_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) { + return at::_ops::split_with_sizes_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim, out); + } + + // aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_with_sizes_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::split_with_sizes_copy_out::redispatch(dispatchKeySet, self, split_sizes, dim, out); + } + + // aten::split_with_sizes_copy.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void split_with_sizes_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { + return at::_ops::split_with_sizes_copy_out::redispatch(dispatchKeySet, self, split_sizes, dim, out); + } + + // aten::view_copy(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor view_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::view_copy::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size)); + } + + // aten::view_copy(Tensor self, SymInt[] size) -> Tensor + inline at::Tensor view_copy_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::view_copy::redispatch(dispatchKeySet, self, size); + } + + // aten::view_copy.dtype(Tensor self, ScalarType dtype) -> Tensor + inline at::Tensor view_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype) { + return at::_ops::view_copy_dtype::redispatch(dispatchKeySet, self, dtype); + } + + // aten::unfold_copy(Tensor self, int dimension, int size, int step) -> Tensor + inline at::Tensor unfold_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { + return at::_ops::unfold_copy::redispatch(dispatchKeySet, self, dimension, size, step); + } + + // aten::alias_copy(Tensor self) -> Tensor + inline at::Tensor alias_copy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::alias_copy::redispatch(dispatchKeySet, self); + } + + // aten::to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor + inline at::Tensor to_padded_tensor(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=c10::nullopt) { + return at::_ops::to_padded_tensor::redispatch(dispatchKeySet, self, padding, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt); + } + + // aten::to_padded_tensor(Tensor self, float padding, SymInt[]? output_size=None) -> Tensor + inline at::Tensor to_padded_tensor_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=c10::nullopt) { + return at::_ops::to_padded_tensor::redispatch(dispatchKeySet, self, padding, output_size); + } + + // aten::_nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor + inline at::Tensor _nested_tensor_softmax_with_shape(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & query) { + return at::_ops::_nested_tensor_softmax_with_shape::redispatch(dispatchKeySet, self, query); + } + + // aten::_transformer_encoder_layer_fwd(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None) -> Tensor + inline at::Tensor _transformer_encoder_layer_fwd(c10::DispatchKeySet dispatchKeySet, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional & mask={}, c10::optional mask_type=c10::nullopt) { + return at::_ops::_transformer_encoder_layer_fwd::redispatch(dispatchKeySet, src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type); + } + + // aten::_native_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None) -> (Tensor, Tensor) + inline ::std::tuple _native_multi_head_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask={}, bool need_weights=true, bool average_attn_weights=true, c10::optional mask_type=c10::nullopt) { + return at::_ops::_native_multi_head_attention::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); + } + + // aten::scaled_dot_product_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> Tensor + inline at::Tensor scaled_dot_product_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask={}, double dropout_p=0.0, bool is_causal=false, c10::optional scale=c10::nullopt) { + return at::_ops::scaled_dot_product_attention::redispatch(dispatchKeySet, query, key, value, attn_mask, dropout_p, is_causal, scale); + } + + // aten::_fused_sdp_choice(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> int + inline int64_t _fused_sdp_choice(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask={}, double dropout_p=0.0, bool is_causal=false, c10::optional scale=c10::nullopt) { + return at::_ops::_fused_sdp_choice::redispatch(dispatchKeySet, query, key, value, attn_mask, dropout_p, is_causal, scale); + } + + // aten::_scaled_dot_product_attention_math(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, Tensor? dropout_mask=None, *, float? scale=None) -> (Tensor, Tensor) + inline ::std::tuple _scaled_dot_product_attention_math(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask={}, double dropout_p=0.0, bool is_causal=false, const c10::optional & dropout_mask={}, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_attention_math::redispatch(dispatchKeySet, query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale); + } + + // aten::_scaled_dot_product_flash_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) + inline ::std::tuple _scaled_dot_product_flash_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_flash_attention::redispatch(dispatchKeySet, query, key, value, dropout_p, is_causal, return_debug_mask, scale); + } + + // aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp) + inline ::std::tuple _scaled_dot_product_flash_attention_for_cpu(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, const c10::optional & attn_mask={}, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_flash_attention_for_cpu::redispatch(dispatchKeySet, query, key, value, dropout_p, is_causal, attn_mask, scale); + } + + // aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value) + inline ::std::tuple _scaled_dot_product_flash_attention_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_flash_attention_backward::redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + + // aten::_scaled_dot_product_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value) + inline ::std::tuple _scaled_dot_product_flash_attention_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_flash_attention_backward::redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + + // aten::_scaled_dot_product_flash_attention_for_cpu_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, float dropout_p, bool is_causal, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor grad_query, Tensor grad_key, Tensor grad_value) + inline ::std::tuple _scaled_dot_product_flash_attention_for_cpu_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, double dropout_p, bool is_causal, const c10::optional & attn_mask={}, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_flash_attention_for_cpu_backward::redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, dropout_p, is_causal, attn_mask, scale); + } + + // aten::_scaled_dot_product_efficient_attention(Tensor query, Tensor key, Tensor value, Tensor? attn_bias, bool compute_log_sumexp, float dropout_p=0.0, bool is_causal=False, *, float? scale=None) -> (Tensor output, Tensor log_sumexp, Tensor philox_seed, Tensor philox_offset) + inline ::std::tuple _scaled_dot_product_efficient_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_bias, bool compute_log_sumexp, double dropout_p=0.0, bool is_causal=false, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_efficient_attention::redispatch(dispatchKeySet, query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, scale); + } + + // aten::_scaled_dot_product_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor attn_bias, Tensor out, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, float dropout_p, bool[4] grad_input_mask, bool is_causal=False, *, float? scale=None) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _scaled_dot_product_efficient_attention_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, double dropout_p, ::std::array grad_input_mask, bool is_causal=false, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_efficient_attention_backward::redispatch(dispatchKeySet, grad_out_, query, key, value, attn_bias, out, logsumexp, philox_seed, philox_offset, dropout_p, grad_input_mask, is_causal, scale); + } + + // aten::_scaled_dot_product_cudnn_attention(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, bool return_debug_mask=False, *, float? scale=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset) + inline ::std::tuple _scaled_dot_product_cudnn_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, bool return_debug_mask=false, c10::optional scale=c10::nullopt) { + return at::_ops::_scaled_dot_product_cudnn_attention::redispatch(dispatchKeySet, query, key, value, dropout_p, is_causal, return_debug_mask, scale); + } + + // aten::_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) + inline ::std::tuple _flash_attention_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & cum_seq_q, const c10::optional & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, bool return_debug_mask, c10::optional scale=c10::nullopt) { + return at::_ops::_flash_attention_forward::redispatch(dispatchKeySet, query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale); + } + + // aten::_flash_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? cum_seq_q, Tensor? cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, bool return_debug_mask, *, float? scale=None) -> (Tensor output, Tensor softmax_logsumexp, Tensor philox_seed, Tensor philox_offset, Tensor debug_attn_mask) + inline ::std::tuple _flash_attention_forward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & cum_seq_q, const c10::optional & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, c10::optional scale=c10::nullopt) { + return at::_ops::_flash_attention_forward::redispatch(dispatchKeySet, query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale); + } + + // aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _flash_attention_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, int64_t max_q, int64_t max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale=c10::nullopt) { + return at::_ops::_flash_attention_backward::redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + + // aten::_flash_attention_backward(Tensor grad_out, Tensor query, Tensor key, Tensor value, Tensor out, Tensor logsumexp, Tensor cum_seq_q, Tensor cum_seq_k, SymInt max_q, SymInt max_k, float dropout_p, bool is_causal, Tensor philox_seed, Tensor philox_offset, *, float? scale=None) -> (Tensor, Tensor, Tensor) + inline ::std::tuple _flash_attention_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale=c10::nullopt) { + return at::_ops::_flash_attention_backward::redispatch(dispatchKeySet, grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + + // aten::_efficient_attention_forward(Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, int? max_seqlen_q, int? max_seqlen_k, float dropout_p, int custom_mask_type, bool compute_log_sumexp=False, *, float? scale=None, Tensor? causal_diagonal=None, Tensor? seqlen_k=None) -> (Tensor output, Tensor logsumexp, Tensor philox_seed, Tensor philox_offset, SymInt max_seqlen_batch_q, SymInt max_seqlen_batch_k) + inline ::std::tuple _efficient_attention_forward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & bias, const c10::optional & cu_seqlens_q, const c10::optional & cu_seqlens_k, c10::optional max_seqlen_q, c10::optional max_seqlen_k, double dropout_p, int64_t custom_mask_type, bool compute_log_sumexp=false, c10::optional scale=c10::nullopt, const c10::optional & causal_diagonal={}, const c10::optional & seqlen_k={}) { + return at::_ops::_efficient_attention_forward::redispatch(dispatchKeySet, query, key, value, bias, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, custom_mask_type, compute_log_sumexp, scale, causal_diagonal, seqlen_k); + } + + // aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _efficient_attention_backward(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & bias, const at::Tensor & out, const c10::optional & cu_seqlens_q, const c10::optional & cu_seqlens_k, int64_t max_seqlen_q, int64_t max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, c10::optional scale=c10::nullopt, c10::optional num_splits_key=c10::nullopt) { + return at::_ops::_efficient_attention_backward::redispatch(dispatchKeySet, grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key); + } + + // aten::_efficient_attention_backward(Tensor grad_out_, Tensor query, Tensor key, Tensor value, Tensor? bias, Tensor out, Tensor? cu_seqlens_q, Tensor? cu_seqlens_k, SymInt max_seqlen_q, SymInt max_seqlen_k, Tensor logsumexp, float dropout_p, Tensor philox_seed, Tensor philox_offset, int custom_mask_type, bool bias_requires_grad, *, float? scale=None, int? num_splits_key=None) -> (Tensor, Tensor, Tensor, Tensor) + inline ::std::tuple _efficient_attention_backward_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & bias, const at::Tensor & out, const c10::optional & cu_seqlens_q, const c10::optional & cu_seqlens_k, c10::SymInt max_seqlen_q, c10::SymInt max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, c10::optional scale=c10::nullopt, c10::optional num_splits_key=c10::nullopt) { + return at::_ops::_efficient_attention_backward::redispatch(dispatchKeySet, grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key); + } + + // aten::_triton_scaled_dot_attention(Tensor q, Tensor k, Tensor v, float dropout_p=0.0) -> Tensor + inline at::Tensor _triton_scaled_dot_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p=0.0) { + return at::_ops::_triton_scaled_dot_attention::redispatch(dispatchKeySet, q, k, v, dropout_p); + } + + // aten::_fill_mem_eff_dropout_mask_(Tensor(a!) self, float dropout_p, int seed, int offset) -> Tensor(a!) + inline at::Tensor & _fill_mem_eff_dropout_mask_(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, double dropout_p, int64_t seed, int64_t offset) { + return at::_ops::_fill_mem_eff_dropout_mask_::redispatch(dispatchKeySet, self, dropout_p, seed, offset); + } + + // aten::_triton_multi_head_attention(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None) -> Tensor + inline at::Tensor _triton_multi_head_attention(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask={}) { + return at::_ops::_triton_multi_head_attention::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask); + } + + // aten::special_airy_ai(Tensor x) -> Tensor + inline at::Tensor special_airy_ai(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x) { + return at::_ops::special_airy_ai::redispatch(dispatchKeySet, x); + } + + // aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_airy_ai_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x) { + return at::_ops::special_airy_ai_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_airy_ai.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_airy_ai_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { + return at::_ops::special_airy_ai_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_bessel_j0(Tensor self) -> Tensor + inline at::Tensor special_bessel_j0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_bessel_j0::redispatch(dispatchKeySet, self); + } + + // aten::special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_j0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_bessel_j0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_j0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_j0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_bessel_j0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_j1(Tensor self) -> Tensor + inline at::Tensor special_bessel_j1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_bessel_j1::redispatch(dispatchKeySet, self); + } + + // aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_j1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_bessel_j1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_j1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_j1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_bessel_j1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_y0(Tensor self) -> Tensor + inline at::Tensor special_bessel_y0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_bessel_y0::redispatch(dispatchKeySet, self); + } + + // aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_y0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_bessel_y0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_y0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_y0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_bessel_y0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_y1(Tensor self) -> Tensor + inline at::Tensor special_bessel_y1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_bessel_y1::redispatch(dispatchKeySet, self); + } + + // aten::special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_y1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_bessel_y1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_bessel_y1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_bessel_y1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_t::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_t_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_t_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_t_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_t_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_t_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_t_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_t_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_t_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_u::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_u_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_u_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_u_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_u_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_u_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_u_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_u_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_u_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_v::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_v_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_v_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_v_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_v_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_v_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_v_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_v_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_v_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_w::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_w_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_w_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_w_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_w_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_chebyshev_polynomial_w_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_w_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_chebyshev_polynomial_w_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_chebyshev_polynomial_w_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_hermite_polynomial_h(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_h::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_h.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_hermite_polynomial_h(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_h_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_h.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_hermite_polynomial_h(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_hermite_polynomial_h_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_h_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_h_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_h_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_h_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_hermite_polynomial_h_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_h.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_h_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_h_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_hermite_polynomial_he(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_he::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_he.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_hermite_polynomial_he(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_he_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_he.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_hermite_polynomial_he(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_hermite_polynomial_he_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_he_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_he_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_hermite_polynomial_he_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_he_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_hermite_polynomial_he_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_hermite_polynomial_he.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_hermite_polynomial_he_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_hermite_polynomial_he_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_laguerre_polynomial_l(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_laguerre_polynomial_l::redispatch(dispatchKeySet, x, n); + } + + // aten::special_laguerre_polynomial_l.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_laguerre_polynomial_l(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_laguerre_polynomial_l_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_laguerre_polynomial_l.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_laguerre_polynomial_l(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_laguerre_polynomial_l_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_laguerre_polynomial_l_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_laguerre_polynomial_l_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_laguerre_polynomial_l_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_laguerre_polynomial_l_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_laguerre_polynomial_l_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_laguerre_polynomial_l.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_laguerre_polynomial_l_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_laguerre_polynomial_l_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_legendre_polynomial_p(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_legendre_polynomial_p::redispatch(dispatchKeySet, x, n); + } + + // aten::special_legendre_polynomial_p.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_legendre_polynomial_p(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_legendre_polynomial_p_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_legendre_polynomial_p.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_legendre_polynomial_p(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_legendre_polynomial_p_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_legendre_polynomial_p_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_legendre_polynomial_p_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_legendre_polynomial_p_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_legendre_polynomial_p_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_legendre_polynomial_p_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_legendre_polynomial_p.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_legendre_polynomial_p_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_legendre_polynomial_p_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_modified_bessel_i0(Tensor self) -> Tensor + inline at::Tensor special_modified_bessel_i0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_modified_bessel_i0::redispatch(dispatchKeySet, self); + } + + // aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_i0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_modified_bessel_i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_i0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_i0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_modified_bessel_i0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_i1(Tensor self) -> Tensor + inline at::Tensor special_modified_bessel_i1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_modified_bessel_i1::redispatch(dispatchKeySet, self); + } + + // aten::special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_i1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_modified_bessel_i1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_i1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_i1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_modified_bessel_i1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_k0(Tensor self) -> Tensor + inline at::Tensor special_modified_bessel_k0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_modified_bessel_k0::redispatch(dispatchKeySet, self); + } + + // aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_k0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_modified_bessel_k0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_k0.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_k0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_modified_bessel_k0_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_k1(Tensor self) -> Tensor + inline at::Tensor special_modified_bessel_k1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::special_modified_bessel_k1::redispatch(dispatchKeySet, self); + } + + // aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_k1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::special_modified_bessel_k1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_modified_bessel_k1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_modified_bessel_k1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::special_modified_bessel_k1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::special_scaled_modified_bessel_k0(Tensor x) -> Tensor + inline at::Tensor special_scaled_modified_bessel_k0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x) { + return at::_ops::special_scaled_modified_bessel_k0::redispatch(dispatchKeySet, x); + } + + // aten::special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_scaled_modified_bessel_k0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x) { + return at::_ops::special_scaled_modified_bessel_k0_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_scaled_modified_bessel_k0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_scaled_modified_bessel_k0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { + return at::_ops::special_scaled_modified_bessel_k0_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_scaled_modified_bessel_k1(Tensor x) -> Tensor + inline at::Tensor special_scaled_modified_bessel_k1(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x) { + return at::_ops::special_scaled_modified_bessel_k1::redispatch(dispatchKeySet, x); + } + + // aten::special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_scaled_modified_bessel_k1_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x) { + return at::_ops::special_scaled_modified_bessel_k1_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_scaled_modified_bessel_k1.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_scaled_modified_bessel_k1_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { + return at::_ops::special_scaled_modified_bessel_k1_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_t(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_t_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_t_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_u.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_u.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_u(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_u_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_u.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_u_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_v(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_v_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_v_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w(Tensor x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_w.x_scalar(Scalar x, Tensor n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_w.n_scalar(Tensor x, Scalar n) -> Tensor + inline at::Tensor special_shifted_chebyshev_polynomial_w(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar::redispatch(dispatchKeySet, x, n); + } + + // aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_w_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & x, const at::Tensor & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Scalar & n) { + return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_shifted_chebyshev_polynomial_w.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_shifted_chebyshev_polynomial_w_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out) { + return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar_out::redispatch(dispatchKeySet, x, n, out); + } + + // aten::special_spherical_bessel_j0(Tensor x) -> Tensor + inline at::Tensor special_spherical_bessel_j0(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x) { + return at::_ops::special_spherical_bessel_j0::redispatch(dispatchKeySet, x); + } + + // aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_spherical_bessel_j0_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x) { + return at::_ops::special_spherical_bessel_j0_out::redispatch(dispatchKeySet, x, out); + } + + // aten::special_spherical_bessel_j0.out(Tensor x, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & special_spherical_bessel_j0_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, at::Tensor & out) { + return at::_ops::special_spherical_bessel_j0_out::redispatch(dispatchKeySet, x, out); + } + + // aten::_foobar(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True) -> Tensor + inline at::Tensor _foobar(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool arg1=true, bool arg2=true, bool arg3=true) { + return at::_ops::_foobar::redispatch(dispatchKeySet, self, arg1, arg2, arg3); + } + + // aten::_fused_adam_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_adam_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam_::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adam_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_adam_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam__tensor_lr::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adamw_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_adamw_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw_::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adamw_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_adamw_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw__tensor_lr::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_sgd_(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_sgd_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd_::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + + // aten::_fused_sgd_.tensor_lr(Tensor(a!)[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> () + inline void _fused_sgd_(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd__tensor_lr::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + + // aten::_propagate_xla_data(Tensor input, Tensor output) -> () + inline void _propagate_xla_data(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & output) { + return at::_ops::_propagate_xla_data::redispatch(dispatchKeySet, input, output); + } + + // aten::_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _new_zeros_with_same_feature_meta_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims=0) { + return at::_ops::_new_zeros_with_same_feature_meta_out::redispatch(dispatchKeySet, self, other, self_num_batch_dims, out); + } + + // aten::_new_zeros_with_same_feature_meta.out(Tensor self, Tensor other, *, int self_num_batch_dims=0, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _new_zeros_with_same_feature_meta_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims, at::Tensor & out) { + return at::_ops::_new_zeros_with_same_feature_meta_out::redispatch(dispatchKeySet, self, other, self_num_batch_dims, out); + } + + // aten::_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _cudnn_ctc_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { + return at::_ops::_cudnn_ctc_loss_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0, out1); + } + + // aten::_cudnn_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank, bool deterministic, bool zero_infinity, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _cudnn_ctc_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_cudnn_ctc_loss_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity, out0, out1); + } + + // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_rnn_flatten_weight_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { + return at::_ops::_cudnn_rnn_flatten_weight_out::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); + } + + // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_rnn_flatten_weight_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) { + return at::_ops::_cudnn_rnn_flatten_weight_out::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); + } + + // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_rnn_flatten_weight_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { + return at::_ops::_cudnn_rnn_flatten_weight_out::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); + } + + // aten::_cudnn_rnn_flatten_weight.out(Tensor[] weight_arr, int weight_stride0, SymInt input_size, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, bool bidirectional, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_rnn_flatten_weight_symint_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out) { + return at::_ops::_cudnn_rnn_flatten_weight_out::redispatch(dispatchKeySet, weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional, out); + } + + // aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _cudnn_rnn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::_cudnn_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4); + } + + // aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _cudnn_rnn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { + return at::_ops::_cudnn_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, out0, out1, out2, out3, out4); + } + + // aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _cudnn_rnn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::_cudnn_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4); + } + + // aten::_cudnn_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor? weight_buf, Tensor hx, Tensor? cx, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _cudnn_rnn_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { + return at::_ops::_cudnn_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4); + } + + // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void _cudnn_rnn_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::_cudnn_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void _cudnn_rnn_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { + return at::_ops::_cudnn_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, c10::fromIntArrayRefSlow(batch_sizes), dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void _cudnn_rnn_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::_cudnn_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::_cudnn_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, SymInt hidden_size, SymInt proj_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, SymInt[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void _cudnn_rnn_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { + return at::_ops::_cudnn_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_init_dropout_state_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, double dropout, bool train, int64_t dropout_seed) { + return at::_ops::_cudnn_init_dropout_state_out::redispatch(dispatchKeySet, dropout, train, dropout_seed, out); + } + + // aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cudnn_init_dropout_state_outf(c10::DispatchKeySet dispatchKeySet, double dropout, bool train, int64_t dropout_seed, at::Tensor & out) { + return at::_ops::_cudnn_init_dropout_state_out::redispatch(dispatchKeySet, dropout, train, dropout_seed, out); + } + + // aten::_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _fused_dropout_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::_fused_dropout_out::redispatch(dispatchKeySet, self, p, generator, out0, out1); + } + + // aten::_fused_dropout.out(Tensor self, float p, Generator? generator=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _fused_dropout_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_fused_dropout_out::redispatch(dispatchKeySet, self, p, generator, out0, out1); + } + + // aten::_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_scale_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, double scale) { + return at::_ops::_masked_scale_out::redispatch(dispatchKeySet, self, mask, scale, out); + } + + // aten::_masked_scale.out(Tensor self, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_scale_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, double scale, at::Tensor & out) { + return at::_ops::_masked_scale_out::redispatch(dispatchKeySet, self, mask, scale, out); + } + + // aten::native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple native_dropout_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, double p, c10::optional train) { + return at::_ops::native_dropout_out::redispatch(dispatchKeySet, input, p, train, out0, out1); + } + + // aten::native_dropout.out(Tensor input, float p, bool? train, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple native_dropout_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double p, c10::optional train, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::native_dropout_out::redispatch(dispatchKeySet, input, p, train, out0, out1); + } + + // aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_dropout_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & mask, double scale) { + return at::_ops::native_dropout_backward_out::redispatch(dispatchKeySet, grad_output, mask, scale, out); + } + + // aten::native_dropout_backward.out(Tensor grad_output, Tensor mask, float scale, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_dropout_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & mask, double scale, at::Tensor & out) { + return at::_ops::native_dropout_backward_out::redispatch(dispatchKeySet, grad_output, mask, scale, out); + } + + // aten::_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _conj_physical_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_conj_physical_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_conj_physical.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _conj_physical_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_conj_physical_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _add_relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::_add_relu_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_add_relu.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _add_relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::_add_relu_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & add_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::add_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::add.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & add_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::add_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & affine_grid_generator_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & theta, at::IntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator_out::redispatch(dispatchKeySet, theta, c10::fromIntArrayRefSlow(size), align_corners, out); + } + + // aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & affine_grid_generator_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, at::IntArrayRef size, bool align_corners, at::Tensor & out) { + return at::_ops::affine_grid_generator_out::redispatch(dispatchKeySet, theta, c10::fromIntArrayRefSlow(size), align_corners, out); + } + + // aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & affine_grid_generator_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) { + return at::_ops::affine_grid_generator_out::redispatch(dispatchKeySet, theta, size, align_corners, out); + } + + // aten::affine_grid_generator.out(Tensor theta, SymInt[] size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & affine_grid_generator_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners, at::Tensor & out) { + return at::_ops::affine_grid_generator_out::redispatch(dispatchKeySet, theta, size, align_corners, out); + } + + // aten::_test_functorch_fallback.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_functorch_fallback_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::_test_functorch_fallback_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_test_functorch_fallback.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_functorch_fallback_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::_test_functorch_fallback_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bartlett_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length) { + return at::_ops::bartlett_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::bartlett_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bartlett_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { + return at::_ops::bartlett_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bartlett_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic) { + return at::_ops::bartlett_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::bartlett_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bartlett_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { + return at::_ops::bartlett_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_batch_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) { + return at::_ops::quantized_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, mean, var, eps, output_scale, output_zero_point, out); + } + + // aten::quantized_batch_norm.out(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor var, float eps, float output_scale, int output_zero_point, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_batch_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out) { + return at::_ops::quantized_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, mean, var, eps, output_scale, output_zero_point, out); + } + + // aten::bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & p, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli_Tensor_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::bernoulli.Tensor_out(Tensor self, Tensor p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & p, c10::optional generator, at::Tensor & out) { + return at::_ops::bernoulli_Tensor_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::bernoulli.Tensor(Tensor self, Tensor p, *, Generator? generator=None) -> Tensor + inline at::Tensor bernoulli(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & p, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli_Tensor::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double p=0.5, c10::optional generator=c10::nullopt) { + return at::_ops::bernoulli_float_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::bernoulli.float_out(Tensor self, float p=0.5, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bernoulli_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator, at::Tensor & out) { + return at::_ops::bernoulli_float_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_with_logits_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight={}, const c10::optional & pos_weight={}, int64_t reduction=at::Reduction::Mean) { + return at::_ops::binary_cross_entropy_with_logits_out::redispatch(dispatchKeySet, self, target, weight, pos_weight, reduction, out); + } + + // aten::binary_cross_entropy_with_logits.out(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binary_cross_entropy_with_logits_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, const c10::optional & pos_weight, int64_t reduction, at::Tensor & out) { + return at::_ops::binary_cross_entropy_with_logits_out::redispatch(dispatchKeySet, self, target, weight, pos_weight, reduction, out); + } + + // aten::bincount.out(Tensor self, Tensor? weights=None, int minlength=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bincount_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & weights={}, int64_t minlength=0) { + return at::_ops::bincount_out::redispatch(dispatchKeySet, self, weights, minlength, out); + } + + // aten::bincount.out(Tensor self, Tensor? weights=None, int minlength=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bincount_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & weights, int64_t minlength, at::Tensor & out) { + return at::_ops::bincount_out::redispatch(dispatchKeySet, self, weights, minlength, out); + } + + // aten::blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & blackman_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length) { + return at::_ops::blackman_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::blackman_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & blackman_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { + return at::_ops::blackman_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & blackman_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic) { + return at::_ops::blackman_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::blackman_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & blackman_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { + return at::_ops::blackman_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & block_diag_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList tensors) { + return at::_ops::block_diag_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::block_diag.out(Tensor[] tensors, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & block_diag_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::Tensor & out) { + return at::_ops::block_diag_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & constant_pad_nd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value=0) { + return at::_ops::constant_pad_nd_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad), value, out); + } + + // aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & constant_pad_nd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef pad, const at::Scalar & value, at::Tensor & out) { + return at::_ops::constant_pad_nd_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(pad), value, out); + } + + // aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & constant_pad_nd_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value=0) { + return at::_ops::constant_pad_nd_out::redispatch(dispatchKeySet, self, pad, value, out); + } + + // aten::constant_pad_nd.out(Tensor self, SymInt[] pad, Scalar value=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & constant_pad_nd_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value, at::Tensor & out) { + return at::_ops::constant_pad_nd_out::redispatch(dispatchKeySet, self, pad, value, out); + } + + // aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) { + return at::_ops::convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out); + } + + // aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) { + return at::_ops::convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out); + } + + // aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + return at::_ops::convolution_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out); + } + + // aten::convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) { + return at::_ops::convolution_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out); + } + + // aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask) { + return at::_ops::convolution_backward_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : c10::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalIntArrayRef bias_sizes, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::convolution_backward_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*bias_sizes)) : c10::nullopt, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::convolution_backward_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[]? bias_sizes, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::convolution_backward_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2); + } + + // aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_overrideable_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups) { + return at::_ops::convolution_overrideable_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out); + } + + // aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_overrideable_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, at::Tensor & out) { + return at::_ops::convolution_overrideable_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, out); + } + + // aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_overrideable_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + return at::_ops::convolution_overrideable_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out); + } + + // aten::convolution_overrideable.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & convolution_overrideable_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out) { + return at::_ops::convolution_overrideable_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out); + } + + // aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_overrideable_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask) { + return at::_ops::convolution_backward_overrideable_out::redispatch(dispatchKeySet, grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_overrideable_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::convolution_backward_overrideable_out::redispatch(dispatchKeySet, grad_output, input, weight, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_overrideable_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::convolution_backward_overrideable_out::redispatch(dispatchKeySet, grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2); + } + + // aten::convolution_backward_overrideable.out(Tensor grad_output, Tensor input, Tensor weight, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple convolution_backward_overrideable_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::convolution_backward_overrideable_out::redispatch(dispatchKeySet, grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask, out0, out1, out2); + } + + // aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) { + return at::_ops::_convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out); + } + + // aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, int64_t groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) { + return at::_ops::_convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), transposed, c10::fromIntArrayRefSlow(output_padding), groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out); + } + + // aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) { + return at::_ops::_convolution_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out); + } + + // aten::_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32, at::Tensor & out) { + return at::_ops::_convolution_out::redispatch(dispatchKeySet, input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32, out); + } + + // aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_tbc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad=0) { + return at::_ops::conv_tbc_out::redispatch(dispatchKeySet, self, weight, bias, pad, out); + } + + // aten::conv_tbc.out(Tensor self, Tensor weight, Tensor bias, int pad=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_tbc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad, at::Tensor & out) { + return at::_ops::conv_tbc_out::redispatch(dispatchKeySet, self, weight, bias, pad, out); + } + + // aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::copy.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { + return at::_ops::copy_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _copy_from_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & dst, bool non_blocking=false) { + return at::_ops::_copy_from_out::redispatch(dispatchKeySet, self, dst, non_blocking, out); + } + + // aten::_copy_from.out(Tensor self, Tensor dst, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _copy_from_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, bool non_blocking, at::Tensor & out) { + return at::_ops::_copy_from_out::redispatch(dispatchKeySet, self, dst, non_blocking, out); + } + + // aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _copy_from_and_resize_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & dst) { + return at::_ops::_copy_from_and_resize_out::redispatch(dispatchKeySet, self, dst, out); + } + + // aten::_copy_from_and_resize.out(Tensor self, Tensor dst, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _copy_from_and_resize_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & dst, at::Tensor & out) { + return at::_ops::_copy_from_and_resize_out::redispatch(dispatchKeySet, self, dst, out); + } + + // aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & count_nonzero_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::count_nonzero_dim_IntList_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::count_nonzero.dim_IntList_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & count_nonzero_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { + return at::_ops::count_nonzero_dim_IntList_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & count_nonzero_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt) { + return at::_ops::count_nonzero_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::count_nonzero.out(Tensor self, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & count_nonzero_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dim, at::Tensor & out) { + return at::_ops::count_nonzero_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_affine_grid_generator_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) { + return at::_ops::cudnn_affine_grid_generator_out::redispatch(dispatchKeySet, theta, N, C, H, W, out); + } + + // aten::cudnn_affine_grid_generator.out(Tensor theta, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_affine_grid_generator_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { + return at::_ops::cudnn_affine_grid_generator_out::redispatch(dispatchKeySet, theta, N, C, H, W, out); + } + + // aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_affine_grid_generator_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { + return at::_ops::cudnn_affine_grid_generator_backward_out::redispatch(dispatchKeySet, grad, N, C, H, W, out); + } + + // aten::cudnn_affine_grid_generator_backward.out(Tensor grad, int N, int C, int H, int W, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_affine_grid_generator_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W, at::Tensor & out) { + return at::_ops::cudnn_affine_grid_generator_backward_out::redispatch(dispatchKeySet, grad, N, C, H, W, out); + } + + // aten::cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple cudnn_batch_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + return at::_ops::cudnn_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2, out3); + } + + // aten::cudnn_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple cudnn_batch_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { + return at::_ops::cudnn_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2, out3); + } + + // aten::cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple cudnn_batch_norm_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon, const at::Tensor & reserveSpace) { + return at::_ops::cudnn_batch_norm_backward_out::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace, out0, out1, out2); + } + + // aten::cudnn_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, Tensor reserveSpace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple cudnn_batch_norm_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon, const at::Tensor & reserveSpace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::cudnn_batch_norm_backward_out::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace, out0, out1, out2); + } + + // aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_transpose_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_transpose_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) { + return at::_ops::cudnn_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_transpose_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + return at::_ops::cudnn_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::cudnn_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_transpose_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32, at::Tensor & out) { + return at::_ops::cudnn_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32, out); + } + + // aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_transpose_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::_mps_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_transpose_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::_mps_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_transpose_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::_mps_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups, out); + } + + // aten::_mps_convolution_transpose.out(Tensor self, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_transpose_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::_mps_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, padding, output_padding, stride, dilation, groups, out); + } + + // aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mps_convolution_transpose_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask) { + return at::_ops::mps_convolution_transpose_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1); + } + + // aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mps_convolution_transpose_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::mps_convolution_transpose_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1); + } + + // aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mps_convolution_transpose_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::mps_convolution_transpose_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1); + } + + // aten::mps_convolution_transpose_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mps_convolution_transpose_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::mps_convolution_transpose_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask, out0, out1); + } + + // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::cudnn_convolution_relu_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::cudnn_convolution_relu_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_relu_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::cudnn_convolution_relu_out::redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups, out); + } + + // aten::cudnn_convolution_relu.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_relu_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::cudnn_convolution_relu_out::redispatch(dispatchKeySet, self, weight, bias, stride, padding, dilation, groups, out); + } + + // aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_add_relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::cudnn_convolution_add_relu_out::redispatch(dispatchKeySet, self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_add_relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::cudnn_convolution_add_relu_out::redispatch(dispatchKeySet, self, weight, z, alpha, bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_add_relu_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::cudnn_convolution_add_relu_out::redispatch(dispatchKeySet, self, weight, z, alpha, bias, stride, padding, dilation, groups, out); + } + + // aten::cudnn_convolution_add_relu.out(Tensor self, Tensor weight, Tensor z, Scalar? alpha, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_convolution_add_relu_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::cudnn_convolution_add_relu_out::redispatch(dispatchKeySet, self, weight, z, alpha, bias, stride, padding, dilation, groups, out); + } + + // aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_grid_sampler_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & grid) { + return at::_ops::cudnn_grid_sampler_out::redispatch(dispatchKeySet, self, grid, out); + } + + // aten::cudnn_grid_sampler.out(Tensor self, Tensor grid, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cudnn_grid_sampler_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid, at::Tensor & out) { + return at::_ops::cudnn_grid_sampler_out::redispatch(dispatchKeySet, self, grid, out); + } + + // aten::cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple cudnn_grid_sampler_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output) { + return at::_ops::cudnn_grid_sampler_backward_out::redispatch(dispatchKeySet, self, grid, grad_output, out0, out1); + } + + // aten::cudnn_grid_sampler_backward.out(Tensor self, Tensor grid, Tensor grad_output, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple cudnn_grid_sampler_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::cudnn_grid_sampler_backward_out::redispatch(dispatchKeySet, self, grid, grad_output, out0, out1); + } + + // aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _ctc_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); + } + + // aten::_ctc_loss.out(Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _ctc_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_ctc_loss_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); + } + + // aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _ctc_loss_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank=0, bool zero_infinity=false) { + return at::_ops::_ctc_loss_Tensor_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); + } + + // aten::_ctc_loss.Tensor_out(Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, int blank=0, bool zero_infinity=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _ctc_loss_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_ctc_loss_Tensor_out::redispatch(dispatchKeySet, log_probs, targets, input_lengths, target_lengths, blank, zero_infinity, out0, out1); + } + + // aten::_ctc_loss_backward.out(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _ctc_loss_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity=false) { + return at::_ops::_ctc_loss_backward_out::redispatch(dispatchKeySet, grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity, out); + } + + // aten::_ctc_loss_backward.out(Tensor grad, Tensor log_probs, Tensor targets, int[] input_lengths, int[] target_lengths, Tensor neg_log_likelihood, Tensor log_alpha, int blank, bool zero_infinity=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _ctc_loss_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity, at::Tensor & out) { + return at::_ops::_ctc_loss_backward_out::redispatch(dispatchKeySet, grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity, out); + } + + // aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diag_embed_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1) { + return at::_ops::diag_embed_out::redispatch(dispatchKeySet, self, offset, dim1, dim2, out); + } + + // aten::diag_embed.out(Tensor self, int offset=0, int dim1=-2, int dim2=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diag_embed_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { + return at::_ops::diag_embed_out::redispatch(dispatchKeySet, self, offset, dim1, dim2, out); + } + + // aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) { + return at::_ops::diagonal_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out); + } + + // aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { + return at::_ops::diagonal_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), offset, dim1, dim2, out); + } + + // aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) { + return at::_ops::diagonal_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, offset, dim1, dim2, out); + } + + // aten::diagonal_backward.out(Tensor grad_output, SymInt[] input_sizes, int offset, int dim1, int dim2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { + return at::_ops::diagonal_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, offset, dim1, dim2, out); + } + + // aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::div_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::div.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::div_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + return at::_ops::div_Scalar_mode_out::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::div.Scalar_mode_out(Tensor self, Scalar other, *, str? rounding_mode, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & div_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode, at::Tensor & out) { + return at::_ops::div_Scalar_mode_out::redispatch(dispatchKeySet, self, other, rounding_mode, out); + } + + // aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) { + return at::_ops::embedding_out::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse, out); + } + + // aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, int64_t padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) { + return at::_ops::embedding_out::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse, out); + } + + // aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx=-1, bool scale_grad_by_freq=false, bool sparse=false) { + return at::_ops::embedding_out::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse, out); + } + + // aten::embedding.out(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse, at::Tensor & out) { + return at::_ops::embedding_out::redispatch(dispatchKeySet, weight, indices, padding_idx, scale_grad_by_freq, sparse, out); + } + + // aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_dense_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { + return at::_ops::embedding_dense_backward_out::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out); + } + + // aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_dense_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq, at::Tensor & out) { + return at::_ops::embedding_dense_backward_out::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out); + } + + // aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_dense_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) { + return at::_ops::embedding_dense_backward_out::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out); + } + + // aten::embedding_dense_backward.out(Tensor grad_output, Tensor indices, SymInt num_weights, SymInt padding_idx, bool scale_grad_by_freq, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_dense_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, at::Tensor & out) { + return at::_ops::embedding_dense_backward_out::redispatch(dispatchKeySet, grad_output, indices, num_weights, padding_idx, scale_grad_by_freq, out); + } + + // aten::embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_renorm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { + return at::_ops::embedding_renorm_out::redispatch(dispatchKeySet, self, indices, max_norm, norm_type, out); + } + + // aten::embedding_renorm.out(Tensor self, Tensor indices, float max_norm, float norm_type, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & embedding_renorm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type, at::Tensor & out) { + return at::_ops::embedding_renorm_out::redispatch(dispatchKeySet, self, indices, max_norm, norm_type, out); + } + + // aten::embedding_renorm(Tensor self, Tensor indices, float max_norm, float norm_type) -> Tensor + inline at::Tensor embedding_renorm(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { + return at::_ops::embedding_renorm::redispatch(dispatchKeySet, self, indices, max_norm, norm_type); + } + + // aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple _embedding_bag_forward_only_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_forward_only_out::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); + } + + // aten::_embedding_bag_forward_only.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple _embedding_bag_forward_only_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { + return at::_ops::_embedding_bag_forward_only_out::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); + } + + // aten::_embedding_bag.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple _embedding_bag_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq=false, int64_t mode=0, bool sparse=false, const c10::optional & per_sample_weights={}, bool include_last_offset=false, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_out::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); + } + + // aten::_embedding_bag.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple _embedding_bag_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { + return at::_ops::_embedding_bag_out::redispatch(dispatchKeySet, weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx, out0, out1, out2, out3); + } + + // aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_dense_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_dense_backward_out::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out); + } + + // aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_dense_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx, at::Tensor & out) { + return at::_ops::_embedding_bag_dense_backward_out::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out); + } + + // aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_dense_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_dense_backward_out::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out); + } + + // aten::_embedding_bag_dense_backward.out(Tensor grad, Tensor indices, Tensor offset2bag, Tensor bag_size, Tensor maximum_indices, SymInt num_weights, bool scale_grad_by_freq, int mode, Tensor? per_sample_weights, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_dense_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx, at::Tensor & out) { + return at::_ops::_embedding_bag_dense_backward_out::redispatch(dispatchKeySet, grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx, out); + } + + // aten::_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_per_sample_weights_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx=-1) { + return at::_ops::_embedding_bag_per_sample_weights_backward_out::redispatch(dispatchKeySet, grad, weight, indices, offsets, offset2bag, mode, padding_idx, out); + } + + // aten::_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _embedding_bag_per_sample_weights_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx, at::Tensor & out) { + return at::_ops::_embedding_bag_per_sample_weights_backward_out::redispatch(dispatchKeySet, grad, weight, indices, offsets, offset2bag, mode, padding_idx, out); + } + + // aten::empty.names_out(int[] size, *, Dimname[]? names, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional names, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_names_out::redispatch(dispatchKeySet, size, names, memory_format, out); + } + + // aten::empty.names_out(int[] size, *, Dimname[]? names, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, c10::optional memory_format, at::Tensor & out) { + return at::_ops::empty_names_out::redispatch(dispatchKeySet, size, names, memory_format, out); + } + + // aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_permuted_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, at::IntArrayRef physical_layout) { + return at::_ops::empty_permuted_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), physical_layout, out); + } + + // aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_permuted_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) { + return at::_ops::empty_permuted_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), physical_layout, out); + } + + // aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_permuted_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, at::IntArrayRef physical_layout) { + return at::_ops::empty_permuted_out::redispatch(dispatchKeySet, size, physical_layout, out); + } + + // aten::empty_permuted.out(SymInt[] size, int[] physical_layout, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_permuted_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::IntArrayRef physical_layout, at::Tensor & out) { + return at::_ops::empty_permuted_out::redispatch(dispatchKeySet, size, physical_layout, out); + } + + // aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::new_empty_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::new_empty_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::new_empty_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::new_empty.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::new_empty_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_strided_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { + return at::_ops::new_empty_strided_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_strided_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::new_empty_strided_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_strided_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + return at::_ops::new_empty_strided_out::redispatch(dispatchKeySet, self, size, stride, out); + } + + // aten::new_empty_strided.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_empty_strided_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { + return at::_ops::new_empty_strided_out::redispatch(dispatchKeySet, self, size, stride, out); + } + + // aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_full_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value) { + return at::_ops::new_full_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), fill_value, out); + } + + // aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_full_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) { + return at::_ops::new_full_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), fill_value, out); + } + + // aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_full_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value) { + return at::_ops::new_full_out::redispatch(dispatchKeySet, self, size, fill_value, out); + } + + // aten::new_full.out(Tensor self, SymInt[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_full_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, at::Tensor & out) { + return at::_ops::new_full_out::redispatch(dispatchKeySet, self, size, fill_value, out); + } + + // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_zeros_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::new_zeros_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_zeros_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::new_zeros_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_zeros_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::new_zeros_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::new_zeros.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_zeros_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::new_zeros_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_ones_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::new_ones_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_ones_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::new_ones_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_ones_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::new_ones_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::new_ones.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & new_ones_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::new_ones_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_affine_quantized_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); + } + + // aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_affine_quantized_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scale, zero_point, memory_format, out); + } + + // aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_affine_quantized_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, double scale=1, int64_t zero_point=0, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_affine_quantized_out::redispatch(dispatchKeySet, size, scale, zero_point, memory_format, out); + } + + // aten::_empty_affine_quantized.out(SymInt[] size, *, float scale=1, int zero_point=0, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_affine_quantized_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, double scale, int64_t zero_point, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_affine_quantized_out::redispatch(dispatchKeySet, size, scale, zero_point, memory_format, out); + } + + // aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_per_channel_affine_quantized_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_per_channel_affine_quantized_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out); + } + + // aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_per_channel_affine_quantized_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_per_channel_affine_quantized_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), scales, zero_points, axis, memory_format, out); + } + + // aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_per_channel_affine_quantized_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional memory_format=MemoryFormat::Contiguous) { + return at::_ops::_empty_per_channel_affine_quantized_out::redispatch(dispatchKeySet, size, scales, zero_points, axis, memory_format, out); + } + + // aten::_empty_per_channel_affine_quantized.out(SymInt[] size, *, Tensor scales, Tensor zero_points, int axis, MemoryFormat? memory_format=contiguous_format, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _empty_per_channel_affine_quantized_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_empty_per_channel_affine_quantized_out::redispatch(dispatchKeySet, size, scales, zero_points, axis, memory_format, out); + } + + // aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), memory_format, out); + } + + // aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional memory_format, const at::Tensor & out) { + return at::_ops::resize_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), memory_format, out); + } + + // aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_out::redispatch(dispatchKeySet, self, size, memory_format, out); + } + + // aten::resize.out(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format, const at::Tensor & out) { + return at::_ops::resize_out::redispatch(dispatchKeySet, self, size, memory_format, out); + } + + // aten::resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor resize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), memory_format); + } + + // aten::resize(Tensor self, SymInt[] size, *, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor resize_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize::redispatch(dispatchKeySet, self, size, memory_format); + } + + // aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _resize_output_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::Device device) { + return at::_ops::_resize_output_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), device, out); + } + + // aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _resize_output_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device, const at::Tensor & out) { + return at::_ops::_resize_output_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), device, out); + } + + // aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _resize_output_symint_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) { + return at::_ops::_resize_output_out::redispatch(dispatchKeySet, self, size, device, out); + } + + // aten::_resize_output.out(Tensor self, SymInt[] size, Device device, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & _resize_output_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device, const at::Tensor & out) { + return at::_ops::_resize_output_out::redispatch(dispatchKeySet, self, size, device, out); + } + + // aten::_resize_output(Tensor self, SymInt[] size, Device device) -> Tensor + inline at::Tensor _resize_output(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Device device) { + return at::_ops::_resize_output::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), device); + } + + // aten::_resize_output(Tensor self, SymInt[] size, Device device) -> Tensor + inline at::Tensor _resize_output_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) { + return at::_ops::_resize_output::redispatch(dispatchKeySet, self, size, device); + } + + // aten::empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_quantized_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, const at::Tensor & qtensor, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_quantized_out::redispatch(dispatchKeySet, size, qtensor, memory_format, out); + } + + // aten::empty_quantized.out(int[] size, Tensor qtensor, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_quantized_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Tensor & qtensor, c10::optional memory_format, at::Tensor & out) { + return at::_ops::empty_quantized_out::redispatch(dispatchKeySet, size, qtensor, memory_format, out); + } + + // aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::empty_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::empty_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::empty_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_strided_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, at::IntArrayRef stride) { + return at::_ops::empty_strided_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_strided_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::empty_strided_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_strided_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + return at::_ops::empty_strided_out::redispatch(dispatchKeySet, size, stride, out); + } + + // aten::empty_strided.out(SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & empty_strided_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { + return at::_ops::empty_strided_out::redispatch(dispatchKeySet, size, stride, out); + } + + // aten::fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & value) { + return at::_ops::fill_Scalar_out::redispatch(dispatchKeySet, self, value, out); + } + + // aten::fill.Scalar_out(Tensor self, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & value, at::Tensor & out) { + return at::_ops::fill_Scalar_out::redispatch(dispatchKeySet, self, value, out); + } + + // aten::fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & value) { + return at::_ops::fill_Tensor_out::redispatch(dispatchKeySet, self, value, out); + } + + // aten::fill.Tensor_out(Tensor self, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & value, at::Tensor & out) { + return at::_ops::fill_Tensor_out::redispatch(dispatchKeySet, self, value, out); + } + + // aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_divide_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::floor_divide_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::floor_divide.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & floor_divide_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::floor_divide_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::full.names_out(int[] size, Scalar fill_value, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional names) { + return at::_ops::full_names_out::redispatch(dispatchKeySet, size, fill_value, names, out); + } + + // aten::full.names_out(int[] size, Scalar fill_value, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, const at::Scalar & fill_value, c10::optional names, at::Tensor & out) { + return at::_ops::full_names_out::redispatch(dispatchKeySet, size, fill_value, names, out); + } + + // aten::full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & fill_value, c10::optional memory_format=c10::nullopt) { + return at::_ops::full_like_out::redispatch(dispatchKeySet, self, fill_value, memory_format, out); + } + + // aten::full_like.out(Tensor self, Scalar fill_value, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & full_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & fill_value, c10::optional memory_format, at::Tensor & out) { + return at::_ops::full_like_out::redispatch(dispatchKeySet, self, fill_value, memory_format, out); + } + + // aten::from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & from_file_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::string_view filename, c10::optional shared=c10::nullopt, c10::optional size=0) { + return at::_ops::from_file_out::redispatch(dispatchKeySet, filename, shared, size, out); + } + + // aten::from_file.out(str filename, bool? shared=None, int? size=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & from_file_outf(c10::DispatchKeySet dispatchKeySet, c10::string_view filename, c10::optional shared, c10::optional size, at::Tensor & out) { + return at::_ops::from_file_out::redispatch(dispatchKeySet, filename, shared, size, out); + } + + // aten::grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & grid_sampler_2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::grid_sampler_2d_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::grid_sampler_2d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & grid_sampler_2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { + return at::_ops::grid_sampler_2d_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple grid_sampler_2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + return at::_ops::grid_sampler_2d_backward_out::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); + } + + // aten::grid_sampler_2d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple grid_sampler_2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::grid_sampler_2d_backward_out::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); + } + + // aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _grid_sampler_2d_cpu_fallback_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::_grid_sampler_2d_cpu_fallback_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::_grid_sampler_2d_cpu_fallback.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _grid_sampler_2d_cpu_fallback_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { + return at::_ops::_grid_sampler_2d_cpu_fallback_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & grid_sampler_3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + return at::_ops::grid_sampler_3d_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::grid_sampler_3d.out(Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & grid_sampler_3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, at::Tensor & out) { + return at::_ops::grid_sampler_3d_out::redispatch(dispatchKeySet, input, grid, interpolation_mode, padding_mode, align_corners, out); + } + + // aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple grid_sampler_3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + return at::_ops::grid_sampler_3d_backward_out::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); + } + + // aten::grid_sampler_3d_backward.out(Tensor grad_output, Tensor input, Tensor grid, int interpolation_mode, int padding_mode, bool align_corners, bool[2] output_mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple grid_sampler_3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::grid_sampler_3d_backward_out::redispatch(dispatchKeySet, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask, out0, out1); + } + + // aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hann_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length) { + return at::_ops::hann_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hann_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { + return at::_ops::hann_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hann_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic) { + return at::_ops::hann_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hann_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { + return at::_ops::hann_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length) { + return at::_ops::hamming_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::hamming_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { + return at::_ops::hamming_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic) { + return at::_ops::hamming_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::hamming_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { + return at::_ops::hamming_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic, double alpha) { + return at::_ops::hamming_window_periodic_alpha_out::redispatch(dispatchKeySet, window_length, periodic, alpha, out); + } + + // aten::hamming_window.periodic_alpha_out(int window_length, bool periodic, float alpha, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, at::Tensor & out) { + return at::_ops::hamming_window_periodic_alpha_out::redispatch(dispatchKeySet, window_length, periodic, alpha, out); + } + + // aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic, double alpha, double beta) { + return at::_ops::hamming_window_periodic_alpha_beta_out::redispatch(dispatchKeySet, window_length, periodic, alpha, beta, out); + } + + // aten::hamming_window.periodic_alpha_beta_out(int window_length, bool periodic, float alpha, float beta, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hamming_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double alpha, double beta, at::Tensor & out) { + return at::_ops::hamming_window_periodic_alpha_beta_out::redispatch(dispatchKeySet, window_length, periodic, alpha, beta, out); + } + + // aten::kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length) { + return at::_ops::kaiser_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::kaiser_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, at::Tensor & out) { + return at::_ops::kaiser_window_out::redispatch(dispatchKeySet, window_length, out); + } + + // aten::kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic) { + return at::_ops::kaiser_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::kaiser_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, at::Tensor & out) { + return at::_ops::kaiser_window_periodic_out::redispatch(dispatchKeySet, window_length, periodic, out); + } + + // aten::kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t window_length, bool periodic, double beta) { + return at::_ops::kaiser_window_beta_out::redispatch(dispatchKeySet, window_length, periodic, beta, out); + } + + // aten::kaiser_window.beta_out(int window_length, bool periodic, float beta, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & kaiser_window_outf(c10::DispatchKeySet dispatchKeySet, int64_t window_length, bool periodic, double beta, at::Tensor & out) { + return at::_ops::kaiser_window_beta_out::redispatch(dispatchKeySet, window_length, periodic, beta, out); + } + + // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps) { + return at::_ops::native_group_norm_out::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); + } + + // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_group_norm_out::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); + } + + // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { + return at::_ops::native_group_norm_out::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); + } + + // aten::native_group_norm.out(Tensor input, Tensor? weight, Tensor? bias, SymInt N, SymInt C, SymInt HxW, int group, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_group_norm_out::redispatch(dispatchKeySet, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); + } + + // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array output_mask) { + return at::_ops::native_group_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); + } + + // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, int64_t N, int64_t C, int64_t HxW, int64_t group, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_group_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); + } + + // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array output_mask) { + return at::_ops::native_group_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); + } + + // aten::native_group_norm_backward.out(Tensor grad_out, Tensor input, Tensor mean, Tensor rstd, Tensor? weight, SymInt N, SymInt C, SymInt HxW, int group, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_group_norm_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_group_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask, out0, out1, out2); + } + + // aten::index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_put_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false) { + return at::_ops::index_put_out::redispatch(dispatchKeySet, self, indices, values, accumulate, out); + } + + // aten::index_put.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_put_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate, at::Tensor & out) { + return at::_ops::index_put_out::redispatch(dispatchKeySet, self, indices, values, accumulate, out); + } + + // aten::_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _index_put_impl_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) { + return at::_ops::_index_put_impl_out::redispatch(dispatchKeySet, self, indices, values, accumulate, unsafe, out); + } + + // aten::_index_put_impl.out(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _index_put_impl_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate, bool unsafe, at::Tensor & out) { + return at::_ops::_index_put_impl_out::redispatch(dispatchKeySet, self, indices, values, accumulate, unsafe, out); + } + + // aten::_index_put_impl(Tensor self, Tensor?[] indices, Tensor values, bool accumulate=False, bool unsafe=False) -> Tensor + inline at::Tensor _index_put_impl(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate=false, bool unsafe=false) { + return at::_ops::_index_put_impl::redispatch(dispatchKeySet, self, indices, values, accumulate, unsafe); + } + + // aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isnan_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::isnan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::isnan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isnan_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::isnan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, at::IntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps) { + return at::_ops::native_layer_norm_out::redispatch(dispatchKeySet, input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2); + } + + // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::IntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_layer_norm_out::redispatch(dispatchKeySet, input, c10::fromIntArrayRefSlow(normalized_shape), weight, bias, eps, out0, out1, out2); + } + + // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps) { + return at::_ops::native_layer_norm_out::redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps, out0, out1, out2); + } + + // aten::native_layer_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight, Tensor? bias, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_layer_norm_out::redispatch(dispatchKeySet, input, normalized_shape, weight, bias, eps, out0, out1, out2); + } + + // aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask) { + return at::_ops::native_layer_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2); + } + + // aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, at::IntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_layer_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, c10::fromIntArrayRefSlow(normalized_shape), mean, rstd, weight, bias, output_mask, out0, out1, out2); + } + + // aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask) { + return at::_ops::native_layer_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2); + } + + // aten::native_layer_norm_backward.out(Tensor grad_out, Tensor input, SymInt[] normalized_shape, Tensor mean, Tensor rstd, Tensor? weight, Tensor? bias, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_layer_norm_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_layer_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask, out0, out1, out2); + } + + // aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple linear_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + return at::_ops::linear_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); + } + + // aten::linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple linear_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::linear_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); + } + + // aten::mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_linear_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias={}) { + return at::_ops::mkldnn_linear_out::redispatch(dispatchKeySet, self, weight, bias, out); + } + + // aten::mkldnn_linear.out(Tensor self, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_linear_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::Tensor & out) { + return at::_ops::mkldnn_linear_out::redispatch(dispatchKeySet, self, weight, bias, out); + } + + // aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_linear_backward_input_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) { + return at::_ops::mkldnn_linear_backward_input_out::redispatch(dispatchKeySet, input_size, grad_output, weight, out); + } + + // aten::mkldnn_linear_backward_input.out(int[] input_size, Tensor grad_output, Tensor weight, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_linear_backward_input_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight, at::Tensor & out) { + return at::_ops::mkldnn_linear_backward_input_out::redispatch(dispatchKeySet, input_size, grad_output, weight, out); + } + + // aten::mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mkldnn_linear_backward_weights_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) { + return at::_ops::mkldnn_linear_backward_weights_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_defined, out0, out1); + } + + // aten::mkldnn_linear_backward_weights.out(Tensor grad_output, Tensor input, Tensor weight, bool bias_defined, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple mkldnn_linear_backward_weights_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::mkldnn_linear_backward_weights_out::redispatch(dispatchKeySet, grad_output, input, weight, bias_defined, out0, out1); + } + + // aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mkldnn_linear_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + return at::_ops::mkldnn_linear_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); + } + + // aten::mkldnn_linear_backward.out(Tensor self, Tensor grad_output, Tensor weight, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mkldnn_linear_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::mkldnn_linear_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, output_mask, out0, out1, out2); + } + + // aten::matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple matmul_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array mask) { + return at::_ops::matmul_backward_out::redispatch(dispatchKeySet, grad, self, other, mask, out0, out1); + } + + // aten::matmul_backward.out(Tensor grad, Tensor self, Tensor other, bool[2] mask, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple matmul_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array mask, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::matmul_backward_out::redispatch(dispatchKeySet, grad, self, other, mask, out0, out1); + } + + // aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _aminmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self) { + return at::_ops::_aminmax_out::redispatch(dispatchKeySet, self, out0, out1); + } + + // aten::_aminmax.out(Tensor self, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _aminmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_aminmax_out::redispatch(dispatchKeySet, self, out0, out1); + } + + // aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _aminmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, int64_t dim, bool keepdim=false) { + return at::_ops::_aminmax_dim_out::redispatch(dispatchKeySet, self, dim, keepdim, out0, out1); + } + + // aten::_aminmax.dim_out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _aminmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_aminmax_dim_out::redispatch(dispatchKeySet, self, dim, keepdim, out0, out1); + } + + // aten::max_pool2d_backward.out(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::max_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::max_pool2d_backward.out(Tensor grad_output, Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & max_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::max_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::mkldnn_max_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool2d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool2d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::mkldnn_max_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::mkldnn_max_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool3d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::mkldnn_max_pool3d_backward_out::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::mkldnn_max_pool3d_backward.out(Tensor grad_output, Tensor output, Tensor input, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_max_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::mkldnn_max_pool3d_backward_out::redispatch(dispatchKeySet, grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool1d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool1d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool1d.out(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=0, int[1] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool1d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::quantized_max_pool1d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool2d.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::quantized_max_pool2d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride={}, at::IntArrayRef padding=0, at::IntArrayRef dilation=1, bool ceil_mode=false) { + return at::_ops::quantized_max_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::quantized_max_pool3d.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantized_max_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, at::Tensor & out) { + return at::_ops::quantized_max_pool3d_out::redispatch(dispatchKeySet, self, kernel_size, stride, padding, dilation, ceil_mode, out); + } + + // aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & median_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::median_out::redispatch(dispatchKeySet, self, out); + } + + // aten::median.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & median_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::median_out::redispatch(dispatchKeySet, self, out); + } + + // aten::nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanmedian_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::nanmedian_out::redispatch(dispatchKeySet, self, out); + } + + // aten::nanmedian.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & nanmedian_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::nanmedian_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::_mps_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::_mps_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::_mps_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, out); + } + + // aten::_mps_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mps_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::_mps_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, out); + } + + // aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mps_convolution_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask) { + return at::_ops::mps_convolution_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2); + } + + // aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mps_convolution_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::mps_convolution_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, output_mask, out0, out1, out2); + } + + // aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mps_convolution_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + return at::_ops::mps_convolution_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2); + } + + // aten::mps_convolution_backward.out(Tensor self, Tensor grad_output, Tensor weight, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple mps_convolution_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::mps_convolution_backward_out::redispatch(dispatchKeySet, self, grad_output, weight, padding, stride, dilation, groups, output_mask, out0, out1, out2); + } + + // aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups) { + return at::_ops::mkldnn_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::mkldnn_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + return at::_ops::mkldnn_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, out); + } + + // aten::mkldnn_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::mkldnn_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, out); + } + + // aten::mkldnn_rnn_layer.out(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple mkldnn_rnn_layer_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) { + return at::_ops::mkldnn_rnn_layer_out::redispatch(dispatchKeySet, input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0, out1, out2, out3); + } + + // aten::mkldnn_rnn_layer.out(Tensor input, Tensor weight0, Tensor weight1, Tensor weight2, Tensor weight3, Tensor hx_, Tensor cx_, bool reverse, int[] batch_sizes, int mode, int hidden_size, int num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple mkldnn_rnn_layer_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { + return at::_ops::mkldnn_rnn_layer_out::redispatch(dispatchKeySet, input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train, out0, out1, out2, out3); + } + + // aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) + inline ::std::tuple mkldnn_rnn_layer_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { + return at::_ops::mkldnn_rnn_layer_backward_out::redispatch(dispatchKeySet, input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6); + } + + // aten::mkldnn_rnn_layer_backward.out(Tensor input, Tensor weight1, Tensor weight2, Tensor weight3, Tensor weight4, Tensor hx_, Tensor cx_tmp, Tensor output, Tensor hy_, Tensor cy_, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, bool reverse, int mode, int hidden_size, int num_layers, bool has_biases, bool train, bool bidirectional, int[] batch_sizes, bool batch_first, Tensor workspace, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5, Tensor(g!) out6) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!), Tensor(g!)) + inline ::std::tuple mkldnn_rnn_layer_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6) { + return at::_ops::mkldnn_rnn_layer_backward_out::redispatch(dispatchKeySet, input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace, out0, out1, out2, out3, out4, out5, out6); + } + + // aten::miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple miopen_batch_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + return at::_ops::miopen_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2); + } + + // aten::miopen_batch_norm.out(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple miopen_batch_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::miopen_batch_norm_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon, out0, out1, out2); + } + + // aten::miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple miopen_batch_norm_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon) { + return at::_ops::miopen_batch_norm_backward_out::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, out0, out1, out2); + } + + // aten::miopen_batch_norm_backward.out(Tensor input, Tensor grad_output, Tensor weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var, float epsilon, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple miopen_batch_norm_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::miopen_batch_norm_backward_out::redispatch(dispatchKeySet, input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, out0, out1, out2); + } + + // aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_transpose_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_transpose_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef output_padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(output_padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_transpose_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_convolution_transpose.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] output_padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_convolution_transpose_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_convolution_transpose_out::redispatch(dispatchKeySet, self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_depthwise_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_depthwise_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_depthwise_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_depthwise_convolution_out::redispatch(dispatchKeySet, self, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, benchmark, deterministic, out); + } + + // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_depthwise_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + return at::_ops::miopen_depthwise_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_depthwise_convolution.out(Tensor self, Tensor weight, Tensor? bias, SymInt[] padding, SymInt[] stride, SymInt[] dilation, SymInt groups, bool benchmark, bool deterministic, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & miopen_depthwise_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, at::Tensor & out) { + return at::_ops::miopen_depthwise_convolution_out::redispatch(dispatchKeySet, self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic, out); + } + + // aten::miopen_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple miopen_rnn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state) { + return at::_ops::miopen_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4); + } + + // aten::miopen_rnn.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple miopen_rnn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { + return at::_ops::miopen_rnn_out::redispatch(dispatchKeySet, input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, out0, out1, out2, out3, out4); + } + + // aten::miopen_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void miopen_rnn_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + return at::_ops::miopen_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::miopen_rnn_backward.out(Tensor input, Tensor[] weight, int weight_stride0, Tensor weight_buf, Tensor hx, Tensor? cx, Tensor output, Tensor? grad_output, Tensor? grad_hy, Tensor? grad_cy, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state, Tensor reserve, bool[4] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!)[] out3) -> () + inline void miopen_rnn_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { + return at::_ops::miopen_rnn_backward_out::redispatch(dispatchKeySet, input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + + // aten::_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sparse_matmul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::_sparse_sparse_matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_sparse_sparse_matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sparse_matmul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::_sparse_sparse_matmul_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mul_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::mul_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::mul.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mul_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::mul_Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_native_batch_norm_legit_functional(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor, Tensor running_mean_out, Tensor running_var_out) + inline ::std::tuple _native_batch_norm_legit_functional(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_functional::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, training, momentum, eps); + } + + // aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _native_batch_norm_legit_no_training_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) { + return at::_ops::_native_batch_norm_legit_no_training_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2); + } + + // aten::_native_batch_norm_legit_no_training.out(Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float momentum, float eps, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _native_batch_norm_legit_no_training_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_native_batch_norm_legit_no_training_out::redispatch(dispatchKeySet, input, weight, bias, running_mean, running_var, momentum, eps, out0, out1, out2); + } + + // aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_stats_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, double eps) { + return at::_ops::batch_norm_stats_out::redispatch(dispatchKeySet, input, eps, out0, out1); + } + + // aten::batch_norm_stats.out(Tensor input, float eps, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_stats_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, double eps, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::batch_norm_stats_out::redispatch(dispatchKeySet, input, eps, out0, out1); + } + + // aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_gather_stats_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, int64_t count) { + return at::_ops::batch_norm_gather_stats_out::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1); + } + + // aten::batch_norm_gather_stats.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_gather_stats_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, int64_t count, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::batch_norm_gather_stats_out::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, count, out0, out1); + } + + // aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_gather_stats_with_counts_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, const at::Tensor & counts) { + return at::_ops::batch_norm_gather_stats_with_counts_out::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1); + } + + // aten::batch_norm_gather_stats_with_counts.out(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, Tensor counts, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_gather_stats_with_counts_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, const at::Tensor & counts, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::batch_norm_gather_stats_with_counts_out::redispatch(dispatchKeySet, input, mean, invstd, running_mean, running_var, momentum, eps, counts, out0, out1); + } + + // aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_batch_norm_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask) { + return at::_ops::native_batch_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); + } + + // aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple native_batch_norm_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_batch_norm_backward_out::redispatch(dispatchKeySet, grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); + } + + // aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple batch_norm_backward_reduce_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, bool input_g, bool weight_g, bool bias_g) { + return at::_ops::batch_norm_backward_reduce_out::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3); + } + + // aten::batch_norm_backward_reduce.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!)) + inline ::std::tuple batch_norm_backward_reduce_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, bool input_g, bool weight_g, bool bias_g, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3) { + return at::_ops::batch_norm_backward_reduce_out::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g, out0, out1, out2, out3); + } + + // aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & batch_norm_backward_elemt_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) { + return at::_ops::batch_norm_backward_elemt_out::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count, out); + } + + // aten::batch_norm_backward_elemt.out(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & batch_norm_backward_elemt_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count, at::Tensor & out) { + return at::_ops::batch_norm_backward_elemt_out::redispatch(dispatchKeySet, grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count, out); + } + + // aten::batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_update_stats_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum) { + return at::_ops::batch_norm_update_stats_out::redispatch(dispatchKeySet, input, running_mean, running_var, momentum, out0, out1); + } + + // aten::batch_norm_update_stats.out(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple batch_norm_update_stats_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::batch_norm_update_stats_out::redispatch(dispatchKeySet, input, running_mean, running_var, momentum, out0, out1); + } + + // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nnpack_spatial_convolution_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride=1) { + return at::_ops::_nnpack_spatial_convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nnpack_spatial_convolution_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, at::IntArrayRef padding, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::_nnpack_spatial_convolution_out::redispatch(dispatchKeySet, input, weight, bias, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nnpack_spatial_convolution_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride=c10::SymInt(1)) { + return at::_ops::_nnpack_spatial_convolution_out::redispatch(dispatchKeySet, input, weight, bias, padding, stride, out); + } + + // aten::_nnpack_spatial_convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[2] padding, SymInt[2] stride=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nnpack_spatial_convolution_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, at::Tensor & out) { + return at::_ops::_nnpack_spatial_convolution_out::redispatch(dispatchKeySet, input, weight, bias, padding, stride, out); + } + + // aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional names) { + return at::_ops::ones_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::ones.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::ones_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::ones_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::ones_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ones_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::ones_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _euclidean_dist_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x1, const at::Tensor & x2) { + return at::_ops::_euclidean_dist_out::redispatch(dispatchKeySet, x1, x2, out); + } + + // aten::_euclidean_dist.out(Tensor x1, Tensor x2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _euclidean_dist_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, at::Tensor & out) { + return at::_ops::_euclidean_dist_out::redispatch(dispatchKeySet, x1, x2, out); + } + + // aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cdist_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional compute_mode) { + return at::_ops::_cdist_forward_out::redispatch(dispatchKeySet, x1, x2, p, compute_mode, out); + } + + // aten::_cdist_forward.out(Tensor x1, Tensor x2, float p, int? compute_mode, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cdist_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional compute_mode, at::Tensor & out) { + return at::_ops::_cdist_forward_out::redispatch(dispatchKeySet, x1, x2, p, compute_mode, out); + } + + // aten::_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cdist_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) { + return at::_ops::_cdist_backward_out::redispatch(dispatchKeySet, grad, x1, x2, p, cdist, out); + } + + // aten::_cdist_backward.out(Tensor grad, Tensor x1, Tensor x2, float p, Tensor cdist, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cdist_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist, at::Tensor & out) { + return at::_ops::_cdist_backward_out::redispatch(dispatchKeySet, grad, x1, x2, p, cdist, out); + } + + // aten::_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pdist_forward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double p=2) { + return at::_ops::_pdist_forward_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::_pdist_forward.out(Tensor self, float p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pdist_forward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, at::Tensor & out) { + return at::_ops::_pdist_forward_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pdist_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { + return at::_ops::_pdist_backward_out::redispatch(dispatchKeySet, grad, self, p, pdist, out); + } + + // aten::_pdist_backward.out(Tensor grad, Tensor self, float p, Tensor pdist, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pdist_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist, at::Tensor & out) { + return at::_ops::_pdist_backward_out::redispatch(dispatchKeySet, grad, self, p, pdist, out); + } + + // aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pixel_shuffle_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t upscale_factor) { + return at::_ops::pixel_shuffle_out::redispatch(dispatchKeySet, self, upscale_factor, out); + } + + // aten::pixel_shuffle.out(Tensor self, int upscale_factor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pixel_shuffle_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t upscale_factor, at::Tensor & out) { + return at::_ops::pixel_shuffle_out::redispatch(dispatchKeySet, self, upscale_factor, out); + } + + // aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pixel_unshuffle_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t downscale_factor) { + return at::_ops::pixel_unshuffle_out::redispatch(dispatchKeySet, self, downscale_factor, out); + } + + // aten::pixel_unshuffle.out(Tensor self, int downscale_factor, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & pixel_unshuffle_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t downscale_factor, at::Tensor & out) { + return at::_ops::pixel_unshuffle_out::redispatch(dispatchKeySet, self, downscale_factor, out); + } + + // aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & channel_shuffle_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t groups) { + return at::_ops::channel_shuffle_out::redispatch(dispatchKeySet, self, groups, out); + } + + // aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & channel_shuffle_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t groups, at::Tensor & out) { + return at::_ops::channel_shuffle_out::redispatch(dispatchKeySet, self, groups, out); + } + + // aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & channel_shuffle_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymInt groups) { + return at::_ops::channel_shuffle_out::redispatch(dispatchKeySet, self, groups, out); + } + + // aten::channel_shuffle.out(Tensor self, SymInt groups, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & channel_shuffle_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt groups, at::Tensor & out) { + return at::_ops::channel_shuffle_out::redispatch(dispatchKeySet, self, groups, out); + } + + // aten::_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pin_memory_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional device=c10::nullopt) { + return at::_ops::_pin_memory_out::redispatch(dispatchKeySet, self, device, out); + } + + // aten::_pin_memory.out(Tensor self, Device? device=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _pin_memory_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional device, at::Tensor & out) { + return at::_ops::_pin_memory_out::redispatch(dispatchKeySet, self, device, out); + } + + // aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scalar_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & s) { + return at::_ops::scalar_tensor_out::redispatch(dispatchKeySet, s, out); + } + + // aten::scalar_tensor.out(Scalar s, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & scalar_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & s, at::Tensor & out) { + return at::_ops::scalar_tensor_out::redispatch(dispatchKeySet, s, out); + } + + // aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional names) { + return at::_ops::rand_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, out); + } + + // aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::rand_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, out); + } + + // aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional names) { + return at::_ops::rand_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::rand.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::rand_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional generator, c10::optional names) { + return at::_ops::rand_generator_with_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, out); + } + + // aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, at::Tensor & out) { + return at::_ops::rand_generator_with_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, out); + } + + // aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional generator, c10::optional names) { + return at::_ops::rand_generator_with_names_out::redispatch(dispatchKeySet, size, generator, names, out); + } + + // aten::rand.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, at::Tensor & out) { + return at::_ops::rand_generator_with_names_out::redispatch(dispatchKeySet, size, generator, names, out); + } + + // aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::rand_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::rand_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rand_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::rand_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t high, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_out::redispatch(dispatchKeySet, self, high, memory_format, out); + } + + // aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t high, c10::optional memory_format, at::Tensor & out) { + return at::_ops::randint_like_out::redispatch(dispatchKeySet, self, high, memory_format, out); + } + + // aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymInt high, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_out::redispatch(dispatchKeySet, self, high, memory_format, out); + } + + // aten::randint_like.out(Tensor self, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt high, c10::optional memory_format, at::Tensor & out) { + return at::_ops::randint_like_out::redispatch(dispatchKeySet, self, high, memory_format, out); + } + + // aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t low, int64_t high, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_low_dtype_out::redispatch(dispatchKeySet, self, low, high, memory_format, out); + } + + // aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t low, int64_t high, c10::optional memory_format, at::Tensor & out) { + return at::_ops::randint_like_low_dtype_out::redispatch(dispatchKeySet, self, low, high, memory_format, out); + } + + // aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymInt low, c10::SymInt high, c10::optional memory_format=c10::nullopt) { + return at::_ops::randint_like_low_dtype_out::redispatch(dispatchKeySet, self, low, high, memory_format, out); + } + + // aten::randint_like.low_dtype_out(Tensor self, SymInt low, SymInt high, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randint_like_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt low, c10::SymInt high, c10::optional memory_format, at::Tensor & out) { + return at::_ops::randint_like_low_dtype_out::redispatch(dispatchKeySet, self, low, high, memory_format, out); + } + + // aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional names) { + return at::_ops::randn_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, out); + } + + // aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::randn_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), names, out); + } + + // aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional names) { + return at::_ops::randn_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::randn.names_out(SymInt[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::randn_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional generator, c10::optional names) { + return at::_ops::randn_generator_with_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, out); + } + + // aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional generator, c10::optional names, at::Tensor & out) { + return at::_ops::randn_generator_with_names_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), generator, names, out); + } + + // aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size, c10::optional generator, c10::optional names) { + return at::_ops::randn_generator_with_names_out::redispatch(dispatchKeySet, size, generator, names, out); + } + + // aten::randn.generator_with_names_out(SymInt[] size, *, Generator? generator, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, c10::optional generator, c10::optional names, at::Tensor & out) { + return at::_ops::randn_generator_with_names_out::redispatch(dispatchKeySet, size, generator, names, out); + } + + // aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::randn_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::randn_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & randn_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::randn_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef repeats) { + return at::_ops::repeat_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(repeats), out); + } + + // aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(repeats), out); + } + + // aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef repeats) { + return at::_ops::repeat_out::redispatch(dispatchKeySet, self, repeats, out); + } + + // aten::repeat.out(Tensor self, SymInt[] repeats, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef repeats, at::Tensor & out) { + return at::_ops::repeat_out::redispatch(dispatchKeySet, self, repeats, out); + } + + // aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_interleave_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & repeats, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_Tensor_out::redispatch(dispatchKeySet, repeats, output_size.has_value() ? c10::make_optional(c10::SymInt(*output_size)) : c10::nullopt, out); + } + + // aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_interleave_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & repeats, c10::optional output_size, at::Tensor & out) { + return at::_ops::repeat_interleave_Tensor_out::redispatch(dispatchKeySet, repeats, output_size.has_value() ? c10::make_optional(c10::SymInt(*output_size)) : c10::nullopt, out); + } + + // aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_interleave_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & repeats, c10::optional output_size=c10::nullopt) { + return at::_ops::repeat_interleave_Tensor_out::redispatch(dispatchKeySet, repeats, output_size, out); + } + + // aten::repeat_interleave.Tensor_out(Tensor repeats, *, SymInt? output_size=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & repeat_interleave_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & repeats, c10::optional output_size, at::Tensor & out) { + return at::_ops::repeat_interleave_Tensor_out::redispatch(dispatchKeySet, repeats, output_size, out); + } + + // aten::_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mkldnn_reshape_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef shape) { + return at::_ops::_mkldnn_reshape_out::redispatch(dispatchKeySet, self, shape, out); + } + + // aten::_mkldnn_reshape.out(Tensor self, int[] shape, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mkldnn_reshape_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shape, at::Tensor & out) { + return at::_ops::_mkldnn_reshape_out::redispatch(dispatchKeySet, self, shape, out); + } + + // aten::relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & relu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::relu_out::redispatch(dispatchKeySet, self, out); + } + + // aten::relu.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & relu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::relu_out::redispatch(dispatchKeySet, self, out); + } + + // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index) { + return at::_ops::select_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out); + } + + // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t index, at::Tensor & out) { + return at::_ops::select_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, index, out); + } + + // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { + return at::_ops::select_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, dim, index, out); + } + + // aten::select_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index, at::Tensor & out) { + return at::_ops::select_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, dim, index, out); + } + + // aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & celu_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & alpha=1.0) { + return at::_ops::celu_out::redispatch(dispatchKeySet, self, alpha, out); + } + + // aten::celu.out(Tensor self, Scalar alpha=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & celu_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::celu_out::redispatch(dispatchKeySet, self, alpha, out); + } + + // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step) { + return at::_ops::slice_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out); + } + + // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, at::IntArrayRef input_sizes, int64_t dim, int64_t start, int64_t end, int64_t step, at::Tensor & out) { + return at::_ops::slice_backward_out::redispatch(dispatchKeySet, grad_output, c10::fromIntArrayRefSlow(input_sizes), dim, start, end, step, out); + } + + // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) { + return at::_ops::slice_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, dim, start, end, step, out); + } + + // aten::slice_backward.out(Tensor grad_output, SymInt[] input_sizes, int dim, SymInt start, SymInt end, SymInt step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step, at::Tensor & out) { + return at::_ops::slice_backward_out::redispatch(dispatchKeySet, grad_output, input_sizes, dim, start, end, step, out); + } + + // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_scatter_out::redispatch(dispatchKeySet, self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out); + } + + // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional start, c10::optional end, int64_t step, at::Tensor & out) { + return at::_ops::slice_scatter_out::redispatch(dispatchKeySet, self, src, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out); + } + + // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_scatter_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_scatter_out::redispatch(dispatchKeySet, self, src, dim, start, end, step, out); + } + + // aten::slice_scatter.out(Tensor self, Tensor src, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_scatter_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step, at::Tensor & out) { + return at::_ops::slice_scatter_out::redispatch(dispatchKeySet, self, src, dim, start, end, step, out); + } + + // aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index) { + return at::_ops::select_scatter_out::redispatch(dispatchKeySet, self, src, dim, index, out); + } + + // aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, int64_t index, at::Tensor & out) { + return at::_ops::select_scatter_out::redispatch(dispatchKeySet, self, src, dim, index, out); + } + + // aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_scatter_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) { + return at::_ops::select_scatter_out::redispatch(dispatchKeySet, self, src, dim, index, out); + } + + // aten::select_scatter.out(Tensor self, Tensor src, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_scatter_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index, at::Tensor & out) { + return at::_ops::select_scatter_out::redispatch(dispatchKeySet, self, src, dim, index, out); + } + + // aten::diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) { + return at::_ops::diagonal_scatter_out::redispatch(dispatchKeySet, self, src, offset, dim1, dim2, out); + } + + // aten::diagonal_scatter.out(Tensor self, Tensor src, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { + return at::_ops::diagonal_scatter_out::redispatch(dispatchKeySet, self, src, offset, dim1, dim2, out); + } + + // aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_scatter_out::redispatch(dispatchKeySet, self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt, out); + } + + // aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset, at::Tensor & out) { + return at::_ops::as_strided_scatter_out::redispatch(dispatchKeySet, self, src, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt, out); + } + + // aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_scatter_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_scatter_out::redispatch(dispatchKeySet, self, src, size, stride, storage_offset, out); + } + + // aten::as_strided_scatter.out(Tensor self, Tensor src, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_scatter_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset, at::Tensor & out) { + return at::_ops::as_strided_scatter_out::redispatch(dispatchKeySet, self, src, size, stride, storage_offset, out); + } + + // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, int64_t split_size, int64_t dim=0) { + return at::_ops::unsafe_split_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t split_size, int64_t dim, at::TensorList out) { + return at::_ops::unsafe_split_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_symint_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, c10::SymInt split_size, int64_t dim=0) { + return at::_ops::unsafe_split_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::unsafe_split.Tensor_out(Tensor self, SymInt split_size, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { + return at::_ops::unsafe_split_Tensor_out::redispatch(dispatchKeySet, self, split_size, dim, out); + } + + // aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_with_sizes_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::unsafe_split_with_sizes_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim, out); + } + + // aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_with_sizes_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef split_sizes, int64_t dim, at::TensorList out) { + return at::_ops::unsafe_split_with_sizes_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(split_sizes), dim, out); + } + + // aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_with_sizes_symint_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim=0) { + return at::_ops::unsafe_split_with_sizes_out::redispatch(dispatchKeySet, self, split_sizes, dim, out); + } + + // aten::unsafe_split_with_sizes.out(Tensor self, SymInt[] split_sizes, int dim=0, *, Tensor(a!)[] out) -> () + inline void unsafe_split_with_sizes_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { + return at::_ops::unsafe_split_with_sizes_out::redispatch(dispatchKeySet, self, split_sizes, dim, out); + } + + // aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::sum_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::sum.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, at::Tensor & out) { + return at::_ops::sum_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple std_mean_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::std_mean_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out0, out1); + } + + // aten::std_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple std_mean_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::std_mean_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out0, out1); + } + + // aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::prod_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::prod.out(Tensor self, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & prod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, at::Tensor & out) { + return at::_ops::prod_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mkldnn_transpose_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::_mkldnn_transpose_out::redispatch(dispatchKeySet, self, dim0, dim1, out); + } + + // aten::_mkldnn_transpose.out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _mkldnn_transpose_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { + return at::_ops::_mkldnn_transpose_out::redispatch(dispatchKeySet, self, dim0, dim1, out); + } + + // aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & flip_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::flip_out::redispatch(dispatchKeySet, self, dims, out); + } + + // aten::flip.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & flip_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) { + return at::_ops::flip_out::redispatch(dispatchKeySet, self, dims, out); + } + + // aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & roll_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims={}) { + return at::_ops::roll_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(shifts), dims, out); + } + + // aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & roll_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) { + return at::_ops::roll_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(shifts), dims, out); + } + + // aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & roll_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims={}) { + return at::_ops::roll_out::redispatch(dispatchKeySet, self, shifts, dims, out); + } + + // aten::roll.out(Tensor self, SymInt[1] shifts, int[1] dims=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & roll_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims, at::Tensor & out) { + return at::_ops::roll_out::redispatch(dispatchKeySet, self, shifts, dims, out); + } + + // aten::rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rot90_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t k=1, at::IntArrayRef dims={0,1}) { + return at::_ops::rot90_out::redispatch(dispatchKeySet, self, k, dims, out); + } + + // aten::rot90.out(Tensor self, int k=1, int[] dims=[0,1], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rot90_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t k, at::IntArrayRef dims, at::Tensor & out) { + return at::_ops::rot90_out::redispatch(dispatchKeySet, self, k, dims, out); + } + + // aten::_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _transform_bias_rescale_qkv_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) { + return at::_ops::_transform_bias_rescale_qkv_out::redispatch(dispatchKeySet, qkv, qkv_bias, num_heads, out0, out1, out2); + } + + // aten::_transform_bias_rescale_qkv.out(Tensor qkv, Tensor qkv_bias, int num_heads, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _transform_bias_rescale_qkv_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_transform_bias_rescale_qkv_out::redispatch(dispatchKeySet, qkv, qkv_bias, num_heads, out0, out1, out2); + } + + // aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_from_mask_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) { + return at::_ops::_nested_tensor_from_mask_out::redispatch(dispatchKeySet, t, mask, mask_check, out); + } + + // aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_from_mask_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & t, const at::Tensor & mask, bool mask_check, at::Tensor & out) { + return at::_ops::_nested_tensor_from_mask_out::redispatch(dispatchKeySet, t, mask, mask_check, out); + } + + // aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_from_padded_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213=false) { + return at::_ops::_nested_from_padded_out::redispatch(dispatchKeySet, padded, cpu_nested_shape_example, fuse_transform_0213, out); + } + + // aten::_nested_from_padded.out(Tensor padded, Tensor cpu_nested_shape_example, bool fuse_transform_0213=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_from_padded_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213, at::Tensor & out) { + return at::_ops::_nested_from_padded_out::redispatch(dispatchKeySet, padded, cpu_nested_shape_example, fuse_transform_0213, out); + } + + // aten::_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_size_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_nested_tensor_size_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_tensor_size.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_size_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_nested_tensor_size_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_strides_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_nested_tensor_strides_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_tensor_strides.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_strides_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_nested_tensor_strides_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_tensor_storage_offsets.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_storage_offsets_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_nested_tensor_storage_offsets_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_tensor_storage_offsets.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_storage_offsets_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_nested_tensor_storage_offsets_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_from_padded_and_nested_example_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & padded, const at::Tensor & nt_example) { + return at::_ops::_nested_from_padded_and_nested_example_out::redispatch(dispatchKeySet, padded, nt_example, out); + } + + // aten::_nested_from_padded_and_nested_example.out(Tensor padded, Tensor nt_example, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_from_padded_and_nested_example_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & padded, const at::Tensor & nt_example, at::Tensor & out) { + return at::_ops::_nested_from_padded_and_nested_example_out::redispatch(dispatchKeySet, padded, nt_example, out); + } + + // aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_view_from_buffer_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) { + return at::_ops::_nested_view_from_buffer_copy_out::redispatch(dispatchKeySet, self, nested_size, nested_strides, offsets, out); + } + + // aten::_nested_view_from_buffer_copy.out(Tensor self, Tensor nested_size, Tensor nested_strides, Tensor offsets, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_view_from_buffer_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets, at::Tensor & out) { + return at::_ops::_nested_view_from_buffer_copy_out::redispatch(dispatchKeySet, self, nested_size, nested_strides, offsets, out); + } + + // aten::_nested_view_from_jagged_copy.out(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_view_from_jagged_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths={}, int64_t ragged_idx=1) { + return at::_ops::_nested_view_from_jagged_copy_out::redispatch(dispatchKeySet, self, offsets, dummy, lengths, ragged_idx, out); + } + + // aten::_nested_view_from_jagged_copy.out(Tensor self, Tensor offsets, Tensor dummy, Tensor? lengths=None, int ragged_idx=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_view_from_jagged_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths, int64_t ragged_idx, at::Tensor & out) { + return at::_ops::_nested_view_from_jagged_copy_out::redispatch(dispatchKeySet, self, offsets, dummy, lengths, ragged_idx, out); + } + + // aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_get_values_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_nested_get_values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_nested_get_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_get_values_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_nested_get_values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _trilinear_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim=1) { + return at::_ops::_trilinear_out::redispatch(dispatchKeySet, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out); + } + + // aten::_trilinear.out(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _trilinear_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim, at::Tensor & out) { + return at::_ops::_trilinear_out::redispatch(dispatchKeySet, i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim, out); + } + + // aten::_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _unique_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, bool sorted=true, bool return_inverse=false) { + return at::_ops::_unique_out::redispatch(dispatchKeySet, self, sorted, return_inverse, out0, out1); + } + + // aten::_unique.out(Tensor self, bool sorted=True, bool return_inverse=False, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _unique_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_unique_out::redispatch(dispatchKeySet, self, sorted, return_inverse, out0, out1); + } + + // aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_dim_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool sorted=true, bool return_inverse=false, bool return_counts=false) { + return at::_ops::unique_dim_out::redispatch(dispatchKeySet, self, dim, sorted, return_inverse, return_counts, out0, out1, out2); + } + + // aten::unique_dim.out(Tensor self, int dim, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_dim_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::unique_dim_out::redispatch(dispatchKeySet, self, dim, sorted, return_inverse, return_counts, out0, out1, out2); + } + + // aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_consecutive_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool return_inverse=false, bool return_counts=false, c10::optional dim=c10::nullopt) { + return at::_ops::unique_consecutive_out::redispatch(dispatchKeySet, self, return_inverse, return_counts, dim, out0, out1, out2); + } + + // aten::unique_consecutive.out(Tensor self, bool return_inverse=False, bool return_counts=False, int? dim=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_consecutive_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional dim, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::unique_consecutive_out::redispatch(dispatchKeySet, self, return_inverse, return_counts, dim, out0, out1, out2); + } + + // aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_dim_consecutive_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, int64_t dim, bool return_inverse=false, bool return_counts=false) { + return at::_ops::unique_dim_consecutive_out::redispatch(dispatchKeySet, self, dim, return_inverse, return_counts, out0, out1, out2); + } + + // aten::unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple unique_dim_consecutive_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::unique_dim_consecutive_out::redispatch(dispatchKeySet, self, dim, return_inverse, return_counts, out0, out1, out2); + } + + // aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _unique2_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & self, bool sorted=true, bool return_inverse=false, bool return_counts=false) { + return at::_ops::_unique2_out::redispatch(dispatchKeySet, self, sorted, return_inverse, return_counts, out0, out1, out2); + } + + // aten::_unique2.out(Tensor self, bool sorted=True, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _unique2_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_unique2_out::redispatch(dispatchKeySet, self, sorted, return_inverse, return_counts, out0, out1, out2); + } + + // aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _unsafe_view_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_unsafe_view_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _unsafe_view_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::_unsafe_view_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _unsafe_view_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::_unsafe_view_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::_unsafe_view.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _unsafe_view_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::_unsafe_view_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple var_mean_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, at::OptionalIntArrayRef dim=c10::nullopt, const c10::optional & correction=c10::nullopt, bool keepdim=false) { + return at::_ops::var_mean_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out0, out1); + } + + // aten::var_mean.correction_out(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple var_mean_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::var_mean_correction_out::redispatch(dispatchKeySet, self, dim, correction, keepdim, out0, out1); + } + + // aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _weight_norm_interface_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & v, const at::Tensor & g, int64_t dim=0) { + return at::_ops::_weight_norm_interface_out::redispatch(dispatchKeySet, v, g, dim, out0, out1); + } + + // aten::_weight_norm_interface.out(Tensor v, Tensor g, int dim=0, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _weight_norm_interface_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & v, const at::Tensor & g, int64_t dim, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_weight_norm_interface_out::redispatch(dispatchKeySet, v, g, dim, out0, out1); + } + + // aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _weight_norm_interface_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { + return at::_ops::_weight_norm_interface_backward_out::redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim, out0, out1); + } + + // aten::_weight_norm_interface_backward.out(Tensor grad_w, Tensor saved_v, Tensor saved_g, Tensor saved_norms, int dim, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _weight_norm_interface_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_weight_norm_interface_backward_out::redispatch(dispatchKeySet, grad_w, saved_v, saved_g, saved_norms, dim, out0, out1); + } + + // aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size, c10::optional names) { + return at::_ops::zeros_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::zeros.names_out(int[] size, *, Dimname[]? names, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, c10::optional names, at::Tensor & out) { + return at::_ops::zeros_names_out::redispatch(dispatchKeySet, size, names, out); + } + + // aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _efficientzerotensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::_efficientzerotensor_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _efficientzerotensor_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::_efficientzerotensor_out::redispatch(dispatchKeySet, c10::fromIntArrayRefSlow(size), out); + } + + // aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _efficientzerotensor_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, c10::SymIntArrayRef size) { + return at::_ops::_efficientzerotensor_out::redispatch(dispatchKeySet, size, out); + } + + // aten::_efficientzerotensor.out(SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _efficientzerotensor_symint_outf(c10::DispatchKeySet dispatchKeySet, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::_efficientzerotensor_out::redispatch(dispatchKeySet, size, out); + } + + // aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_like_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::zeros_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::zeros_like.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zeros_like_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::zeros_like_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _standard_gamma_grad_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & output) { + return at::_ops::_standard_gamma_grad_out::redispatch(dispatchKeySet, self, output, out); + } + + // aten::_standard_gamma_grad.out(Tensor self, Tensor output, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _standard_gamma_grad_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & output, at::Tensor & out) { + return at::_ops::_standard_gamma_grad_out::redispatch(dispatchKeySet, self, output, out); + } + + // aten::_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _standard_gamma_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::_standard_gamma_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::_standard_gamma.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _standard_gamma_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator, at::Tensor & out) { + return at::_ops::_standard_gamma_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _dirichlet_grad_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { + return at::_ops::_dirichlet_grad_out::redispatch(dispatchKeySet, x, alpha, total, out); + } + + // aten::_dirichlet_grad.out(Tensor x, Tensor alpha, Tensor total, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _dirichlet_grad_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total, at::Tensor & out) { + return at::_ops::_dirichlet_grad_out::redispatch(dispatchKeySet, x, alpha, total, out); + } + + // aten::_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sample_dirichlet_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::_sample_dirichlet_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::_sample_dirichlet.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sample_dirichlet_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator, at::Tensor & out) { + return at::_ops::_sample_dirichlet_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & poisson_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::poisson_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::poisson.out(Tensor self, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & poisson_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator, at::Tensor & out) { + return at::_ops::poisson_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binomial_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & count, const at::Tensor & prob, c10::optional generator=c10::nullopt) { + return at::_ops::binomial_out::redispatch(dispatchKeySet, count, prob, generator, out); + } + + // aten::binomial.out(Tensor count, Tensor prob, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & binomial_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & count, const at::Tensor & prob, c10::optional generator, at::Tensor & out) { + return at::_ops::binomial_out::redispatch(dispatchKeySet, count, prob, generator, out); + } + + // aten::native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & p=2) { + return at::_ops::native_norm_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::native_norm.out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, at::Tensor & out) { + return at::_ops::native_norm_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + return at::_ops::native_norm_ScalarOpt_dim_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::native_norm.ScalarOpt_dim_dtype_out(Tensor self, Scalar? p, int[1] dim, bool keepdim, ScalarType? dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & native_norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::native_norm_ScalarOpt_dim_dtype_out::redispatch(dispatchKeySet, self, p, dim, keepdim, dtype, out); + } + + // aten::_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::_sparse_sum_dim_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::_sparse_sum.dim_out(Tensor self, int[1] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { + return at::_ops::_sparse_sum_dim_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sum_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::_sparse_sum_backward_out::redispatch(dispatchKeySet, grad, self, dim, out); + } + + // aten::_sparse_sum_backward.out(Tensor grad, Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_sum_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { + return at::_ops::_sparse_sum_backward_out::redispatch(dispatchKeySet, grad, self, dim, out); + } + + // aten::_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_csr_sum_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_csr_sum_dim_dtype_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::_sparse_csr_sum.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_csr_sum_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::_sparse_csr_sum_dim_dtype_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_csr_prod_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false, c10::optional dtype=c10::nullopt) { + return at::_ops::_sparse_csr_prod_dim_dtype_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::_sparse_csr_prod.dim_dtype_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_csr_prod_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional dtype, at::Tensor & out) { + return at::_ops::_sparse_csr_prod_dim_dtype_out::redispatch(dispatchKeySet, self, dim, keepdim, dtype, out); + } + + // aten::_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_sparse_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_sparse_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { + return at::_ops::_sparse_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_softmax_backward_data_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + return at::_ops::_sparse_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, self, out); + } + + // aten::_sparse_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_softmax_backward_data_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_sparse_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, self, out); + } + + // aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_log_softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, bool half_to_float) { + return at::_ops::_sparse_log_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_sparse_log_softmax.out(Tensor self, int dim, bool half_to_float, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_log_softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool half_to_float, at::Tensor & out) { + return at::_ops::_sparse_log_softmax_out::redispatch(dispatchKeySet, self, dim, half_to_float, out); + } + + // aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_log_softmax_backward_data_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + return at::_ops::_sparse_log_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, self, out); + } + + // aten::_sparse_log_softmax_backward_data.out(Tensor grad_output, Tensor output, int dim, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_log_softmax_backward_data_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_sparse_log_softmax_backward_data_out::redispatch(dispatchKeySet, grad_output, output, dim, self, out); + } + + // aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _spdiags_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional layout=c10::nullopt) { + return at::_ops::_spdiags_out::redispatch(dispatchKeySet, diagonals, offsets, shape, layout, out); + } + + // aten::_spdiags.out(Tensor diagonals, Tensor offsets, int[] shape, Layout? layout=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _spdiags_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional layout, at::Tensor & out) { + return at::_ops::_spdiags_out::redispatch(dispatchKeySet, diagonals, offsets, shape, layout, out); + } + + // aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const c10::optional & p, at::ScalarType dtype) { + return at::_ops::norm_ScalarOpt_dtype_out::redispatch(dispatchKeySet, self, p, dtype, out); + } + + // aten::norm.ScalarOpt_dtype_out(Tensor self, Scalar? p, *, ScalarType dtype, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::optional & p, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::norm_ScalarOpt_dtype_out::redispatch(dispatchKeySet, self, p, dtype, out); + } + + // aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & p=2) { + return at::_ops::norm_Scalar_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::norm.Scalar_out(Tensor self, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & norm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & p, at::Tensor & out) { + return at::_ops::norm_Scalar_out::redispatch(dispatchKeySet, self, p, out); + } + + // aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clone_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional memory_format=c10::nullopt) { + return at::_ops::clone_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::clone.out(Tensor self, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & clone_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional memory_format, at::Tensor & out) { + return at::_ops::clone_out::redispatch(dispatchKeySet, self, memory_format, out); + } + + // aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_as_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, const at::Tensor & the_template, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_as_out::redispatch(dispatchKeySet, self, the_template, memory_format, out); + } + + // aten::resize_as.out(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_as_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional memory_format, const at::Tensor & out) { + return at::_ops::resize_as_out::redispatch(dispatchKeySet, self, the_template, memory_format, out); + } + + // aten::resize_as(Tensor self, Tensor the_template, *, MemoryFormat? memory_format=None) -> Tensor + inline at::Tensor resize_as(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, c10::optional memory_format=c10::nullopt) { + return at::_ops::resize_as::redispatch(dispatchKeySet, self, the_template, memory_format); + } + + // aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_as_sparse_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, const at::Tensor & the_template) { + return at::_ops::resize_as_sparse_out::redispatch(dispatchKeySet, self, the_template, out); + } + + // aten::resize_as_sparse.out(Tensor self, Tensor the_template, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & resize_as_sparse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template, const at::Tensor & out) { + return at::_ops::resize_as_sparse_out::redispatch(dispatchKeySet, self, the_template, out); + } + + // aten::resize_as_sparse(Tensor self, Tensor the_template) -> Tensor + inline at::Tensor resize_as_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & the_template) { + return at::_ops::resize_as_sparse::redispatch(dispatchKeySet, self, the_template); + } + + // aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zero_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::zero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::zero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & zero_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::zero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::zero(Tensor self) -> Tensor + inline at::Tensor zero(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::zero::redispatch(dispatchKeySet, self); + } + + // aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sub_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::sub_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::sub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sub_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::sub_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsub_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::rsub_Tensor_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::rsub.Tensor_out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsub_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::rsub_Tensor_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsub_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha=1) { + return at::_ops::rsub_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::rsub.Scalar_out(Tensor self, Scalar other, Scalar alpha=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rsub_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::rsub_Scalar_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_addmm_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta=1, const at::Scalar & alpha=1) { + return at::_ops::_sparse_addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::_sparse_addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_addmm_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, at::Tensor & out) { + return at::_ops::_sparse_addmm_out::redispatch(dispatchKeySet, self, mat1, mat2, beta, alpha, out); + } + + // aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_coo_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::IntArrayRef size) { + return at::_ops::sparse_coo_tensor_size_out::redispatch(dispatchKeySet, size, out); + } + + // aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_coo_tensor_outf(c10::DispatchKeySet dispatchKeySet, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::sparse_coo_tensor_size_out::redispatch(dispatchKeySet, size, out); + } + + // aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size) { + return at::_ops::_sparse_coo_tensor_with_dims_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, out); + } + + // aten::_sparse_coo_tensor_with_dims.out(int sparse_dim, int dense_dim, int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_outf(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::_sparse_coo_tensor_with_dims_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, out); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_outf(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, at::IntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional is_coalesced, at::Tensor & out) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, c10::fromIntArrayRefSlow(size), indices, values, is_coalesced, out); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional is_coalesced=c10::nullopt) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, indices, values, is_coalesced, out); + } + + // aten::_sparse_coo_tensor_with_dims_and_tensors.out(int sparse_dim, int dense_dim, SymInt[] size, Tensor indices, Tensor values, *, bool? is_coalesced=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_coo_tensor_with_dims_and_tensors_symint_outf(c10::DispatchKeySet dispatchKeySet, int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional is_coalesced, at::Tensor & out) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors_out::redispatch(dispatchKeySet, sparse_dim, dense_dim, size, indices, values, is_coalesced, out); + } + + // aten::sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & sparse_resize_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize_out::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim, out); + } + + // aten::sparse_resize.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & sparse_resize_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out) { + return at::_ops::sparse_resize_out::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim, out); + } + + // aten::sparse_resize(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor + inline at::Tensor sparse_resize(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim); + } + + // aten::sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & sparse_resize_and_clear_out(c10::DispatchKeySet dispatchKeySet, const at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize_and_clear_out::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim, out); + } + + // aten::sparse_resize_and_clear.out(Tensor self, int[] size, int sparse_dim, int dense_dim, *, Tensor(a!) out) -> Tensor(a!) + inline const at::Tensor & sparse_resize_and_clear_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim, const at::Tensor & out) { + return at::_ops::sparse_resize_and_clear_out::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim, out); + } + + // aten::sparse_resize_and_clear(Tensor self, int[] size, int sparse_dim, int dense_dim) -> Tensor + inline at::Tensor sparse_resize_and_clear(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + return at::_ops::sparse_resize_and_clear::redispatch(dispatchKeySet, self, size, sparse_dim, dense_dim); + } + + // aten::sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_mask_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask) { + return at::_ops::sparse_mask_out::redispatch(dispatchKeySet, self, mask, out); + } + + // aten::sparse_mask.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & sparse_mask_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, at::Tensor & out) { + return at::_ops::sparse_mask_out::redispatch(dispatchKeySet, self, mask, out); + } + + // aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_mask_projection_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches=false) { + return at::_ops::_sparse_mask_projection_out::redispatch(dispatchKeySet, self, mask, accumulate_matches, out); + } + + // aten::_sparse_mask_projection.out(Tensor self, Tensor mask, bool accumulate_matches=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_mask_projection_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches, at::Tensor & out) { + return at::_ops::_sparse_mask_projection_out::redispatch(dispatchKeySet, self, mask, accumulate_matches, out); + } + + // aten::_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_dense_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dtype=c10::nullopt, c10::optional masked_grad=c10::nullopt) { + return at::_ops::_to_dense_out::redispatch(dispatchKeySet, self, dtype, masked_grad, out); + } + + // aten::_to_dense.out(Tensor self, ScalarType? dtype=None, bool? masked_grad=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_dense_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional masked_grad, at::Tensor & out) { + return at::_ops::_to_dense_out::redispatch(dispatchKeySet, self, dtype, masked_grad, out); + } + + // aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _coalesce_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_coalesce_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_coalesce.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _coalesce_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_coalesce_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _coalesced_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool coalesced) { + return at::_ops::_coalesced_out::redispatch(dispatchKeySet, self, coalesced, out); + } + + // aten::_coalesced.out(Tensor self, bool coalesced, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _coalesced_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool coalesced, at::Tensor & out) { + return at::_ops::_coalesced_out::redispatch(dispatchKeySet, self, coalesced, out); + } + + // aten::_coalesced(Tensor self, bool coalesced) -> Tensor + inline at::Tensor _coalesced(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool coalesced) { + return at::_ops::_coalesced::redispatch(dispatchKeySet, self, coalesced); + } + + // aten::copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copy_sparse_to_sparse_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy_sparse_to_sparse_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::copy_sparse_to_sparse.out(Tensor self, Tensor src, bool non_blocking=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & copy_sparse_to_sparse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out) { + return at::_ops::copy_sparse_to_sparse_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::copy_sparse_to_sparse(Tensor self, Tensor src, bool non_blocking=False) -> Tensor + inline at::Tensor copy_sparse_to_sparse(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & src, bool non_blocking=false) { + return at::_ops::copy_sparse_to_sparse::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::_to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t sparse_dim) { + return at::_ops::_to_sparse_sparse_dim_out::redispatch(dispatchKeySet, self, sparse_dim, out); + } + + // aten::_to_sparse.sparse_dim_out(Tensor self, int sparse_dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t sparse_dim, at::Tensor & out) { + return at::_ops::_to_sparse_sparse_dim_out::redispatch(dispatchKeySet, self, sparse_dim, out); + } + + // aten::_to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional layout=c10::nullopt, at::OptionalIntArrayRef blocksize=c10::nullopt, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_out::redispatch(dispatchKeySet, self, layout, blocksize, dense_dim, out); + } + + // aten::_to_sparse.out(Tensor self, *, Layout? layout=None, int[2]? blocksize=None, int? dense_dim=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional layout, at::OptionalIntArrayRef blocksize, c10::optional dense_dim, at::Tensor & out) { + return at::_ops::_to_sparse_out::redispatch(dispatchKeySet, self, layout, blocksize, dense_dim, out); + } + + // aten::_to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_csr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_csr_out::redispatch(dispatchKeySet, self, dense_dim, out); + } + + // aten::_to_sparse_csr.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_csr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim, at::Tensor & out) { + return at::_ops::_to_sparse_csr_out::redispatch(dispatchKeySet, self, dense_dim, out); + } + + // aten::_to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_csc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_csc_out::redispatch(dispatchKeySet, self, dense_dim, out); + } + + // aten::_to_sparse_csc.out(Tensor self, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_csc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dense_dim, at::Tensor & out) { + return at::_ops::_to_sparse_csc_out::redispatch(dispatchKeySet, self, dense_dim, out); + } + + // aten::_to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_bsr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_bsr_out::redispatch(dispatchKeySet, self, blocksize, dense_dim, out); + } + + // aten::_to_sparse_bsr.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_bsr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim, at::Tensor & out) { + return at::_ops::_to_sparse_bsr_out::redispatch(dispatchKeySet, self, blocksize, dense_dim, out); + } + + // aten::_to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_bsc_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim=c10::nullopt) { + return at::_ops::_to_sparse_bsc_out::redispatch(dispatchKeySet, self, blocksize, dense_dim, out); + } + + // aten::_to_sparse_bsc.out(Tensor self, int[2] blocksize, int? dense_dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_sparse_bsc_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim, at::Tensor & out) { + return at::_ops::_to_sparse_bsc_out::redispatch(dispatchKeySet, self, blocksize, dense_dim, out); + } + + // aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_mkldnn_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional dtype=c10::nullopt) { + return at::_ops::to_mkldnn_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::to_mkldnn.out(Tensor self, ScalarType? dtype=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_mkldnn_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, at::Tensor & out) { + return at::_ops::to_mkldnn_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv2d_weight_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=c10::nullopt) { + return at::_ops::mkldnn_reorder_conv2d_weight_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*input_size)) : c10::nullopt, out); + } + + // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv2d_weight_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::OptionalIntArrayRef input_size, at::Tensor & out) { + return at::_ops::mkldnn_reorder_conv2d_weight_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, input_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*input_size)) : c10::nullopt, out); + } + + // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv2d_weight_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1, at::OptionalSymIntArrayRef input_size=c10::nullopt) { + return at::_ops::mkldnn_reorder_conv2d_weight_out::redispatch(dispatchKeySet, self, padding, stride, dilation, groups, input_size, out); + } + + // aten::mkldnn_reorder_conv2d_weight.out(Tensor self, SymInt[2] padding=0, SymInt[2] stride=1, SymInt[2] dilation=1, SymInt groups=1, SymInt[]? input_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv2d_weight_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out) { + return at::_ops::mkldnn_reorder_conv2d_weight_out::redispatch(dispatchKeySet, self, padding, stride, dilation, groups, input_size, out); + } + + // aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv3d_weight_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1) { + return at::_ops::mkldnn_reorder_conv3d_weight_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv3d_weight_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef padding, at::IntArrayRef stride, at::IntArrayRef dilation, int64_t groups, at::Tensor & out) { + return at::_ops::mkldnn_reorder_conv3d_weight_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(dilation), groups, out); + } + + // aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv3d_weight_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef dilation=c10::SymInt(1), c10::SymInt groups=1) { + return at::_ops::mkldnn_reorder_conv3d_weight_out::redispatch(dispatchKeySet, self, padding, stride, dilation, groups, out); + } + + // aten::mkldnn_reorder_conv3d_weight.out(Tensor self, SymInt[3] padding=0, SymInt[3] stride=1, SymInt[3] dilation=1, SymInt groups=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_reorder_conv3d_weight_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::Tensor & out) { + return at::_ops::mkldnn_reorder_conv3d_weight_out::redispatch(dispatchKeySet, self, padding, stride, dilation, groups, out); + } + + // aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_dynamic_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::ScalarType dtype, bool reduce_range) { + return at::_ops::quantize_per_tensor_dynamic_out::redispatch(dispatchKeySet, self, dtype, reduce_range, out); + } + + // aten::quantize_per_tensor_dynamic.out(Tensor self, ScalarType dtype, bool reduce_range, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_dynamic_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, bool reduce_range, at::Tensor & out) { + return at::_ops::quantize_per_tensor_dynamic_out::redispatch(dispatchKeySet, self, dtype, reduce_range, out); + } + + // aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, dtype, out); + } + + // aten::quantize_per_tensor.out(Tensor self, float scale, int zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::quantize_per_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, dtype, out); + } + + // aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor_tensor_qparams_out::redispatch(dispatchKeySet, self, scale, zero_point, dtype, out); + } + + // aten::quantize_per_tensor.tensor_qparams_out(Tensor self, Tensor scale, Tensor zero_point, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::quantize_per_tensor_tensor_qparams_out::redispatch(dispatchKeySet, self, scale, zero_point, dtype, out); + } + + // aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> () + inline void quantize_per_tensor_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) { + return at::_ops::quantize_per_tensor_tensors_out::redispatch(dispatchKeySet, tensors, scales, zero_points, dtype, out); + } + + // aten::quantize_per_tensor.tensors_out(Tensor[] tensors, Tensor scales, Tensor zero_points, ScalarType dtype, *, Tensor(a!)[] out) -> () + inline void quantize_per_tensor_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype, at::TensorList out) { + return at::_ops::quantize_per_tensor_tensors_out::redispatch(dispatchKeySet, tensors, scales, zero_points, dtype, out); + } + + // aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_channel_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { + return at::_ops::quantize_per_channel_out::redispatch(dispatchKeySet, self, scales, zero_points, axis, dtype, out); + } + + // aten::quantize_per_channel.out(Tensor self, Tensor scales, Tensor zero_points, int axis, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & quantize_per_channel_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::quantize_per_channel_out::redispatch(dispatchKeySet, self, scales, zero_points, axis, dtype, out); + } + + // aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dequantize_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::dequantize_self_out::redispatch(dispatchKeySet, self, out); + } + + // aten::dequantize.self_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dequantize_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::dequantize_self_out::redispatch(dispatchKeySet, self, out); + } + + // aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> () + inline void dequantize_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList tensors) { + return at::_ops::dequantize_tensors_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::dequantize.tensors_out(Tensor[] tensors, *, Tensor(a!)[] out) -> () + inline void dequantize_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList tensors, at::TensorList out) { + return at::_ops::dequantize_tensors_out::redispatch(dispatchKeySet, tensors, out); + } + + // aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & q_per_channel_scales_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::q_per_channel_scales_out::redispatch(dispatchKeySet, self, out); + } + + // aten::q_per_channel_scales.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & q_per_channel_scales_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::q_per_channel_scales_out::redispatch(dispatchKeySet, self, out); + } + + // aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & q_per_channel_zero_points_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::q_per_channel_zero_points_out::redispatch(dispatchKeySet, self, out); + } + + // aten::q_per_channel_zero_points.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & q_per_channel_zero_points_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::q_per_channel_zero_points_out::redispatch(dispatchKeySet, self, out); + } + + // aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & int_repr_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::int_repr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::int_repr.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & int_repr_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::int_repr_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_per_tensor_quantized_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double scale, int64_t zero_point) { + return at::_ops::_make_per_tensor_quantized_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, out); + } + + // aten::_make_per_tensor_quantized_tensor.out(Tensor self, float scale, int zero_point, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_per_tensor_quantized_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, at::Tensor & out) { + return at::_ops::_make_per_tensor_quantized_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, out); + } + + // aten::_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_per_channel_quantized_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) { + return at::_ops::_make_per_channel_quantized_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, out); + } + + // aten::_make_per_channel_quantized_tensor.out(Tensor self, Tensor scale, Tensor zero_point, int axis, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_per_channel_quantized_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, at::Tensor & out) { + return at::_ops::_make_per_channel_quantized_tensor_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, out); + } + + // aten::fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fake_quantize_per_tensor_affine_cachemask_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask_out::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max, out0, out1); + } + + // aten::fake_quantize_per_tensor_affine_cachemask.out(Tensor self, float scale, int zero_point, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fake_quantize_per_tensor_affine_cachemask_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask_out::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max, out0, out1); + } + + // aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) { + return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::redispatch(dispatchKeySet, self, scale, zero_point, fake_quant_enabled, quant_min, quant_max, out0, out1); + } + + // aten::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams.out(Tensor self, Tensor scale, Tensor zero_point, Tensor fake_quant_enabled, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_out::redispatch(dispatchKeySet, self, scale, zero_point, fake_quant_enabled, quant_min, quant_max, out0, out1); + } + + // aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fake_quantize_learnable_per_tensor_affine_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine_out::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max, grad_factor, out); + } + + // aten::_fake_quantize_learnable_per_tensor_affine.out(Tensor self, Tensor scale, Tensor zero_point, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fake_quantize_learnable_per_tensor_affine_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine_out::redispatch(dispatchKeySet, self, scale, zero_point, quant_min, quant_max, grad_factor, out); + } + + // aten::fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fake_quantize_per_channel_affine_cachemask_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { + return at::_ops::fake_quantize_per_channel_affine_cachemask_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, out0, out1); + } + + // aten::fake_quantize_per_channel_affine_cachemask.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple fake_quantize_per_channel_affine_cachemask_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::fake_quantize_per_channel_affine_cachemask_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, out0, out1); + } + + // aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fake_quantize_learnable_per_channel_affine_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor=1.0) { + return at::_ops::_fake_quantize_learnable_per_channel_affine_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out); + } + + // aten::_fake_quantize_learnable_per_channel_affine.out(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max, float grad_factor=1.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fake_quantize_learnable_per_channel_affine_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor, at::Tensor & out) { + return at::_ops::_fake_quantize_learnable_per_channel_affine_out::redispatch(dispatchKeySet, self, scale, zero_point, axis, quant_min, quant_max, grad_factor, out); + } + + // aten::_fused_moving_avg_obs_fq_helper.out(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False, *, Tensor(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!)) + inline ::std::tuple _fused_moving_avg_obs_fq_helper_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) { + return at::_ops::_fused_moving_avg_obs_fq_helper_out::redispatch(dispatchKeySet, self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0, out1); + } + + // aten::_fused_moving_avg_obs_fq_helper.out(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor(a!) running_min, Tensor(b!) running_max, Tensor(c!) scale, Tensor(d!) zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False, *, Tensor(e!) out0, Tensor(f!) out1) -> (Tensor(e!), Tensor(f!)) + inline ::std::tuple _fused_moving_avg_obs_fq_helper_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, at::Tensor & running_min, at::Tensor & running_max, at::Tensor & scale, at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_fused_moving_avg_obs_fq_helper_out::redispatch(dispatchKeySet, self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant, out0, out1); + } + + // aten::_fused_moving_avg_obs_fq_helper_functional(Tensor self, Tensor observer_on, Tensor fake_quant_on, Tensor running_min, Tensor running_max, Tensor scale, Tensor zero_point, float averaging_const, int quant_min, int quant_max, int ch_axis, bool per_row_fake_quant=False, bool symmetric_quant=False) -> (Tensor output, Tensor mask, Tensor running_min_out, Tensor running_max_out, Tensor scale_out, Tensor zero_point_out) + inline ::std::tuple _fused_moving_avg_obs_fq_helper_functional(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, const at::Tensor & running_min, const at::Tensor & running_max, const at::Tensor & scale, const at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant=false, bool symmetric_quant=false) { + return at::_ops::_fused_moving_avg_obs_fq_helper_functional::redispatch(dispatchKeySet, self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); + } + + // aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool non_blocking=false, c10::optional memory_format=c10::nullopt) { + return at::_ops::_to_copy_out::redispatch(dispatchKeySet, self, non_blocking, memory_format, out); + } + + // aten::_to_copy.out(Tensor self, *, bool non_blocking=False, MemoryFormat? memory_format=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _to_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool non_blocking, c10::optional memory_format, at::Tensor & out) { + return at::_ops::_to_copy_out::redispatch(dispatchKeySet, self, non_blocking, memory_format, out); + } + + // aten::_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!)) + inline ::std::tuple _lstm_mps_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::_lstm_mps_out::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4, out5); + } + + // aten::_lstm_mps.out(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4, Tensor(f!) out5) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!), Tensor(f!)) + inline ::std::tuple _lstm_mps_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5) { + return at::_ops::_lstm_mps_out::redispatch(dispatchKeySet, input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2, out3, out4, out5); + } + + // aten::lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> () + inline void lstm_mps_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::TensorList out1, at::TensorList out2, const c10::optional & grad_y, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + return at::_ops::lstm_mps_backward_out::redispatch(dispatchKeySet, grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2); + } + + // aten::lstm_mps_backward.out(Tensor? grad_y, Tensor? grad_hy, Tensor? grad_cy, Tensor z_state, Tensor cell_state_fwd, Tensor input, Tensor layersOutputs, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, Tensor(a!) out0, Tensor(b!)[] out1, Tensor(c!)[] out2) -> () + inline void lstm_mps_backward_outf(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_y, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2) { + return at::_ops::lstm_mps_backward_out::redispatch(dispatchKeySet, grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2); + } + + // aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _thnn_fused_lstm_cell_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias={}, const c10::optional & hidden_bias={}) { + return at::_ops::_thnn_fused_lstm_cell_out::redispatch(dispatchKeySet, input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2); + } + + // aten::_thnn_fused_lstm_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor cx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _thnn_fused_lstm_cell_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias, const c10::optional & hidden_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_thnn_fused_lstm_cell_out::redispatch(dispatchKeySet, input_gates, hidden_gates, cx, input_bias, hidden_bias, out0, out1, out2); + } + + // aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _thnn_fused_lstm_cell_backward_impl_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { + return at::_ops::_thnn_fused_lstm_cell_backward_impl_out::redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2); + } + + // aten::_thnn_fused_lstm_cell_backward_impl.out(Tensor? grad_hy, Tensor? grad_cy, Tensor cx, Tensor cy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _thnn_fused_lstm_cell_backward_impl_outf(c10::DispatchKeySet dispatchKeySet, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_thnn_fused_lstm_cell_backward_impl_out::redispatch(dispatchKeySet, grad_hy, grad_cy, cx, cy, workspace, has_bias, out0, out1, out2); + } + + // aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _thnn_fused_gru_cell_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias={}, const c10::optional & hidden_bias={}) { + return at::_ops::_thnn_fused_gru_cell_out::redispatch(dispatchKeySet, input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1); + } + + // aten::_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _thnn_fused_gru_cell_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_thnn_fused_gru_cell_out::redispatch(dispatchKeySet, input_gates, hidden_gates, hx, input_bias, hidden_bias, out0, out1); + } + + // aten::_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _thnn_fused_gru_cell_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) { + return at::_ops::_thnn_fused_gru_cell_backward_out::redispatch(dispatchKeySet, grad_hy, workspace, has_bias, out0, out1, out2, out3, out4); + } + + // aten::_thnn_fused_gru_cell_backward.out(Tensor grad_hy, Tensor workspace, bool has_bias, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3, Tensor(e!) out4) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!), Tensor(e!)) + inline ::std::tuple _thnn_fused_gru_cell_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4) { + return at::_ops::_thnn_fused_gru_cell_backward_out::redispatch(dispatchKeySet, grad_hy, workspace, has_bias, out0, out1, out2, out3, out4); + } + + // aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _pack_padded_sequence_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & input, const at::Tensor & lengths, bool batch_first) { + return at::_ops::_pack_padded_sequence_out::redispatch(dispatchKeySet, input, lengths, batch_first, out0, out1); + } + + // aten::_pack_padded_sequence.out(Tensor input, Tensor lengths, bool batch_first, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _pack_padded_sequence_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & lengths, bool batch_first, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_pack_padded_sequence_out::redispatch(dispatchKeySet, input, lengths, batch_first, out0, out1); + } + + // aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Storage source) { + return at::_ops::set_source_Storage_out::redispatch(dispatchKeySet, self, source, out); + } + + // aten::set.source_Storage_out(Tensor self, Storage source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, at::Tensor & out) { + return at::_ops::set_source_Storage_out::redispatch(dispatchKeySet, self, source, out); + } + + // aten::set.source_Storage(Tensor self, Storage source) -> Tensor + inline at::Tensor set(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source) { + return at::_ops::set_source_Storage::redispatch(dispatchKeySet, self, source); + } + + // aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) { + return at::_ops::set_source_Storage_storage_offset_out::redispatch(dispatchKeySet, self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::set_source_Storage_storage_offset_out::redispatch(dispatchKeySet, self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) { + return at::_ops::set_source_Storage_storage_offset_out::redispatch(dispatchKeySet, self, source, storage_offset, size, stride, out); + } + + // aten::set.source_Storage_storage_offset_out(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[], *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { + return at::_ops::set_source_Storage_storage_offset_out::redispatch(dispatchKeySet, self, source, storage_offset, size, stride, out); + } + + // aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor + inline at::Tensor set(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, int64_t storage_offset, at::IntArrayRef size, at::IntArrayRef stride={}) { + return at::_ops::set_source_Storage_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride)); + } + + // aten::set.source_Storage_storage_offset(Tensor self, Storage source, SymInt storage_offset, SymInt[] size, SymInt[] stride=[]) -> Tensor + inline at::Tensor set_symint(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride={}) { + return at::_ops::set_source_Storage_storage_offset::redispatch(dispatchKeySet, self, source, storage_offset, size, stride); + } + + // aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & source) { + return at::_ops::set_source_Tensor_out::redispatch(dispatchKeySet, self, source, out); + } + + // aten::set.source_Tensor_out(Tensor self, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & source, at::Tensor & out) { + return at::_ops::set_source_Tensor_out::redispatch(dispatchKeySet, self, source, out); + } + + // aten::set.source_Tensor(Tensor self, Tensor source) -> Tensor + inline at::Tensor set(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & source) { + return at::_ops::set_source_Tensor::redispatch(dispatchKeySet, self, source); + } + + // aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::set_out::redispatch(dispatchKeySet, self, out); + } + + // aten::set.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & set_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::set_out::redispatch(dispatchKeySet, self, out); + } + + // aten::set(Tensor self) -> Tensor + inline at::Tensor set(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self) { + return at::_ops::set::redispatch(dispatchKeySet, self); + } + + // aten::lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::lift_out::redispatch(dispatchKeySet, self, out); + } + + // aten::lift.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lift_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::lift_out::redispatch(dispatchKeySet, self, out); + } + + // aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lift_fresh_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::lift_fresh_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::lift_fresh_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & lift_fresh_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::lift_fresh_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { + return at::_ops::masked_fill_Scalar_out::redispatch(dispatchKeySet, self, mask, value, out); + } + + // aten::masked_fill.Scalar_out(Tensor self, Tensor mask, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value, at::Tensor & out) { + return at::_ops::masked_fill_Scalar_out::redispatch(dispatchKeySet, self, mask, value, out); + } + + // aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { + return at::_ops::masked_fill_Tensor_out::redispatch(dispatchKeySet, self, mask, value, out); + } + + // aten::masked_fill.Tensor_out(Tensor self, Tensor mask, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value, at::Tensor & out) { + return at::_ops::masked_fill_Tensor_out::redispatch(dispatchKeySet, self, mask, value, out); + } + + // aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_scatter_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { + return at::_ops::masked_scatter_out::redispatch(dispatchKeySet, self, mask, source, out); + } + + // aten::masked_scatter.out(Tensor self, Tensor mask, Tensor source, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & masked_scatter_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source, at::Tensor & out) { + return at::_ops::masked_scatter_out::redispatch(dispatchKeySet, self, mask, source, out); + } + + // aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_softmax_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & mask, c10::optional dim=c10::nullopt, c10::optional mask_type=c10::nullopt) { + return at::_ops::_masked_softmax_out::redispatch(dispatchKeySet, self, mask, dim, mask_type, out); + } + + // aten::_masked_softmax.out(Tensor self, Tensor mask, int? dim=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_softmax_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & mask, c10::optional dim, c10::optional mask_type, at::Tensor & out) { + return at::_ops::_masked_softmax_out::redispatch(dispatchKeySet, self, mask, dim, mask_type, out); + } + + // aten::_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_softmax_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional dim=c10::nullopt) { + return at::_ops::_masked_softmax_backward_out::redispatch(dispatchKeySet, grad_output, output, mask, dim, out); + } + + // aten::_masked_softmax_backward.out(Tensor grad_output, Tensor output, Tensor mask, int? dim=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _masked_softmax_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional dim, at::Tensor & out) { + return at::_ops::_masked_softmax_backward_out::redispatch(dispatchKeySet, grad_output, output, mask, dim, out); + } + + // aten::put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & put_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate=false) { + return at::_ops::put_out::redispatch(dispatchKeySet, self, index, source, accumulate, out); + } + + // aten::put.out(Tensor self, Tensor index, Tensor source, bool accumulate=False, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & put_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate, at::Tensor & out) { + return at::_ops::put_out::redispatch(dispatchKeySet, self, index, source, accumulate, out); + } + + // aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + return at::_ops::index_fill_int_Scalar_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::index_fill.int_Scalar_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) { + return at::_ops::index_fill_int_Scalar_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_fill_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { + return at::_ops::index_fill_int_Tensor_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::index_fill.int_Tensor_out(Tensor self, int dim, Tensor index, Tensor value, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & index_fill_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value, at::Tensor & out) { + return at::_ops::index_fill_int_Tensor_out::redispatch(dispatchKeySet, self, dim, index, value, out); + } + + // aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_and_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_and.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_and_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_and_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_or_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_or.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_or_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_or_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_xor_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_xor.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_xor_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_xor_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __lshift___out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__lshift___Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__lshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __lshift___outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::__lshift___Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __lshift___out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__lshift___Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__lshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __lshift___outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::__lshift___Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_left_shift_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_left_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_left_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_left_shift_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __rshift___out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Scalar & other) { + return at::_ops::__rshift___Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__rshift__.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __rshift___outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Scalar & other, at::Tensor & out) { + return at::_ops::__rshift___Scalar_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __rshift___out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other) { + return at::_ops::__rshift___Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::__rshift__.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & __rshift___outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::__rshift___Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::bitwise_right_shift_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::bitwise_right_shift.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bitwise_right_shift_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::bitwise_right_shift_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt) { + return at::_ops::random_from_out::redispatch(dispatchKeySet, self, from, to, generator, out); + } + + // aten::random.from_out(Tensor self, int from, int? to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator, at::Tensor & out) { + return at::_ops::random_from_out::redispatch(dispatchKeySet, self, from, to, generator, out); + } + + // aten::random.from(Tensor self, int from, int? to, *, Generator? generator=None) -> Tensor + inline at::Tensor random(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator=c10::nullopt) { + return at::_ops::random_from::redispatch(dispatchKeySet, self, from, to, generator); + } + + // aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt) { + return at::_ops::random_to_out::redispatch(dispatchKeySet, self, to, generator, out); + } + + // aten::random.to_out(Tensor self, int to, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t to, c10::optional generator, at::Tensor & out) { + return at::_ops::random_to_out::redispatch(dispatchKeySet, self, to, generator, out); + } + + // aten::random.to(Tensor self, int to, *, Generator? generator=None) -> Tensor + inline at::Tensor random(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t to, c10::optional generator=c10::nullopt) { + return at::_ops::random_to::redispatch(dispatchKeySet, self, to, generator); + } + + // aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::random_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::random.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & random_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator, at::Tensor & out) { + return at::_ops::random_out::redispatch(dispatchKeySet, self, generator, out); + } + + // aten::random(Tensor self, *, Generator? generator=None) -> Tensor + inline at::Tensor random(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional generator=c10::nullopt) { + return at::_ops::random::redispatch(dispatchKeySet, self, generator); + } + + // aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & uniform_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double from=0, double to=1, c10::optional generator=c10::nullopt) { + return at::_ops::uniform_out::redispatch(dispatchKeySet, self, from, to, generator, out); + } + + // aten::uniform.out(Tensor self, float from=0, float to=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & uniform_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from, double to, c10::optional generator, at::Tensor & out) { + return at::_ops::uniform_out::redispatch(dispatchKeySet, self, from, to, generator, out); + } + + // aten::uniform(Tensor self, float from=0, float to=1, *, Generator? generator=None) -> Tensor + inline at::Tensor uniform(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double from=0, double to=1, c10::optional generator=c10::nullopt) { + return at::_ops::uniform::redispatch(dispatchKeySet, self, from, to, generator); + } + + // aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cauchy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double median=0, double sigma=1, c10::optional generator=c10::nullopt) { + return at::_ops::cauchy_out::redispatch(dispatchKeySet, self, median, sigma, generator, out); + } + + // aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & cauchy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double median, double sigma, c10::optional generator, at::Tensor & out) { + return at::_ops::cauchy_out::redispatch(dispatchKeySet, self, median, sigma, generator, out); + } + + // aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor + inline at::Tensor cauchy(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double median=0, double sigma=1, c10::optional generator=c10::nullopt) { + return at::_ops::cauchy::redispatch(dispatchKeySet, self, median, sigma, generator); + } + + // aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double mean=1, double std=2, c10::optional generator=c10::nullopt) { + return at::_ops::log_normal_out::redispatch(dispatchKeySet, self, mean, std, generator, out); + } + + // aten::log_normal.out(Tensor self, float mean=1, float std=2, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & log_normal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional generator, at::Tensor & out) { + return at::_ops::log_normal_out::redispatch(dispatchKeySet, self, mean, std, generator, out); + } + + // aten::log_normal(Tensor self, float mean=1, float std=2, *, Generator? generator=None) -> Tensor + inline at::Tensor log_normal(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean=1, double std=2, c10::optional generator=c10::nullopt) { + return at::_ops::log_normal::redispatch(dispatchKeySet, self, mean, std, generator); + } + + // aten::exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exponential_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double lambd=1, c10::optional generator=c10::nullopt) { + return at::_ops::exponential_out::redispatch(dispatchKeySet, self, lambd, generator, out); + } + + // aten::exponential.out(Tensor self, float lambd=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & exponential_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double lambd, c10::optional generator, at::Tensor & out) { + return at::_ops::exponential_out::redispatch(dispatchKeySet, self, lambd, generator, out); + } + + // aten::exponential(Tensor self, float lambd=1, *, Generator? generator=None) -> Tensor + inline at::Tensor exponential(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double lambd=1, c10::optional generator=c10::nullopt) { + return at::_ops::exponential::redispatch(dispatchKeySet, self, lambd, generator); + } + + // aten::geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & geometric_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::geometric_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::geometric.out(Tensor self, float p, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & geometric_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator, at::Tensor & out) { + return at::_ops::geometric_out::redispatch(dispatchKeySet, self, p, generator, out); + } + + // aten::geometric(Tensor self, float p, *, Generator? generator=None) -> Tensor + inline at::Tensor geometric(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double p, c10::optional generator=c10::nullopt) { + return at::_ops::geometric::redispatch(dispatchKeySet, self, p, generator); + } + + // aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tril_indices_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) { + return at::_ops::tril_indices_out::redispatch(dispatchKeySet, row, col, offset, out); + } + + // aten::tril_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & tril_indices_outf(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out) { + return at::_ops::tril_indices_out::redispatch(dispatchKeySet, row, col, offset, out); + } + + // aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & triu_indices_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, int64_t row, int64_t col, int64_t offset=0) { + return at::_ops::triu_indices_out::redispatch(dispatchKeySet, row, col, offset, out); + } + + // aten::triu_indices.out(int row, int col, int offset=0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & triu_indices_outf(c10::DispatchKeySet dispatchKeySet, int64_t row, int64_t col, int64_t offset, at::Tensor & out) { + return at::_ops::triu_indices_out::redispatch(dispatchKeySet, row, col, offset, out); + } + + // aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & trace_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::trace_out::redispatch(dispatchKeySet, self, out); + } + + // aten::trace.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & trace_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::trace_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cholesky_solve_helper_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & A, bool upper) { + return at::_ops::_cholesky_solve_helper_out::redispatch(dispatchKeySet, self, A, upper, out); + } + + // aten::_cholesky_solve_helper.out(Tensor self, Tensor A, bool upper, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _cholesky_solve_helper_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & A, bool upper, at::Tensor & out) { + return at::_ops::_cholesky_solve_helper_out::redispatch(dispatchKeySet, self, A, upper, out); + } + + // aten::dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dist_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & other, const at::Scalar & p=2) { + return at::_ops::dist_out::redispatch(dispatchKeySet, self, other, p, out); + } + + // aten::dist.out(Tensor self, Tensor other, Scalar p=2, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & dist_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & other, const at::Scalar & p, at::Tensor & out) { + return at::_ops::dist_out::redispatch(dispatchKeySet, self, other, p, out); + } + + // aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> () + inline void _histogramdd_bin_edges_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_bin_edges_out::redispatch(dispatchKeySet, self, bins, range, weight, density, out); + } + + // aten::_histogramdd_bin_edges.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!)[] out) -> () + inline void _histogramdd_bin_edges_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density, at::TensorList out) { + return at::_ops::_histogramdd_bin_edges_out::redispatch(dispatchKeySet, self, bins, range, weight, density, out); + } + + // aten::_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _histogramdd_from_bin_cts_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range=c10::nullopt, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_from_bin_cts_out::redispatch(dispatchKeySet, self, bins, range, weight, density, out); + } + + // aten::_histogramdd_from_bin_cts.out(Tensor self, int[] bins, *, float[]? range=None, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _histogramdd_from_bin_cts_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density, at::Tensor & out) { + return at::_ops::_histogramdd_from_bin_cts_out::redispatch(dispatchKeySet, self, bins, range, weight, density, out); + } + + // aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _histogramdd_from_bin_tensors_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::TensorList bins, const c10::optional & weight={}, bool density=false) { + return at::_ops::_histogramdd_from_bin_tensors_out::redispatch(dispatchKeySet, self, bins, weight, density, out); + } + + // aten::_histogramdd_from_bin_tensors.out(Tensor self, Tensor[] bins, *, Tensor? weight=None, bool density=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _histogramdd_from_bin_tensors_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::TensorList bins, const c10::optional & weight, bool density, at::Tensor & out) { + return at::_ops::_histogramdd_from_bin_tensors_out::redispatch(dispatchKeySet, self, bins, weight, density, out); + } + + // aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & other) { + return at::_ops::remainder_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::remainder.Scalar_Tensor_out(Scalar self, Tensor other, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & remainder_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & other, at::Tensor & out) { + return at::_ops::remainder_Scalar_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argsort_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool stable, int64_t dim=-1, bool descending=false) { + return at::_ops::argsort_stable_out::redispatch(dispatchKeySet, self, stable, dim, descending, out); + } + + // aten::argsort.stable_out(Tensor self, *, bool stable, int dim=-1, bool descending=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & argsort_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool stable, int64_t dim, bool descending, at::Tensor & out) { + return at::_ops::argsort_stable_out::redispatch(dispatchKeySet, self, stable, dim, descending, out); + } + + // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { + return at::_ops::unfold_backward_out::redispatch(dispatchKeySet, grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out); + } + + // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, at::IntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) { + return at::_ops::unfold_backward_out::redispatch(dispatchKeySet, grad_in, c10::fromIntArrayRefSlow(input_sizes), dim, size, step, out); + } + + // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { + return at::_ops::unfold_backward_out::redispatch(dispatchKeySet, grad_in, input_sizes, dim, size, step, out); + } + + // aten::unfold_backward.out(Tensor grad_in, SymInt[] input_sizes, int dim, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step, at::Tensor & out) { + return at::_ops::unfold_backward_out::redispatch(dispatchKeySet, grad_in, input_sizes, dim, size, step, out); + } + + // aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double mean=0, double std=1, c10::optional generator=c10::nullopt) { + return at::_ops::normal_out::redispatch(dispatchKeySet, self, mean, std, generator, out); + } + + // aten::normal.out(Tensor self, float mean=0, float std=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & normal_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double mean, double std, c10::optional generator, at::Tensor & out) { + return at::_ops::normal_out::redispatch(dispatchKeySet, self, mean, std, generator, out); + } + + // aten::_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> () + inline void _amp_foreach_non_finite_check_and_unscale_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale_out::redispatch(dispatchKeySet, self, found_inf, inv_scale, out); + } + + // aten::_amp_foreach_non_finite_check_and_unscale.out(Tensor[] self, Tensor(b!) found_inf, Tensor inv_scale, *, Tensor(a!)[] out) -> () + inline void _amp_foreach_non_finite_check_and_unscale_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale, at::TensorList out) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale_out::redispatch(dispatchKeySet, self, found_inf, inv_scale, out); + } + + // aten::_amp_foreach_non_finite_check_and_unscale(Tensor[] self, Tensor found_inf, Tensor inv_scale) -> (Tensor[] self_out, Tensor found_inf_out) + inline ::std::tuple<::std::vector,at::Tensor> _amp_foreach_non_finite_check_and_unscale(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & found_inf, const at::Tensor & inv_scale) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale::redispatch(dispatchKeySet, self, found_inf, inv_scale); + } + + // aten::_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _amp_update_scale_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { + return at::_ops::_amp_update_scale_out::redispatch(dispatchKeySet, self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval, out); + } + + // aten::_amp_update_scale.out(Tensor self, Tensor(b!) growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _amp_update_scale_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval, at::Tensor & out) { + return at::_ops::_amp_update_scale_out::redispatch(dispatchKeySet, self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval, out); + } + + // aten::_amp_update_scale(Tensor self, Tensor growth_tracker, Tensor found_inf, float scale_growth_factor, float scale_backoff_factor, int growth_interval) -> (Tensor, Tensor growth_tracker_out) + inline ::std::tuple _amp_update_scale(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { + return at::_ops::_amp_update_scale::redispatch(dispatchKeySet, self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval); + } + + // aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_add_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_add_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_add.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_add_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_add_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_add_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add_List_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_add.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_add_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { + return at::_ops::_foreach_add_List_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_add_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_add_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_add.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_add_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_add_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_add_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_add_Tensor_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_add.Tensor_out(Tensor[] self, Tensor other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_add_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other, const at::Scalar & alpha, at::TensorList out) { + return at::_ops::_foreach_add_Tensor_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_sub_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_sub_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_sub.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_sub_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_sub_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_sub_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other, const at::Scalar & alpha=1) { + return at::_ops::_foreach_sub_List_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_sub.List_out(Tensor[] self, Tensor[] other, *, Scalar alpha=1, Tensor(a!)[] out) -> () + inline void _foreach_sub_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { + return at::_ops::_foreach_sub_List_out::redispatch(dispatchKeySet, self, other, alpha, out); + } + + // aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_sub_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_sub_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_sub.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_sub_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_sub_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_mul_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_mul.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_mul_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_mul_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_mul.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_mul_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_mul_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_mul.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_mul_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_mul.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_mul_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_mul.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> () + inline void _foreach_mul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other, at::TensorList out) { + return at::_ops::_foreach_mul_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_div_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_div_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_div.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_div_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_div_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_div_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_div_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_div.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_div_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_div_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_div_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_div_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_div.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_div_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_div_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_div.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> () + inline void _foreach_div_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Tensor & other) { + return at::_ops::_foreach_div_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_div.Tensor_out(Tensor[] self, Tensor other, *, Tensor(a!)[] out) -> () + inline void _foreach_div_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Tensor & other, at::TensorList out) { + return at::_ops::_foreach_div_Tensor_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_max_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_clamp_max.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_clamp_max_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_max_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_clamp_max.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_clamp_max_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_max_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_clamp_max.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_max_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_clamp_max_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_clamp_min_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_clamp_min.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_clamp_min_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_clamp_min_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_clamp_min.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_clamp_min_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_clamp_min_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_clamp_min.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_clamp_min_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_clamp_min_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_maximum_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_maximum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_maximum_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_maximum_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_maximum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_maximum_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_maximum_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_maximum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_maximum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_maximum_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & scalar) { + return at::_ops::_foreach_minimum_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_minimum.Scalar_out(Tensor[] self, Scalar scalar, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + return at::_ops::_foreach_minimum_Scalar_out::redispatch(dispatchKeySet, self, scalar, out); + } + + // aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList other) { + return at::_ops::_foreach_minimum_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_minimum.List_out(Tensor[] self, Tensor[] other, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList other, at::TensorList out) { + return at::_ops::_foreach_minimum_List_out::redispatch(dispatchKeySet, self, other, out); + } + + // aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef scalars) { + return at::_ops::_foreach_minimum_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_minimum.ScalarList_out(Tensor[] self, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_minimum_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_minimum_ScalarList_out::redispatch(dispatchKeySet, self, scalars, out); + } + + // aten::_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcdiv_Scalar_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::_foreach_addcdiv.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { + return at::_ops::_foreach_addcdiv_Scalar_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcdiv_ScalarList_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcdiv.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_addcdiv_ScalarList_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcdiv_Tensor_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcdiv.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcdiv_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { + return at::_ops::_foreach_addcdiv_Tensor_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value=1) { + return at::_ops::_foreach_addcmul_Scalar_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::_foreach_addcmul.Scalar_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar value=1, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { + return at::_ops::_foreach_addcmul_Scalar_out::redispatch(dispatchKeySet, self, tensor1, tensor2, value, out); + } + + // aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + return at::_ops::_foreach_addcmul_ScalarList_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcmul.ScalarList_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Scalar[] scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars, at::TensorList out) { + return at::_ops::_foreach_addcmul_ScalarList_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + return at::_ops::_foreach_addcmul_Tensor_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_addcmul.Tensor_out(Tensor[] self, Tensor[] tensor1, Tensor[] tensor2, Tensor scalars, *, Tensor(a!)[] out) -> () + inline void _foreach_addcmul_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { + return at::_ops::_foreach_addcmul_Tensor_out::redispatch(dispatchKeySet, self, tensor1, tensor2, scalars, out); + } + + // aten::_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_abs_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_abs_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_abs.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_abs_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_abs_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_acos_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_acos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_acos.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_acos_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_acos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_asin_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_asin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_asin.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_asin_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_asin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_atan_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_atan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_atan.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_atan_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_atan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_ceil_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_ceil_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_ceil.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_ceil_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_ceil_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_cos_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_cos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_cos.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_cos_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_cos_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_cosh_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_cosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_cosh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_cosh_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_cosh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_erf_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_erf.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_erf_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_erf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_erfc_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_erfc.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_erfc_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_erfc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_exp_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_exp.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_exp_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_expm1_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_expm1.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_expm1_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_expm1_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_floor_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_floor_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_floor.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_floor_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_floor_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_frac_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_frac_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_frac.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_frac_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_frac_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> () + inline void _foreach_lerp_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensors1, at::TensorList weights) { + return at::_ops::_foreach_lerp_List_out::redispatch(dispatchKeySet, self, tensors1, weights, out); + } + + // aten::_foreach_lerp.List_out(Tensor[] self, Tensor[] tensors1, Tensor[] weights, *, Tensor(a!)[] out) -> () + inline void _foreach_lerp_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out) { + return at::_ops::_foreach_lerp_List_out::redispatch(dispatchKeySet, self, tensors1, weights, out); + } + + // aten::_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> () + inline void _foreach_lerp_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { + return at::_ops::_foreach_lerp_Scalar_out::redispatch(dispatchKeySet, self, tensors1, weight, out); + } + + // aten::_foreach_lerp.Scalar_out(Tensor[] self, Tensor[] tensors1, Scalar weight, *, Tensor(a!)[] out) -> () + inline void _foreach_lerp_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out) { + return at::_ops::_foreach_lerp_Scalar_out::redispatch(dispatchKeySet, self, tensors1, weight, out); + } + + // aten::_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_lgamma_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_lgamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_lgamma.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_lgamma_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_lgamma_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_log_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_log_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log10_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_log10_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log10.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log10_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_log10_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log1p_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log1p.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log1p_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_log1p_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log2_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_log2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_log2.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_log2_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_log2_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_neg_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_neg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_neg.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_neg_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_neg_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, *, Tensor(a!)[] out) -> () + inline void _foreach_norm_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & ord=2) { + return at::_ops::_foreach_norm_Scalar_out::redispatch(dispatchKeySet, self, ord, out); + } + + // aten::_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, *, Tensor(a!)[] out) -> () + inline void _foreach_norm_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & ord, at::TensorList out) { + return at::_ops::_foreach_norm_Scalar_out::redispatch(dispatchKeySet, self, ord, out); + } + + // aten::_foreach_pow.List_out(Tensor[] self, Tensor[] exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList exponent) { + return at::_ops::_foreach_pow_List_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_pow.List_out(Tensor[] self, Tensor[] exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList exponent, at::TensorList out) { + return at::_ops::_foreach_pow_List_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_pow.Scalar_out(Tensor[] self, Scalar exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, const at::Scalar & exponent) { + return at::_ops::_foreach_pow_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_pow.Scalar_out(Tensor[] self, Scalar exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & exponent, at::TensorList out) { + return at::_ops::_foreach_pow_Scalar_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_pow.ScalarList_out(Tensor[] self, Scalar[] exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::ArrayRef exponent) { + return at::_ops::_foreach_pow_ScalarList_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_pow.ScalarList_out(Tensor[] self, Scalar[] exponent, *, Tensor(a!)[] out) -> () + inline void _foreach_pow_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::ArrayRef exponent, at::TensorList out) { + return at::_ops::_foreach_pow_ScalarList_out::redispatch(dispatchKeySet, self, exponent, out); + } + + // aten::_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_reciprocal_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_reciprocal_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_reciprocal_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_reciprocal_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_round_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_round_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_round.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_round_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_round_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sigmoid_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sigmoid.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sigmoid_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_sigmoid_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sign.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sign_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_sign_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sign.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sign_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_sign_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sin_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_sin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sin.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sin_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_sin_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sinh_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_sinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sinh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sinh_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_sinh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sqrt_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_sqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_sqrt.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_sqrt_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_sqrt_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_tan_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_tan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_tan.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_tan_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_tan_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_tanh_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_tanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_tanh.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_tanh_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_tanh_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_trunc_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_trunc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_trunc.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_trunc_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_trunc_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_zero_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self) { + return at::_ops::_foreach_zero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> () + inline void _foreach_zero_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out) { + return at::_ops::_foreach_zero_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out + inline ::std::vector _foreach_zero(c10::DispatchKeySet dispatchKeySet, at::TensorList self) { + return at::_ops::_foreach_zero::redispatch(dispatchKeySet, self); + } + + // aten::_foreach_copy.out(Tensor[] self, Tensor[] src, bool non_blocking=False, *, Tensor(a!)[] out) -> () + inline void _foreach_copy_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList src, bool non_blocking=false) { + return at::_ops::_foreach_copy_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::_foreach_copy.out(Tensor[] self, Tensor[] src, bool non_blocking=False, *, Tensor(a!)[] out) -> () + inline void _foreach_copy_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList src, bool non_blocking, at::TensorList out) { + return at::_ops::_foreach_copy_out::redispatch(dispatchKeySet, self, src, non_blocking, out); + } + + // aten::_foreach_copy(Tensor[] self, Tensor[] src, bool non_blocking=False) -> Tensor[] self_out + inline ::std::vector _foreach_copy(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList src, bool non_blocking=false) { + return at::_ops::_foreach_copy::redispatch(dispatchKeySet, self, src, non_blocking); + } + + // aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bucketize_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32=false, bool right=false) { + return at::_ops::bucketize_Scalar_out::redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); + } + + // aten::bucketize.Scalar_out(Scalar self, Tensor boundaries, *, bool out_int32=False, bool right=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & bucketize_outf(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right, at::Tensor & out) { + return at::_ops::bucketize_Scalar_out::redispatch(dispatchKeySet, self, boundaries, out_int32, right, out); + } + + // aten::glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_jvp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) { + return at::_ops::glu_jvp_out::redispatch(dispatchKeySet, glu, x, dx, dim, out); + } + + // aten::glu_jvp.out(Tensor glu, Tensor x, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_jvp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim, at::Tensor & out) { + return at::_ops::glu_jvp_out::redispatch(dispatchKeySet, glu, x, dx, dim, out); + } + + // aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_backward_jvp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { + return at::_ops::glu_backward_jvp_out::redispatch(dispatchKeySet, grad_x, grad_glu, x, dgrad_glu, dx, dim, out); + } + + // aten::glu_backward_jvp.out(Tensor grad_x, Tensor grad_glu, Tensor x, Tensor dgrad_glu, Tensor dx, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & glu_backward_jvp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out) { + return at::_ops::glu_backward_jvp_out::redispatch(dispatchKeySet, grad_x, grad_glu, x, dgrad_glu, dx, dim, out); + } + + // aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardswish_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::hardswish_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::hardswish_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & hardswish_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { + return at::_ops::hardswish_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rrelu_with_noise_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result) { + return at::_ops::rrelu_with_noise_backward_out::redispatch(dispatchKeySet, grad_output, self, noise, lower, upper, training, self_is_result, out); + } + + // aten::rrelu_with_noise_backward.out(Tensor grad_output, Tensor self, Tensor noise, Scalar lower, Scalar upper, bool training, bool self_is_result, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & rrelu_with_noise_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result, at::Tensor & out) { + return at::_ops::rrelu_with_noise_backward_out::redispatch(dispatchKeySet, grad_output, self, noise, lower, upper, training, self_is_result, out); + } + + // aten::mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_adaptive_avg_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::mkldnn_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & mkldnn_adaptive_avg_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { + return at::_ops::mkldnn_adaptive_avg_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::_adaptive_avg_pool2d.out(Tensor self, SymInt[2] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool2d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::_adaptive_avg_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::_adaptive_avg_pool2d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool2d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(output_size), out); + } + + // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef output_size) { + return at::_ops::_adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::_adaptive_avg_pool3d.out(Tensor self, SymInt[3] output_size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool3d_out::redispatch(dispatchKeySet, self, output_size, out); + } + + // aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad_output, const at::Tensor & self) { + return at::_ops::_adaptive_avg_pool3d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::_adaptive_avg_pool3d_backward.out(Tensor grad_output, Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _adaptive_avg_pool3d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_adaptive_avg_pool3d_backward_out::redispatch(dispatchKeySet, grad_output, self, out); + } + + // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array output_mask) { + return at::_ops::_slow_conv2d_backward_output_mask_out::redispatch(dispatchKeySet, grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2); + } + + // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_slow_conv2d_backward_output_mask_out::redispatch(dispatchKeySet, grad_output, self, weight, c10::fromIntArrayRefSlow(kernel_size), c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), output_mask, out0, out1, out2); + } + + // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask) { + return at::_ops::_slow_conv2d_backward_output_mask_out::redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2); + } + + // aten::_slow_conv2d_backward.output_mask_out(Tensor grad_output, Tensor self, Tensor weight, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) + inline ::std::tuple _slow_conv2d_backward_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::_slow_conv2d_backward_output_mask_out::redispatch(dispatchKeySet, grad_output, self, weight, kernel_size, stride, padding, output_mask, out0, out1, out2); + } + + // aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_depthwise3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_depthwise3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_depthwise3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + return at::_ops::conv_depthwise3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::conv_depthwise3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias, SymInt[3] stride, SymInt[3] padding, SymInt[3] dilation, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & conv_depthwise3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::conv_depthwise3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated2d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated2d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated2d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::slow_conv_dilated2d.out(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias=None, SymInt[2] stride=1, SymInt[2] padding=0, SymInt[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated2d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated2d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated3d_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias={}, at::IntArrayRef stride=1, at::IntArrayRef padding=0, at::IntArrayRef dilation=1) { + return at::_ops::slow_conv_dilated3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated3d_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated3d_out::redispatch(dispatchKeySet, self, weight, c10::fromIntArrayRefSlow(kernel_size), bias, c10::fromIntArrayRefSlow(stride), c10::fromIntArrayRefSlow(padding), c10::fromIntArrayRefSlow(dilation), out); + } + + // aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated3d_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef dilation=c10::SymInt(1)) { + return at::_ops::slow_conv_dilated3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::slow_conv_dilated3d.out(Tensor self, Tensor weight, SymInt[3] kernel_size, Tensor? bias=None, SymInt[3] stride=1, SymInt[3] padding=0, SymInt[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slow_conv_dilated3d_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out) { + return at::_ops::slow_conv_dilated3d_out::redispatch(dispatchKeySet, self, weight, kernel_size, bias, stride, padding, dilation, out); + } + + // aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isinf_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::isinf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::isinf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & isinf_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::isinf_out::redispatch(dispatchKeySet, self, out); + } + + // aten::linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_exp_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::linalg_matrix_exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::linalg_matrix_exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & linalg_matrix_exp_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::linalg_matrix_exp_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_intlist_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & values, at::OptionalIntArrayRef addends) { + return at::_ops::_test_optional_intlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_optional_intlist.out(Tensor values, int[]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_intlist_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { + return at::_ops::_test_optional_intlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_filled_intlist_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & values, at::OptionalIntArrayRef addends) { + return at::_ops::_test_optional_filled_intlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_optional_filled_intlist.out(Tensor values, int[2]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_filled_intlist_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, at::OptionalIntArrayRef addends, at::Tensor & out) { + return at::_ops::_test_optional_filled_intlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_floatlist_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & values, c10::optional> addends) { + return at::_ops::_test_optional_floatlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_optional_floatlist.out(Tensor values, float[]? addends, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_optional_floatlist_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & values, c10::optional> addends, at::Tensor & out) { + return at::_ops::_test_optional_floatlist_out::redispatch(dispatchKeySet, values, addends, out); + } + + // aten::_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_warn_in_autograd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_test_warn_in_autograd_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_warn_in_autograd.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_warn_in_autograd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_test_warn_in_autograd_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_autograd_multiple_dispatch_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_autograd_multiple_dispatch.fullcoverage_out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_autograd_multiple_dispatch_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_test_autograd_multiple_dispatch_fullcoverage_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_autograd_multiple_dispatch_view_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_test_autograd_multiple_dispatch_view_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_test_autograd_multiple_dispatch_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _test_autograd_multiple_dispatch_view_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_test_autograd_multiple_dispatch_view_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & segment_reduce_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths={}, const c10::optional & indices={}, const c10::optional & offsets={}, int64_t axis=0, bool unsafe=false, const c10::optional & initial=c10::nullopt) { + return at::_ops::segment_reduce_out::redispatch(dispatchKeySet, data, reduce, lengths, indices, offsets, axis, unsafe, initial, out); + } + + // aten::segment_reduce.out(Tensor data, str reduce, *, Tensor? lengths=None, Tensor? indices=None, Tensor? offsets=None, int axis=0, bool unsafe=False, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & segment_reduce_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths, const c10::optional & indices, const c10::optional & offsets, int64_t axis, bool unsafe, const c10::optional & initial, at::Tensor & out) { + return at::_ops::segment_reduce_out::redispatch(dispatchKeySet, data, reduce, lengths, indices, offsets, axis, unsafe, initial, out); + } + + // aten::_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _segment_reduce_backward_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths={}, const c10::optional & offsets={}, int64_t axis=0, const c10::optional & initial=c10::nullopt) { + return at::_ops::_segment_reduce_backward_out::redispatch(dispatchKeySet, grad, output, data, reduce, lengths, offsets, axis, initial, out); + } + + // aten::_segment_reduce_backward.out(Tensor grad, Tensor output, Tensor data, str reduce, *, Tensor? lengths=None, Tensor? offsets=None, int axis=0, Scalar? initial=None, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _segment_reduce_backward_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths, const c10::optional & offsets, int64_t axis, const c10::optional & initial, at::Tensor & out) { + return at::_ops::_segment_reduce_backward_out::redispatch(dispatchKeySet, grad, output, data, reduce, lengths, offsets, axis, initial, out); + } + + // aten::_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_from_tensor_list_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, at::TensorList list, c10::optional dtype=c10::nullopt, c10::optional layout=c10::nullopt, c10::optional device=c10::nullopt, c10::optional pin_memory=c10::nullopt) { + return at::_ops::_nested_tensor_from_tensor_list_out::redispatch(dispatchKeySet, list, dtype, layout, device, pin_memory, out); + } + + // aten::_nested_tensor_from_tensor_list.out(Tensor[] list, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _nested_tensor_from_tensor_list_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList list, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, at::Tensor & out) { + return at::_ops::_nested_tensor_from_tensor_list_out::redispatch(dispatchKeySet, list, dtype, layout, device, pin_memory, out); + } + + // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fw_primal_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t level) { + return at::_ops::_fw_primal_copy_out::redispatch(dispatchKeySet, self, level, out); + } + + // aten::_fw_primal_copy.out(Tensor self, int level, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _fw_primal_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t level, at::Tensor & out) { + return at::_ops::_fw_primal_copy_out::redispatch(dispatchKeySet, self, level, out); + } + + // aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_dual_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { + return at::_ops::_make_dual_copy_out::redispatch(dispatchKeySet, primal, tangent, level, out); + } + + // aten::_make_dual_copy.out(Tensor primal, Tensor tangent, int level, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _make_dual_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & primal, const at::Tensor & tangent, int64_t level, at::Tensor & out) { + return at::_ops::_make_dual_copy_out::redispatch(dispatchKeySet, primal, tangent, level, out); + } + + // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_as_real_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::view_as_real_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::view_as_real_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_as_real_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::view_as_real_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_as_complex_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::view_as_complex_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::view_as_complex_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_as_complex_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::view_as_complex_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _conj_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_conj_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_conj_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _conj_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_conj_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _neg_view_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_neg_view_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_neg_view_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _neg_view_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_neg_view_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt, out); + } + + // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, c10::optional storage_offset, at::Tensor & out) { + return at::_ops::as_strided_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), storage_offset.has_value() ? c10::make_optional(c10::SymInt(*storage_offset)) : c10::nullopt, out); + } + + // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset=c10::nullopt) { + return at::_ops::as_strided_copy_out::redispatch(dispatchKeySet, self, size, stride, storage_offset, out); + } + + // aten::as_strided_copy.out(Tensor self, SymInt[] size, SymInt[] stride, SymInt? storage_offset=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & as_strided_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset, at::Tensor & out) { + return at::_ops::as_strided_copy_out::redispatch(dispatchKeySet, self, size, stride, storage_offset, out); + } + + // aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_broadcast_to_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::_sparse_broadcast_to_copy_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::_sparse_broadcast_to_copy.out(Tensor self, int[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _sparse_broadcast_to_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::_sparse_broadcast_to_copy_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t offset=0, int64_t dim1=0, int64_t dim2=1) { + return at::_ops::diagonal_copy_out::redispatch(dispatchKeySet, self, offset, dim1, dim2, out); + } + + // aten::diagonal_copy.out(Tensor self, int offset=0, int dim1=0, int dim2=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & diagonal_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out) { + return at::_ops::diagonal_copy_out::redispatch(dispatchKeySet, self, offset, dim1, dim2, out); + } + + // aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expand_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, bool implicit=false) { + return at::_ops::expand_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), implicit, out); + } + + // aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expand_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, bool implicit, at::Tensor & out) { + return at::_ops::expand_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), implicit, out); + } + + // aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expand_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit=false) { + return at::_ops::expand_copy_out::redispatch(dispatchKeySet, self, size, implicit, out); + } + + // aten::expand_copy.out(Tensor self, SymInt[] size, *, bool implicit=False, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & expand_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, bool implicit, at::Tensor & out) { + return at::_ops::expand_copy_out::redispatch(dispatchKeySet, self, size, implicit, out); + } + + // aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & permute_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dims) { + return at::_ops::permute_copy_out::redispatch(dispatchKeySet, self, dims, out); + } + + // aten::permute_copy.out(Tensor self, int[] dims, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & permute_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dims, at::Tensor & out) { + return at::_ops::permute_copy_out::redispatch(dispatchKeySet, self, dims, out); + } + + // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _reshape_alias_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride) { + return at::_ops::_reshape_alias_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _reshape_alias_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::IntArrayRef stride, at::Tensor & out) { + return at::_ops::_reshape_alias_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), c10::fromIntArrayRefSlow(stride), out); + } + + // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _reshape_alias_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + return at::_ops::_reshape_alias_copy_out::redispatch(dispatchKeySet, self, size, stride, out); + } + + // aten::_reshape_alias_copy.out(Tensor self, SymInt[] size, SymInt[] stride, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _reshape_alias_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, at::Tensor & out) { + return at::_ops::_reshape_alias_copy_out::redispatch(dispatchKeySet, self, size, stride, out); + } + + // aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, int64_t index) { + return at::_ops::select_copy_int_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, int64_t index, at::Tensor & out) { + return at::_ops::select_copy_int_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim, c10::SymInt index) { + return at::_ops::select_copy_int_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::select_copy.int_out(Tensor self, int dim, SymInt index, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & select_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::SymInt index, at::Tensor & out) { + return at::_ops::select_copy_int_out::redispatch(dispatchKeySet, self, dim, index, out); + } + + // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & detach_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::detach_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::detach_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & detach_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::detach_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, int64_t step=1) { + return at::_ops::slice_copy_Tensor_out::redispatch(dispatchKeySet, self, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out); + } + + // aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional start, c10::optional end, int64_t step, at::Tensor & out) { + return at::_ops::slice_copy_Tensor_out::redispatch(dispatchKeySet, self, dim, start.has_value() ? c10::make_optional(c10::SymInt(*start)) : c10::nullopt, end.has_value() ? c10::make_optional(c10::SymInt(*end)) : c10::nullopt, step, out); + } + + // aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim=0, c10::optional start=c10::nullopt, c10::optional end=c10::nullopt, c10::SymInt step=1) { + return at::_ops::slice_copy_Tensor_out::redispatch(dispatchKeySet, self, dim, start, end, step, out); + } + + // aten::slice_copy.Tensor_out(Tensor self, int dim=0, SymInt? start=None, SymInt? end=None, SymInt step=1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & slice_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step, at::Tensor & out) { + return at::_ops::slice_copy_Tensor_out::redispatch(dispatchKeySet, self, dim, start, end, step, out); + } + + // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::squeeze_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::squeeze_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::squeeze_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim) { + return at::_ops::squeeze_copy_dim_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::squeeze_copy.dim_out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { + return at::_ops::squeeze_copy_dim_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef dim) { + return at::_ops::squeeze_copy_dims_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::squeeze_copy.dims_out(Tensor self, int[] dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & squeeze_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out) { + return at::_ops::squeeze_copy_dims_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & t_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::t_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::t_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & t_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::t_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & transpose_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim0, int64_t dim1) { + return at::_ops::transpose_copy_int_out::redispatch(dispatchKeySet, self, dim0, dim1, out); + } + + // aten::transpose_copy.int_out(Tensor self, int dim0, int dim1, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & transpose_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim0, int64_t dim1, at::Tensor & out) { + return at::_ops::transpose_copy_int_out::redispatch(dispatchKeySet, self, dim0, dim1, out); + } + + // aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unsqueeze_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dim) { + return at::_ops::unsqueeze_copy_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::unsqueeze_copy.out(Tensor self, int dim, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unsqueeze_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, at::Tensor & out) { + return at::_ops::unsqueeze_copy_out::redispatch(dispatchKeySet, self, dim, out); + } + + // aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _values_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::_values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::_values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _values_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::_values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & values_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::values_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & values_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::values_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & crow_indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::crow_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::crow_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & crow_indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::crow_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col_indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::col_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::col_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & col_indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::col_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ccol_indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::ccol_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::ccol_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & ccol_indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::ccol_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & row_indices_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::row_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::row_indices_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & row_indices_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::row_indices_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::IntArrayRef size) { + return at::_ops::view_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::IntArrayRef size, at::Tensor & out) { + return at::_ops::view_copy_out::redispatch(dispatchKeySet, self, c10::fromIntArrayRefSlow(size), out); + } + + // aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef size) { + return at::_ops::view_copy_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::view_copy.out(Tensor self, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out) { + return at::_ops::view_copy_out::redispatch(dispatchKeySet, self, size, out); + } + + // aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, at::ScalarType dtype) { + return at::_ops::view_copy_dtype_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::view_copy.dtype_out(Tensor self, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & view_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::ScalarType dtype, at::Tensor & out) { + return at::_ops::view_copy_dtype_out::redispatch(dispatchKeySet, self, dtype, out); + } + + // aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { + return at::_ops::unfold_copy_out::redispatch(dispatchKeySet, self, dimension, size, step, out); + } + + // aten::unfold_copy.out(Tensor self, int dimension, int size, int step, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & unfold_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dimension, int64_t size, int64_t step, at::Tensor & out) { + return at::_ops::unfold_copy_out::redispatch(dispatchKeySet, self, dimension, size, step, out); + } + + // aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & alias_copy_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self) { + return at::_ops::alias_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::alias_copy.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & alias_copy_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out) { + return at::_ops::alias_copy_out::redispatch(dispatchKeySet, self, out); + } + + // aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_padded_tensor_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size=c10::nullopt) { + return at::_ops::to_padded_tensor_out::redispatch(dispatchKeySet, self, padding, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, out); + } + + // aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_padded_tensor_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size, at::Tensor & out) { + return at::_ops::to_padded_tensor_out::redispatch(dispatchKeySet, self, padding, output_size.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*output_size)) : c10::nullopt, out); + } + + // aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_padded_tensor_symint_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size=c10::nullopt) { + return at::_ops::to_padded_tensor_out::redispatch(dispatchKeySet, self, padding, output_size, out); + } + + // aten::to_padded_tensor.out(Tensor self, float padding, SymInt[]? output_size=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & to_padded_tensor_symint_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size, at::Tensor & out) { + return at::_ops::to_padded_tensor_out::redispatch(dispatchKeySet, self, padding, output_size, out); + } + + // aten::_transformer_encoder_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _transformer_encoder_layer_fwd_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional & mask={}, c10::optional mask_type=c10::nullopt) { + return at::_ops::_transformer_encoder_layer_fwd_out::redispatch(dispatchKeySet, src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type, out); + } + + // aten::_transformer_encoder_layer_fwd.out(Tensor src, int embed_dim, int num_heads, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, bool use_gelu, bool norm_first, float eps, Tensor norm_weight_1, Tensor norm_bias_1, Tensor norm_weight_2, Tensor norm_bias_2, Tensor ffn_weight_1, Tensor ffn_bias_1, Tensor ffn_weight_2, Tensor ffn_bias_2, Tensor? mask=None, int? mask_type=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _transformer_encoder_layer_fwd_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional & mask, c10::optional mask_type, at::Tensor & out) { + return at::_ops::_transformer_encoder_layer_fwd_out::redispatch(dispatchKeySet, src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type, out); + } + + // aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _native_multi_head_attention_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out0, at::Tensor & out1, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask={}, bool need_weights=true, bool average_attn_weights=true, c10::optional mask_type=c10::nullopt) { + return at::_ops::_native_multi_head_attention_out::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1); + } + + // aten::_native_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, bool need_weights=True, bool average_attn_weights=True, int? mask_type=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!)) + inline ::std::tuple _native_multi_head_attention_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask, bool need_weights, bool average_attn_weights, c10::optional mask_type, at::Tensor & out0, at::Tensor & out1) { + return at::_ops::_native_multi_head_attention_out::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type, out0, out1); + } + + // aten::_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _triton_scaled_dot_attention_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p=0.0) { + return at::_ops::_triton_scaled_dot_attention_out::redispatch(dispatchKeySet, q, k, v, dropout_p, out); + } + + // aten::_triton_scaled_dot_attention.out(Tensor q, Tensor k, Tensor v, float dropout_p=0.0, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _triton_scaled_dot_attention_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p, at::Tensor & out) { + return at::_ops::_triton_scaled_dot_attention_out::redispatch(dispatchKeySet, q, k, v, dropout_p, out); + } + + // aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _triton_multi_head_attention_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask={}) { + return at::_ops::_triton_multi_head_attention_out::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out); + } + + // aten::_triton_multi_head_attention.out(Tensor query, Tensor key, Tensor value, int embed_dim, int num_head, Tensor qkv_weight, Tensor qkv_bias, Tensor proj_weight, Tensor proj_bias, Tensor? mask=None, *, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _triton_multi_head_attention_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask, at::Tensor & out) { + return at::_ops::_triton_multi_head_attention_out::redispatch(dispatchKeySet, query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, out); + } + + // aten::_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _foobar_out(c10::DispatchKeySet dispatchKeySet, at::Tensor & out, const at::Tensor & self, bool arg1=true, bool arg2=true, bool arg3=true) { + return at::_ops::_foobar_out::redispatch(dispatchKeySet, self, arg1, arg2, arg3, out); + } + + // aten::_foobar.out(Tensor self, bool arg1=True, bool arg2=True, *, bool arg3=True, Tensor(a!) out) -> Tensor(a!) + inline at::Tensor & _foobar_outf(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, bool arg1, bool arg2, bool arg3, at::Tensor & out) { + return at::_ops::_foobar_out::redispatch(dispatchKeySet, self, arg1, arg2, arg3, out); + } + + // aten::_fused_adam.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adam_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adam.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adam_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_adam_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adam(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adam(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adam.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adam_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam_tensor_lr_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adam.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adam_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_adam_tensor_lr_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adam.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adam(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adam_tensor_lr::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adamw_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adamw.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adamw_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_adamw_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adamw(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, float lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adamw(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_adamw.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adamw_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw_tensor_lr_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adamw.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] exp_avgs, Tensor(d!)[] exp_avg_sqs, Tensor(e!)[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_adamw_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_adamw_tensor_lr_out::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + + // aten::_fused_adamw.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] exp_avgs, Tensor[] exp_avg_sqs, Tensor[] max_exp_avg_sqs, Tensor[] state_steps, *, Tensor lr, float beta1, float beta2, float weight_decay, float eps, bool amsgrad, bool maximize, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] exp_avgs_out, Tensor[] exp_avg_sqs_out, Tensor[] max_exp_avg_sqs_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adamw(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_adamw_tensor_lr::redispatch(dispatchKeySet, self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + + // aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_sgd_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd_out::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + + // aten::_fused_sgd.out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_sgd_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_sgd_out::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + + // aten::_fused_sgd(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, float lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector> _fused_sgd(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + + // aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_sgd_out(c10::DispatchKeySet dispatchKeySet, at::TensorList out, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd_tensor_lr_out::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + + // aten::_fused_sgd.tensor_lr_out(Tensor[] self, Tensor(b!)[] grads, Tensor(c!)[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None, Tensor(a!)[] out) -> () + inline void _fused_sgd_outf(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + return at::_ops::_fused_sgd_tensor_lr_out::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + + // aten::_fused_sgd.tensor_lr(Tensor[] self, Tensor[] grads, Tensor[] momentum_buffer_list, *, float weight_decay, float momentum, Tensor lr, float dampening, bool nesterov, bool maximize, bool is_first_step, Tensor? grad_scale=None, Tensor? found_inf=None) -> (Tensor[] self_out, Tensor[] grads_out, Tensor[] momentum_buffer_list_out) + inline ::std::tuple<::std::vector,::std::vector,::std::vector> _fused_sgd(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale={}, const c10::optional & found_inf={}) { + return at::_ops::_fused_sgd_tensor_lr::redispatch(dispatchKeySet, self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } +} // namespace redispatch + +} diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/SavedTensorHooks.h b/venv/lib/python3.10/site-packages/torch/include/ATen/SavedTensorHooks.h new file mode 100644 index 0000000000000000000000000000000000000000..af821cb908c6a6675d21c3543809f9668ffccb64 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/SavedTensorHooks.h @@ -0,0 +1,52 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include + +namespace at { + +namespace impl { + +struct TORCH_API SavedTensorDefaultHooksTLS { + // PyObject is defined in c10/util/python_stub.h + std::stack> stack; + + // See NOTE: [Disabling SavedTensorDefaultHooks] for context + // NOTE: [disabled_error_message invariant] + // disabled_error_message is nullopt IFF Saved Tensor hooks is enabled + // We did this for efficiency (so we didn't have to keep a separate bool + // around) + c10::optional disabled_error_message; +}; + +} // namespace impl + +struct TORCH_API SavedTensorDefaultHooks { + static void push_hooks(PyObject* pack_hook, PyObject* unpack_hook); + static void pop_hooks(); + static std::pair get_hooks(); + static void lazy_initialize(); + static std::stack> get_stack(); + static void set_stack(std::stack>); + + static const impl::SavedTensorDefaultHooksTLS& get_tls_state(); + static void set_tls_state(const impl::SavedTensorDefaultHooksTLS& tls); + + // NOTE: [Disabling SavedTensorDefaultHooks] + // A developer of a PyTorch feature may choose to disable SavedTensorDefault + // hooks, especially if their feature does not work with it. If they are + // disabled, then the following will raise an error: + // - Attempting to push_hooks + // - calling disable(message) with a non-zero stack (from get_stack) size + static void disable(const std::string& error_message); + static void enable(); + static bool is_enabled(); + static const c10::optional& get_disabled_error_message(); +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Scalar.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Scalar.h new file mode 100644 index 0000000000000000000000000000000000000000..e12557428f15674e4382983c07de64c3e43e8af0 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Scalar.h @@ -0,0 +1,3 @@ +#pragma once + +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ScalarOps.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ScalarOps.h new file mode 100644 index 0000000000000000000000000000000000000000..943ac161d4c18278d245b58257045ee990570de5 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ScalarOps.h @@ -0,0 +1,53 @@ +#pragma once + +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +namespace at::detail { +// When filling a number to 1-element CPU tensor, we want to skip +// everything but manipulate data ptr directly. +// Ideally this fast pass should be implemented in TensorIterator, +// but we also want to skip compute_types which in not avoidable +// in TensorIterator for now. +Tensor& scalar_fill(Tensor& self, const Scalar& value); +TORCH_API Tensor scalar_tensor_static( + const Scalar& s, + c10::optional dtype_opt, + c10::optional device_opt); +} // namespace at::detail + +// This is in the c10 namespace because we use ADL to find the functions in it. +namespace c10 { + +// FIXME: this should be (and was) Scalar::toTensor, but there is currently no +// way to implement this without going through Derived Types (which are not part +// of core). +inline at::Tensor scalar_to_tensor( + const Scalar& s, + const Device device = at::kCPU) { + // This is the fast track we have for CPU scalar tensors. + if (device == at::kCPU) { + return at::detail::scalar_tensor_static(s, s.type(), at::kCPU); + } + return at::scalar_tensor(s, at::device(device).dtype(s.type())); +} + +} // namespace c10 + +namespace at::native { + +inline Tensor wrapped_scalar_tensor( + const Scalar& scalar, + const Device device = at::kCPU) { + auto tensor = scalar_to_tensor(scalar, device); + tensor.unsafeGetTensorImpl()->set_wrapped_number(true); + return tensor; +} + +} // namespace at::native diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/SmallVector.h b/venv/lib/python3.10/site-packages/torch/include/ATen/SmallVector.h new file mode 100644 index 0000000000000000000000000000000000000000..fabfa44190c727c9fdf9aa034d042559da1b621d --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/SmallVector.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/SparseCsrTensorImpl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/SparseCsrTensorImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..515ddc6e7e18d9e11b391ec10ddcb47f6c9838d8 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/SparseCsrTensorImpl.h @@ -0,0 +1,186 @@ +#pragma once + +#include +#include +#include +namespace at { + +// Struct implementing a sparse CSR tensor. It uses three 1-D tensors for +// denoting the data: `crow_indices_`, `col_indices_` and `values_`. +// The `crow_indices_` tensor is a integer tensor of shape `(size(0) + 1)` +// that represents the compressed row indices of the CSR tensor. The +// `col_indices_` tensor is an integer tensor of shape `(nnz())` +// that explicitly stores the column indices of each value of the sparse +// tensor. The `values_` tensor can be of any pytorch-supported data type +// and has shape `(nnz())`. +// +// Since the main advantage of the CSR format over the COO format is speed of +// computation, care must be taken to facilitate smooth interfacing of +// these data structures with optimized libraries such as MKL and MAGMA. +// Since the MKL interface for pytorch currently uses indexing with int32 +// type, it is important to make sure that the `crow_indices` and `col_indices` +// are of type int32 when calling MKL routines such as SPMM or SPMV. +// +// If not calling MKL, it should be alright to use 64 bit integer tensors +// for indexing. +struct TORCH_API SparseCsrTensorImpl : public TensorImpl { + Tensor crow_indices_; + Tensor col_indices_; + Tensor values_; + Layout layout_; + + public: + explicit SparseCsrTensorImpl( + at::DispatchKeySet, + at::Device device, + Layout layout, + const caffe2::TypeMeta); + + void resize_(int64_t nnz, IntArrayRef size); + void resize_and_clear_( + int64_t sparse_dim, + int64_t dense_dim, + IntArrayRef size); + void resize_as_sparse_compressed_tensor_(const Tensor& src); + void set_member_tensors( + const Tensor& crow_indices, + const Tensor& col_indices, + const Tensor& values, + c10::SymIntArrayRef size); + void set_member_tensors( + const Tensor& crow_indices, + const Tensor& col_indices, + const Tensor& values, + IntArrayRef size); + const Tensor& compressed_indices() const { + return crow_indices_; + } + const Tensor& plain_indices() const { + return col_indices_; + } + const Tensor& values() const { + return values_; + } + int64_t nnz() { + return col_indices_.size(-1); + } + + inline int64_t batch_dim() const noexcept { + return crow_indices_.dim() - 1; + } + + inline int64_t sparse_dim() const noexcept { + return 2; + } + + inline int64_t dense_dim() const noexcept { + return values_.dim() - batch_dim() - block_dim() - 1; + } + + private: + inline int64_t block_dim() const noexcept { + return (layout_ == kSparseBsr || layout_ == kSparseBsc ? 2 : 0); + } + + protected: + IntArrayRef strides_custom() const override; + SymIntArrayRef sym_strides_custom() const override; + bool is_contiguous_custom(MemoryFormat) const override; + + public: + void set_size(int64_t dim, int64_t new_size) override; + void set_stride(int64_t dim, int64_t new_stride) override; + void set_storage_offset(int64_t storage_offset) override; + Layout layout_impl() const override { + return layout_; + } + void set_layout(Layout layout) { + switch (layout) { + case kSparseCsr: + case kSparseCsc: + case kSparseBsr: + case kSparseBsc: + layout_ = layout; + break; + default: + TORCH_CHECK(false, "unsupported layout ", layout); + } + } + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive( + key_set(), device(), layout_impl(), dtype()); + copy_tensor_metadata( + /*src_sparse_impl=*/this, + /*dest_sparse_impl=*/impl.get(), + /*version_counter=*/version_counter, + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + c10::VariableVersion&& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive( + key_set(), device(), layout_impl(), dtype()); + copy_tensor_metadata( + /*src_sparse_impl=*/this, + /*dest_sparse_impl=*/impl.get(), + /*version_counter=*/std::move(version_counter), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + private: + explicit SparseCsrTensorImpl( + at::DispatchKeySet key_set, + const caffe2::TypeMeta data_type, + at::Tensor crow_indices, + at::Tensor col_indices, + at::Tensor values, + at::Layout layout); + + const char* tensorimpl_type_name() const override; + + /** + * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / + * storage_offset) from one TensorImpl to another TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE + * [ TensorImpl Shallow-Copying ]. + */ + static void copy_tensor_metadata( + const SparseCsrTensorImpl* src_sparse_impl, + SparseCsrTensorImpl* dest_sparse_impl, + c10::VariableVersion version_counter, + bool allow_tensor_metadata_change) { + TensorImpl::copy_tensor_metadata( + src_sparse_impl, + dest_sparse_impl, + std::move(version_counter), + allow_tensor_metadata_change); + + // Sparse-specific fields + dest_sparse_impl->crow_indices_ = src_sparse_impl->compressed_indices(); + dest_sparse_impl->col_indices_ = src_sparse_impl->plain_indices(); + dest_sparse_impl->values_ = src_sparse_impl->values(); + dest_sparse_impl->layout_ = src_sparse_impl->layout_impl(); + } +}; +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/SparseTensorImpl.h b/venv/lib/python3.10/site-packages/torch/include/ATen/SparseTensorImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..dfb84c02bb34cbc9313d5efaac57069de17d77c4 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/SparseTensorImpl.h @@ -0,0 +1,400 @@ +#pragma once + +#include +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#include +#endif + +namespace at { +struct TORCH_API SparseTensorImpl : public TensorImpl { + // Stored in COO format, indices + values. + + // INVARIANTS: + // sparse_dim: range [0, len(shape)]; sparse_dim + dense_dim = len(shape) + // dense_dim : range [0, len(shape)]; sparse_dim + dense_dim = len(shape) + // _indices.shape: dimensionality: 2, shape: (sparse_dim, nnz) + // _values.shape: dimensionality: 1 + dense_dim. shape: (nnz, + // shape[sparse_dim:]) + + int64_t sparse_dim_ = 0; // number of sparse dimensions + int64_t dense_dim_ = 0; // number of dense dimensions + + Tensor indices_; // always a LongTensor + Tensor values_; + + // A sparse tensor is 'coalesced' if every index occurs at most once in + // the indices tensor, and the indices are in sorted order. (This means + // that it is very easy to convert a coalesced tensor to CSR format: you + // need only compute CSR format indices.) + // + // Most math operations can only be performed on coalesced sparse tensors, + // because many algorithms proceed by merging two sorted lists (of indices). + bool coalesced_ = false; + + // compute_numel with integer multiplication overflow check, see gh-57542 + void refresh_numel() { + TensorImpl::safe_refresh_numel(); + } + + public: + // Public for now... + explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta); + + void release_resources() override; + + int64_t nnz() const { + return values_.size(0); + } + + c10::SymInt sym_nnz() const { + return values_.sym_size(0); + } + int64_t sparse_dim() const { + return sparse_dim_; + } + int64_t dense_dim() const { + return dense_dim_; + } + bool coalesced() const { + return coalesced_; + } + Tensor indices() const { + return indices_; + } + Tensor values() const { + return values_; + } + + void set_size(int64_t dim, int64_t new_size) override; + void set_stride(int64_t dim, int64_t new_stride) override; + void set_storage_offset(int64_t storage_offset) override; + +#ifdef DEBUG + bool has_storage() const override; +#endif + + // WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim + // with respect to indices and values + void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) { + TORCH_CHECK( + allow_tensor_metadata_change(), + "raw_resize_ ", + err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK( + !has_symbolic_sizes_strides_, + "raw_resize_ called on tensor with symbolic shape") + set_sizes_and_strides(size, std::vector(size.size())); + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + refresh_numel(); + } + + // NOTE: This function preserves invariants of sparse_dim/dense_dim with + // respect to indices and values. + // + // NOTE: This function supports the following cases: + // 1. When we keep the number of dense dimensions unchanged, and NOT shrinking + // the size of any of the dense dimensions. + // 2. When we keep the number of sparse dimensions unchanged, and NOT + // shrinking the size of any of the sparse dimensions. + // 3. When the sparse tensor has zero nnz, in which case we are free to change + // the shapes of both its sparse and dense dimensions. + // + // This function DOESN'T support (and will throw an error) the following + // cases: + // 1. When we attempt to change the number of sparse dimensions on a non-empty + // sparse tensor (such an operation will invalidate the indices stored). + // 2. When we attempt to change the number of dense dimensions on a non-empty + // sparse tensor (such an operation will behave differently from an equivalent + // dense tensor's resize method, and for API consistency we don't support it). + // 3. When we attempt to shrink the size of any of the dense dimensions on a + // non-empty sparse tensor (such an operation will behave differently from an + // equivalent dense tensor's resize method, and for API consistency we don't + // support it). + // 4. When we attempt to shrink the size of any of the sparse dimensions on a + // non-empty sparse tensor (this could make some of the stored indices + // out-of-bound and thus unsafe). + template + void _resize_(int64_t sparse_dim, int64_t dense_dim, ArrayRef size) { + TORCH_CHECK( + allow_tensor_metadata_change(), + "resize_ ", + err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK( + !has_symbolic_sizes_strides_, + "resize_ called on tensor with symbolic shape") + TORCH_CHECK( + sparse_dim + dense_dim == static_cast(size.size()), + "number of dimensions must be sparse_dim (", + sparse_dim, + ") + dense_dim (", + dense_dim, + "), but got ", + size.size()); + if (nnz() > 0) { + auto alt_options_msg = + "You could try the following options:\n\ +1. If you need an empty sparse tensor of this size, call `x = torch.sparse_coo_tensor(size)`.\n\ +2. If you need to resize this tensor, you have the following options:\n\ + 1. For both sparse and dense dimensions, keep the number of them constant and the size of them non-shrinking, and then try the same call again.\n\ + 2. Or, create a new sparse tensor with the correct indices and values from this sparse tensor."; + + TORCH_CHECK( + sparse_dim == sparse_dim_, + "changing the number of sparse dimensions (from ", + sparse_dim_, + " to ", + sparse_dim, + ") on a non-empty sparse tensor is not supported.\n", + alt_options_msg); + + TORCH_CHECK( + dense_dim == dense_dim_, + "changing the number of dense dimensions (from ", + dense_dim_, + " to ", + dense_dim, + ") on a non-empty sparse tensor is not supported.\n", + alt_options_msg); + + bool shrinking_sparse_dims = false; + bool shrinking_dense_dim = false; + auto sparse_size_original = generic_sizes().slice(0, sparse_dim); + auto sparse_size_new = size.slice(0, sparse_dim); + for (const auto i : c10::irange(sparse_dim)) { + if (sparse_size_new[i] < sparse_size_original[i]) { + shrinking_sparse_dims = true; + break; + } + } + auto dense_size_original = generic_sizes().slice(sparse_dim); + auto dense_size_new = size.slice(sparse_dim); + for (const auto i : c10::irange(dense_dim)) { + if (dense_size_new[i] < dense_size_original[i]) { + shrinking_dense_dim = true; + break; + } + } + + TORCH_CHECK( + !shrinking_sparse_dims, + "shrinking the size of sparse dimensions (from ", + sparse_size_original, + " to ", + sparse_size_new, + ") on a non-empty sparse tensor is not supported.\n", + alt_options_msg); + + TORCH_CHECK( + !shrinking_dense_dim, + "shrinking the size of dense dimensions (from ", + dense_size_original, + " to ", + dense_size_new, + ") on a non-empty sparse tensor is not supported.\n", + alt_options_msg); + } + + auto sizes_and_strides = generic_sizes(); + const bool size_equals_sizes = std::equal( + size.begin(), + size.end(), + sizes_and_strides.begin(), + sizes_and_strides.end()); + if ((!size_equals_sizes) || (sparse_dim != sparse_dim_) || + (dense_dim != dense_dim_)) { + auto nnz = at::symint::sizes(values())[0]; + std::vector values_size = {nnz}; + auto dense_size = size.slice(sparse_dim); + values_size.insert( + values_size.end(), dense_size.begin(), dense_size.end()); + at::symint::resize_(values_, values_size); + at::symint::resize_(indices_, {T(sparse_dim), nnz}); + } + + if (!size_equals_sizes) { + set_sizes_and_strides(size, std::vector(size.size())); + } + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + refresh_numel(); + } + + void resize_(int64_t sparse_dim, int64_t dense_dim, ArrayRef size) { + return _resize_(sparse_dim, dense_dim, size); + } + + void resize_( + int64_t sparse_dim, + int64_t dense_dim, + ArrayRef size) { + return _resize_(sparse_dim, dense_dim, size); + } + + // NOTE: this function will resize the sparse tensor and also set `indices` + // and `values` to empty. + void resize_and_clear_( + int64_t sparse_dim, + int64_t dense_dim, + IntArrayRef size) { + TORCH_CHECK( + allow_tensor_metadata_change(), + "resize_and_clear_ ", + err_msg_tensor_metadata_change_not_allowed); + TORCH_CHECK( + !has_symbolic_sizes_strides_, + "resize_and_clear_ called on tensor with symbolic shape") + TORCH_CHECK( + sparse_dim + dense_dim == static_cast(size.size()), + "number of dimensions must be sparse_dim (", + sparse_dim, + ") + dense_dim (", + dense_dim, + "), but got ", + size.size()); + + set_sizes_and_strides(size, std::vector(size.size())); + sparse_dim_ = sparse_dim; + dense_dim_ = dense_dim; + + auto empty_indices = at::empty({sparse_dim, 0}, indices().options()); + std::vector values_size = {0}; + auto dense_size = sizes().slice(sparse_dim); + values_size.insert(values_size.end(), dense_size.begin(), dense_size.end()); + auto empty_values = at::empty(values_size, values().options()); + set_indices_and_values_unsafe(empty_indices, empty_values); + refresh_numel(); + } + + void set_coalesced(bool coalesced) { + TORCH_CHECK( + allow_tensor_metadata_change(), + "set_coalesced ", + err_msg_tensor_metadata_change_not_allowed); + coalesced_ = coalesced; + } + + // NOTE: this function is only used internally and not exposed to Python + // frontend + void set_nnz_and_narrow(int64_t new_nnz) { + TORCH_CHECK( + allow_tensor_metadata_change(), + "set_nnz_and_narrow ", + err_msg_tensor_metadata_change_not_allowed); + AT_ASSERT(new_nnz <= nnz()); + indices_ = indices_.narrow(1, 0, new_nnz); + values_ = values_.narrow(0, 0, new_nnz); + if (new_nnz < 2) { + coalesced_ = true; + } + } + + // Takes indices and values and directly puts them into the sparse tensor, no + // copy. NOTE: this function is unsafe because it doesn't check whether any + // indices are out of boundaries of `sizes`, so it should ONLY be used where + // we know that the indices are guaranteed to be within bounds. This used to + // be called THSTensor_(_move) NB: This used to be able to avoid a refcount + // bump, but I was too lazy to make it happen + void set_indices_and_values_unsafe( + const Tensor& indices, + const Tensor& values); + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + const c10::VariableVersion& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive(key_set(), dtype()); + copy_tensor_metadata( + /*src_sparse_impl=*/this, + /*dest_sparse_impl=*/impl.get(), + /*version_counter=*/version_counter, + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + /** + * Return a TensorImpl that is a shallow-copy of this TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, + * see NOTE [ TensorImpl Shallow-Copying ]. + */ + c10::intrusive_ptr shallow_copy_and_detach( + c10::VariableVersion&& version_counter, + bool allow_tensor_metadata_change) const override { + auto impl = c10::make_intrusive(key_set(), dtype()); + copy_tensor_metadata( + /*src_sparse_impl=*/this, + /*dest_sparse_impl=*/impl.get(), + /*version_counter=*/std::move(version_counter), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change); + impl->refresh_numel(); + return impl; + } + + /** + * Shallow-copies data from another TensorImpl into this TensorImpl. + * + * For why this function doesn't check this TensorImpl's + * `allow_tensor_metadata_change_`, see NOTE [ TensorImpl Shallow-Copying ]. + */ + void shallow_copy_from(const c10::intrusive_ptr& impl) override { + AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set())); + auto sparse_impl = static_cast(impl.get()); + copy_tensor_metadata( + /*src_sparse_impl=*/sparse_impl, + /*dest_sparse_impl=*/this, + /*version_counter=*/version_counter(), + /*allow_tensor_metadata_change=*/allow_tensor_metadata_change()); + refresh_numel(); + } + + private: + explicit SparseTensorImpl( + at::DispatchKeySet, + const caffe2::TypeMeta, + at::Tensor indices, + at::Tensor values); + + /** + * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / + * storage_offset) from one TensorImpl to another TensorImpl. + * + * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE + * [ TensorImpl Shallow-Copying ]. + */ + static void copy_tensor_metadata( + const SparseTensorImpl* src_sparse_impl, + SparseTensorImpl* dest_sparse_impl, + c10::VariableVersion version_counter, + bool allow_tensor_metadata_change) { + TensorImpl::copy_tensor_metadata( + src_sparse_impl, + dest_sparse_impl, + std::move(version_counter), + allow_tensor_metadata_change); + + // Sparse-specific fields + dest_sparse_impl->sparse_dim_ = src_sparse_impl->sparse_dim(); + dest_sparse_impl->dense_dim_ = src_sparse_impl->dense_dim(); + dest_sparse_impl->indices_ = src_sparse_impl->indices(); + dest_sparse_impl->values_ = src_sparse_impl->values(); + dest_sparse_impl->coalesced_ = src_sparse_impl->coalesced(); + } + + const char* tensorimpl_type_name() const override; +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/StorageUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/StorageUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..f7a9fdab0cc732378cb6d64bfcbc219dafdd4ec3 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/StorageUtils.h @@ -0,0 +1,49 @@ +#pragma once + +#include +#include +#include + +namespace at { + +class TensorBase; + +// Here we define a series of utils to create/manipulate ATen backed +// c10 storage implementations. + +/** + * Create a new shared memory storage impl managed by file descriptor + * + * @param size size in bytes + */ +C10_EXPORT c10::intrusive_ptr new_shm_fd_storage(size_t size); + +/** + * Copy src to dst + * Caller must guarantee the validness of the storage objects + * during the entire copy process, esp. when it's async. + * + * This can probably live in c10 namespace later if needed, + * but for now keep it in at to keep implementation simple. + * + * @param dst dst tensor + * @param src src tensor + * @param non_blocking (default false) whether this operation blocks caller + */ +C10_EXPORT void storage_copy( + c10::Storage& dst, + const c10::Storage& src, + bool non_blocking = false); + +/** + * In place change the storage to shm based. + * + * This is only applicable to CPU tensors not already shared. + * Otherwise, it's a no op to mirror the THP tensor behavior: + * https://pytorch.org/docs/stable/generated/torch.Tensor.share_memory_.html + * + * @param t a tensor + */ +C10_EXPORT void share_memory_(TensorBase& t); + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIndexing.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIndexing.h new file mode 100644 index 0000000000000000000000000000000000000000..b6bb4710900c5570496785c502e492ec76520d93 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIndexing.h @@ -0,0 +1,735 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#include +#else +#include +#include +#include +#include +#endif + +#include + +#include + +namespace at::indexing { + +constexpr int64_t INDEX_MIN = c10::SymInt::min_representable_int(); +constexpr int64_t INDEX_MAX = -(INDEX_MIN + 1); + +enum class TensorIndexType { None, Ellipsis, SymInt, Boolean, Slice, Tensor }; + +constexpr c10::nullopt_t None = c10::nullopt; + +struct TORCH_API EllipsisIndexType final { + EllipsisIndexType() = default; +}; +TORCH_API extern const EllipsisIndexType Ellipsis; + +struct TORCH_API Slice final { + public: + Slice( + c10::optional start_index = c10::nullopt, + c10::optional stop_index = c10::nullopt, + c10::optional step_index = c10::nullopt) { + if (!step_index.has_value()) { + step_ = c10::SymInt(1); + } else { + step_ = std::move(step_index).value(); + } + + TORCH_CHECK_VALUE(step_ != 0, "slice step cannot be zero"); + + if (!start_index.has_value()) { + start_ = c10::SymInt(step_ < 0 ? INDEX_MAX : 0); + } else { + start_ = std::move(start_index).value(); + } + + if (!stop_index.has_value()) { + stop_ = c10::SymInt(step_ < 0 ? INDEX_MIN : INDEX_MAX); + } else { + stop_ = std::move(stop_index).value(); + } + } + + inline c10::SymInt start() const { + return start_; + } + + inline c10::SymInt stop() const { + return stop_; + } + + inline c10::SymInt step() const { + return step_; + } + + private: + c10::SymInt start_; + c10::SymInt stop_; + c10::SymInt step_; +}; + +TORCH_API std::ostream& operator<<(std::ostream& stream, const Slice& slice); + +// `at::indexing::TensorIndex` is used for converting C++ tensor indices such as +// `{None, "...", Ellipsis, 0, true, Slice(1, None, 2), torch::tensor({1, 2})}` +// into its equivalent `std::vector`, so that further tensor +// indexing operations can be performed using the supplied indices. +// +// There is one-to-one correspondence between Python and C++ tensor index types: +// Python | C++ +// ----------------------------------------------------- +// `None` | `at::indexing::None` +// `Ellipsis` | `at::indexing::Ellipsis` +// `...` | `"..."` +// `123` | `123` +// `True` / `False` | `true` / `false` +// `:` | `Slice()` / `Slice(None, None)` +// `::` | `Slice()` / `Slice(None, None, None)` +// `1:` | `Slice(1, None)` +// `1::` | `Slice(1, None, None)` +// `:3` | `Slice(None, 3)` +// `:3:` | `Slice(None, 3, None)` +// `::2` | `Slice(None, None, 2)` +// `1:3` | `Slice(1, 3)` +// `1::2` | `Slice(1, None, 2)` +// `:3:2` | `Slice(None, 3, 2)` +// `1:3:2` | `Slice(1, 3, 2)` +// `torch.tensor([1, 2])`) | `torch::tensor({1, 2})` +struct TORCH_API TensorIndex final { + // Case 1: `at::indexing::None` + TensorIndex(c10::nullopt_t) : type_(TensorIndexType::None) {} + + // Case 2: "..." / `at::indexing::Ellipsis` + TensorIndex(at::indexing::EllipsisIndexType) + : type_(TensorIndexType::Ellipsis) {} + TensorIndex(const char* str) : TensorIndex(at::indexing::Ellipsis) { + TORCH_CHECK_VALUE( + strcmp(str, "...") == 0, + "Expected \"...\" to represent an ellipsis index, but got \"", + str, + "\""); + } + + // Case 3: (Sym) Integer value + TensorIndex(SymInt integer) + : integer_(std::move(integer)), type_(TensorIndexType::SymInt) {} + TensorIndex(int64_t integer) : TensorIndex(SymInt(integer)) {} + TensorIndex(int integer) : TensorIndex(SymInt(integer)) {} + + // Case 4: Boolean value + template >> + TensorIndex(T boolean) : boolean_(boolean), type_(TensorIndexType::Boolean) {} + + // Case 5: Slice represented in `at::indexing::Slice` form + TensorIndex(Slice slice) + : slice_(std::move(slice)), type_(TensorIndexType::Slice) {} + + // Case 6: Tensor value + TensorIndex(Tensor tensor) + : tensor_(std::move(tensor)), type_(TensorIndexType::Tensor) {} + + inline bool is_none() const { + return type_ == TensorIndexType::None; + } + + inline bool is_ellipsis() const { + return type_ == TensorIndexType::Ellipsis; + } + + inline bool is_integer() const { + return type_ == TensorIndexType::SymInt; + } + + inline SymInt integer() const { + return integer_; + } + + inline bool is_boolean() const { + return type_ == TensorIndexType::Boolean; + } + + inline bool boolean() const { + return boolean_; + } + + inline bool is_slice() const { + return type_ == TensorIndexType::Slice; + } + + inline const Slice& slice() const { + return slice_; + } + + inline bool is_tensor() const { + return type_ == TensorIndexType::Tensor; + } + + inline const Tensor& tensor() const { + return tensor_; + } + + private: + SymInt integer_ = 0; + bool boolean_ = false; + Slice slice_; + Tensor tensor_; + TensorIndexType type_; +}; + +TORCH_API std::ostream& operator<<( + std::ostream& stream, + const TensorIndex& tensor_index); +TORCH_API std::ostream& operator<<( + std::ostream& stream, + const std::vector& tensor_indices); + +namespace impl { +static inline Tensor applySlice( + const Tensor& self, + int64_t dim, + c10::SymInt start, + c10::SymInt stop, + c10::SymInt step, + bool disable_slice_optimization, + const at::Device& self_device, + const c10::optional& self_sizes) { + // TODO: implement negative step + TORCH_CHECK_VALUE(step > 0, "step must be greater than zero"); + + // See NOTE [nested tensor size for indexing] + if (self_sizes.has_value()) { + // Skip this optimization if we are tracing, as the trace may be polymorphic + // over the shape of the `self` tensor, and we still want to record + // the slice. + SymInt length = (self_device == at::kCPU || self_device == at::kCUDA) + ? (*self_sizes)[dim] + : self.sym_size(dim); + if (!disable_slice_optimization && + TORCH_GUARD_SIZE_OBLIVIOUS(start.sym_eq(0)) && length == stop && + step == 1) { + return self; + } + } + return self.slice_symint( + dim, std::move(start), std::move(stop), std::move(step)); +} + +static inline Tensor applySelect( + const Tensor& self, + int64_t dim, + SymInt index, + int64_t real_dim, + const at::Device& /*self_device*/, + const c10::optional& self_sizes) { + // See NOTE [nested tensor size for indexing] + if (self_sizes.has_value()) { + auto maybe_index = index.maybe_as_int(); + if (maybe_index.has_value()) { + TORCH_CHECK_INDEX( + !(maybe_index.value() == 0 && dim == 0 && self_sizes->empty()), + "invalid index of a 0-dim tensor. ", + "Use `tensor.item()` in Python or `tensor.item()` in C++ to convert a 0-dim tensor to a number"); + } + + auto size = (*self_sizes)[dim]; + // Note: `size >= -index` is not equivalent to `size > -1 - index` if index + // is INT64_MIN For std::numeric_limits::min() result of unary + // minus is undefined by the standard but in practice is equal to self. On + // the other hand, indexing wraping is valid for all negative int64_t + // values, as x[INT64_MIN] is the same as x[INT64_MAX] + TORCH_CHECK_INDEX( + size > -1 - index && size > index, + "index ", + index, + " is out of bounds for dimension ", + real_dim, + " with size ", + size); + } + + // if the index is negative, do not normalize it because that would fix the + // index on the current tensor size in the tracer. aten::select also works on + // negative indices + return self.select_symint(dim, std::move(index)); +} + +static inline Tensor boolToIndexingTensorCPUOrCUDA( + const Tensor& self, + bool value) { + // booleans add a dimension of size 1. true indexes this dimension as if 0:, + // false as empty. + if (value) { + return at::empty({1}, self.options().dtype(kLong)).fill_(0.); + } else { + return at::empty({0}, self.options().dtype(kLong)); + } +} + +static inline Tensor boolToIndexingTensorNonNativeDeviceType( + const Tensor& self, + bool value) { + // booleans add a dimension of size 1. true indexes this dimension as if 0:, + // false as empty. + if (value) { + return at::zeros({1}, self.options().dtype(kLong)); + } else { + return at::empty({0}, self.options().dtype(kLong)); + } +} + +static inline Tensor boolToIndexingTensor( + const Tensor& self, + bool value, + const at::Device& self_device) { + if (self_device == at::kCPU || self_device == at::kCUDA) { + return boolToIndexingTensorCPUOrCUDA(self, value); + } else { + return boolToIndexingTensorNonNativeDeviceType(self, value); + } +} + +static inline Tensor scalarToTensorNonNativeDeviceType( + const Scalar& v, + const TensorOptions& options) { + return at::scalar_tensor(v, options); +} + +static inline void recordTensorIndex( + const Tensor& tensor, + std::vector& outIndices, + int64_t* dim_ptr) { + // TODO: check scalarType + outIndices.resize(*dim_ptr + 1); + outIndices[*dim_ptr] = tensor; + (*dim_ptr)++; +}; + +static inline c10::List> typeConvertIndices( + const Tensor& /*self*/, + std::vector&& indices) { + c10::List> converted_inds; + converted_inds.reserve(indices.size()); + for (auto&& i : std::move(indices)) { + converted_inds.push_back(std::move(i)); + } + return converted_inds; +} + +// NOTE: Why do we mirror instead of replace the `count_specified_dimensions` +// function in torch/csrc/autograd/python_variable_indexing.cpp? It's because +// `count_specified_dimensions` is on the hot path of Python tensor multi-dim +// indexing (i.e. it's called by `applySlicing` which is called by +// `THPVariable_getitem` / `THPVariable_setitem` when handling indexing of more +// than one dimension). If we were to merge the Python/C++ +// `count_specified_dimensions` function, on the Python side we would have to +// construct a `std::vector` container to be consumed by the C++ +// `count_specified_dimensions` function, which adds 100s of nanoseconds +// overhead and is undesirable. +static inline int64_t count_specified_dimensions( + const ArrayRef& indices) { + // Count the number of indexed dimensions (everything but ellipsis and None) + int64_t count = 0; + for (auto& obj : indices) { + if (obj.is_tensor()) { + auto& tensor = obj.tensor(); + if (tensor.scalar_type() == kByte || tensor.scalar_type() == kBool) { + count += tensor.dim(); + } else { + count++; + } + } else if (!obj.is_none() && !obj.is_ellipsis() && !obj.is_boolean()) { + count++; + } + } + return count; +} +} // namespace impl + +// NOTE: Many functions below are only for consumption from Python indexing +// implementation, they include: +// +// - `Tensor scalarToTensor(...)` +// - `IntArrayRef slicePrefix1sSize(...)` +// - `void copy_to(...)` +// - `Tensor handleDimInMultiDimIndexing(...)` +// - `Tensor dispatch_index(...)` +// - `Tensor dispatch_index_put_(...)` +// - `Tensor get_item(...)` +// - `void set_item(...)` +// +// The rest of the functions are in `at::indexing::impl` namespace, signifying +// that they shouldn't be used from Python indexing implementation. +static inline Tensor scalarToTensor( + const Scalar& v, + const TensorOptions& options, + const at::Device& self_device) { + if (self_device == at::kCPU && !v.isSymbolic()) { + return at::detail::scalar_tensor_static( + v, options.dtype_opt()->toScalarType(), self_device); + } else { + return impl::scalarToTensorNonNativeDeviceType(v, options); + } +} + +// To match numpy semantics: +// As a special case for backwards compatibility, +// strip away unit dimensions from the left of 'src' +static inline SymIntArrayRef slicePrefix1sSize(const SymIntArrayRef& sizes) { + size_t first_non1_src = sizes.size(); + for (const auto i : c10::irange(sizes.size())) { + // Unbacked SymInt has different behavior, but this is sound because + // failing to slice will only ever cause an error, not divergent + // behavior + if (!sizes[i].has_hint() || sizes[i] != 1) { + first_non1_src = i; + break; + } + } + + return sizes.slice(first_non1_src); +} + +static inline void copy_to(const Tensor& dst, const Tensor& src) { + if (dst.sym_sizes().equals(src.sym_sizes())) { + // A shortcut to avoid generating hard-coded constant sizes during tracing. + // This is not a perfect solution: when src & dst have different shapes, + // constants will still appear. Users can workaround that case by + // dst[index..] = src.reshape(..) + dst.copy_(src); + return; + } else if (src.dim() == 0 && src.device().type() == at::kCPU) { + dst.fill_(src); + return; + } + auto src_view = src.view_symint(slicePrefix1sSize(src.sym_sizes())); + c10::MaybeOwned b_src = expand_inplace(dst, src_view, "setitem"); + dst.copy_(*b_src); +} + +// See NOTE [ Setting `disable_slice_optimization` when calling C++ tensor +// indexing functions from Python ] +static inline Tensor handleDimInMultiDimIndexing( + const Tensor& prev_dim_result, + const Tensor& original_tensor, + const TensorIndex& index, + int64_t* dim_ptr, + int64_t* specified_dims_ptr, + int64_t real_dim, + std::vector& outIndices, + bool disable_slice_optimization, + const at::Device& original_tensor_device, + const c10::optional& prev_dim_result_sizes) { + if (index.is_integer()) { + return impl::applySelect( + prev_dim_result, + *dim_ptr, + index.integer(), + real_dim, + original_tensor_device, + prev_dim_result_sizes); + } else if (index.is_slice()) { + Tensor result = impl::applySlice( + prev_dim_result, + *dim_ptr, + index.slice().start(), + index.slice().stop(), + index.slice().step(), + /*disable_slice_optimization=*/disable_slice_optimization, + original_tensor_device, + prev_dim_result_sizes); + (*dim_ptr)++; + return result; + } else if (index.is_ellipsis()) { + (*dim_ptr) += original_tensor.dim() - (*specified_dims_ptr); + return prev_dim_result; + } else if (index.is_none()) { + Tensor result = prev_dim_result.unsqueeze(*dim_ptr); + (*dim_ptr)++; + return result; + } else if (index.is_boolean()) { + Tensor result = prev_dim_result.unsqueeze(*dim_ptr); + impl::recordTensorIndex( + impl::boolToIndexingTensor( + result, index.boolean(), original_tensor_device), + outIndices, + dim_ptr); + return result; + } else if (index.is_tensor()) { + Tensor result = prev_dim_result; + const Tensor& tensor = index.tensor(); + auto scalar_type = tensor.scalar_type(); + if (tensor.dim() == 0 && + at::isIntegralType(scalar_type, /*includeBool=*/true)) { + if (scalar_type != at::kByte && scalar_type != at::kBool) { + result = impl::applySelect( + result, + *dim_ptr, + tensor.item(), + real_dim, + original_tensor_device, + prev_dim_result_sizes); + } else { + result = result.unsqueeze(*dim_ptr); + if (scalar_type == at::kBool) { + impl::recordTensorIndex( + impl::boolToIndexingTensor( + result, tensor.item() != 0, original_tensor_device), + outIndices, + dim_ptr); + } else { + impl::recordTensorIndex( + impl::boolToIndexingTensor( + result, tensor.item() != 0, original_tensor_device), + outIndices, + dim_ptr); + } + } + } else { + impl::recordTensorIndex(tensor, outIndices, dim_ptr); + } + return result; + } else { + TORCH_INTERNAL_ASSERT(false, "Invalid TensorIndex type"); + } +} + +namespace impl { +// This mirrors `applySlicing` in +// torch/csrc/autograd/python_variable_indexing.cpp +static inline Tensor applySlicing( + const Tensor& self, + const ArrayRef& indices, + std::vector& outIndices, + bool disable_slice_optimization, + const at::Device& self_device, + const c10::optional& self_sizes) { + int64_t dim = 0; + int64_t specified_dims = impl::count_specified_dimensions(indices); + + // See NOTE [nested tensor size for indexing] + if (self_sizes.has_value()) { + TORCH_CHECK_INDEX( + specified_dims <= (int64_t)self_sizes->size(), + "too many indices for tensor of dimension ", + (int)self_sizes->size()); + } + + Tensor result = self; + for (const auto i : c10::irange(indices.size())) { + auto& obj = indices[i]; + // See NOTE [nested tensor size for indexing] + c10::optional result_sizes = result.is_nested() + ? c10::optional(c10::nullopt) + : c10::optional(result.sym_sizes()); + result = handleDimInMultiDimIndexing( + /*prev_dim_result=*/result, + /*original_tensor=*/self, + /*index=*/obj, + /*dim_ptr=*/&dim, + /*specified_dims_ptr=*/&specified_dims, + /*real_dim=*/static_cast(i), + /*outIndices=*/outIndices, + /*disable_slice_optimization=*/disable_slice_optimization, + /*original_tensor_device=*/self_device, + /*prev_dim_result_sizes=*/result_sizes); + } + return result; +} +} // namespace impl + +static inline Tensor dispatch_index( + const Tensor& self, + std::vector&& indices) { + return self.index(impl::typeConvertIndices(self, std::move(indices))); +} + +static inline Tensor dispatch_index_put_( + Tensor& self, + std::vector&& indices, + const Tensor& value) { + return self.index_put_( + impl::typeConvertIndices(self, std::move(indices)), value); +} + +// NOTE [ Setting `disable_slice_optimization` when calling C++ tensor indexing +// functions from Python ] +// +// Question: When should we set `disable_slice_optimization` to `true` when +// calling C++ tensor indexing functions from Python indexing code? +// +// Answer: What "slice optimization" means: when we have a slicing expression +// like `x[0:5, 0]`, where the sliced tensor was of size 5 in dimension 0, we +// would skip dispatching the actual slice call as an optimization. However, +// here are the cases where we DON'T want this optimization: +// +// 1. When we are doing 1-D slicing (e.g. `tensor[:]`). +// Reason: we always return a shallow copy for expressions such as +// `tensor[:]` / `tensor[...]` / `tensor[:, :]`. (Note that for `tensor[:, +// :]`, we return an alias of `tensor` by doing the following: +// ``` +// Tensor sliced = impl::applySlicing(self, indices, tensorIndices, +// disable_slice_optimization, self_device, self_sizes); if +// (tensorIndices.empty()) { +// if (sliced.is_same(self)) { +// // ensure we return a shallow copy for things like x[...] +// sliced = at::alias(sliced); +// } +// return sliced; +// } +// ```) +// 2. When we are doing JIT tracing. +// Reason: JIT tracing needs the `self.slice(...)` call to properly trace the +// slice operation. + +// This mirrors `THPVariable_getitem` in +// torch/csrc/autograd/python_variable_indexing.cpp See NOTE [ Setting +// `disable_slice_optimization` when calling C++ tensor indexing functions from +// Python ] +static inline Tensor get_item( + const Tensor& self, + const ArrayRef& indices, + bool disable_slice_optimization = false) { + at::Device self_device = self.device(); + // NOTE [nested tensor size for indexing] + // nested tensor does not have a size (yet) so for now we represent its size + // as null may need to be changed after we reach a better solution for nested + // tensor size + c10::optional self_sizes = self.is_nested() + ? c10::optional(c10::nullopt) + : c10::optional(self.sym_sizes()); + + // handle simple types: integers, slices, none, ellipsis, bool + if (indices.size() == 1) { + const TensorIndex& index = indices[0]; + if (index.is_integer()) { + return impl::applySelect( + self, 0, index.integer(), 0, self_device, self_sizes); + } else if (index.is_slice()) { + return impl::applySlice( + self, + 0, + index.slice().start(), + index.slice().stop(), + index.slice().step(), + /*disable_slice_optimization=*/true, + self_device, + self_sizes); + } else if (index.is_none()) { + return self.unsqueeze(0); + } else if (index.is_ellipsis()) { + return at::alias(self); + } else if (index.is_boolean()) { + Tensor result = self.unsqueeze(0); + return dispatch_index( + result, + std::vector{impl::boolToIndexingTensor( + result, index.boolean(), self_device)}); + } + } + + std::vector tensorIndices; + Tensor sliced = impl::applySlicing( + self, + indices, + tensorIndices, + disable_slice_optimization, + self_device, + self_sizes); + if (tensorIndices.empty()) { + if (sliced.is_same(self)) { + // ensure we return a shallow copy for things like x[...] + sliced = at::alias(sliced); + } + return sliced; + } + + // indexing by tensors ("advanced" indexing) + return dispatch_index(sliced, std::move(tensorIndices)); +} + +// This mirrors `THPVariable_setitem` in +// torch/csrc/autograd/python_variable_indexing.cpp for "the assigned value is a +// Tensor" case See NOTE [ Setting `disable_slice_optimization` when calling C++ +// tensor indexing functions from Python ] +static inline void set_item( + const Tensor& self, + const ArrayRef& indices, + const Tensor& value, + bool disable_slice_optimization = false) { + at::Device self_device = self.device(); + SymIntArrayRef self_sizes = self.sym_sizes(); + + // handle simple types: integers, slices, ellipsis, bool + if (indices.size() == 1) { + const TensorIndex& index = indices[0]; + if (index.is_boolean() && !index.boolean()) { + // do nothing for false (technically we should check the size, but we + // don't have real 0-sized shapes. + return; + } else if (index.is_ellipsis()) { + copy_to(self, value); + return; + } else if (index.is_none() || (index.is_boolean() && index.boolean())) { + copy_to(self.unsqueeze(0), value); + return; + } else if (index.is_integer()) { + copy_to( + impl::applySelect( + self, 0, index.integer(), 0, self_device, self_sizes), + value); + return; + } else if (index.is_slice()) { + copy_to( + impl::applySlice( + self, + 0, + index.slice().start(), + index.slice().stop(), + index.slice().step(), + /*disable_slice_optimization=*/disable_slice_optimization, + self_device, + self_sizes), + value); + return; + } + } + + std::vector tensorIndices; + Tensor sliced = impl::applySlicing( + self, + indices, + tensorIndices, + disable_slice_optimization, + self_device, + self_sizes); + if (tensorIndices.empty()) { + copy_to(sliced, value); + return; + } + + SymIntArrayRef valueSizes = value.sym_sizes(); + SymIntArrayRef slicedValueSizes = slicePrefix1sSize(valueSizes); + Tensor valuesSliced; + if (!valueSizes.equals(slicedValueSizes)) { + valuesSliced = value.view_symint(slicedValueSizes); + } else { + valuesSliced = value; + } + dispatch_index_put_(sliced, std::move(tensorIndices), valuesSliced); + return; +} + +} // namespace at::indexing diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIterator.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIterator.h new file mode 100644 index 0000000000000000000000000000000000000000..4a1a2bce60808a67629bcd53492413017cd1d429 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIterator.h @@ -0,0 +1,1002 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace at { +class Tensor; +class OptionalTensorRef; +using NameVector = SmallVector; +} // namespace at + +// TensorIterator is a helper class for element-wise operations, such as +// arithmetic, comparisons, and trigonometric functions. It handles +// broadcasting and type conversions of operands. +// +// This is inspired by NumPy's Array Iterator API (NpyIter). +// +// The files Loops.h and Loops.cuh provide functions to build kernels that +// use TensorIterator. +// +// Example: +// +// auto iter = TensorIteratorConfig() +// .add_output(output) +// .add_input(input) +// .build() +// +// [MyKernel.cpp / MyKernel.cu] +// cpu_kernel(iter, [](float a, float b) { +// return a + b; +// }); +// +// gpu_kernel(iter, []GPU_LAMBDA(float a, float b) -> float { +// return a + b; +// }); +// +// Note [Order of Construction] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// When setting up the tensor iterator configuration, the output Tensors +// have to be added first via +// TensorIteratorConfig::add_owned_output(at::Tensor). After adding all outputs, +// the inputs can be added via +// TensorIteratorConfig::add_owned_input(at::Tensor). +// Adding another output after inputs have been added will rise an exception. +// +// Note [Common Dtype Computation] +// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +// Some operations have a natural notion of a "common dtype" or +// "computation dtype" where all inputs are cast to one dtype, the +// operation is performed, and then the results are cast to all outputs. +// +// TensorIterator infers a common dtype if all inputs have the same dtype, +// and it computes one using type promotion rules on its inputs if +// promote_inputs_to_common_dtype_ is true. Attempting to query +// a common dtype otherwise will throw an exception. +// +// Note that the outputs are not considered when computing a common dtype. + +namespace at { + +namespace internal { +// This parameter is heuristically chosen to determine the minimum number of +// work that warrants parallelism. For example, when summing an array, it is +// deemed inefficient to parallelise over arrays shorter than 32768. Further, +// no parallel algorithm (such as parallel_reduce) should split work into +// smaller than GRAIN_SIZE chunks. +constexpr int64_t GRAIN_SIZE = 32768; + +// Storage for a non-owning Tensor, without needing to include Tensor.h +class TORCH_API OpaqueOptionalTensorRef { + alignas(alignof(TensorBase)) std::array data_{}; + + public: + OpaqueOptionalTensorRef(); + OpaqueOptionalTensorRef(const OpaqueOptionalTensorRef&) = default; + OpaqueOptionalTensorRef& operator=(const OpaqueOptionalTensorRef&) = default; + OpaqueOptionalTensorRef(OpaqueOptionalTensorRef&&) noexcept = default; + OpaqueOptionalTensorRef& operator=(OpaqueOptionalTensorRef&&) noexcept = + default; + ~OpaqueOptionalTensorRef(); + + OptionalTensorRef* get() { + return reinterpret_cast(data_.data()); + } + const OptionalTensorRef* get() const { + return reinterpret_cast(data_.data()); + } + + OptionalTensorRef& operator*() { + return *get(); + } + const OptionalTensorRef& operator*() const { + return *get(); + } + OptionalTensorRef* operator->() { + return get(); + } + const OptionalTensorRef* operator->() const { + return get(); + } + + const Tensor& getTensor() const; +}; +} // namespace internal + +struct TORCH_API OperandInfo { + using StrideVector = SmallVector; + OperandInfo() = default; + C10_ALWAYS_INLINE explicit OperandInfo(c10::MaybeOwned&& t) { + if (t->defined()) { + device = t->device(); + target_dtype = t->scalar_type(); + current_dtype = target_dtype; + } + tensor(std::move(t)); + validate(); + } + + C10_ALWAYS_INLINE OperandInfo(const OperandInfo&) = default; + C10_ALWAYS_INLINE OperandInfo& operator=(const OperandInfo&) = default; + C10_ALWAYS_INLINE OperandInfo(OperandInfo&&) noexcept = default; + C10_ALWAYS_INLINE OperandInfo& operator=(OperandInfo&&) noexcept = default; + C10_ALWAYS_INLINE ~OperandInfo() = default; + + /// The data pointer. This may be different from tensor->data_ptr() if the + /// iterator is split. + void* data = nullptr; + + /// Stride after broadcasting. The stride is in bytes, not number of elements. + StrideVector stride_bytes; + + /// The desired device and type for the operand. For inputs, this specifies + /// that the input should be converted to this type if necessary. For outputs, + /// this specifies which type to allocate. target_dtype and device are + /// initialized with the dtype and device of the tensor but during type + /// promotion target_dtype value can become different from tensor's dtype + /// also, during type promotion target_dtype and device can be set for an + /// undefined tensor so that tensor can be properly constructed later. + c10::optional device = c10::nullopt; + ScalarType target_dtype = ScalarType::Undefined; + // Caches dtype of the tensor, because scalar_type is an expensive operation + // If dtype of the tensor is changed (e.g. as a result of type promotion or in + // allocate_outputs), this + // value should be changed too. + ScalarType current_dtype = ScalarType::Undefined; + + bool is_device_defined() const { + return device.has_value(); + } + bool is_type_defined() const { + return target_dtype != ScalarType::Undefined; + } + TensorOptions options() const { + return TensorOptions(target_dtype).device(device); + } + + bool is_output = false; + + bool will_resize = false; + + bool is_read_write = false; + + bool is_const = false; + + void validate() { + TORCH_CHECK( + !tensor_base_->defined() || tensor_base_->layout() == kStrided, + "unsupported tensor layout: ", + tensor_base_->layout()); + } + + /// The tensor operand. Note that the strides, data pointer, and + /// other attributes may differ due to dimension reordering and + /// coalescing. + const Tensor& tensor() const { + return tensor_storage_.getTensor(); + } + const TensorBase& tensor_base() const { + return *tensor_base_; + } + void tensor(c10::MaybeOwned&& tensor); + + // Save the original tensor operand in cases when an output is modified + // (e.g. if dtype is changed) + const Tensor& original_tensor() const { + return original_tensor_storage_.getTensor(); + } + const TensorBase& original_tensor_base() const { + return *original_tensor_base_; + } + + // Set tensor to a new value, and store the old tensor value in + // original_tensor Should only ever be called once for the lifetime of an + // operand + void exchange_tensor(c10::MaybeOwned&& new_tensor); + + // Move original_tensor back into tensor, exchange_tensor must have been + // called before + void restore_original_tensor(); + + private: + c10::MaybeOwned tensor_base_; + c10::MaybeOwned original_tensor_base_ = + c10::MaybeOwned::owned(std::in_place); + + // We store TensorBase visibly in the header to allow inline access. + // However, we sometimes need a genuine `const Tensor &` for the + // TensorIterator API. So, we also store a non-owning `Tensor` + // object in these `_storage_` variables. + internal::OpaqueOptionalTensorRef tensor_storage_; + internal::OpaqueOptionalTensorRef original_tensor_storage_; +}; + +struct SplitUntil32Bit; + +enum class FastSetupType : uint8_t { + NONE, + CONTIGUOUS, + CHANNELS_LAST, + NON_OVERLAPPING_DENSE +}; + +class TensorIteratorConfig; +struct TensorIterator; + +struct TORCH_API TensorIteratorBase : public impl::MetaBase { + using DimMask = std::bitset<64>; + using PtrVector = SmallVector; + using StrideVector = SmallVector; + + TensorIteratorBase(); + void build(TensorIteratorConfig&); + + // The inner-loop function operates on the fastest moving dimension. It + // implements element-wise operations in terms of 1-d strided tensors. + // + // Arguments: + // data: data pointers for each operand (length `ntensors`) + // strides: stride for each operand (length `ntensors`) + // size: size of inner loop + // + // The `size` often matches shape[0], but may be smaller due to + // parallelization of the inner loop. + using loop2d_t = c10::function_ref< + void(char** data, const int64_t* strides, int64_t size0, int64_t size1)>; + + using loop_subiter_t = c10::function_ref; + + void foreach_reduced_elt(loop_subiter_t loop, bool parallelize = true); + + int ndim() const { + return static_cast(shape_.size()); + } + IntArrayRef shape() const { + return shape_; + } + int64_t numel() const; + int ntensors() const { + return static_cast(operands_.size()); + } + int noutputs() const { + return num_outputs_; + } + int ninputs() const { + return ntensors() - noutputs(); + } + IntArrayRef view_offsets() const { + return view_offsets_; + } + + /// number of elements in the output operand. this is the same as numel() for + /// operations that are not reductions. + int64_t num_output_elements() const; + + /// number of reduced dimensions in a reduction operation + int num_reduce_dims() const; + + /// 1-dimensional iteration and no buffering or type conversion + bool is_trivial_1d() const; + /// Reducible to 1-dimensional and all operands are contiguous + bool is_contiguous() const; + bool is_dim_reduced(int dim) const; + + /// Accessors for each operand + IntArrayRef strides(int64_t arg) const { + return operands_[arg].stride_bytes; + } + void* data_ptr(int64_t arg) const; + ScalarType dtype(int64_t arg = 0) const { + return operands_[arg].current_dtype; + } + ScalarType common_dtype() const { + TORCH_INTERNAL_ASSERT( + common_dtype_ != ScalarType::Undefined, + "Queried for invalid common dtype!"); + return common_dtype_; + } + ScalarType input_dtype(int64_t arg = 0) const { + return operands_[num_outputs_ + arg].current_dtype; + } + Device device(int64_t arg = 0) const { + return operands_[arg].device.value(); + } + c10::DeviceType device_type(int64_t arg = 0) const { + return device(arg).type(); + } + int64_t element_size(int64_t arg) const { + return static_cast(elementSize(dtype(arg))); + } + bool is_scalar(int64_t arg) const; + bool is_cpu_scalar(int64_t arg) const; + + const TensorBase& tensor_base(int64_t arg) const { + return operands_[arg].tensor_base(); + } + const Tensor& tensor(int64_t arg) const { + return operands_[arg].tensor(); + } + + const TensorBase& output_base(int64_t arg = 0) const { + AT_ASSERT(arg < num_outputs_); + return tensor_base(arg); + } + + const Tensor& output(int64_t arg = 0) const { + AT_ASSERT(arg < num_outputs_); + return tensor(arg); + } + + const TensorBase& input_base(int64_t arg = 0) const { + AT_ASSERT(arg >= 0 && arg < ntensors() - num_outputs_); + return tensor_base(num_outputs_ + arg); + } + const Tensor& input(int64_t arg = 0) const { + AT_ASSERT(arg >= 0 && arg < ntensors() - num_outputs_); + return tensor(num_outputs_ + arg); + } + + // Copies from temporary outputs back to the original outputs + // NOTE: only used on CPU + void cast_outputs(); + + /// Removes an operand from this iterator + void remove_operand(int64_t arg); + /// Shrinks an iterated dimension + void narrow(int dim, int64_t start, int64_t size); + /// Narrows every dim after and including `start_dim` to size one. + void select_all_keeping_dim(int start_dim, IntArrayRef starts); + /// Replaces the data pointer for the operand at index `arg`. + /// The new pointer should have the same sizes, strides and dtype as the + /// original + void unsafe_replace_operand(int64_t arg, void* data); + + /// Splits this TensorIterator into two iterators. Together they iterate over + /// the entire operation. Used by `with_32bit_indexing()`. + std::unique_ptr split(int dim); + + /// Returns the dimension with the largest extent: (size[dim]-1) * stride[dim] + int get_dim_to_split() const; + + template + T scalar_value(int64_t arg) { + auto& op = operands_[arg]; + return c10::fetch_and_cast(op.tensor_base().scalar_type(), op.data); + } + + /// Return scalar value from original_tensor_base if it is defined. When + /// common_dtype is Half, casting scalar input to common_dtype might overflow. + /// If the scalar is aleady given in the type of Half, then return scalar + /// value from tensor_base. + template + T original_scalar_value(int64_t arg) { + auto& original_tensor_base = operands_[arg].original_tensor_base(); + if (original_tensor_base.defined()) { + TORCH_INTERNAL_ASSERT( + original_tensor_base.scalar_type() != common_dtype()); + return c10::fetch_and_cast( + original_tensor_base.scalar_type(), + original_tensor_base.const_data_ptr()); + } else { + return scalar_value(arg); + } + } + + private: + template + auto loop_2d_from_1d(const loop1d_t& loop) { + return + [loop, ntensor = ntensors()]( + char** base, const int64_t* strides, int64_t size0, int64_t size1) { + PtrVector data(base, base + ntensor); + const int64_t* outer_strides = &strides[ntensor]; + for (const auto i : c10::irange(size1)) { + if (i > 0) { + for (const auto arg : c10::irange(ntensor)) { + data[arg] += outer_strides[arg]; + } + } + loop(data.data(), strides, size0); + } + }; + } + + public: + template < + typename loop1d_t, + std::enable_if_t< + std::is_convertible_v< + loop1d_t, + c10::function_ref< + void(char**, const int64_t* strides, int64_t size)>>, + int> = 0> + void for_each(loop1d_t loop, int64_t grain_size = at::internal::GRAIN_SIZE) { + for_each(loop_2d_from_1d(loop), grain_size); + } + + void for_each(loop2d_t loop, int64_t grain_size = at::internal::GRAIN_SIZE); + + void parallel_reduce(loop2d_t loop); + + template < + typename loop1d_t, + std::enable_if_t< + std::is_convertible_v< + loop1d_t, + c10::function_ref< + void(char**, const int64_t* strides, int64_t size)>>, + int> = 0> + void serial_for_each(loop1d_t loop, Range range) { + serial_for_each(loop_2d_from_1d(loop), range); + } + + void serial_for_each(loop2d_t loop, Range range) const; + + /// Create a strides array for a Tensor with shape of this iterator. The + /// parameter `element_size` specifies the size of Tensor's data type in + /// bytes (e.g. `4` for `float`) + StrideVector compatible_stride(int64_t element_size) const; + + /// Inverts the re-ordering done by reorder_dimensions. This can only be + /// called *before* coalesce_dimensions() is called. + DimVector invert_perm(IntArrayRef input) const; + + /// Reapply same re-ordering as it is done by reorder_dimensions. This can + /// only be called *before* coalesce_dimensions() is called. + DimVector apply_perm_and_mul(IntArrayRef input, int mul) const; + + /// Helper functions for CPU iteration + StrideVector get_dim_strides(int dim) const; + StrideVector get_strides() const; + StrideVector get_inner_strides() const { + return get_dim_strides(0); + } + PtrVector get_base_ptrs() const; + + // Helper functions for advanced stride manipulations (e.g. torch.flip) + void _unsafe_set_arg_strides(const int64_t arg, IntArrayRef strides) { + operands_[arg].stride_bytes = strides; + } + void _unsafe_set_arg_data(const int64_t arg, void* data) { + operands_[arg].data = data; + } + + /// true if the stride computation can use 32-bit arithmetic. Used by GPU + /// kernels + bool can_use_32bit_indexing() const; + + /// An "iteratable" object that recursively splits this iterator into + /// sub-iterators that can use 32-bit indexing. + SplitUntil32Bit with_32bit_indexing() const; + + /// If the kernel should accumulate into the output. Only relevant for CUDA + /// reductions. + bool should_accumulate() const { + return accumulate_; + } + + /// Whether this iterator produces the actual output, + /// as opposed to something that will be accumulated further. Only relevant + /// for CUDA reductions. + bool is_final_output() const { + return final_output_; + } + + bool has_contiguous_first_dim() const { + if (ndim() == 0) { + return true; + } + + int num_tensors = ntensors(); + for (const auto i : c10::irange(num_tensors)) { + if (strides(i)[0] != element_size(i)) { + return false; + } + } + return true; + } + + void set_output_raw_strided( + int64_t output_idx, + IntArrayRef sizes, + IntArrayRef strides, + TensorOptions options, + DimnameList names) override; + +#define TORCH_DISALLOW_TEMPORARIES_IMPL(methodname, maybestatic) \ + maybestatic void methodname( \ + TensorBase&& out, const TensorBase& a, const TensorBase& b) = delete; \ + maybestatic void methodname( \ + const TensorBase& out, TensorBase&& a, const TensorBase& b) = delete; \ + maybestatic void methodname( \ + const TensorBase& out, const TensorBase& a, TensorBase&& b) = delete; \ + maybestatic void methodname( \ + TensorBase&& out, TensorBase&& a, const TensorBase& b) = delete; \ + maybestatic void methodname( \ + TensorBase&& out, const TensorBase& a, TensorBase&& b) = delete; \ + maybestatic void methodname( \ + const TensorBase& out, TensorBase&& a, TensorBase&& b) = delete; \ + maybestatic void methodname( \ + TensorBase&& out, TensorBase&& a, TensorBase&& b) = delete; + +#define TORCH_DISALLOW_TEMPORARIES(methodname) \ + TORCH_DISALLOW_TEMPORARIES_IMPL(methodname, ) + + void build_binary_float_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + void build_borrowing_binary_float_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_binary_float_op) + void build_binary_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + void build_borrowing_binary_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_binary_op) + void build_unary_float_op(const TensorBase& out, const TensorBase& a); + void build_borrowing_unary_float_op( + const TensorBase& out, + const TensorBase& a); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_unary_float_op) + void build_unary_op(const TensorBase& out, const TensorBase& a); + // Odd special case needed for pow. Has to borrow the output because + // it's a structured kernel, but the argument is potentially a copy. + void build_output_borrowing_argument_owning_unary_op( + const TensorBase& out, + const TensorBase& a); + void build_borrowing_unary_op(const TensorBase& out, const TensorBase& a); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_unary_op) + void build_borrowing_unary_force_boolean_op( + const TensorBase& out, + const TensorBase& a); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_unary_force_boolean_op) + void build_comparison_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + void build_borrowing_comparison_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + TORCH_DISALLOW_TEMPORARIES(build_borrowing_comparison_op) + // Another special case: we need to own the second argument for comparison + // ops. + void build_borrowing_except_last_argument_comparison_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + void build_ternary_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b, + const TensorBase& c); + +#undef TORCH_DISALLOW_TEMPORARIES + protected: + // Mutable reference as it moves tensors out of TensorIteratorConfig + void populate_operands(TensorIteratorConfig&); + void mark_outputs(); + void mark_resize_outputs(const TensorIteratorConfig&); + void compute_mem_overlaps(const TensorIteratorConfig&); + void compute_shape(const TensorIteratorConfig&); + void compute_strides(const TensorIteratorConfig&); + void reorder_dimensions(); + void permute_dimensions(IntArrayRef perm); + void compute_types(const TensorIteratorConfig&); + ScalarType compute_common_dtype(); + void allocate_or_resize_outputs(); + bool fast_set_up(const TensorIteratorConfig&); + FastSetupType compute_fast_setup_type(const TensorIteratorConfig&); + void compute_names(const TensorIteratorConfig&); + void propagate_names_to_outputs(); + void coalesce_dimensions(); + + protected: + /// Records the "computation" shape of the output tensor. The computation + /// shape is different from the regular shape in a few ways: + /// + /// - The shape may be permuted (via permute_dimensions) so that we + /// process the dimensions in the most computationally efficient order + /// (rather than the logical order given to us by the users.) + /// - The shape may have adjacent dimensions collapsed (via + /// coalesce_dimensions) so that we minimize the number of + /// dimensions we have to explicitly iterate over. For example, + /// a pointwise operation on a contiguous tensor "computationally" + /// consists of only a single dimension. + /// + /// In other words, the computation shape is the output shape as it + /// actually matters for implementing the kernel, but not necessarily the + /// output shape that the user will see in the end. + /// + /// The lifecycle of mutations to shape_ in TensorIterator: + /// - declare_static_shape() sets an initial shape explicitly + /// provided by user, otherwise + /// - compute_shape() computes the true (non-computational) shape + /// specified by the user. + /// - reorder_dimensions() reorders dimensions to improve coalescing. + /// - coalesce_dimensions() then coalesces adjacent dimensions when + /// possible. + /// + /// The shape may also be further modified if we create sub-TensorIterators, + /// e.g., via narrow or select_all_keeping_dim. + DimVector shape_; + + /// Temporarily records the permutation computed by reorder_dimensions. + /// This permutation maps the computation output dimension (dim) to + /// the original true output dimension (perm_[dim]). It is used by + /// invert_perm to undo the permutation. After coalesce_dimensions is + /// called, the permutation is no longer valid (as, in general, there + /// is no permutation that will make computation dimensions to + /// output dimensions); methods that manipulate perm_ are obligated + /// to test that !has_coalesced_dimensions + DimVector perm_; + + /// Has coalesce_dimensions() (or any moral equivalent, e.g., fast_build()) + /// been called? This is SOLELY used to check validity of perm_. + bool has_coalesced_dimensions_ = false; + + /// Whether iteration must be fixed. This disables dimension permuting and + /// also changes how for_each divides work among threads. + bool enforce_linear_iteration_ = false; + + /// The index offsets into the original tensors for each dimension. + /// This is only non-zero when you narrow() a TensorIterator (e.g., + /// when you make sub-TensorIterators). + DimVector view_offsets_; + + /// The computed names of the output tensor. Computed by compute_names() + NameVector names_; + + /// The operands of the TensorIterator: both the inputs and outputs. The + /// outputs MUST come first in the operands_ list. There is always an + /// operand for each output of the TensorIterator, even if TensorIterator + /// will ultimately be responsible for allocating the output; in those + /// cases, tensor is simply undefined (and will be populated later + /// during build()). + /// + /// This list is initially populated prior to build(), but build() mutates + /// OperandInfo to populate more information. + SmallVector operands_; + + /// Number of outputs in operands_ (the length of the outputs prefix + /// in operands_). + int num_outputs_ = 0; + + /// Whether or not all operands have the same shape and are 1d+. Having all + /// the same shape affects whether or not the iterator is eligible for fast + /// setup. + bool all_ops_same_shape_ = false; + /// Whether or not all operands are 0d, this affects type promotion + bool all_ops_are_scalars_ = false; + + /// The "computation" dtype of TensorIterator, specifying what the dtype + /// we will do the internal computation in TensorIterator. Typically, + /// this matches the dtype of the output tensors, but not always! + ScalarType common_dtype_ = ScalarType::Undefined; + + /// This is currently defined as kCPU, or the device of the first non-CPU + /// tensor argument. See TensorIteratorBase::compute_types for details. + Device common_device_ = kCPU; + + /// Set by split(), see should_accumulate() and is_final_output() + bool accumulate_ = false; + bool final_output_ = true; + + // From TensorIteratorConfig + bool is_reduction_ = false; + + /// Set by populate_operands(), says if we're handling meta tensors + bool is_meta_ = false; +}; + +struct TORCH_API TensorIterator final : public TensorIteratorBase { + TensorIterator() : TensorIteratorBase() {} + // Slicing is OK, TensorIterator guaranteed NOT to have any fields + TensorIterator(const TensorIteratorBase& iter) : TensorIteratorBase(iter) {} + +#define TORCH_DISALLOW_TEMPORARIES(methodname) \ + TORCH_DISALLOW_TEMPORARIES_IMPL(methodname, static) + + static TensorIterator binary_float_op( + TensorBase& out, + const TensorBase& a, + const TensorBase& b); + static TensorIterator binary_op( + TensorBase& out, + const TensorBase& a, + const TensorBase& b); + static TensorIterator borrowing_binary_op( + const TensorBase& out, + const TensorBase& a, + const TensorBase& b); + TORCH_DISALLOW_TEMPORARIES(borrowing_binary_op) + static TensorIterator comparison_op( + TensorBase& out, + const TensorBase& a, + const TensorBase& b); + static TensorIterator unary_op(TensorBase& out, const TensorBase& a); + static TensorIterator unary_float_op(TensorBase& out, const TensorBase& a); + static TensorIterator nullary_op(TensorBase& out); + static TensorIterator borrowing_nullary_op(const TensorBase& out); + static TensorIterator borrowing_nullary_op(TensorBase&& out) = delete; + static TensorIterator reduce_op(TensorBase& out, const TensorBase& a); + static TensorIterator reduce_op( + TensorBase& out1, + TensorBase& out2, + const TensorBase& a); +#undef TORCH_DISALLOW_TEMPORARIES +#undef TORCH_DISALLOW_TEMPORARIES_IMPL + + const Tensor& maybe_get_output(int64_t output_idx) override; + void set_output_raw_strided( + int64_t output_idx, + IntArrayRef sizes, + IntArrayRef strides, + TensorOptions options, + DimnameList names) override; +}; + +class TORCH_API TensorIteratorConfig final { + public: + friend struct TensorIteratorBase; + friend struct TensorIterator; + + TensorIteratorConfig() = default; + + C10_DISABLE_COPY_AND_ASSIGN(TensorIteratorConfig); + + /// Construction + // Stores input/output Tensors without incrementing the reference count. + // Important: the outputs have to be added before the inputs. + TensorIteratorConfig& add_output(const TensorBase& output) { + return add_borrowed_output(output); + } + TensorIteratorConfig& add_input(const TensorBase& input) { + return add_borrowed_input(input); + } + TensorIteratorConfig& add_const_input(const TensorBase& input) { + return add_borrowed_const_input(input); + } + + // Borrowing from temporaries is unlikely to go well. + TensorIteratorConfig& add_output(TensorBase&& output) = delete; + TensorIteratorConfig& add_input(TensorBase&& input) = delete; + TensorIteratorConfig& add_const_input(TensorBase&& input) = delete; + + // Stores input/output Tensors while incrementing the reference count. + // Note that add_{in,out}put are nearly always what you + // want, and the exception (adding an unnamed temporary) won't + // compile. + TensorIteratorConfig& add_owned_output(const TensorBase& output); + TensorIteratorConfig& add_owned_input(const TensorBase& input); + TensorIteratorConfig& add_owned_const_input(const TensorBase& input); + + // Advanced API: stores input/output Tensors without incrementing + // the reference count. The caller must ensure that these Tensors + // live at least as long as this TensorIteratorConfig and any + // TensorIteratorBase built from this TensorIteratorConfig. + // Important: the outputs have to be added before the inputs. + TensorIteratorConfig& add_borrowed_output(const TensorBase& output); + TensorIteratorConfig& add_borrowed_input(const TensorBase& input); + TensorIteratorConfig& add_borrowed_const_input(const TensorBase& input); + + // Borrowing from temporaries is unlikely to go well. + TensorIteratorConfig& add_borrowed_output(TensorBase&& output) = delete; + TensorIteratorConfig& add_borrowed_input(TensorBase&& input) = delete; + TensorIteratorConfig& add_borrowed_const_input(TensorBase&& input) = delete; + + // Sets the check_mem_overlap_ flag, which is true by default. + // If true, inputs are checked for partial overlap with the outputs and + // outputs are checked for internal overlap (e.g. broadcasted views). An error + // is raised if unacceptable overlap is detected. + // If you're migrating an existing operator to using TensorIterator, please + // consider if the previous implementation checked memory overlap. If it did + // not, and if the operator is idempotent (for example, Tensor.fill_(0)), then + // checking memory overlap is BC-breaking. Please don't check memory overlap + // in that case. + TensorIteratorConfig& set_check_mem_overlap(bool check_mem_overlap) { + check_mem_overlap_ = check_mem_overlap; + return *this; + } + + // Sets the check_all_same_dtype_ flag, which is true by default + // If true, checks that all inputs and defined outputs have the same dtype + // Setting either of promote_inputs_to_common_dtype_ + // or cast_common_dtype_to_outputs_ to true will set + // check_all_same_dtype_ to false. + TensorIteratorConfig& check_all_same_dtype(const bool _check_all_same_dtype) { + check_all_same_dtype_ = _check_all_same_dtype; + return *this; + } + + // Sets the check_all_same_device_ flag, which is true by default + // If true, all operands must be on the same device, with the possible + // exception of CPU scalars, which can be passed to some CUDA kernels + // as kernel arguments. + TensorIteratorConfig& check_all_same_device( + const bool _check_all_same_device) { + check_all_same_device_ = _check_all_same_device; + return *this; + } + + // Sets the enforce_safe_casting_to_output_ flag, which is false by default + // If true, the iterator's "common dtype" must be computable + // (see the [Common Dtype Computation] note) and + // canCast(common dtype, output dtype) must be true for all outputs. + TensorIteratorConfig& enforce_safe_casting_to_output( + const bool _enforce_safe_casting_to_output) { + enforce_safe_casting_to_output_ = _enforce_safe_casting_to_output; + return *this; + } + + // Sets the enforce_linear_iteration_ flag, which is false by default. + // If true, iteration goes in the same order as a C-contiguous tensor + // is layed out in memory. i.e. last dimension iterates fastest. + // + // This iteration order can be less efficient and may even prevent + // vectorization. So only use if the correctness of your kernel depends on it. + TensorIteratorConfig& enforce_linear_iteration( + const bool _enforce_linear_iteration = true) { + enforce_linear_iteration_ = _enforce_linear_iteration; + return *this; + } + + // Sets the promote_inputs_to_common_dtype_ flag, which is false by default + // If true, the iterator's "common dtype" is always computed (see the + // [Common Dtype Computation] note) and, on the CPU, temporary copies of + // the inputs in the common dtype are passed as the actual inputs to + // the operation. + // Setting this flag to true sets check_all_same_dtype_ to false. + TensorIteratorConfig& promote_inputs_to_common_dtype( + const bool _promote_inputs_to_common_dtype) { + promote_inputs_to_common_dtype_ = _promote_inputs_to_common_dtype; + if (_promote_inputs_to_common_dtype) { + check_all_same_dtype_ = false; + } + return *this; + } + + // Sets the promote_integer_inputs_to_float_ flag, which is false by default + // NOTE: If set to true, the promote_inputs_to_common_dtype_ must also be + // true. If true, if the iterator's "common dtype" is an integral type + // (including bool) + // then it is changed to the default float scalar type. + TensorIteratorConfig& promote_integer_inputs_to_float( + const bool _promote_integer_inputs_to_float) { + promote_integer_inputs_to_float_ = _promote_integer_inputs_to_float; + TORCH_INTERNAL_ASSERT( + !promote_integer_inputs_to_float_ || promote_inputs_to_common_dtype_); + return *this; + } + + TensorIteratorConfig& is_reduction(const bool _is_reduction) { + is_reduction_ = _is_reduction; + return *this; + } + + TensorIteratorConfig& allow_cpu_scalars(const bool _allow_cpu_scalars) { + allow_cpu_scalars_ = _allow_cpu_scalars; + return *this; + } + + // Sets the cast_common_dtype_to_outputs_ flag, which is false by default + // If true, the iterator's "common dtype" must be computatable + // (see the [Common Dtype Computation] note) and, on the CPU, temporary + // copies of the outputs are passed as the actual output to the operation. + // These temporaries are then copied to the original outputs after + // the operation is performed (see cast_outputs()). + // Setting this flag to true sets check_all_same_dtype_ to false. + TensorIteratorConfig& cast_common_dtype_to_outputs( + const bool _cast_common_dtype_to_outputs) { + cast_common_dtype_to_outputs_ = _cast_common_dtype_to_outputs; + if (_cast_common_dtype_to_outputs) { + check_all_same_dtype_ = false; + } + return *this; + } + + TensorIteratorConfig& resize_outputs(bool resize_outputs) { + resize_outputs_ = resize_outputs; + return *this; + } + + // Bypass output dtype/device computation and fix the dtype/device as + // specified here. + TensorIteratorConfig& declare_static_dtype_and_device( + ScalarType dtype, + Device device); + TensorIteratorConfig& declare_static_dtype(ScalarType dtype); + TensorIteratorConfig& declare_static_device(Device device); + TensorIteratorConfig& declare_static_shape(IntArrayRef shape); + TensorIteratorConfig& declare_static_shape( + IntArrayRef shape, + IntArrayRef squash_dims); + + // It would be better if this was && qualified, but this would be at the cost + // of a lot of boilerplate above + TensorIterator build() { + TensorIterator iter; + iter.build(*this); + return iter; + } + + private: + bool is_tensor_const(size_t idx); + + SmallVector, 4> tensors_; + int num_outputs_ = 0; + int num_inputs_ = 0; + + c10::optional static_shape_ = c10::nullopt; + c10::optional static_dtype_ = c10::nullopt; + c10::optional static_device_ = c10::nullopt; + bool check_mem_overlap_ = true; + bool allow_cpu_scalars_ = false; + bool is_reduction_ = false; + bool resize_outputs_ = true; + bool check_all_same_dtype_ = true; + bool check_all_same_device_ = true; + bool enforce_safe_casting_to_output_ = false; + bool enforce_linear_iteration_ = false; + bool promote_inputs_to_common_dtype_ = false; + bool promote_integer_inputs_to_float_ = false; + bool cast_common_dtype_to_outputs_ = false; + + SmallVector const_tensor_indices_; +}; + +/// A container-like struct that acts as if it contains splits of a +/// TensorIterator that can use 32-bit indexing. Taken together the splits cover +/// the original TensorIterator. +struct TORCH_API SplitUntil32Bit { + struct TORCH_API iterator { + iterator() = default; + iterator(const TensorIteratorBase& iter); + iterator(iterator&&) = default; + + // Guaranteed to be a TensorIterator proper! + TensorIterator& operator*() const; + iterator& operator++(); + bool operator==(const iterator& other) const { + // two iterators are equal if they are the same object or they're both + // empty + return this == &other || (vec.empty() && other.vec.empty()); + } + // needed for C++11 range-based for loop + bool operator!=(const iterator& other) const { + return !(*this == other); + } + + /// stack of TensorIterators to be split + std::vector> vec; + }; + + SplitUntil32Bit(const TensorIteratorBase& iter) : iter(iter) {} + + iterator begin() const; + iterator end() const; + + private: + // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members) + const TensorIteratorBase& iter; +}; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIteratorInternal.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIteratorInternal.h new file mode 100644 index 0000000000000000000000000000000000000000..ec0cb6c8fdfcb2a36139035340d75d96a7930dfc --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorIteratorInternal.h @@ -0,0 +1,72 @@ +#pragma once +#include +#include +#include + +namespace at { + +struct DimCounter { + DimCounter(IntArrayRef shape, Range range); + + void increment(const std::array& step); + bool is_done() const; + std::array max_2d_step() const; + + IntArrayRef shape; + Range range; + c10::SmallBuffer values; + int64_t offset; +}; + +namespace internal { + +inline void get_data_ptrs( + char** ptrs, + ArrayRef base, + IntArrayRef strides, + IntArrayRef counter) { + const auto ntensors = base.size(); + const auto ndim = counter.size(); + std::copy(base.begin(), base.end(), ptrs); + for (const auto dim : c10::irange(ndim)) { + int64_t value = counter[dim]; + for (const auto arg : c10::irange(ntensors)) { + ptrs[arg] += value * strides[dim * ntensors + arg]; + } + } +} + +inline void serial_for_each( + IntArrayRef shape, + IntArrayRef strides, + char** base_ptrs, + size_t ntensors, + typename TensorIteratorBase::loop2d_t loop, + Range range) { + const auto ndim = shape.size(); + TORCH_INTERNAL_ASSERT_DEBUG_ONLY( + strides.size() == ntensors * std::max(size_t{2}, ndim)); + + if (ndim <= 1) { + if (range.begin == 0) { + loop(base_ptrs, strides.data(), range.size(), 1); + } else { + c10::SmallBuffer ptrs(ntensors); + get_data_ptrs(ptrs.data(), {base_ptrs, ntensors}, strides, {range.begin}); + loop(ptrs.data(), strides.data(), range.size(), 1); + } + } else { + c10::SmallBuffer ptrs(ntensors); + auto counter = DimCounter(shape, range); + while (!counter.is_done()) { + get_data_ptrs( + ptrs.data(), {base_ptrs, ntensors}, strides, counter.values); + auto step = counter.max_2d_step(); + loop(ptrs.data(), strides.data(), step[0], step[1]); + counter.increment(step); + } + } +} + +} // namespace internal +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorNames.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorNames.h new file mode 100644 index 0000000000000000000000000000000000000000..616efc14d2599d7f1a9f73f04fdf960e05bfcf2e --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorNames.h @@ -0,0 +1,75 @@ +#pragma once + +#include + +namespace at::namedinference { + +// TensorName and TensorNames are wrappers around Dimname and DimnameList +// that contain helper functions to make writing name inference rules easier. +// +// A TensorName represents a Dimname associated with some DimnameList (from a +// Tensor). This encapsulates all the information that is needed to check if +// names *match* and to *unify* names. +// +// Definition: Two names in two tensors *match* if they are equal, or if at +// least one of them is a wildcard that can be *refined* to the other name. +// +// Definition: unify(name, other) fails if the names do not match. Otherwise, +// it returns the most refined of name and other. +// +// Here is an example of checking if two names match. +// tensor: Tensor[A, None] +// other: Tensor[A] +// +// Let's say we wish to check if tensor.names[-1] matches other.names[-1]. +// None (in tensor) cannot match A (in other) because if the None were refined +// to A, `tensor` would have duplicate names [A, A]. Therefore we need to check +// tensor.names [A, None] for the existence of A. +struct TORCH_API TensorName { + explicit TensorName(ArrayRef origin, int origin_idx) + : origin_(origin), + name_(origin[maybe_wrap_dim( + origin_idx, + static_cast(origin.size()))]), + origin_idx_(origin_idx) {} + + // op_name is only used for error reporting. + const TensorName& unify(const TensorName& other, const char* op_name) const; + Dimname toDimname() const; + + private: + ArrayRef origin_; + Dimname name_; + int origin_idx_; // A named tensor can have at most 64 dims. + + TORCH_API friend std::ostream& operator<<( + std::ostream& out, + const TensorName& tensorname); +}; + +using TensorNameVec = SmallVector; + +struct TORCH_API TensorNames { + explicit TensorNames(ArrayRef names); + + // Create TensorNames from names[start:end]. Each individual TensorName stores + // `names`, NOT names[start:end], because the original tensor's names are + // `names`. + explicit TensorNames(ArrayRef names, int64_t start, int64_t end); + + // op_name is only used for error reporting. + TensorNames& unifyFromRightInplace( + const TensorNames& other, + const char* op_name = "unify"); + void checkUnique(const char* op_name) const; + + void append(TensorName name); + std::vector toDimnameVec() const; + + private: + explicit TensorNames(TensorNameVec&& names) : names_(std::move(names)){}; + + TensorNameVec names_; +}; + +} // namespace at::namedinference diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOperators.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOperators.h new file mode 100644 index 0000000000000000000000000000000000000000..7567af4cbfe466843b1d48d78ffd259035cd62dc --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOperators.h @@ -0,0 +1,51 @@ +#pragma once + +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +namespace at { + +#define AT_FORALL_BINARY_OPS(_) \ + _(+, x.add(y), y.add(x)) \ + _(*, x.mul(y), y.mul(x)) \ + _(-, \ + x.sub(y), \ + ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).sub_(y)) \ + _(/, \ + x.div(y), \ + ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).div_(y)) \ + _(%, \ + x.remainder(y), \ + ::at::empty_like(y, at::MemoryFormat::Preserve).fill_(x).remainder_(y)) \ + _(&, x.bitwise_and(y), y.bitwise_and(x)) \ + _(|, x.bitwise_or(y), y.bitwise_or(x)) \ + _(^, x.bitwise_xor(y), y.bitwise_xor(x)) \ + _(<, x.lt(y), y.gt(x)) \ + _(<=, x.le(y), y.ge(x)) \ + _(>, x.gt(y), y.lt(x)) \ + _(>=, x.ge(y), y.le(x)) \ + _(==, x.eq(y), y.eq(x)) \ + _(!=, x.ne(y), y.ne(x)) + +#define DEFINE_OPERATOR(op, body, reverse_scalar_body) \ + static inline Tensor operator op(const Tensor& x, const Tensor& y) { \ + return body; \ + } \ + static inline Tensor operator op(const Tensor& x, const Scalar& y) { \ + return body; \ + } \ + static inline Tensor operator op(const Scalar& x, const Tensor& y) { \ + return reverse_scalar_body; \ + } + +AT_FORALL_BINARY_OPS(DEFINE_OPERATOR) +#undef DEFINE_OPERATOR +#undef AT_FORALL_BINARY_OPS + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOptions.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOptions.h new file mode 100644 index 0000000000000000000000000000000000000000..b3edba8efdf726cea92059cb01e34ee25206482c --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorOptions.h @@ -0,0 +1,2 @@ +#pragma once +#include diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorSubclassLikeUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorSubclassLikeUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..a9a0b4ecdcf8b9e323d41f0b39941528a2f0b0cd --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorSubclassLikeUtils.h @@ -0,0 +1,86 @@ +#pragma once +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +namespace at { + +// Note [Tensor-subclass-like Tensors] +// Tensor-subclass-like is defined as: +// - a Tensor subclass (via __torch_dispatch__ in Python or extending +// TensorImpl in C++) +// - anything else that shares the same perils as Tensor subclasses. +// For example, many Tensor subclasses do not have storage and meta Tensors +// do not have storage either, so meta Tensors belong here. +// +// We should ensure that PyTorch internals supports Tensor-subclass-like +// objects. In particular, Tensor-subclass-like objects struggle with two +// classes of operations that are problematic for Tensor subclasses: +// 1. Because some Tensor subclasses do not have storage, .item() or +// .data_ptr() calls are not good. +// 2. Certain in-place operations can eliminate the typing of the Tensor +// subclass. For example: +// >>> torch.zeros(input.sizes(), grad.options()).diag().copy_(input) +// If input is a Tensor subclass, then the above ends up either erroring out +// or returning a regular non-Tensor-subclass Tensor! + +constexpr auto kFunctorchWrappedTensors = DispatchKeySet( + {DispatchKey::FuncTorchGradWrapper, + DispatchKey::FuncTorchBatched, + DispatchKey::Functionalize}); + +constexpr auto kTensorSubclassLike = + kFunctorchWrappedTensors | + DispatchKeySet( + {// WARNING: DO NOT put combined backend component + functionality keys + // here, you will incorrectly always match on the functionality key + // no matter the backend component + DispatchKey::Batched, + DispatchKey::Sparse, + DispatchKey::SparseCsr, + DispatchKey::Python}) | + DispatchKeySet(BackendComponent::MetaBit); + +inline bool isTensorSubclassLike(const Tensor& tensor) { + if (c10::impl::dispatch_mode_enabled()) + return true; + auto key_set = tensor.unsafeGetTensorImpl()->key_set(); + return !(key_set & kTensorSubclassLike).empty(); +} + +inline bool areAnyTensorSubclassLike(TensorList tensors) { + if (c10::impl::dispatch_mode_enabled()) + return true; + return std::any_of(tensors.begin(), tensors.end(), isTensorSubclassLike); +} + +inline bool areAnyOptionalTensorSubclassLike( + const c10::List>& tensors) { + if (c10::impl::dispatch_mode_enabled()) + return true; + return std::any_of( + tensors.begin(), tensors.end(), [](const optional& opt_tensor) { + return ( + opt_tensor.has_value() && isTensorSubclassLike(opt_tensor.value())); + }); +} + +// Helper function to deal testing truthfulness of a scalar tensor +// in a Composite Compliant manner. +// NOTE: This function expects a scalar tensor of boolean dtype. +// Eg. +// Non-Composite Compliant Pattern : (t == 0).all().item() +// Composite Compliant Patter : is_salar_tensor_true((t == 0).all()) +inline bool is_scalar_tensor_true(const Tensor& t) { + TORCH_INTERNAL_ASSERT(t.dim() == 0) + TORCH_INTERNAL_ASSERT(t.scalar_type() == kBool) + return at::equal(t, t.new_ones({}, t.options())); +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TensorUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..4615ab50606ee963c48c5673c93196b4188aa9b2 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TensorUtils.h @@ -0,0 +1,190 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include + +// These functions are NOT in Utils.h, because this file has a dep on Tensor.h + +#define TORCH_CHECK_TENSOR_ALL(cond, ...) \ + TORCH_CHECK((cond)._is_all_true().item(), __VA_ARGS__); + +namespace at { + +// The following are utility functions for checking that arguments +// make sense. These are particularly useful for native functions, +// which do NO argument checking by default. + +struct TORCH_API TensorArg { + // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members) + const Tensor& tensor; + const char* name; + int pos; // 1-indexed + TensorArg(const Tensor& tensor, const char* name, int pos) + : tensor(tensor), name(name), pos(pos) {} + // Try to mitigate any possibility of dangling reference to temporaries. + // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved) + TensorArg(Tensor&& tensor, const char* name, int pos) = delete; + const Tensor* operator->() const { + return &tensor; + } + const Tensor& operator*() const { + return tensor; + } +}; + +struct TORCH_API TensorGeometryArg { + TensorGeometry tensor; + const char* name; + int pos; // 1-indexed + /* implicit */ TensorGeometryArg(TensorArg arg) + : tensor(TensorGeometry{arg.tensor}), name(arg.name), pos(arg.pos) {} + TensorGeometryArg(TensorGeometry tensor, const char* name, int pos) + : tensor(std::move(tensor)), name(name), pos(pos) {} + const TensorGeometry* operator->() const { + return &tensor; + } + const TensorGeometry& operator*() const { + return tensor; + } +}; + +// A string describing which function did checks on its input +// arguments. +// TODO: Consider generalizing this into a call stack. +using CheckedFrom = const char*; + +// The undefined convention: singular operators assume their arguments +// are defined, but functions which take multiple tensors will +// implicitly filter out undefined tensors (to make it easier to perform +// tests which should apply if the tensor is defined, and should not +// otherwise.) +// +// NB: This means that the n-ary operators take lists of TensorArg, +// not TensorGeometryArg, because the Tensor to TensorGeometry +// conversion will blow up if you have undefined tensors. + +TORCH_API std::ostream& operator<<( + std::ostream& out, + const TensorGeometryArg& t); +TORCH_API void checkDim( + CheckedFrom c, + const Tensor& tensor, + const char* name, + int pos, // 1-indexed + int64_t dim); +TORCH_API void checkDim(CheckedFrom c, const TensorGeometryArg& t, int64_t dim); +// NB: this is an inclusive-exclusive range +TORCH_API void checkDimRange( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim_start, + int64_t dim_end); +TORCH_API void checkSameDim( + CheckedFrom c, + const TensorGeometryArg& t1, + const TensorGeometryArg& t2); +TORCH_API void checkContiguous(CheckedFrom c, const TensorGeometryArg& t); +TORCH_API void checkAllContiguous(CheckedFrom c, at::ArrayRef ts); +TORCH_API void checkSize( + CheckedFrom c, + const TensorGeometryArg& t, + IntArrayRef sizes); +TORCH_API void checkSize_symint( + CheckedFrom c, + const TensorGeometryArg& t, + c10::SymIntArrayRef sizes); +TORCH_API void checkSize( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim, + int64_t size); +TORCH_API void checkSize_symint( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t dim, + const c10::SymInt& size); +TORCH_API void checkNumel( + CheckedFrom c, + const TensorGeometryArg& t, + int64_t numel); +TORCH_API void checkSameNumel( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +TORCH_API void checkAllSameNumel(CheckedFrom c, ArrayRef tensors); +TORCH_API void checkScalarType(CheckedFrom c, const TensorArg& t, ScalarType s); +TORCH_API void checkScalarTypes( + CheckedFrom c, + const TensorArg& t, + at::ArrayRef l); +TORCH_API void checkSameGPU( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +TORCH_API void checkAllSameGPU(CheckedFrom c, ArrayRef tensors); +TORCH_API void checkSameType( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +TORCH_API void checkAllSameType(CheckedFrom c, ArrayRef tensors); +TORCH_API void checkSameSize( + CheckedFrom c, + const TensorArg& t1, + const TensorArg& t2); +TORCH_API void checkAllSameSize(CheckedFrom c, ArrayRef tensors); +TORCH_API void checkDefined(CheckedFrom c, const TensorArg& t); +TORCH_API void checkAllDefined(CheckedFrom c, at::ArrayRef t); + +// FixMe: does TensorArg slow things down? +TORCH_API void checkBackend( + CheckedFrom c, + at::ArrayRef t, + at::Backend backend); + +TORCH_API void checkDeviceType( + CheckedFrom c, + at::ArrayRef tensors, + at::DeviceType device_type); + +TORCH_API void checkLayout(CheckedFrom c, const Tensor& t, Layout layout); + +TORCH_API void checkLayout( + CheckedFrom c, + at::ArrayRef tensors, + at::Layout layout); + +// Methods for getting data_ptr if tensor is defined +TORCH_API void* maybe_data_ptr(const Tensor& tensor); +TORCH_API void* maybe_data_ptr(const TensorArg& tensor); + +TORCH_API void check_dim_size( + const Tensor& tensor, + int64_t dim, + int64_t dim_size, + int64_t size); + +namespace detail { +TORCH_API std::vector defaultStrides(IntArrayRef sizes); + +TORCH_API c10::optional> computeStride( + IntArrayRef oldshape, + IntArrayRef oldstride, + IntArrayRef newshape); + +TORCH_API c10::optional computeStride( + c10::SymIntArrayRef oldshape, + c10::SymIntArrayRef oldstride, + c10::SymIntArrayRef newshape); + +TORCH_API c10::optional computeStride( + IntArrayRef oldshape, + IntArrayRef oldstride, + const DimVector& newshape); + +} // namespace detail +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ThreadLocalPythonObjects.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ThreadLocalPythonObjects.h new file mode 100644 index 0000000000000000000000000000000000000000..c45de86db3abeffe22cb8db559f602d88b35be9c --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ThreadLocalPythonObjects.h @@ -0,0 +1,21 @@ +#pragma once + +#include +#include +#include + +namespace at::impl { + +struct TORCH_API ThreadLocalPythonObjects { + static void set(const std::string& key, std::shared_ptr value); + static const std::shared_ptr& get(const std::string& key); + static bool contains(const std::string& key); + + static const ThreadLocalPythonObjects& get_state(); + static void set_state(ThreadLocalPythonObjects state); + + private: + std::unordered_map> obj_dict_; +}; + +} // namespace at::impl diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TracerMode.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TracerMode.h new file mode 100644 index 0000000000000000000000000000000000000000..59bf0bdd2b650dfebc51c873f0e60cd56d654e93 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TracerMode.h @@ -0,0 +1,132 @@ +#pragma once + +#include +#include +#include + +// NOTE [Tracing Mode Switches] +// +// Historically, tracing function was controlled by two switches: +// +// - `AutoDispatchBelowADInplaceOrView` guard +// +// Tracing function used to be script-generated inside `VariableType_*.cpp` +// kernels, sharing the same `Autograd` dispatch key with autograd function. +// Therefore, before tracing function was moved out of VariableType, +// `AutoDispatchBelowADInplaceOrView` guard can also disable tracing as a +// side effect of disabling `Autograd` dispatching. +// +// - `setTracingState()` API in `torch/csrc/jit/frontend/tracer.h` +// +// It stores tracing data in a `TracingState` object in TLS. If the +// `TracingState` object in TLS is `null`, then tracing is paused. +// +// The `TracingState` object is created in `tracer::trace()` - the main +// entrance of tracing function. It's temporarily set to `null` inside +// generated VariableType (now TraceType) to bypass tracing for intermediate +// ops (ops being called by other ops). After the intermediate op call +// finishes it's set back to the original `TracingState` object. +// +// The `TracingState` obect in TLS can also be read/written via its Python +// binding in `python_tracer.cpp`, and `get/setTracingState()` C++ APIs, +// which are also exposed as `TORCH_API`. +// +// Two new switches were introduced since tracing function was moved out of +// VariableType: +// +// - `tracer::impl::set_dispatch_enabled()` API +// +// Unlike the special `Autograd` dispatch key which is included in dispatch +// key set by default, `Tracer` dispatch key is off by default. The +// dispatching switch can be toggled via this new API. +// +// - `tracer::impl::NoTracerDispatchMode` guard +// +// It's used to cover the old semantics of `AutoDispatchBelowADInplaceOrView` +// after tracing was moved out of VariableType. +// +// Before tracing function was moved out of VariableType, tracing was enabled +// when the following conditions are satisfied: +// +// 1) `TracingState` object in TLS != null; +// - Either inside the execution scope of `tracer::trace()`, or +// - Eagerly called `setTracingState()` with non-null object. +// 2) Not inside `AutoDispatchBelowADInplaceOrView` scope; +// +// After: +// +// 1) `TracingState` object in TLS != null; +// 2) Has called `tracer::impl::set_dispatch_enabled(true)`; +// 3) Not inside `tracer::impl::NonDispatchGuard` scope; +// +// [TODOs] +// +// - `setTracingState()` v.s. `tracer::impl::set_dispatch_enabled()` +// +// Currently `set_dispatch_enabled()` is set/unset inside `setTracingState()` +// to keep the semantics exactly the same as before - it's confusing to keep +// both switches, though. We should consider simplifying/limiting the exposed +// `setTracingState()` Python/C++ APIs (and other APIs calling it) so that +// these two can be unified. +// +// - `AutoDispatchBelowADInplaceOrView` v.s. +// `tracer::impl::NoTracerDispatchMode` +// +// We don't need to always set both guards together to keep semantics +// unchanged. For the follow use cases of `AutoDispatchBelowADInplaceOrView` +// we don't need set the new tracer guard: +// +// * Script-generated VariableType kernels. The guard is not necessary as +// tracing is already disabled explicitly by `setTracingState(null)` in +// generated TraceType kernels - we could keep it as is or use the new guard +// instead. +// +// * Custom ops. Will be handled by fallback kernel for `Tracer`. +// +// * Functions that are not likely to be called in tracing context (no python +// binding / not an operator), e.g.: all mobile forward() wrappers, test +// binaries, and etc. +// +// * Where new threads are spawned, e.g.: ATen/native/ConvolutionMM2d.cpp. +// It's not necessary as tracing is off by default. +// +// For the rest of cases we might need have both: +// +// * Functions that might be reachable from eager mode python (especially +// factory methods), e.g.: +// `internal_new_from_data()` in `torch/csrc/utils/tensor_new.cpp`. +// Without the new guard it will add `aten::empty` to the traced graph. +// +// * Some manually maintained functions, e.g.: +// `torch/csrc/autograd/VariableTypeManual.cpp`. +// Set the new guard if it's not obvious whether `setTracingState(null)` +// has been called before it reaches the `AutoDispatchBelowADInplaceOrView` +// guard. +// +// We might need tweak the usage of the new guard to optimize/fix things. +// It should only affect the correctness of tracing function, because the +// guard is essentially no-op when the master `setTracingState()` switch is +// off. + +// TODO: move this from `at::` to `jit::torch::` after +// `aten/src/ATen/cpp_custom_type_hack.h` is removed. + +namespace at::tracer::impl { + +static inline bool is_dispatch_enabled() { + return c10::impl::tls_is_dispatch_key_included(at::DispatchKey::Tracer) && + !c10::impl::tls_is_dispatch_key_excluded(at::DispatchKey::Tracer); +} + +static inline void set_dispatch_enabled(bool enabled) { + TORCH_INTERNAL_ASSERT( + !c10::impl::tls_is_dispatch_key_excluded(at::DispatchKey::Tracer), + "Cannot enable tracing within the scope of NoTracerDispatchMode!"); + c10::impl::tls_set_dispatch_key_included(at::DispatchKey::Tracer, enabled); +} + +struct NoTracerDispatchMode { + c10::impl::ExcludeDispatchKeyGuard guard_{at::DispatchKey::Tracer}; +}; + +} // namespace at::tracer::impl diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/TypeDefault.h b/venv/lib/python3.10/site-packages/torch/include/ATen/TypeDefault.h new file mode 100644 index 0000000000000000000000000000000000000000..0361b9d97d9e7148a5eeb5c9ffa09e815723beb4 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/TypeDefault.h @@ -0,0 +1,30 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace c10 { +struct Storage; +} + +namespace at { + +class Tensor; +using TensorList = ArrayRef; + +class Context; +struct Generator; + +struct Quantizer; +// This is temporary typedef to enable Quantizer in aten native function API +// we'll remove them when we are actually exposing Quantizer class +// to frontend +using ConstQuantizerPtr = const c10::intrusive_ptr&; + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/Version.h b/venv/lib/python3.10/site-packages/torch/include/ATen/Version.h new file mode 100644 index 0000000000000000000000000000000000000000..706da58a5da01c35fda7f2c6374c8f5868f1b642 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/Version.h @@ -0,0 +1,18 @@ +#include + +namespace at { + +/// Returns a detailed string describing the configuration PyTorch. +TORCH_API std::string show_config(); + +TORCH_API std::string get_mkl_version(); + +TORCH_API std::string get_mkldnn_version(); + +TORCH_API std::string get_openmp_version(); + +TORCH_API std::string get_cxx_flags(); + +TORCH_API std::string get_cpu_capability(); + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/VmapGeneratedPlumbing.h b/venv/lib/python3.10/site-packages/torch/include/ATen/VmapGeneratedPlumbing.h new file mode 100644 index 0000000000000000000000000000000000000000..31c73b1a8b11e0dd14fc110572caba85eb75c69b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/VmapGeneratedPlumbing.h @@ -0,0 +1,32614 @@ + +#pragma once +#include +#include + +namespace at { namespace functorch { + +template +at::Tensor _cast_Byte_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Byte::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Char_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Char::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Double_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Double::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Float_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Float::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Int_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Int::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Long_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Long::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Short_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Short::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cast_Half_generated_plumbing(const at::Tensor & self, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_cast_Half::call(self, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _backward_generated_plumbing(const at::Tensor & self, at::TensorList inputs, const c10::optional & gradient, c10::optional retain_graph, bool create_graph) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(inputs, cur_level) && !isBatchedAtLevel(gradient, cur_level)) { + return at::_ops::_backward::call(self, inputs, gradient, retain_graph, create_graph); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional gradient_value; + optional gradient_bdim; + if (gradient) { + std::tie(gradient_value, gradient_bdim) = unwrapTensorAtLevel(gradient.value(), cur_level); + } + batch_rule(self_value, self_bdim, inputs, gradient_value, gradient_bdim, retain_graph, create_graph); +} +template +void set_data_generated_plumbing(at::Tensor & self, const at::Tensor & new_data) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(new_data, cur_level)) { + return at::_ops::set_data::call(self, new_data); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor new_data_value; + optional new_data_bdim; + std::tie(new_data_value, new_data_bdim) = unwrapTensorAtLevel(new_data, cur_level); + batch_rule(self_value, self_bdim, new_data_value, new_data_bdim); +} +template +at::Tensor data_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::data::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & requires_grad__generated_plumbing(at::Tensor & self, bool requires_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::requires_grad_::call(self, requires_grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, requires_grad); + return self; +} +template +void retain_grad_generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::retain_grad::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); +} +template +at::Tensor _fw_primal_generated_plumbing(const at::Tensor & self, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fw_primal::call(self, level); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, level); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _make_dual_generated_plumbing(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(primal, cur_level) && !isBatchedAtLevel(tangent, cur_level)) { + return at::_ops::_make_dual::call(primal, tangent, level); + } + Tensor primal_value; + optional primal_bdim; + std::tie(primal_value, primal_bdim) = unwrapTensorAtLevel(primal, cur_level); + Tensor tangent_value; + optional tangent_bdim; + std::tie(tangent_value, tangent_bdim) = unwrapTensorAtLevel(tangent, cur_level); + auto results = batch_rule(primal_value, primal_bdim, tangent_value, tangent_bdim, level); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _unpack_dual_generated_plumbing(const at::Tensor & dual, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dual, cur_level)) { + return at::_ops::_unpack_dual::call(dual, level); + } + Tensor dual_value; + optional dual_bdim; + std::tie(dual_value, dual_bdim) = unwrapTensorAtLevel(dual, cur_level); + auto results = batch_rule(dual_value, dual_bdim, level); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _new_zeros_with_same_feature_meta_generated_plumbing(const at::Tensor & self, const at::Tensor & other, int64_t self_num_batch_dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_new_zeros_with_same_feature_meta::call(self, other, self_num_batch_dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, self_num_batch_dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rename_generated_plumbing(const at::Tensor & self, c10::optional names) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rename::call(self, names); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, names); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor align_to_generated_plumbing(const at::Tensor & self, at::DimnameList names) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::align_to::call(self, names); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, names); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor align_to_ellipsis_idx_generated_plumbing(const at::Tensor & self, at::DimnameList order, int64_t ellipsis_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::align_to_ellipsis_idx::call(self, order, ellipsis_idx); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, order, ellipsis_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor align_as_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::align_as::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector align_tensors_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::align_tensors::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _assert_async_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_assert_async::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); +} +template +void _assert_async_msg_generated_plumbing(const at::Tensor & self, c10::string_view assert_msg) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_assert_async_msg::call(self, assert_msg); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, assert_msg); +} +template +at::Tensor _functional_assert_scalar_generated_plumbing(const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dep_token, cur_level)) { + return at::_ops::_functional_assert_scalar::call(self, assert_msg, dep_token); + } + Tensor dep_token_value; + optional dep_token_bdim; + std::tie(dep_token_value, dep_token_bdim) = unwrapTensorAtLevel(dep_token, cur_level); + auto results = batch_rule(self, assert_msg, dep_token_value, dep_token_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _functional_assert_async_msg_generated_plumbing(const at::Tensor & self, c10::string_view assert_msg, const at::Tensor & dep_token) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(dep_token, cur_level)) { + return at::_ops::_functional_assert_async_msg::call(self, assert_msg, dep_token); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor dep_token_value; + optional dep_token_bdim; + std::tie(dep_token_value, dep_token_bdim) = unwrapTensorAtLevel(dep_token, cur_level); + auto results = batch_rule(self_value, self_bdim, assert_msg, dep_token_value, dep_token_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _assert_tensor_metadata_generated_plumbing(const at::Tensor & a, at::OptionalSymIntArrayRef size, at::OptionalSymIntArrayRef stride, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(a, cur_level)) { + return at::_ops::_assert_tensor_metadata::call(a, size, stride, dtype); + } + Tensor a_value; + optional a_bdim; + std::tie(a_value, a_bdim) = unwrapTensorAtLevel(a, cur_level); + batch_rule(a_value, a_bdim, size, stride, dtype); +} +template +at::Tensor _functional_sym_constrain_range_generated_plumbing(const at::Scalar & size, c10::optional min, c10::optional max, const at::Tensor & dep_token) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dep_token, cur_level)) { + return at::_ops::_functional_sym_constrain_range::call(size, min, max, dep_token); + } + Tensor dep_token_value; + optional dep_token_bdim; + std::tie(dep_token_value, dep_token_bdim) = unwrapTensorAtLevel(dep_token, cur_level); + auto results = batch_rule(size, min, max, dep_token_value, dep_token_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _functional_sym_constrain_range_for_size_generated_plumbing(const at::Scalar & size, c10::optional min, c10::optional max, const at::Tensor & dep_token) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dep_token, cur_level)) { + return at::_ops::_functional_sym_constrain_range_for_size::call(size, min, max, dep_token); + } + Tensor dep_token_value; + optional dep_token_bdim; + std::tie(dep_token_value, dep_token_bdim) = unwrapTensorAtLevel(dep_token, cur_level); + auto results = batch_rule(size, min, max, dep_token_value, dep_token_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor refine_names_generated_plumbing(const at::Tensor & self, at::DimnameList names) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::refine_names::call(self, names); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, names); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _cudnn_ctc_loss_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level)) { + return at::_ops::_cudnn_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths, target_lengths, blank, deterministic, zero_infinity); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _cudnn_ctc_loss_Tensor_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool deterministic, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level) && !isBatchedAtLevel(input_lengths, cur_level) && !isBatchedAtLevel(target_lengths, cur_level)) { + return at::_ops::_cudnn_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, deterministic, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + Tensor input_lengths_value; + optional input_lengths_bdim; + std::tie(input_lengths_value, input_lengths_bdim) = unwrapTensorAtLevel(input_lengths, cur_level); + Tensor target_lengths_value; + optional target_lengths_bdim; + std::tie(target_lengths_value, target_lengths_bdim) = unwrapTensorAtLevel(target_lengths, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths_value, input_lengths_bdim, target_lengths_value, target_lengths_bdim, blank, deterministic, zero_infinity); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _cudnn_rnn_flatten_weight_generated_plumbing(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight_arr, cur_level)) { + return at::_ops::_cudnn_rnn_flatten_weight::call(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); + } + + auto results = batch_rule(weight_arr, weight_stride0, input_size, mode, hidden_size, proj_size, num_layers, batch_first, bidirectional); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _cudnn_rnn_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const c10::optional & weight_buf, const at::Tensor & hx, const c10::optional & cx, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(weight_buf, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(dropout_state, cur_level)) { + return at::_ops::_cudnn_rnn::call(input, weight, weight_stride0, weight_buf, hx, cx, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + optional weight_buf_value; + optional weight_buf_bdim; + if (weight_buf) { + std::tie(weight_buf_value, weight_buf_bdim) = unwrapTensorAtLevel(weight_buf.value(), cur_level); + } + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight, weight_stride0, weight_buf_value, weight_buf_bdim, hx_value, hx_bdim, cx_value, cx_bdim, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple> _cudnn_rnn_backward_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(weight_buf, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(dropout_state, cur_level) && !isBatchedAtLevel(reserve, cur_level)) { + return at::_ops::_cudnn_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_buf_value; + optional weight_buf_bdim; + std::tie(weight_buf_value, weight_buf_bdim) = unwrapTensorAtLevel(weight_buf, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor reserve_value; + optional reserve_bdim; + std::tie(reserve_value, reserve_bdim) = unwrapTensorAtLevel(reserve, cur_level); + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional grad_output_value; + optional grad_output_bdim; + if (grad_output) { + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight, weight_stride0, weight_buf_value, weight_buf_bdim, hx_value, hx_bdim, cx_value, cx_bdim, output_value, output_bdim, grad_output_value, grad_output_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim, reserve_value, reserve_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _fused_dropout_generated_plumbing(const at::Tensor & self, double p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fused_dropout::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, generator); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _masked_scale_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, double scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_masked_scale::call(self, mask, scale); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, scale); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple native_dropout_generated_plumbing(const at::Tensor & input, double p, c10::optional train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::native_dropout::call(input, p, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, p, train); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor native_dropout_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & mask, double scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::native_dropout_backward::call(grad_output, mask, scale); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, mask_value, mask_bdim, scale); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _sobol_engine_draw_generated_plumbing(const at::Tensor & quasi, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(quasi, cur_level) && !isBatchedAtLevel(sobolstate, cur_level)) { + return at::_ops::_sobol_engine_draw::call(quasi, n, sobolstate, dimension, num_generated, dtype); + } + Tensor quasi_value; + optional quasi_bdim; + std::tie(quasi_value, quasi_bdim) = unwrapTensorAtLevel(quasi, cur_level); + Tensor sobolstate_value; + optional sobolstate_bdim; + std::tie(sobolstate_value, sobolstate_bdim) = unwrapTensorAtLevel(sobolstate, cur_level); + auto results = batch_rule(quasi_value, quasi_bdim, n, sobolstate_value, sobolstate_bdim, dimension, num_generated, dtype); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor & _sobol_engine_ff__generated_plumbing(at::Tensor & self, int64_t n, const at::Tensor & sobolstate, int64_t dimension, int64_t num_generated) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(sobolstate, cur_level)) { + return at::_ops::_sobol_engine_ff_::call(self, n, sobolstate, dimension, num_generated); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor sobolstate_value; + optional sobolstate_bdim; + std::tie(sobolstate_value, sobolstate_bdim) = unwrapTensorAtLevel(sobolstate, cur_level); + batch_rule(self_value, self_bdim, n, sobolstate_value, sobolstate_bdim, dimension, num_generated); + return self; +} +template +at::Tensor & _sobol_engine_scramble__generated_plumbing(at::Tensor & self, const at::Tensor & ltm, int64_t dimension) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(ltm, cur_level)) { + return at::_ops::_sobol_engine_scramble_::call(self, ltm, dimension); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor ltm_value; + optional ltm_bdim; + std::tie(ltm_value, ltm_bdim) = unwrapTensorAtLevel(ltm, cur_level); + batch_rule(self_value, self_bdim, ltm_value, ltm_bdim, dimension); + return self; +} +template +at::Tensor & _sobol_engine_initialize_state__generated_plumbing(at::Tensor & self, int64_t dimension) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sobol_engine_initialize_state_::call(self, dimension); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dimension); + return self; +} +template +at::Tensor _reshape_from_tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & shape) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(shape, cur_level)) { + return at::_ops::_reshape_from_tensor::call(self, shape); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor shape_value; + optional shape_bdim; + std::tie(shape_value, shape_bdim) = unwrapTensorAtLevel(shape, cur_level); + auto results = batch_rule(self_value, self_bdim, shape_value, shape_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _shape_as_tensor_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_shape_as_tensor::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor dropout_generated_plumbing(const at::Tensor & input, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::dropout::call(input, p, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, p, train); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & dropout__generated_plumbing(at::Tensor & self, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::dropout_::call(self, p, train); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, train); + return self; +} +template +at::Tensor feature_dropout_generated_plumbing(const at::Tensor & input, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::feature_dropout::call(input, p, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, p, train); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & feature_dropout__generated_plumbing(at::Tensor & self, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::feature_dropout_::call(self, p, train); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, train); + return self; +} +template +at::Tensor alpha_dropout_generated_plumbing(const at::Tensor & input, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::alpha_dropout::call(input, p, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, p, train); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & alpha_dropout__generated_plumbing(at::Tensor & self, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::alpha_dropout_::call(self, p, train); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, train); + return self; +} +template +at::Tensor feature_alpha_dropout_generated_plumbing(const at::Tensor & input, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::feature_alpha_dropout::call(input, p, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, p, train); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & feature_alpha_dropout__generated_plumbing(at::Tensor & self, double p, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::feature_alpha_dropout_::call(self, p, train); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, train); + return self; +} +template +at::Tensor abs_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::abs::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & abs__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::abs_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor absolute_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::absolute::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & absolute__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::absolute_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor angle_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::angle::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_as_real_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_as_real::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_as_complex_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_as_complex::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sgn_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sgn::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sgn__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sgn_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor chalf_generated_plumbing(const at::Tensor & self, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::chalf::call(self, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor real_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::real::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor imag_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::imag::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _conj_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_conj::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conj_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::conj::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _conj_physical_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_conj_physical::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conj_physical_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::conj_physical::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & conj_physical__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::conj_physical_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor resolve_conj_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::resolve_conj::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor resolve_neg_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::resolve_neg::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _neg_view_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_neg_view::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor acos_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::acos::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & acos__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::acos_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arccos_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arccos::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arccos__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arccos_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor avg_pool1d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::avg_pool1d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, ceil_mode, count_include_pad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor adaptive_avg_pool1d_generated_plumbing(const at::Tensor & self, at::IntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_avg_pool1d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple adaptive_max_pool1d_generated_plumbing(const at::Tensor & self, at::IntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_max_pool1d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor add_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::add_Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & add__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::add__Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return self; +} +template +at::Tensor _add_relu_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_add_relu_Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _add_relu__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_add_relu__Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return self; +} +template +at::Tensor _add_relu_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_add_relu_Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _add_relu__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_add_relu__Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, alpha); + return self; +} +template +at::Tensor add_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::add_Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & add__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::add__Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, alpha); + return self; +} +template +at::Tensor addmv_generated_plumbing(const at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat, cur_level) && !isBatchedAtLevel(vec, cur_level)) { + return at::_ops::addmv::call(self, mat, vec, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat_value; + optional mat_bdim; + std::tie(mat_value, mat_bdim) = unwrapTensorAtLevel(mat, cur_level); + Tensor vec_value; + optional vec_bdim; + std::tie(vec_value, vec_bdim) = unwrapTensorAtLevel(vec, cur_level); + auto results = batch_rule(self_value, self_bdim, mat_value, mat_bdim, vec_value, vec_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & addmv__generated_plumbing(at::Tensor & self, const at::Tensor & mat, const at::Tensor & vec, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat, cur_level) && !isBatchedAtLevel(vec, cur_level)) { + return at::_ops::addmv_::call(self, mat, vec, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat_value; + optional mat_bdim; + std::tie(mat_value, mat_bdim) = unwrapTensorAtLevel(mat, cur_level); + Tensor vec_value; + optional vec_bdim; + std::tie(vec_value, vec_bdim) = unwrapTensorAtLevel(vec, cur_level); + batch_rule(self_value, self_bdim, mat_value, mat_bdim, vec_value, vec_bdim, beta, alpha); + return self; +} +template +at::Tensor addr_generated_plumbing(const at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(vec1, cur_level) && !isBatchedAtLevel(vec2, cur_level)) { + return at::_ops::addr::call(self, vec1, vec2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor vec1_value; + optional vec1_bdim; + std::tie(vec1_value, vec1_bdim) = unwrapTensorAtLevel(vec1, cur_level); + Tensor vec2_value; + optional vec2_bdim; + std::tie(vec2_value, vec2_bdim) = unwrapTensorAtLevel(vec2, cur_level); + auto results = batch_rule(self_value, self_bdim, vec1_value, vec1_bdim, vec2_value, vec2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & addr__generated_plumbing(at::Tensor & self, const at::Tensor & vec1, const at::Tensor & vec2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(vec1, cur_level) && !isBatchedAtLevel(vec2, cur_level)) { + return at::_ops::addr_::call(self, vec1, vec2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor vec1_value; + optional vec1_bdim; + std::tie(vec1_value, vec1_bdim) = unwrapTensorAtLevel(vec1, cur_level); + Tensor vec2_value; + optional vec2_bdim; + std::tie(vec2_value, vec2_bdim) = unwrapTensorAtLevel(vec2, cur_level); + batch_rule(self_value, self_bdim, vec1_value, vec1_bdim, vec2_value, vec2_bdim, beta, alpha); + return self; +} +template +at::Tensor affine_grid_generator_generated_plumbing(const at::Tensor & theta, c10::SymIntArrayRef size, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(theta, cur_level)) { + return at::_ops::affine_grid_generator::call(theta, size, align_corners); + } + Tensor theta_value; + optional theta_bdim; + std::tie(theta_value, theta_bdim) = unwrapTensorAtLevel(theta, cur_level); + auto results = batch_rule(theta_value, theta_bdim, size, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor affine_grid_generator_backward_generated_plumbing(const at::Tensor & grad, c10::SymIntArrayRef size, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level)) { + return at::_ops::affine_grid_generator_backward::call(grad, size, align_corners); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + auto results = batch_rule(grad_value, grad_bdim, size, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _is_all_true_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_is_all_true::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _is_any_true_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_is_any_true::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_check_tensor_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_check_tensor::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_functorch_fallback_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_test_functorch_fallback::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor all_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::all_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor all_dims_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::all_dims::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor all_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::all_dimname::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor any_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::any_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor any_dims_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::any_dims::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor any_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::any_dimname::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _dim_arange_generated_plumbing(const at::Tensor & like, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(like, cur_level)) { + return at::_ops::_dim_arange::call(like, dim); + } + Tensor like_value; + optional like_bdim; + std::tie(like_value, like_bdim) = unwrapTensorAtLevel(like, cur_level); + auto results = batch_rule(like_value, like_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argmax_generated_plumbing(const at::Tensor & self, c10::optional dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argmax::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argmin_generated_plumbing(const at::Tensor & self, c10::optional dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argmin::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor acosh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::acosh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & acosh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::acosh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arccosh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arccosh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arccosh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arccosh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor asinh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::asinh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & asinh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::asinh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arcsinh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arcsinh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arcsinh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arcsinh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor atanh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atanh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & atanh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atanh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arctanh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arctanh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arctanh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arctanh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor as_strided_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::as_strided::call(self, size, stride, storage_offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, stride, storage_offset); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor asin_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::asin::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & asin__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::asin_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arcsin_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arcsin::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arcsin__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arcsin_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor atan_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atan::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & atan__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atan_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor arctan_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arctan::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arctan__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::arctan_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor atleast_1d_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atleast_1d::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector atleast_1d_Sequence_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::atleast_1d_Sequence::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor atleast_2d_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atleast_2d::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector atleast_2d_Sequence_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::atleast_2d_Sequence::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor atleast_3d_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::atleast_3d::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector atleast_3d_Sequence_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::atleast_3d_Sequence::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor baddbmm_generated_plumbing(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(batch1, cur_level) && !isBatchedAtLevel(batch2, cur_level)) { + return at::_ops::baddbmm::call(self, batch1, batch2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor batch1_value; + optional batch1_bdim; + std::tie(batch1_value, batch1_bdim) = unwrapTensorAtLevel(batch1, cur_level); + Tensor batch2_value; + optional batch2_bdim; + std::tie(batch2_value, batch2_bdim) = unwrapTensorAtLevel(batch2, cur_level); + auto results = batch_rule(self_value, self_bdim, batch1_value, batch1_bdim, batch2_value, batch2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & baddbmm__generated_plumbing(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(batch1, cur_level) && !isBatchedAtLevel(batch2, cur_level)) { + return at::_ops::baddbmm_::call(self, batch1, batch2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor batch1_value; + optional batch1_bdim; + std::tie(batch1_value, batch1_bdim) = unwrapTensorAtLevel(batch1, cur_level); + Tensor batch2_value; + optional batch2_bdim; + std::tie(batch2_value, batch2_bdim) = unwrapTensorAtLevel(batch2, cur_level); + batch_rule(self_value, self_bdim, batch1_value, batch1_bdim, batch2_value, batch2_bdim, beta, alpha); + return self; +} +template +at::Tensor batch_norm_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps, bool cudnn_enabled) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::batch_norm::call(input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, training, momentum, eps, cudnn_enabled); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_batch_norm_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(var, cur_level)) { + return at::_ops::quantized_batch_norm::call(input, weight, bias, mean, var, eps, output_scale, output_zero_point); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor var_value; + optional var_bdim; + std::tie(var_value, var_bdim) = unwrapTensorAtLevel(var, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, mean_value, mean_bdim, var_value, var_bdim, eps, output_scale, output_zero_point); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _batch_norm_impl_index_backward_generated_plumbing(int64_t impl_index, const at::Tensor & input, const at::Tensor & grad_output, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var_transform, bool train, double eps, ::std::array output_mask, const at::Tensor & reservedSpace) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level) && !isBatchedAtLevel(save_mean, cur_level) && !isBatchedAtLevel(save_var_transform, cur_level) && !isBatchedAtLevel(reservedSpace, cur_level)) { + return at::_ops::_batch_norm_impl_index_backward::call(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor reservedSpace_value; + optional reservedSpace_bdim; + std::tie(reservedSpace_value, reservedSpace_bdim) = unwrapTensorAtLevel(reservedSpace, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + optional save_mean_value; + optional save_mean_bdim; + if (save_mean) { + std::tie(save_mean_value, save_mean_bdim) = unwrapTensorAtLevel(save_mean.value(), cur_level); + } + optional save_var_transform_value; + optional save_var_transform_bdim; + if (save_var_transform) { + std::tie(save_var_transform_value, save_var_transform_bdim) = unwrapTensorAtLevel(save_var_transform.value(), cur_level); + } + auto results = batch_rule(impl_index, input_value, input_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, save_mean_value, save_mean_bdim, save_var_transform_value, save_var_transform_bdim, train, eps, output_mask, reservedSpace_value, reservedSpace_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor bernoulli_generated_plumbing(const at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bernoulli::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bernoulli__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(p, cur_level)) { + return at::_ops::bernoulli__Tensor::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor p_value; + optional p_bdim; + std::tie(p_value, p_bdim) = unwrapTensorAtLevel(p, cur_level); + batch_rule(self_value, self_bdim, p_value, p_bdim, generator); + return self; +} +template +at::Tensor & bernoulli__float_generated_plumbing(at::Tensor & self, double p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bernoulli__float::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, generator); + return self; +} +template +at::Tensor bernoulli_p_generated_plumbing(const at::Tensor & self, double p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bernoulli_p::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bilinear_generated_plumbing(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & weight, const c10::optional & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input1, cur_level) && !isBatchedAtLevel(input2, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::bilinear::call(input1, input2, weight, bias); + } + Tensor input1_value; + optional input1_bdim; + std::tie(input1_value, input1_bdim) = unwrapTensorAtLevel(input1, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input1_value, input1_bdim, input2_value, input2_bdim, weight_value, weight_bdim, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor binary_cross_entropy_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::binary_cross_entropy::call(self, target, weight, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor binary_cross_entropy_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::binary_cross_entropy_backward::call(grad_output, self, target, weight, reduction); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor binary_cross_entropy_with_logits_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, const c10::optional & pos_weight, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(pos_weight, cur_level)) { + return at::_ops::binary_cross_entropy_with_logits::call(self, target, weight, pos_weight, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional pos_weight_value; + optional pos_weight_bdim; + if (pos_weight) { + std::tie(pos_weight_value, pos_weight_bdim) = unwrapTensorAtLevel(pos_weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, pos_weight_value, pos_weight_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bincount_generated_plumbing(const at::Tensor & self, const c10::optional & weights, int64_t minlength) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weights, cur_level)) { + return at::_ops::bincount::call(self, weights, minlength); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weights_value; + optional weights_bdim; + if (weights) { + std::tie(weights_value, weights_bdim) = unwrapTensorAtLevel(weights.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weights_value, weights_bdim, minlength); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_not_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_not::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_not__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_not_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor copysign_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::copysign_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & copysign__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::copysign__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor copysign_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::copysign_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & copysign__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::copysign__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor _lazy_clone_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_lazy_clone::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logical_not_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logical_not::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & logical_not__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logical_not_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor logical_xor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_xor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & logical_xor__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_xor_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor logical_and_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_and::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & logical_and__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_and_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor logical_or_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_or::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & logical_or__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logical_or_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bmm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::bmm::call(self, mat2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector broadcast_tensors_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::broadcast_tensors::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor broadcast_to_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::broadcast_to::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_broadcast_to_generated_plumbing(const at::Tensor & self, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_broadcast_to::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cat_generated_plumbing(const at::ITensorListRef & tensors, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::cat::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cat_names_generated_plumbing(at::TensorList tensors, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::cat_names::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor concat_generated_plumbing(at::TensorList tensors, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::concat::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor concat_names_generated_plumbing(at::TensorList tensors, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::concat_names::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor concatenate_generated_plumbing(at::TensorList tensors, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::concatenate::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor concatenate_names_generated_plumbing(at::TensorList tensors, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::concatenate_names::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor block_diag_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::block_diag::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ceil_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ceil::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & ceil__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ceil_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor chain_matmul_generated_plumbing(at::TensorList matrices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(matrices, cur_level)) { + return at::_ops::chain_matmul::call(matrices); + } + + auto results = batch_rule(matrices); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unsafe_chunk_generated_plumbing(const at::Tensor & self, int64_t chunks, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unsafe_chunk::call(self, chunks, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, chunks, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector chunk_generated_plumbing(const at::Tensor & self, int64_t chunks, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::chunk::call(self, chunks, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, chunks, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector tensor_split_sections_generated_plumbing(const at::Tensor & self, c10::SymInt sections, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tensor_split_sections::call(self, sections, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sections, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector tensor_split_indices_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef indices, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tensor_split_indices::call(self, indices, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector tensor_split_tensor_indices_or_sections_generated_plumbing(const at::Tensor & self, const at::Tensor & tensor_indices_or_sections, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor_indices_or_sections, cur_level)) { + return at::_ops::tensor_split_tensor_indices_or_sections::call(self, tensor_indices_or_sections, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor_indices_or_sections_value; + optional tensor_indices_or_sections_bdim; + std::tie(tensor_indices_or_sections_value, tensor_indices_or_sections_bdim) = unwrapTensorAtLevel(tensor_indices_or_sections, cur_level); + auto results = batch_rule(self_value, self_bdim, tensor_indices_or_sections_value, tensor_indices_or_sections_bdim, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clamp_generated_plumbing(const at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, min, max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clamp_Tensor_generated_plumbing(const at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clamp_Tensor::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional min_value; + optional min_bdim; + if (min) { + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min.value(), cur_level); + } + optional max_value; + optional max_bdim; + if (max) { + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, min_value, min_bdim, max_value, max_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & clamp__generated_plumbing(at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp_::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, min, max); + return self; +} +template +at::Tensor & clamp__Tensor_generated_plumbing(at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clamp__Tensor::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional min_value; + optional min_bdim; + if (min) { + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min.value(), cur_level); + } + optional max_value; + optional max_bdim; + if (max) { + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max.value(), cur_level); + } + batch_rule(self_value, self_bdim, min_value, min_bdim, max_value, max_bdim); + return self; +} +template +at::Tensor clamp_max_generated_plumbing(const at::Tensor & self, const at::Scalar & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp_max::call(self, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clamp_max_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clamp_max_Tensor::call(self, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor max_value; + optional max_bdim; + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max, cur_level); + auto results = batch_rule(self_value, self_bdim, max_value, max_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & clamp_max__generated_plumbing(at::Tensor & self, const at::Scalar & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp_max_::call(self, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, max); + return self; +} +template +at::Tensor & clamp_max__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clamp_max__Tensor::call(self, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor max_value; + optional max_bdim; + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max, cur_level); + batch_rule(self_value, self_bdim, max_value, max_bdim); + return self; +} +template +at::Tensor clamp_min_generated_plumbing(const at::Tensor & self, const at::Scalar & min) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp_min::call(self, min); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, min); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clamp_min_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & min) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level)) { + return at::_ops::clamp_min_Tensor::call(self, min); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor min_value; + optional min_bdim; + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min, cur_level); + auto results = batch_rule(self_value, self_bdim, min_value, min_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & clamp_min__generated_plumbing(at::Tensor & self, const at::Scalar & min) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clamp_min_::call(self, min); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, min); + return self; +} +template +at::Tensor & clamp_min__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & min) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level)) { + return at::_ops::clamp_min__Tensor::call(self, min); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor min_value; + optional min_bdim; + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min, cur_level); + batch_rule(self_value, self_bdim, min_value, min_bdim); + return self; +} +template +at::Tensor clip_generated_plumbing(const at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clip::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, min, max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clip_Tensor_generated_plumbing(const at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clip_Tensor::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional min_value; + optional min_bdim; + if (min) { + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min.value(), cur_level); + } + optional max_value; + optional max_bdim; + if (max) { + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, min_value, min_bdim, max_value, max_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & clip__generated_plumbing(at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clip_::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, min, max); + return self; +} +template +at::Tensor & clip__Tensor_generated_plumbing(at::Tensor & self, const c10::optional & min, const c10::optional & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(min, cur_level) && !isBatchedAtLevel(max, cur_level)) { + return at::_ops::clip__Tensor::call(self, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional min_value; + optional min_bdim; + if (min) { + std::tie(min_value, min_bdim) = unwrapTensorAtLevel(min.value(), cur_level); + } + optional max_value; + optional max_bdim; + if (max) { + std::tie(max_value, max_bdim) = unwrapTensorAtLevel(max.value(), cur_level); + } + batch_rule(self_value, self_bdim, min_value, min_bdim, max_value, max_bdim); + return self; +} +template +at::Tensor complex_generated_plumbing(const at::Tensor & real, const at::Tensor & imag) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(real, cur_level) && !isBatchedAtLevel(imag, cur_level)) { + return at::_ops::complex::call(real, imag); + } + Tensor real_value; + optional real_bdim; + std::tie(real_value, real_bdim) = unwrapTensorAtLevel(real, cur_level); + Tensor imag_value; + optional imag_bdim; + std::tie(imag_value, imag_bdim) = unwrapTensorAtLevel(imag, cur_level); + auto results = batch_rule(real_value, real_bdim, imag_value, imag_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor polar_generated_plumbing(const at::Tensor & abs, const at::Tensor & angle) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(abs, cur_level) && !isBatchedAtLevel(angle, cur_level)) { + return at::_ops::polar::call(abs, angle); + } + Tensor abs_value; + optional abs_bdim; + std::tie(abs_value, abs_bdim) = unwrapTensorAtLevel(abs, cur_level); + Tensor angle_value; + optional angle_bdim; + std::tie(angle_value, angle_bdim) = unwrapTensorAtLevel(angle, cur_level); + auto results = batch_rule(abs_value, abs_bdim, angle_value, angle_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor constant_pad_nd_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef pad, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::constant_pad_nd::call(self, pad, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, pad, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor contiguous_generated_plumbing(const at::Tensor & self, at::MemoryFormat memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::contiguous::call(self, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor convolution_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, transposed, output_padding, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple convolution_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, at::OptionalSymIntArrayRef bias_sizes, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::convolution_backward::call(grad_output, input, weight, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, weight_value, weight_bdim, bias_sizes, stride, padding, dilation, transposed, output_padding, groups, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor convolution_overrideable_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::convolution_overrideable::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, transposed, output_padding, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple convolution_backward_overrideable_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::convolution_backward_overrideable::call(grad_output, input, weight, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, weight_value, weight_bdim, stride, padding, dilation, transposed, output_padding, groups, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _convolution_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_convolution::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled, allow_tf32); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _convolution_deprecated_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, at::IntArrayRef output_padding, c10::SymInt groups, bool benchmark, bool deterministic, bool cudnn_enabled) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_convolution_deprecated::call(input, weight, bias, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, transposed, output_padding, groups, benchmark, deterministic, cudnn_enabled); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _convolution_mode_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_convolution_mode::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _convolution_double_backward_generated_plumbing(const c10::optional & ggI, const c10::optional & ggW, const c10::optional & ggb, const at::Tensor & gO, const at::Tensor & weight, const at::Tensor & self, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ggI, cur_level) && !isBatchedAtLevel(ggW, cur_level) && !isBatchedAtLevel(ggb, cur_level) && !isBatchedAtLevel(gO, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_convolution_double_backward::call(ggI, ggW, ggb, gO, weight, self, stride, padding, dilation, transposed, output_padding, groups, output_mask); + } + Tensor gO_value; + optional gO_bdim; + std::tie(gO_value, gO_bdim) = unwrapTensorAtLevel(gO, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional ggI_value; + optional ggI_bdim; + if (ggI) { + std::tie(ggI_value, ggI_bdim) = unwrapTensorAtLevel(ggI.value(), cur_level); + } + optional ggW_value; + optional ggW_bdim; + if (ggW) { + std::tie(ggW_value, ggW_bdim) = unwrapTensorAtLevel(ggW.value(), cur_level); + } + optional ggb_value; + optional ggb_bdim; + if (ggb) { + std::tie(ggb_value, ggb_bdim) = unwrapTensorAtLevel(ggb.value(), cur_level); + } + auto results = batch_rule(ggI_value, ggI_bdim, ggW_value, ggW_bdim, ggb_value, ggb_bdim, gO_value, gO_bdim, weight_value, weight_bdim, self_value, self_bdim, stride, padding, dilation, transposed, output_padding, groups, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor conv1d_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv1d::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv2d_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv2d::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv3d_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv3d::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv1d_padding_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv1d_padding::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv2d_padding_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv2d_padding::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv3d_padding_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::string_view padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv3d_padding::call(input, weight, bias, stride, padding, dilation, groups); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv_tbc_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_tbc::call(self, weight, bias, pad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, pad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple conv_tbc_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & input, const at::Tensor & weight, const at::Tensor & bias, int64_t pad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_tbc_backward::call(self, input, weight, bias, pad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(self_value, self_bdim, input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, pad); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor conv_transpose1d_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_transpose1d::call(input, weight, bias, stride, padding, output_padding, groups, dilation); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, output_padding, groups, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv_transpose2d_input_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_transpose2d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, output_padding, groups, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv_transpose3d_input_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_transpose3d_input::call(input, weight, bias, stride, padding, output_padding, groups, dilation); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, output_padding, groups, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor copy_generated_plumbing(const at::Tensor & self, const at::Tensor & src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::copy::call(self, src, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & copy__generated_plumbing(at::Tensor & self, const at::Tensor & src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::copy_::call(self, src, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, src_value, src_bdim, non_blocking); + return self; +} +template +at::Tensor _copy_from_generated_plumbing(const at::Tensor & self, const at::Tensor & dst, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(dst, cur_level)) { + return at::_ops::_copy_from::call(self, dst, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor dst_value; + optional dst_bdim; + std::tie(dst_value, dst_bdim) = unwrapTensorAtLevel(dst, cur_level); + auto results = batch_rule(self_value, self_bdim, dst_value, dst_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _copy_from_and_resize_generated_plumbing(const at::Tensor & self, const at::Tensor & dst) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(dst, cur_level)) { + return at::_ops::_copy_from_and_resize::call(self, dst); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor dst_value; + optional dst_bdim; + std::tie(dst_value, dst_bdim) = unwrapTensorAtLevel(dst, cur_level); + auto results = batch_rule(self_value, self_bdim, dst_value, dst_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cos_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cos::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cos__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cos_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor cosh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cosh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cosh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cosh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor cosine_embedding_loss_generated_plumbing(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input1, cur_level) && !isBatchedAtLevel(input2, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::cosine_embedding_loss::call(input1, input2, target, margin, reduction); + } + Tensor input1_value; + optional input1_bdim; + std::tie(input1_value, input1_bdim) = unwrapTensorAtLevel(input1, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(input1_value, input1_bdim, input2_value, input2_bdim, target_value, target_bdim, margin, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor count_nonzero_dim_IntList_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::count_nonzero_dim_IntList::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor count_nonzero_generated_plumbing(const at::Tensor & self, c10::optional dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::count_nonzero::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cov_generated_plumbing(const at::Tensor & self, int64_t correction, const c10::optional & fweights, const c10::optional & aweights) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(fweights, cur_level) && !isBatchedAtLevel(aweights, cur_level)) { + return at::_ops::cov::call(self, correction, fweights, aweights); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional fweights_value; + optional fweights_bdim; + if (fweights) { + std::tie(fweights_value, fweights_bdim) = unwrapTensorAtLevel(fweights.value(), cur_level); + } + optional aweights_value; + optional aweights_bdim; + if (aweights) { + std::tie(aweights_value, aweights_bdim) = unwrapTensorAtLevel(aweights.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, correction, fweights_value, fweights_bdim, aweights_value, aweights_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor corrcoef_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::corrcoef::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cudnn_affine_grid_generator_generated_plumbing(const at::Tensor & theta, int64_t N, int64_t C, int64_t H, int64_t W) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(theta, cur_level)) { + return at::_ops::cudnn_affine_grid_generator::call(theta, N, C, H, W); + } + Tensor theta_value; + optional theta_bdim; + std::tie(theta_value, theta_bdim) = unwrapTensorAtLevel(theta, cur_level); + auto results = batch_rule(theta_value, theta_bdim, N, C, H, W); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cudnn_affine_grid_generator_backward_generated_plumbing(const at::Tensor & grad, int64_t N, int64_t C, int64_t H, int64_t W) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level)) { + return at::_ops::cudnn_affine_grid_generator_backward::call(grad, N, C, H, W); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + auto results = batch_rule(grad_value, grad_bdim, N, C, H, W); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple cudnn_batch_norm_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::cudnn_batch_norm::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, training, exponential_average_factor, epsilon); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple cudnn_batch_norm_backward_generated_plumbing(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon, const at::Tensor & reserveSpace) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level) && !isBatchedAtLevel(save_mean, cur_level) && !isBatchedAtLevel(save_var, cur_level) && !isBatchedAtLevel(reserveSpace, cur_level)) { + return at::_ops::cudnn_batch_norm_backward::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon, reserveSpace); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor reserveSpace_value; + optional reserveSpace_bdim; + std::tie(reserveSpace_value, reserveSpace_bdim) = unwrapTensorAtLevel(reserveSpace, cur_level); + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + optional save_mean_value; + optional save_mean_bdim; + if (save_mean) { + std::tie(save_mean_value, save_mean_bdim) = unwrapTensorAtLevel(save_mean.value(), cur_level); + } + optional save_var_value; + optional save_var_bdim; + if (save_var) { + std::tie(save_var_value, save_var_bdim) = unwrapTensorAtLevel(save_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, save_mean_value, save_mean_bdim, save_var_value, save_var_bdim, epsilon, reserveSpace_value, reserveSpace_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor cudnn_convolution_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::cudnn_convolution::call(self, weight, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cudnn_convolution_transpose_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic, bool allow_tf32) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::cudnn_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, padding, output_padding, stride, dilation, groups, benchmark, deterministic, allow_tf32); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _mps_convolution_transpose_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_mps_convolution_transpose::call(self, weight, padding, output_padding, stride, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, padding, output_padding, stride, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple mps_convolution_transpose_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::mps_convolution_transpose_backward::call(self, grad_output, weight, padding, output_padding, stride, dilation, groups, output_mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, padding, output_padding, stride, dilation, groups, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor cudnn_convolution_relu_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::cudnn_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cudnn_convolution_add_relu_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(z, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::cudnn_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor z_value; + optional z_bdim; + std::tie(z_value, z_bdim) = unwrapTensorAtLevel(z, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, z_value, z_bdim, alpha, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cudnn_grid_sampler_generated_plumbing(const at::Tensor & self, const at::Tensor & grid) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::cudnn_grid_sampler::call(self, grid); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(self_value, self_bdim, grid_value, grid_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple cudnn_grid_sampler_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grid, const at::Tensor & grad_output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grid, cur_level) && !isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::cudnn_grid_sampler_backward::call(self, grid, grad_output); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(self_value, self_bdim, grid_value, grid_bdim, grad_output_value, grad_output_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple cummax_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cummax::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple cummax_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cummax_dimname::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +void _cummax_helper_generated_plumbing(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(values, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::_cummax_helper::call(self, values, indices, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + batch_rule(self_value, self_bdim, values_value, values_bdim, indices_value, indices_bdim, dim); +} +template +::std::tuple cummin_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cummin::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple cummin_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cummin_dimname::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +void _cummin_helper_generated_plumbing(const at::Tensor & self, at::Tensor & values, at::Tensor & indices, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(values, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::_cummin_helper::call(self, values, indices, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + batch_rule(self_value, self_bdim, values_value, values_bdim, indices_value, indices_bdim, dim); +} +template +at::Tensor cummaxmin_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & indices, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::cummaxmin_backward::call(grad, input, indices, dim); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_value, input_bdim, indices_value, indices_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cumprod_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumprod::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cumprod__generated_plumbing(at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumprod_::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim, dtype); + return self; +} +template +at::Tensor cumprod_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumprod_dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cumprod__dimname_generated_plumbing(at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumprod__dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim, dtype); + return self; +} +template +at::Tensor cumprod_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & input, int64_t dim, const at::Tensor & output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::cumprod_backward::call(grad, input, dim, output); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_value, input_bdim, dim, output_value, output_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cumsum_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumsum::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cumsum__generated_plumbing(at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumsum_::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim, dtype); + return self; +} +template +at::Tensor cumsum_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumsum_dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & cumsum__dimname_generated_plumbing(at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cumsum__dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim, dtype); + return self; +} +template +at::Tensor cumulative_trapezoid_x_generated_plumbing(const at::Tensor & y, const at::Tensor & x, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level) && !isBatchedAtLevel(x, cur_level)) { + return at::_ops::cumulative_trapezoid_x::call(y, x, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(y_value, y_bdim, x_value, x_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cumulative_trapezoid_dx_generated_plumbing(const at::Tensor & y, const at::Scalar & dx, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level)) { + return at::_ops::cumulative_trapezoid_dx::call(y, dx, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + auto results = batch_rule(y_value, y_bdim, dx, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ctc_loss_IntList_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level)) { + return at::_ops::ctc_loss_IntList::call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths, target_lengths, blank, reduction, zero_infinity); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ctc_loss_Tensor_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, int64_t reduction, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level) && !isBatchedAtLevel(input_lengths, cur_level) && !isBatchedAtLevel(target_lengths, cur_level)) { + return at::_ops::ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, reduction, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + Tensor input_lengths_value; + optional input_lengths_bdim; + std::tie(input_lengths_value, input_lengths_bdim) = unwrapTensorAtLevel(input_lengths, cur_level); + Tensor target_lengths_value; + optional target_lengths_bdim; + std::tie(target_lengths_value, target_lengths_bdim) = unwrapTensorAtLevel(target_lengths, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths_value, input_lengths_bdim, target_lengths_value, target_lengths_bdim, blank, reduction, zero_infinity); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _ctc_loss_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level)) { + return at::_ops::_ctc_loss::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths, target_lengths, blank, zero_infinity); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _ctc_loss_Tensor_generated_plumbing(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level) && !isBatchedAtLevel(input_lengths, cur_level) && !isBatchedAtLevel(target_lengths, cur_level)) { + return at::_ops::_ctc_loss_Tensor::call(log_probs, targets, input_lengths, target_lengths, blank, zero_infinity); + } + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + Tensor input_lengths_value; + optional input_lengths_bdim; + std::tie(input_lengths_value, input_lengths_bdim) = unwrapTensorAtLevel(input_lengths, cur_level); + Tensor target_lengths_value; + optional target_lengths_bdim; + std::tie(target_lengths_value, target_lengths_bdim) = unwrapTensorAtLevel(target_lengths, cur_level); + auto results = batch_rule(log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths_value, input_lengths_bdim, target_lengths_value, target_lengths_bdim, blank, zero_infinity); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _ctc_loss_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level) && !isBatchedAtLevel(neg_log_likelihood, cur_level) && !isBatchedAtLevel(log_alpha, cur_level)) { + return at::_ops::_ctc_loss_backward::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + Tensor neg_log_likelihood_value; + optional neg_log_likelihood_bdim; + std::tie(neg_log_likelihood_value, neg_log_likelihood_bdim) = unwrapTensorAtLevel(neg_log_likelihood, cur_level); + Tensor log_alpha_value; + optional log_alpha_bdim; + std::tie(log_alpha_value, log_alpha_bdim) = unwrapTensorAtLevel(log_alpha, cur_level); + auto results = batch_rule(grad_value, grad_bdim, log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths, target_lengths, neg_log_likelihood_value, neg_log_likelihood_bdim, log_alpha_value, log_alpha_bdim, blank, zero_infinity); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _ctc_loss_backward_Tensor_generated_plumbing(const at::Tensor & grad, const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, const at::Tensor & neg_log_likelihood, const at::Tensor & log_alpha, int64_t blank, bool zero_infinity) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(log_probs, cur_level) && !isBatchedAtLevel(targets, cur_level) && !isBatchedAtLevel(input_lengths, cur_level) && !isBatchedAtLevel(target_lengths, cur_level) && !isBatchedAtLevel(neg_log_likelihood, cur_level) && !isBatchedAtLevel(log_alpha, cur_level)) { + return at::_ops::_ctc_loss_backward_Tensor::call(grad, log_probs, targets, input_lengths, target_lengths, neg_log_likelihood, log_alpha, blank, zero_infinity); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor log_probs_value; + optional log_probs_bdim; + std::tie(log_probs_value, log_probs_bdim) = unwrapTensorAtLevel(log_probs, cur_level); + Tensor targets_value; + optional targets_bdim; + std::tie(targets_value, targets_bdim) = unwrapTensorAtLevel(targets, cur_level); + Tensor input_lengths_value; + optional input_lengths_bdim; + std::tie(input_lengths_value, input_lengths_bdim) = unwrapTensorAtLevel(input_lengths, cur_level); + Tensor target_lengths_value; + optional target_lengths_bdim; + std::tie(target_lengths_value, target_lengths_bdim) = unwrapTensorAtLevel(target_lengths, cur_level); + Tensor neg_log_likelihood_value; + optional neg_log_likelihood_bdim; + std::tie(neg_log_likelihood_value, neg_log_likelihood_bdim) = unwrapTensorAtLevel(neg_log_likelihood, cur_level); + Tensor log_alpha_value; + optional log_alpha_bdim; + std::tie(log_alpha_value, log_alpha_bdim) = unwrapTensorAtLevel(log_alpha, cur_level); + auto results = batch_rule(grad_value, grad_bdim, log_probs_value, log_probs_bdim, targets_value, targets_bdim, input_lengths_value, input_lengths_bdim, target_lengths_value, target_lengths_bdim, neg_log_likelihood_value, neg_log_likelihood_bdim, log_alpha_value, log_alpha_bdim, blank, zero_infinity); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diag_embed_generated_plumbing(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diag_embed::call(self, offset, dim1, dim2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagflat_generated_plumbing(const at::Tensor & self, int64_t offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diagflat::call(self, offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, offset); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagonal_generated_plumbing(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diagonal::call(self, offset, dim1, dim2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_diagonal_generated_plumbing(const at::Tensor & A, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_diagonal::call(A, offset, dim1, dim2); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagonal_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname outdim, at::Dimname dim1, at::Dimname dim2, int64_t offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diagonal_Dimname::call(self, outdim, dim1, dim2, offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, outdim, dim1, dim2, offset); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagonal_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::diagonal_backward::call(grad_output, input_sizes, offset, dim1, dim2); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_sizes, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & fill_diagonal__generated_plumbing(at::Tensor & self, const at::Scalar & fill_value, bool wrap) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fill_diagonal_::call(self, fill_value, wrap); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, fill_value, wrap); + return self; +} +template +at::Tensor diff_generated_plumbing(const at::Tensor & self, int64_t n, int64_t dim, const c10::optional & prepend, const c10::optional & append) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(prepend, cur_level) && !isBatchedAtLevel(append, cur_level)) { + return at::_ops::diff::call(self, n, dim, prepend, append); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional prepend_value; + optional prepend_bdim; + if (prepend) { + std::tie(prepend_value, prepend_bdim) = unwrapTensorAtLevel(prepend.value(), cur_level); + } + optional append_value; + optional append_bdim; + if (append) { + std::tie(append_value, append_bdim) = unwrapTensorAtLevel(append.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, n, dim, prepend_value, prepend_bdim, append_value, append_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_scalarint_generated_plumbing(const at::Tensor & self, const c10::optional & spacing, c10::optional dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gradient_scalarint::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_scalararray_generated_plumbing(const at::Tensor & self, const at::Scalar & spacing, at::IntArrayRef dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gradient_scalararray::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_array_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gradient_array::call(self, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_scalarrayint_generated_plumbing(const at::Tensor & self, at::ArrayRef spacing, c10::optional dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gradient_scalarrayint::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_scalarrayarray_generated_plumbing(const at::Tensor & self, at::ArrayRef spacing, at::IntArrayRef dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gradient_scalarrayarray::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_tensorarrayint_generated_plumbing(const at::Tensor & self, at::TensorList spacing, c10::optional dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(spacing, cur_level)) { + return at::_ops::gradient_tensorarrayint::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector gradient_tensorarray_generated_plumbing(const at::Tensor & self, at::TensorList spacing, at::IntArrayRef dim, int64_t edge_order) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(spacing, cur_level)) { + return at::_ops::gradient_tensorarray::call(self, spacing, dim, edge_order); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, spacing, dim, edge_order); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor div_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::div_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & div__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::div__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor div_Tensor_mode_generated_plumbing(const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::div_Tensor_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, rounding_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & div__Tensor_mode_generated_plumbing(at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::div__Tensor_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, rounding_mode); + return self; +} +template +at::Tensor div_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::div_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & div__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::div__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor div_Scalar_mode_generated_plumbing(const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::div_Scalar_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, rounding_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & div__Scalar_mode_generated_plumbing(at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::div__Scalar_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, rounding_mode); + return self; +} +template +at::Tensor divide_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::divide_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & divide__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::divide__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor divide_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::divide_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & divide__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::divide__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor divide_Tensor_mode_generated_plumbing(const at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::divide_Tensor_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, rounding_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & divide__Tensor_mode_generated_plumbing(at::Tensor & self, const at::Tensor & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::divide__Tensor_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, rounding_mode); + return self; +} +template +at::Tensor divide_Scalar_mode_generated_plumbing(const at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::divide_Scalar_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, rounding_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & divide__Scalar_mode_generated_plumbing(at::Tensor & self, const at::Scalar & other, c10::optional rounding_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::divide__Scalar_mode::call(self, other, rounding_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, rounding_mode); + return self; +} +template +at::Tensor true_divide_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::true_divide_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & true_divide__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::true_divide__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor true_divide_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::true_divide_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & true_divide__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::true_divide__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor dot_generated_plumbing(const at::Tensor & self, const at::Tensor & tensor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor, cur_level)) { + return at::_ops::dot::call(self, tensor); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor_value; + optional tensor_bdim; + std::tie(tensor_value, tensor_bdim) = unwrapTensorAtLevel(tensor, cur_level); + auto results = batch_rule(self_value, self_bdim, tensor_value, tensor_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor vdot_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::vdot::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor einsum_generated_plumbing(c10::string_view equation, at::TensorList tensors, at::OptionalIntArrayRef path) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::einsum::call(equation, tensors, path); + } + + auto results = batch_rule(equation, tensors, path); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor embedding_generated_plumbing(const at::Tensor & weight, const at::Tensor & indices, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding::call(weight, indices, padding_idx, scale_grad_by_freq, sparse); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(weight_value, weight_bdim, indices_value, indices_bdim, padding_idx, scale_grad_by_freq, sparse); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor embedding_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq, bool sparse) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq, sparse); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_value, grad_bdim, indices_value, indices_bdim, num_weights, padding_idx, scale_grad_by_freq, sparse); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor embedding_dense_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & indices, c10::SymInt num_weights, c10::SymInt padding_idx, bool scale_grad_by_freq) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding_dense_backward::call(grad_output, indices, num_weights, padding_idx, scale_grad_by_freq); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, indices_value, indices_bdim, num_weights, padding_idx, scale_grad_by_freq); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & embedding_renorm__generated_plumbing(at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding_renorm_::call(self, indices, max_norm, norm_type); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + batch_rule(self_value, self_bdim, indices_value, indices_bdim, max_norm, norm_type); + return self; +} +template +at::Tensor embedding_sparse_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & indices, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding_sparse_backward::call(grad, indices, num_weights, padding_idx, scale_grad_by_freq); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_value, grad_bdim, indices_value, indices_bdim, num_weights, padding_idx, scale_grad_by_freq); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _embedding_bag_forward_only_generated_plumbing(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::_embedding_bag_forward_only::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(weight_value, weight_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, scale_grad_by_freq, mode, sparse, per_sample_weights_value, per_sample_weights_bdim, include_last_offset, padding_idx); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _rowwise_prune_generated_plumbing(const at::Tensor & weight, const at::Tensor & mask, at::ScalarType compressed_indices_dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_rowwise_prune::call(weight, mask, compressed_indices_dtype); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(weight_value, weight_bdim, mask_value, mask_bdim, compressed_indices_dtype); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor row_stack_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::row_stack::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple embedding_bag_generated_plumbing(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::embedding_bag::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(weight_value, weight_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, scale_grad_by_freq, mode, sparse, per_sample_weights_value, per_sample_weights_bdim, include_last_offset); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple embedding_bag_padding_idx_generated_plumbing(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, c10::optional padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::embedding_bag_padding_idx::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(weight_value, weight_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, scale_grad_by_freq, mode, sparse, per_sample_weights_value, per_sample_weights_bdim, include_last_offset, padding_idx); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _embedding_bag_generated_plumbing(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::_embedding_bag::call(weight, indices, offsets, scale_grad_by_freq, mode, sparse, per_sample_weights, include_last_offset, padding_idx); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(weight_value, weight_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, scale_grad_by_freq, mode, sparse, per_sample_weights_value, per_sample_weights_bdim, include_last_offset, padding_idx); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +at::Tensor _embedding_bag_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(offset2bag, cur_level) && !isBatchedAtLevel(bag_size, cur_level) && !isBatchedAtLevel(maximum_indices, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::_embedding_bag_backward::call(grad, indices, offsets, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights, padding_idx); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + Tensor offset2bag_value; + optional offset2bag_bdim; + std::tie(offset2bag_value, offset2bag_bdim) = unwrapTensorAtLevel(offset2bag, cur_level); + Tensor bag_size_value; + optional bag_size_bdim; + std::tie(bag_size_value, bag_size_bdim) = unwrapTensorAtLevel(bag_size, cur_level); + Tensor maximum_indices_value; + optional maximum_indices_bdim; + std::tie(maximum_indices_value, maximum_indices_bdim) = unwrapTensorAtLevel(maximum_indices, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(grad_value, grad_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, offset2bag_value, offset2bag_bdim, bag_size_value, bag_size_bdim, maximum_indices_value, maximum_indices_bdim, num_weights, scale_grad_by_freq, mode, sparse, per_sample_weights_value, per_sample_weights_bdim, padding_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _embedding_bag_sparse_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, const at::Tensor & bag_size, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(offset2bag, cur_level) && !isBatchedAtLevel(bag_size, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::_embedding_bag_sparse_backward::call(grad, indices, offsets, offset2bag, bag_size, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + Tensor offset2bag_value; + optional offset2bag_bdim; + std::tie(offset2bag_value, offset2bag_bdim) = unwrapTensorAtLevel(offset2bag, cur_level); + Tensor bag_size_value; + optional bag_size_bdim; + std::tie(bag_size_value, bag_size_bdim) = unwrapTensorAtLevel(bag_size, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(grad_value, grad_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, offset2bag_value, offset2bag_bdim, bag_size_value, bag_size_bdim, num_weights, scale_grad_by_freq, mode, per_sample_weights_value, per_sample_weights_bdim, padding_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _embedding_bag_dense_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offset2bag, cur_level) && !isBatchedAtLevel(bag_size, cur_level) && !isBatchedAtLevel(maximum_indices, cur_level) && !isBatchedAtLevel(per_sample_weights, cur_level)) { + return at::_ops::_embedding_bag_dense_backward::call(grad, indices, offset2bag, bag_size, maximum_indices, num_weights, scale_grad_by_freq, mode, per_sample_weights, padding_idx); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offset2bag_value; + optional offset2bag_bdim; + std::tie(offset2bag_value, offset2bag_bdim) = unwrapTensorAtLevel(offset2bag, cur_level); + Tensor bag_size_value; + optional bag_size_bdim; + std::tie(bag_size_value, bag_size_bdim) = unwrapTensorAtLevel(bag_size, cur_level); + Tensor maximum_indices_value; + optional maximum_indices_bdim; + std::tie(maximum_indices_value, maximum_indices_bdim) = unwrapTensorAtLevel(maximum_indices, cur_level); + optional per_sample_weights_value; + optional per_sample_weights_bdim; + if (per_sample_weights) { + std::tie(per_sample_weights_value, per_sample_weights_bdim) = unwrapTensorAtLevel(per_sample_weights.value(), cur_level); + } + auto results = batch_rule(grad_value, grad_bdim, indices_value, indices_bdim, offset2bag_value, offset2bag_bdim, bag_size_value, bag_size_bdim, maximum_indices_value, maximum_indices_bdim, num_weights, scale_grad_by_freq, mode, per_sample_weights_value, per_sample_weights_bdim, padding_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _embedding_bag_per_sample_weights_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(offset2bag, cur_level)) { + return at::_ops::_embedding_bag_per_sample_weights_backward::call(grad, weight, indices, offsets, offset2bag, mode, padding_idx); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + Tensor offset2bag_value; + optional offset2bag_bdim; + std::tie(offset2bag_value, offset2bag_bdim) = unwrapTensorAtLevel(offset2bag, cur_level); + auto results = batch_rule(grad_value, grad_bdim, weight_value, weight_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, offset2bag_value, offset2bag_bdim, mode, padding_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor new_empty_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::new_empty::call(self, size, dtype, layout, device, pin_memory); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor new_empty_strided_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::new_empty_strided::call(self, size, stride, dtype, layout, device, pin_memory); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, stride, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor new_full_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::new_full::call(self, size, fill_value, dtype, layout, device, pin_memory); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, fill_value, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor new_zeros_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::new_zeros::call(self, size, dtype, layout, device, pin_memory); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor new_ones_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::new_ones::call(self, size, dtype, layout, device, pin_memory); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _empty_per_channel_affine_quantized_generated_plumbing(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(scales, cur_level) && !isBatchedAtLevel(zero_points, cur_level)) { + return at::_ops::_empty_per_channel_affine_quantized::call(size, scales, zero_points, axis, dtype, layout, device, pin_memory, memory_format); + } + Tensor scales_value; + optional scales_bdim; + std::tie(scales_value, scales_bdim) = unwrapTensorAtLevel(scales, cur_level); + Tensor zero_points_value; + optional zero_points_bdim; + std::tie(zero_points_value, zero_points_bdim) = unwrapTensorAtLevel(zero_points, cur_level); + auto results = batch_rule(size, scales_value, scales_bdim, zero_points_value, zero_points_bdim, axis, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +const at::Tensor & _resize_output__generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_resize_output_::call(self, size, device); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, size, device); + return self; +} +template +at::Tensor empty_quantized_generated_plumbing(at::IntArrayRef size, const at::Tensor & qtensor, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(qtensor, cur_level)) { + return at::_ops::empty_quantized::call(size, qtensor, dtype, layout, device, pin_memory, memory_format); + } + Tensor qtensor_value; + optional qtensor_bdim; + std::tie(qtensor_value, qtensor_bdim) = unwrapTensorAtLevel(qtensor, cur_level); + auto results = batch_rule(size, qtensor_value, qtensor_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor empty_like_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::empty_like::call(self, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor erf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & erf__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erf_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor erfc_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erfc::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & erfc__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erfc_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor exp_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exp::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & exp__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exp_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor exp2_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exp2::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & exp2__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exp2_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor expm1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::expm1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & expm1__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::expm1_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor expand_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::expand::call(self, size, implicit); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, implicit); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor expand_as_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::expand_as::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flatten_using_ints_generated_plumbing(const at::Tensor & self, int64_t start_dim, int64_t end_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flatten_using_ints::call(self, start_dim, end_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, start_dim, end_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flatten_named_out_dim_generated_plumbing(const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flatten_named_out_dim::call(self, start_dim, end_dim, out_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, start_dim, end_dim, out_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flatten_using_names_generated_plumbing(const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flatten_using_names::call(self, start_dim, end_dim, out_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, start_dim, end_dim, out_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flatten_DimnameList_generated_plumbing(const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flatten_DimnameList::call(self, dims, out_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dims, out_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unflatten_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::SymIntArrayRef sizes) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unflatten_int::call(self, dim, sizes); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, sizes); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unflatten_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::SymIntArrayRef sizes, at::DimnameList names) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unflatten_Dimname::call(self, dim, sizes, names); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, sizes, names); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fill_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fill_Scalar::call(self, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fill_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::fill_Tensor::call(self, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + auto results = batch_rule(self_value, self_bdim, value_value, value_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & fill__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fill__Scalar::call(self, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, value); + return self; +} +template +at::Tensor & fill__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::fill__Tensor::call(self, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + batch_rule(self_value, self_bdim, value_value, value_bdim); + return self; +} +template +at::Tensor floor_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::floor::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & floor__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::floor_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor floor_divide_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::floor_divide::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & floor_divide__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::floor_divide__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor floor_divide_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::floor_divide_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & floor_divide__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::floor_divide__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor frac_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::frac::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & frac__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::frac_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor full_like_generated_plumbing(const at::Tensor & self, const at::Scalar & fill_value, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::full_like::call(self, fill_value, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, fill_value, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gcd_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::gcd::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & gcd__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::gcd_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor lcm_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::lcm::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & lcm__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::lcm_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor grid_sampler_generated_plumbing(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::grid_sampler::call(input, grid, interpolation_mode, padding_mode, align_corners); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor grid_sampler_2d_generated_plumbing(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::grid_sampler_2d::call(input, grid, interpolation_mode, padding_mode, align_corners); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple grid_sampler_2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::grid_sampler_2d_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _grid_sampler_2d_cpu_fallback_generated_plumbing(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::_grid_sampler_2d_cpu_fallback::call(input, grid, interpolation_mode, padding_mode, align_corners); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _grid_sampler_2d_cpu_fallback_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::_grid_sampler_2d_cpu_fallback_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor grid_sampler_3d_generated_plumbing(const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::grid_sampler_3d::call(input, grid, interpolation_mode, padding_mode, align_corners); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple grid_sampler_3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & grid, int64_t interpolation_mode, int64_t padding_mode, bool align_corners, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grid, cur_level)) { + return at::_ops::grid_sampler_3d_backward::call(grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grid_value; + optional grid_bdim; + std::tie(grid_value, grid_bdim) = unwrapTensorAtLevel(grid, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, grid_value, grid_bdim, interpolation_mode, padding_mode, align_corners, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor hinge_embedding_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, double margin, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::hinge_embedding_loss::call(self, target, margin, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, margin, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor group_norm_generated_plumbing(const at::Tensor & input, int64_t num_groups, const c10::optional & weight, const c10::optional & bias, double eps, bool cudnn_enabled) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::group_norm::call(input, num_groups, weight, bias, eps, cudnn_enabled); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, num_groups, weight_value, weight_bdim, bias_value, bias_bdim, eps, cudnn_enabled); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple native_group_norm_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::native_group_norm::call(input, weight, bias, N, C, HxW, group, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, N, C, HxW, group, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple native_group_norm_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(rstd, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::native_group_norm_backward::call(grad_out, input, mean, rstd, weight, N, C, HxW, group, output_mask); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor rstd_value; + optional rstd_bdim; + std::tie(rstd_value, rstd_bdim) = unwrapTensorAtLevel(rstd, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, input_value, input_bdim, mean_value, mean_bdim, rstd_value, rstd_bdim, weight_value, weight_bdim, N, C, HxW, group, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _fft_r2c_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, bool onesided) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fft_r2c::call(self, dim, normalization, onesided); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, normalization, onesided); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _fft_c2r_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, int64_t normalization, c10::SymInt last_dim_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fft_c2r::call(self, dim, normalization, last_dim_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, normalization, last_dim_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _fft_c2c_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef dim, int64_t normalization, bool forward) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fft_c2c::call(self, dim, normalization, forward); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, normalization, forward); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _validate_compressed_sparse_indices_generated_plumbing(bool is_crow, const at::Tensor & compressed_idx, const at::Tensor & plain_idx, int64_t cdim, int64_t dim, int64_t nnz) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_idx, cur_level) && !isBatchedAtLevel(plain_idx, cur_level)) { + return at::_ops::_validate_compressed_sparse_indices::call(is_crow, compressed_idx, plain_idx, cdim, dim, nnz); + } + Tensor compressed_idx_value; + optional compressed_idx_bdim; + std::tie(compressed_idx_value, compressed_idx_bdim) = unwrapTensorAtLevel(compressed_idx, cur_level); + Tensor plain_idx_value; + optional plain_idx_bdim; + std::tie(plain_idx_value, plain_idx_bdim) = unwrapTensorAtLevel(plain_idx, cur_level); + batch_rule(is_crow, compressed_idx_value, compressed_idx_bdim, plain_idx_value, plain_idx_bdim, cdim, dim, nnz); +} +template +at::Tensor index_Tensor_generated_plumbing(const at::Tensor & self, const c10::List> & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::index_Tensor::call(self, indices); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _unsafe_index_Tensor_generated_plumbing(const at::Tensor & self, const c10::List> & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::_unsafe_index_Tensor::call(self, indices); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_copy__generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_copy_::call(self, dim, index, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim); + return self; +} +template +at::Tensor index_copy_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_copy::call(self, dim, index, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_copy__dimname_generated_plumbing(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_copy__dimname::call(self, dim, index, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim); + return self; +} +template +at::Tensor index_copy_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_copy_dimname::call(self, dim, index, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_put__generated_plumbing(at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::index_put_::call(self, indices, values, accumulate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(self_value, self_bdim, indices, values_value, values_bdim, accumulate); + return self; +} +template +at::Tensor index_put_generated_plumbing(const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::index_put::call(self, indices, values, accumulate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(self_value, self_bdim, indices, values_value, values_bdim, accumulate); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _unsafe_index_put_generated_plumbing(const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_unsafe_index_put::call(self, indices, values, accumulate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(self_value, self_bdim, indices, values_value, values_bdim, accumulate); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _index_put_impl__generated_plumbing(at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate, bool unsafe) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_index_put_impl_::call(self, indices, values, accumulate, unsafe); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(self_value, self_bdim, indices, values_value, values_bdim, accumulate, unsafe); + return self; +} +template +at::Tensor instance_norm_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool use_input_stats, double momentum, double eps, bool cudnn_enabled) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::instance_norm::call(input, weight, bias, running_mean, running_var, use_input_stats, momentum, eps, cudnn_enabled); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, use_input_stats, momentum, eps, cudnn_enabled); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isclose_generated_plumbing(const at::Tensor & self, const at::Tensor & other, double rtol, double atol, bool equal_nan) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::isclose::call(self, other, rtol, atol, equal_nan); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, rtol, atol, equal_nan); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isin_Tensor_Tensor_generated_plumbing(const at::Tensor & elements, const at::Tensor & test_elements, bool assume_unique, bool invert) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(elements, cur_level) && !isBatchedAtLevel(test_elements, cur_level)) { + return at::_ops::isin_Tensor_Tensor::call(elements, test_elements, assume_unique, invert); + } + Tensor elements_value; + optional elements_bdim; + std::tie(elements_value, elements_bdim) = unwrapTensorAtLevel(elements, cur_level); + Tensor test_elements_value; + optional test_elements_bdim; + std::tie(test_elements_value, test_elements_bdim) = unwrapTensorAtLevel(test_elements, cur_level); + auto results = batch_rule(elements_value, elements_bdim, test_elements_value, test_elements_bdim, assume_unique, invert); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isin_Tensor_Scalar_generated_plumbing(const at::Tensor & elements, const at::Scalar & test_element, bool assume_unique, bool invert) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(elements, cur_level)) { + return at::_ops::isin_Tensor_Scalar::call(elements, test_element, assume_unique, invert); + } + Tensor elements_value; + optional elements_bdim; + std::tie(elements_value, elements_bdim) = unwrapTensorAtLevel(elements, cur_level); + auto results = batch_rule(elements_value, elements_bdim, test_element, assume_unique, invert); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isin_Scalar_Tensor_generated_plumbing(const at::Scalar & element, const at::Tensor & test_elements, bool assume_unique, bool invert) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(test_elements, cur_level)) { + return at::_ops::isin_Scalar_Tensor::call(element, test_elements, assume_unique, invert); + } + Tensor test_elements_value; + optional test_elements_bdim; + std::tie(test_elements_value, test_elements_bdim) = unwrapTensorAtLevel(test_elements, cur_level); + auto results = batch_rule(element, test_elements_value, test_elements_bdim, assume_unique, invert); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isnan_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isnan::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isreal_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isreal::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor kl_div_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction, bool log_target) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::kl_div::call(self, target, reduction, log_target); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction, log_target); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor kron_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::kron::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple kthvalue_generated_plumbing(const at::Tensor & self, int64_t k, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::kthvalue::call(self, k, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, k, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple kthvalue_dimname_generated_plumbing(const at::Tensor & self, int64_t k, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::kthvalue_dimname::call(self, k, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, k, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor layer_norm_generated_plumbing(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps, bool cudnn_enable) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::layer_norm::call(input, normalized_shape, weight, bias, eps, cudnn_enable); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, normalized_shape, weight_value, weight_bdim, bias_value, bias_bdim, eps, cudnn_enable); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple native_layer_norm_generated_plumbing(const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const c10::optional & weight, const c10::optional & bias, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::native_layer_norm::call(input, normalized_shape, weight, bias, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, normalized_shape, weight_value, weight_bdim, bias_value, bias_bdim, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple native_layer_norm_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & input, c10::SymIntArrayRef normalized_shape, const at::Tensor & mean, const at::Tensor & rstd, const c10::optional & weight, const c10::optional & bias, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(rstd, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::native_layer_norm_backward::call(grad_out, input, normalized_shape, mean, rstd, weight, bias, output_mask); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor rstd_value; + optional rstd_bdim; + std::tie(rstd_value, rstd_bdim) = unwrapTensorAtLevel(rstd, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, input_value, input_bdim, normalized_shape, mean_value, mean_bdim, rstd_value, rstd_bdim, weight_value, weight_bdim, bias_value, bias_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor nan_to_num_generated_plumbing(const at::Tensor & self, c10::optional nan, c10::optional posinf, c10::optional neginf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nan_to_num::call(self, nan, posinf, neginf); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, nan, posinf, neginf); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & nan_to_num__generated_plumbing(at::Tensor & self, c10::optional nan, c10::optional posinf, c10::optional neginf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nan_to_num_::call(self, nan, posinf, neginf); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, nan, posinf, neginf); + return self; +} +template +at::Tensor linear_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::linear::call(input, weight, bias); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linear_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::linear_backward::call(self, grad_output, weight, output_mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor mkldnn_linear_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::mkldnn_linear::call(self, weight, bias); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_linear_backward_input_generated_plumbing(at::IntArrayRef input_size, const at::Tensor & grad_output, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::mkldnn_linear_backward_input::call(input_size, grad_output, weight); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(input_size, grad_output_value, grad_output_bdim, weight_value, weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple mkldnn_linear_backward_weights_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & input, const at::Tensor & weight, bool bias_defined) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::mkldnn_linear_backward_weights::call(grad_output, input, weight, bias_defined); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_value, input_bdim, weight_value, weight_bdim, bias_defined); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple mkldnn_linear_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::mkldnn_linear_backward::call(self, grad_output, weight, output_mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _cslt_compress_generated_plumbing(const at::Tensor & input) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_cslt_compress::call(input); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cslt_sparse_mm_generated_plumbing(const at::Tensor & compressed_A, const at::Tensor & dense_B, const c10::optional & bias, const c10::optional & alpha, c10::optional out_dtype, bool transpose_result, int64_t alg_id) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_A, cur_level) && !isBatchedAtLevel(dense_B, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(alpha, cur_level)) { + return at::_ops::_cslt_sparse_mm::call(compressed_A, dense_B, bias, alpha, out_dtype, transpose_result, alg_id); + } + Tensor compressed_A_value; + optional compressed_A_bdim; + std::tie(compressed_A_value, compressed_A_bdim) = unwrapTensorAtLevel(compressed_A, cur_level); + Tensor dense_B_value; + optional dense_B_bdim; + std::tie(dense_B_value, dense_B_bdim) = unwrapTensorAtLevel(dense_B, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional alpha_value; + optional alpha_bdim; + if (alpha) { + std::tie(alpha_value, alpha_bdim) = unwrapTensorAtLevel(alpha.value(), cur_level); + } + auto results = batch_rule(compressed_A_value, compressed_A_bdim, dense_B_value, dense_B_bdim, bias_value, bias_bdim, alpha_value, alpha_bdim, out_dtype, transpose_result, alg_id); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_semi_structured_linear_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & meta, const c10::optional & bias, c10::optional activation, c10::optional out_dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(meta, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_sparse_semi_structured_linear::call(input, weight, meta, bias, activation, out_dtype); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor meta_value; + optional meta_bdim; + std::tie(meta_value, meta_bdim) = unwrapTensorAtLevel(meta, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, meta_value, meta_bdim, bias_value, bias_bdim, activation, out_dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _mixed_dtypes_linear_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & scale, const c10::optional & bias, c10::optional activation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_mixed_dtypes_linear::call(input, weight, scale, bias, activation); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, scale_value, scale_bdim, bias_value, bias_bdim, activation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_linear_int8_weight_fp32_activation_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(packed, cur_level) && !isBatchedAtLevel(col_offsets, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::fbgemm_linear_int8_weight_fp32_activation::call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor packed_value; + optional packed_bdim; + std::tie(packed_value, packed_bdim) = unwrapTensorAtLevel(packed, cur_level); + Tensor col_offsets_value; + optional col_offsets_bdim; + std::tie(col_offsets_value, col_offsets_bdim) = unwrapTensorAtLevel(col_offsets, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, packed_value, packed_bdim, col_offsets_value, col_offsets_bdim, weight_scale, weight_zero_point, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_linear_int8_weight_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const at::Tensor & packed, const at::Tensor & col_offsets, const at::Scalar & weight_scale, const at::Scalar & weight_zero_point, const at::Tensor & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(packed, cur_level) && !isBatchedAtLevel(col_offsets, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::fbgemm_linear_int8_weight::call(input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor packed_value; + optional packed_bdim; + std::tie(packed_value, packed_bdim) = unwrapTensorAtLevel(packed, cur_level); + Tensor col_offsets_value; + optional col_offsets_bdim; + std::tie(col_offsets_value, col_offsets_bdim) = unwrapTensorAtLevel(col_offsets, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, packed_value, packed_bdim, col_offsets_value, col_offsets_bdim, weight_scale, weight_zero_point, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_pack_gemm_matrix_fp16_generated_plumbing(const at::Tensor & input) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::fbgemm_pack_gemm_matrix_fp16::call(input); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_linear_fp16_weight_fp32_activation_generated_plumbing(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(packed_weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::fbgemm_linear_fp16_weight_fp32_activation::call(input, packed_weight, bias); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor packed_weight_value; + optional packed_weight_bdim; + std::tie(packed_weight_value, packed_weight_bdim) = unwrapTensorAtLevel(packed_weight, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(input_value, input_bdim, packed_weight_value, packed_weight_bdim, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_linear_fp16_weight_generated_plumbing(const at::Tensor & input, const at::Tensor & packed_weight, const at::Tensor & bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(packed_weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::fbgemm_linear_fp16_weight::call(input, packed_weight, bias); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor packed_weight_value; + optional packed_weight_bdim; + std::tie(packed_weight_value, packed_weight_bdim) = unwrapTensorAtLevel(packed_weight, cur_level); + Tensor bias_value; + optional bias_bdim; + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias, cur_level); + auto results = batch_rule(input_value, input_bdim, packed_weight_value, packed_weight_bdim, bias_value, bias_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_pack_quantized_matrix_generated_plumbing(const at::Tensor & input) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::fbgemm_pack_quantized_matrix::call(input); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fbgemm_pack_quantized_matrix_KN_generated_plumbing(const at::Tensor & input, int64_t K, int64_t N) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::fbgemm_pack_quantized_matrix_KN::call(input, K, N); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, K, N); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ldexp_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ldexp_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & ldexp__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ldexp_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor linspace_Tensor_Tensor_generated_plumbing(const at::Tensor & start, const at::Tensor & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(start, cur_level) && !isBatchedAtLevel(end, cur_level)) { + return at::_ops::linspace_Tensor_Tensor::call(start, end, steps, dtype, layout, device, pin_memory); + } + Tensor start_value; + optional start_bdim; + std::tie(start_value, start_bdim) = unwrapTensorAtLevel(start, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + auto results = batch_rule(start_value, start_bdim, end_value, end_bdim, steps, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linspace_Tensor_Scalar_generated_plumbing(const at::Tensor & start, const at::Scalar & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(start, cur_level)) { + return at::_ops::linspace_Tensor_Scalar::call(start, end, steps, dtype, layout, device, pin_memory); + } + Tensor start_value; + optional start_bdim; + std::tie(start_value, start_bdim) = unwrapTensorAtLevel(start, cur_level); + auto results = batch_rule(start_value, start_bdim, end, steps, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linspace_Scalar_Tensor_generated_plumbing(const at::Scalar & start, const at::Tensor & end, int64_t steps, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(end, cur_level)) { + return at::_ops::linspace_Scalar_Tensor::call(start, end, steps, dtype, layout, device, pin_memory); + } + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + auto results = batch_rule(start, end_value, end_bdim, steps, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor log_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & log__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor log10_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log10::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & log10__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log10_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor log1p_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log1p::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & log1p__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log1p_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor log2_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log2::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & log2__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log2_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor logaddexp_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logaddexp::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logaddexp2_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::logaddexp2::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor xlogy_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::xlogy_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor xlogy_Scalar_Self_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::xlogy_Scalar_Self::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor xlogy_Scalar_Other_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::xlogy_Scalar_Other::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & xlogy__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::xlogy__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor & xlogy__Scalar_Other_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::xlogy__Scalar_Other::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor logspace_Tensor_Tensor_generated_plumbing(const at::Tensor & start, const at::Tensor & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(start, cur_level) && !isBatchedAtLevel(end, cur_level)) { + return at::_ops::logspace_Tensor_Tensor::call(start, end, steps, base, dtype, layout, device, pin_memory); + } + Tensor start_value; + optional start_bdim; + std::tie(start_value, start_bdim) = unwrapTensorAtLevel(start, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + auto results = batch_rule(start_value, start_bdim, end_value, end_bdim, steps, base, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logspace_Tensor_Scalar_generated_plumbing(const at::Tensor & start, const at::Scalar & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(start, cur_level)) { + return at::_ops::logspace_Tensor_Scalar::call(start, end, steps, base, dtype, layout, device, pin_memory); + } + Tensor start_value; + optional start_bdim; + std::tie(start_value, start_bdim) = unwrapTensorAtLevel(start, cur_level); + auto results = batch_rule(start_value, start_bdim, end, steps, base, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logspace_Scalar_Tensor_generated_plumbing(const at::Scalar & start, const at::Tensor & end, int64_t steps, double base, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(end, cur_level)) { + return at::_ops::logspace_Scalar_Tensor::call(start, end, steps, base, dtype, layout, device, pin_memory); + } + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + auto results = batch_rule(start, end_value, end_bdim, steps, base, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor log_softmax_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_softmax_int::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor log_softmax_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_softmax_Dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _log_softmax_generated_plumbing(const at::Tensor & self, int64_t dim, bool half_to_float) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_log_softmax::call(self, dim, half_to_float); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, half_to_float); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _log_softmax_backward_data_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::_log_softmax_backward_data::call(grad_output, output, dim, input_dtype); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, dim, input_dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _logcumsumexp_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_logcumsumexp::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logcumsumexp_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logcumsumexp::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logcumsumexp_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logcumsumexp_dimname::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logsumexp_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logsumexp::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logsumexp_names_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logsumexp_names::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor margin_ranking_loss_generated_plumbing(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input1, cur_level) && !isBatchedAtLevel(input2, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::margin_ranking_loss::call(input1, input2, target, margin, reduction); + } + Tensor input1_value; + optional input1_bdim; + std::tie(input1_value, input1_bdim) = unwrapTensorAtLevel(input1, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(input1_value, input1_bdim, input2_value, input2_bdim, target_value, target_bdim, margin, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor matmul_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::matmul::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple matmul_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & other, ::std::array mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::matmul_backward::call(grad, self, other, mask); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, other_value, other_bdim, mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor matrix_power_generated_plumbing(const at::Tensor & self, int64_t n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::matrix_power::call(self, n); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor matrix_exp_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::matrix_exp::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor matrix_exp_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad, cur_level)) { + return at::_ops::matrix_exp_backward::call(self, grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_value, grad_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _aminmax_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_aminmax::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _aminmax_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_aminmax_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple aminmax_generated_plumbing(const at::Tensor & self, c10::optional dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::aminmax::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _compute_linear_combination_generated_plumbing(const at::Tensor & input, const at::Tensor & coefficients) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(coefficients, cur_level)) { + return at::_ops::_compute_linear_combination::call(input, coefficients); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor coefficients_value; + optional coefficients_bdim; + std::tie(coefficients_value, coefficients_bdim) = unwrapTensorAtLevel(coefficients, cur_level); + auto results = batch_rule(input_value, input_bdim, coefficients_value, coefficients_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple max_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple max_names_dim_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_names_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor value_selecting_reduction_backward_generated_plumbing(const at::Tensor & grad, int64_t dim, const at::Tensor & indices, c10::SymIntArrayRef sizes, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::value_selecting_reduction_backward::call(grad, dim, indices, sizes, keepdim); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_value, grad_bdim, dim, indices_value, indices_bdim, sizes, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor amax_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::amax::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple max_pool1d_with_indices_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool1d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor max_pool1d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool1d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool2d_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_max_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_max_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(input, cur_level)) { + return at::_ops::mkldnn_max_pool2d_backward::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, input_value, input_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_max_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_max_pool3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & input, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(input, cur_level)) { + return at::_ops::mkldnn_max_pool3d_backward::call(grad_output, output, input, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, input_value, input_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_max_pool1d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantized_max_pool1d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_max_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantized_max_pool2d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_max_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantized_max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool3d::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mean_generated_plumbing(const at::Tensor & self, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mean::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mean_dim_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mean_dim::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mean_names_dim_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mean_names_dim::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nanmean_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nanmean::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor median_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::median::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple median_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::median_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple median_names_dim_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::median_names_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor nanmedian_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nanmedian::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple nanmedian_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nanmedian_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple nanmedian_names_dim_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nanmedian_names_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple min_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::min_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple min_names_dim_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::min_names_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor amin_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::amin::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _mps_convolution_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_mps_convolution::call(self, weight, bias, padding, stride, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, stride, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple mps_convolution_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad_output, const at::Tensor & weight, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::mps_convolution_backward::call(self, grad_output, weight, padding, stride, dilation, groups, output_mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, padding, stride, dilation, groups, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor mkldnn_convolution_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::mkldnn_convolution::call(self, weight, bias, padding, stride, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, stride, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple mkldnn_rnn_layer_generated_plumbing(const at::Tensor & input, const at::Tensor & weight0, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & hx_, const at::Tensor & cx_, bool reverse, at::IntArrayRef batch_sizes, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool bidirectional, bool batch_first, bool train) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight0, cur_level) && !isBatchedAtLevel(weight1, cur_level) && !isBatchedAtLevel(weight2, cur_level) && !isBatchedAtLevel(weight3, cur_level) && !isBatchedAtLevel(hx_, cur_level) && !isBatchedAtLevel(cx_, cur_level)) { + return at::_ops::mkldnn_rnn_layer::call(input, weight0, weight1, weight2, weight3, hx_, cx_, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight0_value; + optional weight0_bdim; + std::tie(weight0_value, weight0_bdim) = unwrapTensorAtLevel(weight0, cur_level); + Tensor weight1_value; + optional weight1_bdim; + std::tie(weight1_value, weight1_bdim) = unwrapTensorAtLevel(weight1, cur_level); + Tensor weight2_value; + optional weight2_bdim; + std::tie(weight2_value, weight2_bdim) = unwrapTensorAtLevel(weight2, cur_level); + Tensor weight3_value; + optional weight3_bdim; + std::tie(weight3_value, weight3_bdim) = unwrapTensorAtLevel(weight3, cur_level); + Tensor hx__value; + optional hx__bdim; + std::tie(hx__value, hx__bdim) = unwrapTensorAtLevel(hx_, cur_level); + Tensor cx__value; + optional cx__bdim; + std::tie(cx__value, cx__bdim) = unwrapTensorAtLevel(cx_, cur_level); + auto results = batch_rule(input_value, input_bdim, weight0_value, weight0_bdim, weight1_value, weight1_bdim, weight2_value, weight2_bdim, weight3_value, weight3_bdim, hx__value, hx__bdim, cx__value, cx__bdim, reverse, batch_sizes, mode, hidden_size, num_layers, has_biases, bidirectional, batch_first, train); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple mkldnn_rnn_layer_backward_generated_plumbing(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight1, cur_level) && !isBatchedAtLevel(weight2, cur_level) && !isBatchedAtLevel(weight3, cur_level) && !isBatchedAtLevel(weight4, cur_level) && !isBatchedAtLevel(hx_, cur_level) && !isBatchedAtLevel(cx_tmp, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(hy_, cur_level) && !isBatchedAtLevel(cy_, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(workspace, cur_level)) { + return at::_ops::mkldnn_rnn_layer_backward::call(input, weight1, weight2, weight3, weight4, hx_, cx_tmp, output, hy_, cy_, grad_output, grad_hy, grad_cy, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight1_value; + optional weight1_bdim; + std::tie(weight1_value, weight1_bdim) = unwrapTensorAtLevel(weight1, cur_level); + Tensor weight2_value; + optional weight2_bdim; + std::tie(weight2_value, weight2_bdim) = unwrapTensorAtLevel(weight2, cur_level); + Tensor weight3_value; + optional weight3_bdim; + std::tie(weight3_value, weight3_bdim) = unwrapTensorAtLevel(weight3, cur_level); + Tensor weight4_value; + optional weight4_bdim; + std::tie(weight4_value, weight4_bdim) = unwrapTensorAtLevel(weight4, cur_level); + Tensor hx__value; + optional hx__bdim; + std::tie(hx__value, hx__bdim) = unwrapTensorAtLevel(hx_, cur_level); + Tensor cx_tmp_value; + optional cx_tmp_bdim; + std::tie(cx_tmp_value, cx_tmp_bdim) = unwrapTensorAtLevel(cx_tmp, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor hy__value; + optional hy__bdim; + std::tie(hy__value, hy__bdim) = unwrapTensorAtLevel(hy_, cur_level); + Tensor cy__value; + optional cy__bdim; + std::tie(cy__value, cy__bdim) = unwrapTensorAtLevel(cy_, cur_level); + Tensor workspace_value; + optional workspace_bdim; + std::tie(workspace_value, workspace_bdim) = unwrapTensorAtLevel(workspace, cur_level); + optional grad_output_value; + optional grad_output_bdim; + if (grad_output) { + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight1_value, weight1_bdim, weight2_value, weight2_bdim, weight3_value, weight3_bdim, weight4_value, weight4_bdim, hx__value, hx__bdim, cx_tmp_value, cx_tmp_bdim, output_value, output_bdim, hy__value, hy__bdim, cy__value, cy__bdim, grad_output_value, grad_output_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, reverse, mode, hidden_size, num_layers, has_biases, train, bidirectional, batch_sizes, batch_first, workspace_value, workspace_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level), makeBatched(std::get<10>(results), std::get<11>(results), cur_level), makeBatched(std::get<12>(results), std::get<13>(results), cur_level)); +} +template +::std::tuple miopen_batch_norm_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double exponential_average_factor, double epsilon) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::miopen_batch_norm::call(input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, training, exponential_average_factor, epsilon); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple miopen_batch_norm_backward_generated_plumbing(const at::Tensor & input, const at::Tensor & grad_output, const at::Tensor & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var, double epsilon) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level) && !isBatchedAtLevel(save_mean, cur_level) && !isBatchedAtLevel(save_var, cur_level)) { + return at::_ops::miopen_batch_norm_backward::call(input, grad_output, weight, running_mean, running_var, save_mean, save_var, epsilon); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + optional save_mean_value; + optional save_mean_bdim; + if (save_mean) { + std::tie(save_mean_value, save_mean_bdim) = unwrapTensorAtLevel(save_mean.value(), cur_level); + } + optional save_var_value; + optional save_var_bdim; + if (save_var) { + std::tie(save_var_value, save_var_bdim) = unwrapTensorAtLevel(save_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, grad_output_value, grad_output_bdim, weight_value, weight_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, save_mean_value, save_mean_bdim, save_var_value, save_var_bdim, epsilon); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor miopen_convolution_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::miopen_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, stride, dilation, groups, benchmark, deterministic); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor miopen_convolution_transpose_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::miopen_convolution_transpose::call(self, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, output_padding, stride, dilation, groups, benchmark, deterministic); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor miopen_depthwise_convolution_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, bool benchmark, bool deterministic) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::miopen_depthwise_convolution::call(self, weight, bias, padding, stride, dilation, groups, benchmark, deterministic); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, stride, dilation, groups, benchmark, deterministic); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor miopen_convolution_relu_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::miopen_convolution_relu::call(self, weight, bias, stride, padding, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor miopen_convolution_add_relu_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, const at::Tensor & z, const c10::optional & alpha, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(z, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::miopen_convolution_add_relu::call(self, weight, z, alpha, bias, stride, padding, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor z_value; + optional z_bdim; + std::tie(z_value, z_bdim) = unwrapTensorAtLevel(z, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, z_value, z_bdim, alpha, bias_value, bias_bdim, stride, padding, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple miopen_rnn_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & hx, const c10::optional & cx, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(dropout_state, cur_level)) { + return at::_ops::miopen_rnn::call(input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight, weight_stride0, hx_value, hx_bdim, cx_value, cx_bdim, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple> miopen_rnn_backward_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(weight_buf, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(dropout_state, cur_level) && !isBatchedAtLevel(reserve, cur_level)) { + return at::_ops::miopen_rnn_backward::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_buf_value; + optional weight_buf_bdim; + std::tie(weight_buf_value, weight_buf_bdim) = unwrapTensorAtLevel(weight_buf, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor reserve_value; + optional reserve_bdim; + std::tie(reserve_value, reserve_bdim) = unwrapTensorAtLevel(reserve, cur_level); + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional grad_output_value; + optional grad_output_bdim; + if (grad_output) { + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight, weight_stride0, weight_buf_value, weight_buf_bdim, hx_value, hx_bdim, cx_value, cx_bdim, output_value, output_bdim, grad_output_value, grad_output_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim, reserve_value, reserve_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +at::Tensor mm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::mm::call(self, mat2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _int_mm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::_int_mm::call(self, mat2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _convert_weight_to_int4pack_generated_plumbing(const at::Tensor & self, int64_t innerKTiles) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_convert_weight_to_int4pack::call(self, innerKTiles); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, innerKTiles); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _weight_int4pack_mm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2, int64_t qGroupSize, const at::Tensor & qScaleAndZeros) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level) && !isBatchedAtLevel(qScaleAndZeros, cur_level)) { + return at::_ops::_weight_int4pack_mm::call(self, mat2, qGroupSize, qScaleAndZeros); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + Tensor qScaleAndZeros_value; + optional qScaleAndZeros_bdim; + std::tie(qScaleAndZeros_value, qScaleAndZeros_bdim) = unwrapTensorAtLevel(qScaleAndZeros, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim, qGroupSize, qScaleAndZeros_value, qScaleAndZeros_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _weight_int8pack_mm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2, const at::Tensor & scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level) && !isBatchedAtLevel(scales, cur_level)) { + return at::_ops::_weight_int8pack_mm::call(self, mat2, scales); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + Tensor scales_value; + optional scales_bdim; + std::tie(scales_value, scales_bdim) = unwrapTensorAtLevel(scales, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim, scales_value, scales_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_mm_generated_plumbing(const at::Tensor & sparse, const at::Tensor & dense) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(sparse, cur_level) && !isBatchedAtLevel(dense, cur_level)) { + return at::_ops::_sparse_mm::call(sparse, dense); + } + Tensor sparse_value; + optional sparse_bdim; + std::tie(sparse_value, sparse_bdim) = unwrapTensorAtLevel(sparse, cur_level); + Tensor dense_value; + optional dense_bdim; + std::tie(dense_value, dense_bdim) = unwrapTensorAtLevel(dense, cur_level); + auto results = batch_rule(sparse_value, sparse_bdim, dense_value, dense_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_mm_reduce_generated_plumbing(const at::Tensor & sparse, const at::Tensor & dense, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(sparse, cur_level) && !isBatchedAtLevel(dense, cur_level)) { + return at::_ops::_sparse_mm_reduce::call(sparse, dense, reduce); + } + Tensor sparse_value; + optional sparse_bdim; + std::tie(sparse_value, sparse_bdim) = unwrapTensorAtLevel(sparse, cur_level); + Tensor dense_value; + optional dense_bdim; + std::tie(dense_value, dense_bdim) = unwrapTensorAtLevel(dense, cur_level); + auto results = batch_rule(sparse_value, sparse_bdim, dense_value, dense_bdim, reduce); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sparse_matmul_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_sparse_sparse_matmul::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple mode_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mode::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple mode_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mode_dimname::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor mul_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::mul_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & mul__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::mul__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor mul_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mul_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & mul__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mul__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor multiply_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::multiply_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & multiply__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::multiply__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor multiply_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::multiply_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & multiply__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::multiply__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor mv_generated_plumbing(const at::Tensor & self, const at::Tensor & vec) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(vec, cur_level)) { + return at::_ops::mv::call(self, vec); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor vec_value; + optional vec_bdim; + std::tie(vec_value, vec_bdim) = unwrapTensorAtLevel(vec, cur_level); + auto results = batch_rule(self_value, self_bdim, vec_value, vec_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mvlgamma_generated_plumbing(const at::Tensor & self, int64_t p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mvlgamma::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & mvlgamma__generated_plumbing(at::Tensor & self, int64_t p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mvlgamma_::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p); + return self; +} +template +at::Tensor narrow_copy_generated_plumbing(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::narrow_copy::call(self, dim, start, length); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, start, length); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor narrow_generated_plumbing(const at::Tensor & self, int64_t dim, c10::SymInt start, c10::SymInt length) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::narrow::call(self, dim, start, length); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, start, length); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor narrow_Tensor_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & start, c10::SymInt length) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(start, cur_level)) { + return at::_ops::narrow_Tensor::call(self, dim, start, length); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor start_value; + optional start_bdim; + std::tie(start_value, start_bdim) = unwrapTensorAtLevel(start, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, start_value, start_bdim, length); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple native_batch_norm_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const c10::optional & running_mean, const c10::optional & running_var, bool training, double momentum, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::native_batch_norm::call(input, weight, bias, running_mean, running_var, training, momentum, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, training, momentum, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _native_batch_norm_legit_no_training_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, double momentum, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::_native_batch_norm_legit_no_training::call(input, weight, bias, running_mean, running_var, momentum, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor running_mean_value; + optional running_mean_bdim; + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean, cur_level); + Tensor running_var_value; + optional running_var_bdim; + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, momentum, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _native_batch_norm_legit_no_stats_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, bool training, double momentum, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_native_batch_norm_legit_no_stats::call(input, weight, bias, training, momentum, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, training, momentum, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple batch_norm_stats_generated_plumbing(const at::Tensor & input, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::batch_norm_stats::call(input, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor batch_norm_elemt_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & invstd, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(invstd, cur_level)) { + return at::_ops::batch_norm_elemt::call(input, weight, bias, mean, invstd, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor invstd_value; + optional invstd_bdim; + std::tie(invstd_value, invstd_bdim) = unwrapTensorAtLevel(invstd, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, mean_value, mean_bdim, invstd_value, invstd_bdim, eps); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple batch_norm_gather_stats_generated_plumbing(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, int64_t count) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(invstd, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::batch_norm_gather_stats::call(input, mean, invstd, running_mean, running_var, momentum, eps, count); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor invstd_value; + optional invstd_bdim; + std::tie(invstd_value, invstd_bdim) = unwrapTensorAtLevel(invstd, cur_level); + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, mean_value, mean_bdim, invstd_value, invstd_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, momentum, eps, count); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple batch_norm_gather_stats_with_counts_generated_plumbing(const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & running_mean, const c10::optional & running_var, double momentum, double eps, const at::Tensor & counts) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(invstd, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level) && !isBatchedAtLevel(counts, cur_level)) { + return at::_ops::batch_norm_gather_stats_with_counts::call(input, mean, invstd, running_mean, running_var, momentum, eps, counts); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor invstd_value; + optional invstd_bdim; + std::tie(invstd_value, invstd_bdim) = unwrapTensorAtLevel(invstd, cur_level); + Tensor counts_value; + optional counts_bdim; + std::tie(counts_value, counts_bdim) = unwrapTensorAtLevel(counts, cur_level); + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, mean_value, mean_bdim, invstd_value, invstd_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, momentum, eps, counts_value, counts_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple native_batch_norm_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level) && !isBatchedAtLevel(save_mean, cur_level) && !isBatchedAtLevel(save_invstd, cur_level)) { + return at::_ops::native_batch_norm_backward::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + optional save_mean_value; + optional save_mean_bdim; + if (save_mean) { + std::tie(save_mean_value, save_mean_bdim) = unwrapTensorAtLevel(save_mean.value(), cur_level); + } + optional save_invstd_value; + optional save_invstd_bdim; + if (save_invstd) { + std::tie(save_invstd_value, save_invstd_bdim) = unwrapTensorAtLevel(save_invstd.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, input_value, input_bdim, weight_value, weight_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, save_mean_value, save_mean_bdim, save_invstd_value, save_invstd_bdim, train, eps, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple batch_norm_backward_reduce_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, bool input_g, bool weight_g, bool bias_g) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(invstd, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::batch_norm_backward_reduce::call(grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor invstd_value; + optional invstd_bdim; + std::tie(invstd_value, invstd_bdim) = unwrapTensorAtLevel(invstd, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, input_value, input_bdim, mean_value, mean_bdim, invstd_value, invstd_bdim, weight_value, weight_bdim, input_g, weight_g, bias_g); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +at::Tensor batch_norm_backward_elemt_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & input, const at::Tensor & mean, const at::Tensor & invstd, const c10::optional & weight, const at::Tensor & sum_dy, const at::Tensor & sum_dy_xmu, const at::Tensor & count) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(invstd, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(sum_dy, cur_level) && !isBatchedAtLevel(sum_dy_xmu, cur_level) && !isBatchedAtLevel(count, cur_level)) { + return at::_ops::batch_norm_backward_elemt::call(grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor invstd_value; + optional invstd_bdim; + std::tie(invstd_value, invstd_bdim) = unwrapTensorAtLevel(invstd, cur_level); + Tensor sum_dy_value; + optional sum_dy_bdim; + std::tie(sum_dy_value, sum_dy_bdim) = unwrapTensorAtLevel(sum_dy, cur_level); + Tensor sum_dy_xmu_value; + optional sum_dy_xmu_bdim; + std::tie(sum_dy_xmu_value, sum_dy_xmu_bdim) = unwrapTensorAtLevel(sum_dy_xmu, cur_level); + Tensor count_value; + optional count_bdim; + std::tie(count_value, count_bdim) = unwrapTensorAtLevel(count, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, input_value, input_bdim, mean_value, mean_bdim, invstd_value, invstd_bdim, weight_value, weight_bdim, sum_dy_value, sum_dy_bdim, sum_dy_xmu_value, sum_dy_xmu_bdim, count_value, count_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple batch_norm_update_stats_generated_plumbing(const at::Tensor & input, const c10::optional & running_mean, const c10::optional & running_var, double momentum) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::batch_norm_update_stats::call(input, running_mean, running_var, momentum); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional running_mean_value; + optional running_mean_bdim; + if (running_mean) { + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean.value(), cur_level); + } + optional running_var_value; + optional running_var_bdim; + if (running_var) { + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, momentum); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _nnpack_spatial_convolution_generated_plumbing(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_nnpack_spatial_convolution::call(input, weight, bias, padding, stride); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, padding, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ones_like_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ones_like::call(self, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pairwise_distance_generated_plumbing(const at::Tensor & x1, const at::Tensor & x2, double p, double eps, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level)) { + return at::_ops::pairwise_distance::call(x1, x2, p, eps, keepdim); + } + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + auto results = batch_rule(x1_value, x1_bdim, x2_value, x2_bdim, p, eps, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cdist_generated_plumbing(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional compute_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level)) { + return at::_ops::cdist::call(x1, x2, p, compute_mode); + } + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + auto results = batch_rule(x1_value, x1_bdim, x2_value, x2_bdim, p, compute_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _euclidean_dist_generated_plumbing(const at::Tensor & x1, const at::Tensor & x2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level)) { + return at::_ops::_euclidean_dist::call(x1, x2); + } + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + auto results = batch_rule(x1_value, x1_bdim, x2_value, x2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cdist_forward_generated_plumbing(const at::Tensor & x1, const at::Tensor & x2, double p, c10::optional compute_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level)) { + return at::_ops::_cdist_forward::call(x1, x2, p, compute_mode); + } + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + auto results = batch_rule(x1_value, x1_bdim, x2_value, x2_bdim, p, compute_mode); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cdist_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & x1, const at::Tensor & x2, double p, const at::Tensor & cdist) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level) && !isBatchedAtLevel(cdist, cur_level)) { + return at::_ops::_cdist_backward::call(grad, x1, x2, p, cdist); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + Tensor cdist_value; + optional cdist_bdim; + std::tie(cdist_value, cdist_bdim) = unwrapTensorAtLevel(cdist, cur_level); + auto results = batch_rule(grad_value, grad_bdim, x1_value, x1_bdim, x2_value, x2_bdim, p, cdist_value, cdist_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pdist_generated_plumbing(const at::Tensor & self, double p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pdist::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _pdist_forward_generated_plumbing(const at::Tensor & self, double p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_pdist_forward::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _pdist_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, double p, const at::Tensor & pdist) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(pdist, cur_level)) { + return at::_ops::_pdist_backward::call(grad, self, p, pdist); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor pdist_value; + optional pdist_bdim; + std::tie(pdist_value, pdist_bdim) = unwrapTensorAtLevel(pdist, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, p, pdist_value, pdist_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cosine_similarity_generated_plumbing(const at::Tensor & x1, const at::Tensor & x2, int64_t dim, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x1, cur_level) && !isBatchedAtLevel(x2, cur_level)) { + return at::_ops::cosine_similarity::call(x1, x2, dim, eps); + } + Tensor x1_value; + optional x1_bdim; + std::tie(x1_value, x1_bdim) = unwrapTensorAtLevel(x1, cur_level); + Tensor x2_value; + optional x2_bdim; + std::tie(x2_value, x2_bdim) = unwrapTensorAtLevel(x2, cur_level); + auto results = batch_rule(x1_value, x1_bdim, x2_value, x2_bdim, dim, eps); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor permute_generated_plumbing(const at::Tensor & self, at::IntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::permute::call(self, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor movedim_intlist_generated_plumbing(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::movedim_intlist::call(self, source, destination); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source, destination); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor movedim_int_generated_plumbing(const at::Tensor & self, int64_t source, int64_t destination) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::movedim_int::call(self, source, destination); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source, destination); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor moveaxis_intlist_generated_plumbing(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::moveaxis_intlist::call(self, source, destination); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source, destination); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor moveaxis_int_generated_plumbing(const at::Tensor & self, int64_t source, int64_t destination) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::moveaxis_int::call(self, source, destination); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source, destination); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor numpy_T_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::numpy_T::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor matrix_H_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::matrix_H::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mT_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mT::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mH_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mH::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor adjoint_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adjoint::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pixel_shuffle_generated_plumbing(const at::Tensor & self, int64_t upscale_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pixel_shuffle::call(self, upscale_factor); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, upscale_factor); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pixel_unshuffle_generated_plumbing(const at::Tensor & self, int64_t downscale_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pixel_unshuffle::call(self, downscale_factor); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, downscale_factor); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor channel_shuffle_generated_plumbing(const at::Tensor & self, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::channel_shuffle::call(self, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor native_channel_shuffle_generated_plumbing(const at::Tensor & self, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::native_channel_shuffle::call(self, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pin_memory_generated_plumbing(const at::Tensor & self, c10::optional device) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pin_memory::call(self, device); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, device); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _pin_memory_generated_plumbing(const at::Tensor & self, c10::optional device) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_pin_memory::call(self, device); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, device); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pinverse_generated_plumbing(const at::Tensor & self, double rcond) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pinverse::call(self, rcond); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, rcond); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor poisson_nll_loss_generated_plumbing(const at::Tensor & input, const at::Tensor & target, bool log_input, bool full, double eps, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::poisson_nll_loss::call(input, target, log_input, full, eps, reduction); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(input_value, input_bdim, target_value, target_bdim, log_input, full, eps, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rad2deg_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rad2deg::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & rad2deg__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rad2deg_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor deg2rad_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::deg2rad::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & deg2rad__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::deg2rad_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor rand_like_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rand_like::call(self, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor randint_like_generated_plumbing(const at::Tensor & self, c10::SymInt high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::randint_like::call(self, high, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, high, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor randint_like_low_dtype_generated_plumbing(const at::Tensor & self, c10::SymInt low, c10::SymInt high, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::randint_like_low_dtype::call(self, low, high, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, low, high, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor randn_like_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::randn_like::call(self, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ravel_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ravel::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reciprocal_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reciprocal::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & reciprocal__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reciprocal_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor neg_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::neg::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & neg__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::neg_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor negative_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::negative::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & negative__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::negative_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor repeat_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef repeats) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::repeat::call(self, repeats); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, repeats); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor repeat_interleave_Tensor_generated_plumbing(const at::Tensor & repeats, c10::optional output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(repeats, cur_level)) { + return at::_ops::repeat_interleave_Tensor::call(repeats, output_size); + } + Tensor repeats_value; + optional repeats_bdim; + std::tie(repeats_value, repeats_bdim) = unwrapTensorAtLevel(repeats, cur_level); + auto results = batch_rule(repeats_value, repeats_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor repeat_interleave_self_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & repeats, c10::optional dim, c10::optional output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(repeats, cur_level)) { + return at::_ops::repeat_interleave_self_Tensor::call(self, repeats, dim, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor repeats_value; + optional repeats_bdim; + std::tie(repeats_value, repeats_bdim) = unwrapTensorAtLevel(repeats, cur_level); + auto results = batch_rule(self_value, self_bdim, repeats_value, repeats_bdim, dim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor repeat_interleave_self_int_generated_plumbing(const at::Tensor & self, c10::SymInt repeats, c10::optional dim, c10::optional output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::repeat_interleave_self_int::call(self, repeats, dim, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, repeats, dim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reshape_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef shape) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reshape::call(self, shape); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, shape); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _reshape_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_reshape_copy::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _reshape_alias_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_reshape_alias::call(self, size, stride); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _mkldnn_reshape_generated_plumbing(const at::Tensor & self, at::IntArrayRef shape) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_mkldnn_reshape::call(self, shape); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, shape); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reshape_as_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::reshape_as::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor round_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::round::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & round__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::round_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor round_decimals_generated_plumbing(const at::Tensor & self, int64_t decimals) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::round_decimals::call(self, decimals); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, decimals); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & round__decimals_generated_plumbing(at::Tensor & self, int64_t decimals) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::round__decimals::call(self, decimals); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, decimals); + return self; +} +template +at::Tensor rrelu_generated_plumbing(const at::Tensor & self, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rrelu::call(self, lower, upper, training, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, lower, upper, training, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & rrelu__generated_plumbing(at::Tensor & self, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rrelu_::call(self, lower, upper, training, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, lower, upper, training, generator); + return self; +} +template +at::Tensor relu_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::relu::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & relu__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::relu_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor relu6_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::relu6::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & relu6__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::relu6_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor prelu_generated_plumbing(const at::Tensor & self, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::prelu::call(self, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _prelu_kernel_generated_plumbing(const at::Tensor & self, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_prelu_kernel::call(self, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _prelu_kernel_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_prelu_kernel_backward::call(grad_output, self, weight); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, weight_value, weight_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor & gelu__generated_plumbing(at::Tensor & self, c10::string_view approximate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gelu_::call(self, approximate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, approximate); + return self; +} +template +at::Tensor gelu_generated_plumbing(const at::Tensor & self, c10::string_view approximate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gelu::call(self, approximate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, approximate); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gelu_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::gelu_backward::call(grad_output, self, approximate); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, approximate); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor infinitely_differentiable_gelu_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::infinitely_differentiable_gelu_backward::call(grad, self); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hardshrink_generated_plumbing(const at::Tensor & self, const at::Scalar & lambd) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardshrink::call(self, lambd); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, lambd); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hardshrink_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & self, const at::Scalar & lambd) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardshrink_backward::call(grad_out, self, lambd); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_out_value, grad_out_bdim, self_value, self_bdim, lambd); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rsqrt_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rsqrt::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & rsqrt__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rsqrt_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor select_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, int64_t index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::select_Dimname::call(self, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor select_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::SymInt index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::select_int::call(self, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor select_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::select_backward::call(grad_output, input_sizes, dim, index); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_sizes, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_select_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim, c10::SymInt index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_select_backward::call(grad_output, self, dim, index); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor selu_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::selu::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & selu__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::selu_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor celu_generated_plumbing(const at::Tensor & self, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::celu::call(self, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & celu__generated_plumbing(at::Tensor & self, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::celu_::call(self, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, alpha); + return self; +} +template +at::Tensor silu_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::silu::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & silu__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::silu_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor silu_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::silu_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mish_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mish::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & mish__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mish_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor mish_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::mish_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sigmoid_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sigmoid::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sigmoid__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sigmoid_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor logit_generated_plumbing(const at::Tensor & self, c10::optional eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logit::call(self, eps); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, eps); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & logit__generated_plumbing(at::Tensor & self, c10::optional eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logit_::call(self, eps); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, eps); + return self; +} +template +at::Tensor sin_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sin::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sin__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sin_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor sinc_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sinc::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sinc__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sinc_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor sinh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sinh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sinh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sinh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor detach_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::detach::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slice_Tensor_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::slice_Tensor::call(self, dim, start, end, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, start, end, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slice_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef input_sizes, int64_t dim, c10::SymInt start, c10::SymInt end, c10::SymInt step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::slice_backward::call(grad_output, input_sizes, dim, start, end, step); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, input_sizes, dim, start, end, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slice_inverse_generated_plumbing(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::slice_inverse::call(self, src, dim, start, end, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, dim, start, end, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slice_scatter_generated_plumbing(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::slice_scatter::call(self, src, dim, start, end, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, dim, start, end, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor select_scatter_generated_plumbing(const at::Tensor & self, const at::Tensor & src, int64_t dim, c10::SymInt index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::select_scatter::call(self, src, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagonal_scatter_generated_plumbing(const at::Tensor & self, const at::Tensor & src, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::diagonal_scatter::call(self, src, offset, dim1, dim2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor as_strided_scatter_generated_plumbing(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::as_strided_scatter::call(self, src, size, stride, storage_offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, size, stride, storage_offset); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor smm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::smm::call(self, mat2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor softmax_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::softmax_int::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor softmax_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::softmax_Dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _softmax_generated_plumbing(const at::Tensor & self, int64_t dim, bool half_to_float) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_softmax::call(self, dim, half_to_float); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, half_to_float); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _softmax_backward_data_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::_softmax_backward_data::call(grad_output, output, dim, input_dtype); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, dim, input_dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unsafe_split_Tensor_generated_plumbing(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unsafe_split_Tensor::call(self, split_size, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_size, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector split_Tensor_generated_plumbing(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::split_Tensor::call(self, split_size, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_size, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector split_sizes_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::split_sizes::call(self, split_size, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_size, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unsafe_split_with_sizes_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unsafe_split_with_sizes::call(self, split_sizes, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_sizes, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector split_with_sizes_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::split_with_sizes::call(self, split_sizes, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_sizes, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector hsplit_int_generated_plumbing(const at::Tensor & self, int64_t sections) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hsplit_int::call(self, sections); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sections); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector hsplit_array_generated_plumbing(const at::Tensor & self, at::IntArrayRef indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hsplit_array::call(self, indices); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector vsplit_int_generated_plumbing(const at::Tensor & self, int64_t sections) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::vsplit_int::call(self, sections); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sections); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector vsplit_array_generated_plumbing(const at::Tensor & self, at::IntArrayRef indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::vsplit_array::call(self, indices); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector dsplit_int_generated_plumbing(const at::Tensor & self, int64_t sections) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::dsplit_int::call(self, sections); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sections); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector dsplit_array_generated_plumbing(const at::Tensor & self, at::IntArrayRef indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::dsplit_array::call(self, indices); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, indices); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_dim_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_dim::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_dimname::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_dims_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_dims::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sspaddmm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::sspaddmm::call(self, mat1, mat2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _chunk_cat_generated_plumbing(at::TensorList tensors, int64_t dim, int64_t num_chunks) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::_chunk_cat::call(tensors, dim, num_chunks); + } + + auto results = batch_rule(tensors, dim, num_chunks); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor stack_generated_plumbing(at::TensorList tensors, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::stack::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _stack_generated_plumbing(at::TensorList tensors, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::_stack::call(tensors, dim); + } + + auto results = batch_rule(tensors, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hstack_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::hstack::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor vstack_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::vstack::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor dstack_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::dstack::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor stft_generated_plumbing(const at::Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const c10::optional & window, bool normalized, c10::optional onesided, c10::optional return_complex) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(window, cur_level)) { + return at::_ops::stft::call(self, n_fft, hop_length, win_length, window, normalized, onesided, return_complex); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional window_value; + optional window_bdim; + if (window) { + std::tie(window_value, window_bdim) = unwrapTensorAtLevel(window.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, n_fft, hop_length, win_length, window_value, window_bdim, normalized, onesided, return_complex); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor stft_center_generated_plumbing(const at::Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const c10::optional & window, bool center, c10::string_view pad_mode, bool normalized, c10::optional onesided, c10::optional return_complex) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(window, cur_level)) { + return at::_ops::stft_center::call(self, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional window_value; + optional window_bdim; + if (window) { + std::tie(window_value, window_bdim) = unwrapTensorAtLevel(window.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, n_fft, hop_length, win_length, window_value, window_bdim, center, pad_mode, normalized, onesided, return_complex); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor istft_generated_plumbing(const at::Tensor & self, int64_t n_fft, c10::optional hop_length, c10::optional win_length, const c10::optional & window, bool center, bool normalized, c10::optional onesided, c10::optional length, bool return_complex) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(window, cur_level)) { + return at::_ops::istft::call(self, n_fft, hop_length, win_length, window, center, normalized, onesided, length, return_complex); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional window_value; + optional window_bdim; + if (window) { + std::tie(window_value, window_bdim) = unwrapTensorAtLevel(window.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, n_fft, hop_length, win_length, window_value, window_bdim, center, normalized, onesided, length, return_complex); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sum_generated_plumbing(const at::Tensor & self, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sum::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sum_dim_IntList_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sum_dim_IntList::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sum_dim_DimnameList_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sum_dim_DimnameList::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_sum_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_sum_backward::call(grad, self, dim, keepdim); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nansum_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nansum::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sum_to_size_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sum_to_size::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sqrt_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sqrt::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sqrt__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sqrt_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor square_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::square::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & square__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::square_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor std_generated_plumbing(const at::Tensor & self, bool unbiased) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std::call(self, unbiased); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, unbiased); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor std_dim_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor std_correction_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_correction::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple std_mean_generated_plumbing(const at::Tensor & self, bool unbiased) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_mean::call(self, unbiased); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, unbiased); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple std_mean_dim_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_mean_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple std_mean_correction_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_mean_correction::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple std_mean_names_dim_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_mean_names_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple std_mean_correction_names_generated_plumbing(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_mean_correction_names::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor std_names_dim_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_names_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor std_correction_names_generated_plumbing(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::std_correction_names::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor prod_generated_plumbing(const at::Tensor & self, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::prod::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor prod_dim_int_generated_plumbing(const at::Tensor & self, int64_t dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::prod_dim_int::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor prod_dim_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::prod_dim_Dimname::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor t_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::t::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor tan_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tan::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & tan__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tan_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor tanh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tanh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & tanh__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tanh_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor tensordot_generated_plumbing(const at::Tensor & self, const at::Tensor & other, at::IntArrayRef dims_self, at::IntArrayRef dims_other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::tensordot::call(self, other, dims_self, dims_other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, dims_self, dims_other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor threshold_generated_plumbing(const at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::threshold::call(self, threshold, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, threshold, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & threshold__generated_plumbing(at::Tensor & self, const at::Scalar & threshold, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::threshold_::call(self, threshold, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, threshold, value); + return self; +} +template +at::Tensor threshold_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & threshold) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::threshold_backward::call(grad_output, self, threshold); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, threshold); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor tile_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tile::call(self, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor transpose_int_generated_plumbing(const at::Tensor & self, int64_t dim0, int64_t dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::transpose_int::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim0, dim1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor transpose_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim0, at::Dimname dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::transpose_Dimname::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim0, dim1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _mkldnn_transpose_generated_plumbing(const at::Tensor & self, int64_t dim0, int64_t dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_mkldnn_transpose::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim0, dim1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _mkldnn_transpose__generated_plumbing(at::Tensor & self, int64_t dim0, int64_t dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_mkldnn_transpose_::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim0, dim1); + return self; +} +template +at::Tensor one_hot_generated_plumbing(const at::Tensor & self, int64_t num_classes) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::one_hot::call(self, num_classes); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, num_classes); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flip_generated_plumbing(const at::Tensor & self, at::IntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flip::call(self, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fliplr_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fliplr::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flipud_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::flipud::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor roll_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef shifts, at::IntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::roll::call(self, shifts, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, shifts, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rot90_generated_plumbing(const at::Tensor & self, int64_t k, at::IntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rot90::call(self, k, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, k, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trapezoid_x_generated_plumbing(const at::Tensor & y, const at::Tensor & x, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level) && !isBatchedAtLevel(x, cur_level)) { + return at::_ops::trapezoid_x::call(y, x, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(y_value, y_bdim, x_value, x_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trapezoid_dx_generated_plumbing(const at::Tensor & y, const at::Scalar & dx, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level)) { + return at::_ops::trapezoid_dx::call(y, dx, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + auto results = batch_rule(y_value, y_bdim, dx, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trapz_x_generated_plumbing(const at::Tensor & y, const at::Tensor & x, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level) && !isBatchedAtLevel(x, cur_level)) { + return at::_ops::trapz_x::call(y, x, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(y_value, y_bdim, x_value, x_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trapz_dx_generated_plumbing(const at::Tensor & y, double dx, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(y, cur_level)) { + return at::_ops::trapz_dx::call(y, dx, dim); + } + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + auto results = batch_rule(y_value, y_bdim, dx, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _transform_bias_rescale_qkv_generated_plumbing(const at::Tensor & qkv, const at::Tensor & qkv_bias, int64_t num_heads) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(qkv, cur_level) && !isBatchedAtLevel(qkv_bias, cur_level)) { + return at::_ops::_transform_bias_rescale_qkv::call(qkv, qkv_bias, num_heads); + } + Tensor qkv_value; + optional qkv_bdim; + std::tie(qkv_value, qkv_bdim) = unwrapTensorAtLevel(qkv, cur_level); + Tensor qkv_bias_value; + optional qkv_bias_bdim; + std::tie(qkv_bias_value, qkv_bias_bdim) = unwrapTensorAtLevel(qkv_bias, cur_level); + auto results = batch_rule(qkv_value, qkv_bdim, qkv_bias_value, qkv_bias_bdim, num_heads); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _nested_tensor_from_mask_generated_plumbing(const at::Tensor & t, const at::Tensor & mask, bool mask_check) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(t, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_nested_tensor_from_mask::call(t, mask, mask_check); + } + Tensor t_value; + optional t_bdim; + std::tie(t_value, t_bdim) = unwrapTensorAtLevel(t, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(t_value, t_bdim, mask_value, mask_bdim, mask_check); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_from_padded_generated_plumbing(const at::Tensor & padded, const at::Tensor & cpu_nested_shape_example, bool fuse_transform_0213) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(padded, cur_level) && !isBatchedAtLevel(cpu_nested_shape_example, cur_level)) { + return at::_ops::_nested_from_padded::call(padded, cpu_nested_shape_example, fuse_transform_0213); + } + Tensor padded_value; + optional padded_bdim; + std::tie(padded_value, padded_bdim) = unwrapTensorAtLevel(padded, cur_level); + Tensor cpu_nested_shape_example_value; + optional cpu_nested_shape_example_bdim; + std::tie(cpu_nested_shape_example_value, cpu_nested_shape_example_bdim) = unwrapTensorAtLevel(cpu_nested_shape_example, cur_level); + auto results = batch_rule(padded_value, padded_bdim, cpu_nested_shape_example_value, cpu_nested_shape_example_bdim, fuse_transform_0213); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_tensor_size_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_tensor_size::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_tensor_strides_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_tensor_strides::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_tensor_storage_offsets_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_tensor_storage_offsets::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_from_padded_and_nested_example_generated_plumbing(const at::Tensor & padded, const at::Tensor & nt_example) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(padded, cur_level) && !isBatchedAtLevel(nt_example, cur_level)) { + return at::_ops::_nested_from_padded_and_nested_example::call(padded, nt_example); + } + Tensor padded_value; + optional padded_bdim; + std::tie(padded_value, padded_bdim) = unwrapTensorAtLevel(padded, cur_level); + Tensor nt_example_value; + optional nt_example_bdim; + std::tie(nt_example_value, nt_example_bdim) = unwrapTensorAtLevel(nt_example, cur_level); + auto results = batch_rule(padded_value, padded_bdim, nt_example_value, nt_example_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_view_from_buffer_generated_plumbing(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(nested_size, cur_level) && !isBatchedAtLevel(nested_strides, cur_level) && !isBatchedAtLevel(offsets, cur_level)) { + return at::_ops::_nested_view_from_buffer::call(self, nested_size, nested_strides, offsets); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor nested_size_value; + optional nested_size_bdim; + std::tie(nested_size_value, nested_size_bdim) = unwrapTensorAtLevel(nested_size, cur_level); + Tensor nested_strides_value; + optional nested_strides_bdim; + std::tie(nested_strides_value, nested_strides_bdim) = unwrapTensorAtLevel(nested_strides, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + auto results = batch_rule(self_value, self_bdim, nested_size_value, nested_size_bdim, nested_strides_value, nested_strides_bdim, offsets_value, offsets_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_view_from_buffer_copy_generated_plumbing(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(nested_size, cur_level) && !isBatchedAtLevel(nested_strides, cur_level) && !isBatchedAtLevel(offsets, cur_level)) { + return at::_ops::_nested_view_from_buffer_copy::call(self, nested_size, nested_strides, offsets); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor nested_size_value; + optional nested_size_bdim; + std::tie(nested_size_value, nested_size_bdim) = unwrapTensorAtLevel(nested_size, cur_level); + Tensor nested_strides_value; + optional nested_strides_bdim; + std::tie(nested_strides_value, nested_strides_bdim) = unwrapTensorAtLevel(nested_strides, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + auto results = batch_rule(self_value, self_bdim, nested_size_value, nested_size_bdim, nested_strides_value, nested_strides_bdim, offsets_value, offsets_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_view_from_jagged_generated_plumbing(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths, int64_t ragged_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(dummy, cur_level) && !isBatchedAtLevel(lengths, cur_level)) { + return at::_ops::_nested_view_from_jagged::call(self, offsets, dummy, lengths, ragged_idx); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + Tensor dummy_value; + optional dummy_bdim; + std::tie(dummy_value, dummy_bdim) = unwrapTensorAtLevel(dummy, cur_level); + optional lengths_value; + optional lengths_bdim; + if (lengths) { + std::tie(lengths_value, lengths_bdim) = unwrapTensorAtLevel(lengths.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, offsets_value, offsets_bdim, dummy_value, dummy_bdim, lengths_value, lengths_bdim, ragged_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_view_from_jagged_copy_generated_plumbing(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths, int64_t ragged_idx) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(offsets, cur_level) && !isBatchedAtLevel(dummy, cur_level) && !isBatchedAtLevel(lengths, cur_level)) { + return at::_ops::_nested_view_from_jagged_copy::call(self, offsets, dummy, lengths, ragged_idx); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + Tensor dummy_value; + optional dummy_bdim; + std::tie(dummy_value, dummy_bdim) = unwrapTensorAtLevel(dummy, cur_level); + optional lengths_value; + optional lengths_bdim; + if (lengths) { + std::tie(lengths_value, lengths_bdim) = unwrapTensorAtLevel(lengths.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, offsets_value, offsets_bdim, dummy_value, dummy_bdim, lengths_value, lengths_bdim, ragged_idx); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_get_values_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_get_values::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_get_values_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_get_values_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_get_offsets_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_get_offsets::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_get_lengths_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_nested_get_lengths::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_get_jagged_dummy_generated_plumbing(const at::Tensor & any) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(any, cur_level)) { + return at::_ops::_nested_get_jagged_dummy::call(any); + } + Tensor any_value; + optional any_bdim; + std::tie(any_value, any_bdim) = unwrapTensorAtLevel(any, cur_level); + auto results = batch_rule(any_value, any_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _trilinear_generated_plumbing(const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(i1, cur_level) && !isBatchedAtLevel(i2, cur_level) && !isBatchedAtLevel(i3, cur_level)) { + return at::_ops::_trilinear::call(i1, i2, i3, expand1, expand2, expand3, sumdim, unroll_dim); + } + Tensor i1_value; + optional i1_bdim; + std::tie(i1_value, i1_bdim) = unwrapTensorAtLevel(i1, cur_level); + Tensor i2_value; + optional i2_bdim; + std::tie(i2_value, i2_bdim) = unwrapTensorAtLevel(i2, cur_level); + Tensor i3_value; + optional i3_bdim; + std::tie(i3_value, i3_bdim) = unwrapTensorAtLevel(i3, cur_level); + auto results = batch_rule(i1_value, i1_bdim, i2_value, i2_bdim, i3_value, i3_bdim, expand1, expand2, expand3, sumdim, unroll_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor triplet_margin_loss_generated_plumbing(const at::Tensor & anchor, const at::Tensor & positive, const at::Tensor & negative, double margin, double p, double eps, bool swap, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(anchor, cur_level) && !isBatchedAtLevel(positive, cur_level) && !isBatchedAtLevel(negative, cur_level)) { + return at::_ops::triplet_margin_loss::call(anchor, positive, negative, margin, p, eps, swap, reduction); + } + Tensor anchor_value; + optional anchor_bdim; + std::tie(anchor_value, anchor_bdim) = unwrapTensorAtLevel(anchor, cur_level); + Tensor positive_value; + optional positive_bdim; + std::tie(positive_value, positive_bdim) = unwrapTensorAtLevel(positive, cur_level); + Tensor negative_value; + optional negative_bdim; + std::tie(negative_value, negative_bdim) = unwrapTensorAtLevel(negative, cur_level); + auto results = batch_rule(anchor_value, anchor_bdim, positive_value, positive_bdim, negative_value, negative_bdim, margin, p, eps, swap, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trunc_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::trunc::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & trunc__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::trunc_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor fix_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fix::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & fix__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fix_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor type_as_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::type_as::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _unique_generated_plumbing(const at::Tensor & self, bool sorted, bool return_inverse) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_unique::call(self, sorted, return_inverse); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sorted, return_inverse); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple unique_dim_generated_plumbing(const at::Tensor & self, int64_t dim, bool sorted, bool return_inverse, bool return_counts) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unique_dim::call(self, dim, sorted, return_inverse, return_counts); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, sorted, return_inverse, return_counts); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple unique_consecutive_generated_plumbing(const at::Tensor & self, bool return_inverse, bool return_counts, c10::optional dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unique_consecutive::call(self, return_inverse, return_counts, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, return_inverse, return_counts, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple unique_dim_consecutive_generated_plumbing(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unique_dim_consecutive::call(self, dim, return_inverse, return_counts); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, return_inverse, return_counts); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _unique2_generated_plumbing(const at::Tensor & self, bool sorted, bool return_inverse, bool return_counts) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_unique2::call(self, sorted, return_inverse, return_counts); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sorted, return_inverse, return_counts); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _unsafe_view_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_unsafe_view::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unsqueeze_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unsqueeze::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor vander_generated_plumbing(const at::Tensor & x, c10::optional N, bool increasing) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::vander::call(x, N, increasing); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, N, increasing); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor var_generated_plumbing(const at::Tensor & self, bool unbiased) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var::call(self, unbiased); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, unbiased); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor var_dim_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor var_correction_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_correction::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor var_names_dim_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_names_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor var_correction_names_generated_plumbing(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_correction_names::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple var_mean_generated_plumbing(const at::Tensor & self, bool unbiased) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_mean::call(self, unbiased); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, unbiased); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple var_mean_dim_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_mean_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple var_mean_correction_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_mean_correction::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple var_mean_names_dim_generated_plumbing(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_mean_names_dim::call(self, dim, unbiased, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, unbiased, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple var_mean_correction_names_generated_plumbing(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::var_mean_correction_names::call(self, dim, correction, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, correction, keepdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor view_as_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::view_as::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor where_self_generated_plumbing(const at::Tensor & condition, const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(condition, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::where_self::call(condition, self, other); + } + Tensor condition_value; + optional condition_bdim; + std::tie(condition_value, condition_bdim) = unwrapTensorAtLevel(condition, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(condition_value, condition_bdim, self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor where_ScalarSelf_generated_plumbing(const at::Tensor & condition, const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(condition, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::where_ScalarSelf::call(condition, self, other); + } + Tensor condition_value; + optional condition_bdim; + std::tie(condition_value, condition_bdim) = unwrapTensorAtLevel(condition, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(condition_value, condition_bdim, self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor where_ScalarOther_generated_plumbing(const at::Tensor & condition, const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(condition, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::where_ScalarOther::call(condition, self, other); + } + Tensor condition_value; + optional condition_bdim; + std::tie(condition_value, condition_bdim) = unwrapTensorAtLevel(condition, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(condition_value, condition_bdim, self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor where_Scalar_generated_plumbing(const at::Tensor & condition, const at::Scalar & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(condition, cur_level)) { + return at::_ops::where_Scalar::call(condition, self, other); + } + Tensor condition_value; + optional condition_bdim; + std::tie(condition_value, condition_bdim) = unwrapTensorAtLevel(condition, cur_level); + auto results = batch_rule(condition_value, condition_bdim, self, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector where_generated_plumbing(const at::Tensor & condition) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(condition, cur_level)) { + return at::_ops::where::call(condition); + } + Tensor condition_value; + optional condition_bdim; + std::tie(condition_value, condition_bdim) = unwrapTensorAtLevel(condition, cur_level); + auto results = batch_rule(condition_value, condition_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_except_dim_generated_plumbing(const at::Tensor & v, int64_t pow, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(v, cur_level)) { + return at::_ops::norm_except_dim::call(v, pow, dim); + } + Tensor v_value; + optional v_bdim; + std::tie(v_value, v_bdim) = unwrapTensorAtLevel(v, cur_level); + auto results = batch_rule(v_value, v_bdim, pow, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _weight_norm_generated_plumbing(const at::Tensor & v, const at::Tensor & g, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(v, cur_level) && !isBatchedAtLevel(g, cur_level)) { + return at::_ops::_weight_norm::call(v, g, dim); + } + Tensor v_value; + optional v_bdim; + std::tie(v_value, v_bdim) = unwrapTensorAtLevel(v, cur_level); + Tensor g_value; + optional g_bdim; + std::tie(g_value, g_bdim) = unwrapTensorAtLevel(g, cur_level); + auto results = batch_rule(v_value, v_bdim, g_value, g_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _weight_norm_interface_generated_plumbing(const at::Tensor & v, const at::Tensor & g, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(v, cur_level) && !isBatchedAtLevel(g, cur_level)) { + return at::_ops::_weight_norm_interface::call(v, g, dim); + } + Tensor v_value; + optional v_bdim; + std::tie(v_value, v_bdim) = unwrapTensorAtLevel(v, cur_level); + Tensor g_value; + optional g_bdim; + std::tie(g_value, g_bdim) = unwrapTensorAtLevel(g, cur_level); + auto results = batch_rule(v_value, v_bdim, g_value, g_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _weight_norm_interface_backward_generated_plumbing(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_w, cur_level) && !isBatchedAtLevel(saved_v, cur_level) && !isBatchedAtLevel(saved_g, cur_level) && !isBatchedAtLevel(saved_norms, cur_level)) { + return at::_ops::_weight_norm_interface_backward::call(grad_w, saved_v, saved_g, saved_norms, dim); + } + Tensor grad_w_value; + optional grad_w_bdim; + std::tie(grad_w_value, grad_w_bdim) = unwrapTensorAtLevel(grad_w, cur_level); + Tensor saved_v_value; + optional saved_v_bdim; + std::tie(saved_v_value, saved_v_bdim) = unwrapTensorAtLevel(saved_v, cur_level); + Tensor saved_g_value; + optional saved_g_bdim; + std::tie(saved_g_value, saved_g_bdim) = unwrapTensorAtLevel(saved_g, cur_level); + Tensor saved_norms_value; + optional saved_norms_bdim; + std::tie(saved_norms_value, saved_norms_bdim) = unwrapTensorAtLevel(saved_norms, cur_level); + auto results = batch_rule(grad_w_value, grad_w_bdim, saved_v_value, saved_v_bdim, saved_g_value, saved_g_bdim, saved_norms_value, saved_norms_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _weight_norm_differentiable_backward_generated_plumbing(const at::Tensor & grad_w, const at::Tensor & saved_v, const at::Tensor & saved_g, const at::Tensor & saved_norms, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_w, cur_level) && !isBatchedAtLevel(saved_v, cur_level) && !isBatchedAtLevel(saved_g, cur_level) && !isBatchedAtLevel(saved_norms, cur_level)) { + return at::_ops::_weight_norm_differentiable_backward::call(grad_w, saved_v, saved_g, saved_norms, dim); + } + Tensor grad_w_value; + optional grad_w_bdim; + std::tie(grad_w_value, grad_w_bdim) = unwrapTensorAtLevel(grad_w, cur_level); + Tensor saved_v_value; + optional saved_v_bdim; + std::tie(saved_v_value, saved_v_bdim) = unwrapTensorAtLevel(saved_v, cur_level); + Tensor saved_g_value; + optional saved_g_bdim; + std::tie(saved_g_value, saved_g_bdim) = unwrapTensorAtLevel(saved_g, cur_level); + Tensor saved_norms_value; + optional saved_norms_bdim; + std::tie(saved_norms_value, saved_norms_bdim) = unwrapTensorAtLevel(saved_norms, cur_level); + auto results = batch_rule(grad_w_value, grad_w_bdim, saved_v_value, saved_v_bdim, saved_g_value, saved_g_bdim, saved_norms_value, saved_norms_bdim, dim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor zeros_like_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::zeros_like::call(self, dtype, layout, device, pin_memory, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _standard_gamma_grad_generated_plumbing(const at::Tensor & self, const at::Tensor & output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::_standard_gamma_grad::call(self, output); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(self_value, self_bdim, output_value, output_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _standard_gamma_generated_plumbing(const at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_standard_gamma::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _dirichlet_grad_generated_plumbing(const at::Tensor & x, const at::Tensor & alpha, const at::Tensor & total) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(alpha, cur_level) && !isBatchedAtLevel(total, cur_level)) { + return at::_ops::_dirichlet_grad::call(x, alpha, total); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor alpha_value; + optional alpha_bdim; + std::tie(alpha_value, alpha_bdim) = unwrapTensorAtLevel(alpha, cur_level); + Tensor total_value; + optional total_bdim; + std::tie(total_value, total_bdim) = unwrapTensorAtLevel(total, cur_level); + auto results = batch_rule(x_value, x_bdim, alpha_value, alpha_bdim, total_value, total_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sample_dirichlet_generated_plumbing(const at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sample_dirichlet::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor poisson_generated_plumbing(const at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::poisson::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor binomial_generated_plumbing(const at::Tensor & count, const at::Tensor & prob, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(count, cur_level) && !isBatchedAtLevel(prob, cur_level)) { + return at::_ops::binomial::call(count, prob, generator); + } + Tensor count_value; + optional count_bdim; + std::tie(count_value, count_bdim) = unwrapTensorAtLevel(count, cur_level); + Tensor prob_value; + optional prob_bdim; + std::tie(prob_value, prob_bdim) = unwrapTensorAtLevel(prob, cur_level); + auto results = batch_rule(count_value, count_bdim, prob_value, prob_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor native_norm_generated_plumbing(const at::Tensor & self, const at::Scalar & p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::native_norm::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor native_norm_ScalarOpt_dim_dtype_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::native_norm_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sum_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_sum::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sum_dtype_generated_plumbing(const at::Tensor & self, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_sum_dtype::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sum_dim_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_sum_dim::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sum_dim_dtype_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_sum_dim_dtype::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_sum_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, at::IntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_sum_backward::call(grad, self, dim); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_csr_sum_dim_dtype_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_csr_sum_dim_dtype::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_csr_prod_dim_dtype_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_csr_prod_dim_dtype::call(self, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_softmax_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_softmax_int::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_softmax_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_softmax_Dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_softmax_generated_plumbing(const at::Tensor & self, int64_t dim, bool half_to_float) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_softmax::call(self, dim, half_to_float); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, half_to_float); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_softmax_backward_data_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_softmax_backward_data::call(grad_output, output, dim, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, dim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_log_softmax_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_log_softmax_int::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_log_softmax_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_log_softmax_Dimname::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_log_softmax_generated_plumbing(const at::Tensor & self, int64_t dim, bool half_to_float) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_log_softmax::call(self, dim, half_to_float); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, half_to_float); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_log_softmax_backward_data_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_log_softmax_backward_data::call(grad_output, output, dim, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, dim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _spdiags_generated_plumbing(const at::Tensor & diagonals, const at::Tensor & offsets, at::IntArrayRef shape, c10::optional layout) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(diagonals, cur_level) && !isBatchedAtLevel(offsets, cur_level)) { + return at::_ops::_spdiags::call(diagonals, offsets, shape, layout); + } + Tensor diagonals_value; + optional diagonals_bdim; + std::tie(diagonals_value, diagonals_bdim) = unwrapTensorAtLevel(diagonals, cur_level); + Tensor offsets_value; + optional offsets_bdim; + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets, cur_level); + auto results = batch_rule(diagonals_value, diagonals_bdim, offsets_value, offsets_bdim, shape, layout); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_ScalarOpt_dtype_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_ScalarOpt_dtype::call(self, p, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_Scalar::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_ScalarOpt_dim_dtype_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_ScalarOpt_dim_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_ScalarOpt_dim::call(self, p, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_names_ScalarOpt_dim_dtype_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_names_ScalarOpt_dim_dtype::call(self, p, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor norm_names_ScalarOpt_dim_generated_plumbing(const at::Tensor & self, const c10::optional & p, at::DimnameList dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::norm_names_ScalarOpt_dim::call(self, p, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple frexp_Tensor_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::frexp_Tensor::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor frobenius_norm_dim_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::frobenius_norm_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nuclear_norm_generated_plumbing(const at::Tensor & self, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nuclear_norm::call(self, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nuclear_norm_dim_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nuclear_norm_dim::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor clone_generated_plumbing(const at::Tensor & self, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::clone::call(self, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor positive_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::positive::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +const at::Tensor & resize_as_sparse__generated_plumbing(const at::Tensor & self, const at::Tensor & the_template) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(the_template, cur_level)) { + return at::_ops::resize_as_sparse_::call(self, the_template); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor the_template_value; + optional the_template_bdim; + std::tie(the_template_value, the_template_bdim) = unwrapTensorAtLevel(the_template, cur_level); + batch_rule(self_value, self_bdim, the_template_value, the_template_bdim); + return self; +} +template +at::Tensor & zero__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::zero_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor sub_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::sub_Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sub__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::sub__Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return self; +} +template +at::Tensor sub_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sub_Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sub__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sub__Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, alpha); + return self; +} +template +at::Tensor subtract_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::subtract_Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & subtract__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::subtract__Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return self; +} +template +at::Tensor subtract_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::subtract_Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & subtract__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::subtract__Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other, alpha); + return self; +} +template +at::Tensor rsub_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::rsub_Tensor::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor heaviside_generated_plumbing(const at::Tensor & self, const at::Tensor & values) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::heaviside::call(self, values); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(self_value, self_bdim, values_value, values_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & heaviside__generated_plumbing(at::Tensor & self, const at::Tensor & values) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::heaviside_::call(self, values); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(self_value, self_bdim, values_value, values_bdim); + return self; +} +template +at::Tensor rsub_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::rsub_Scalar::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_addmm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::_sparse_addmm::call(self, mat1, mat2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_sampled_addmm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::sparse_sampled_addmm::call(self, mat1, mat2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _sparse_mm_reduce_impl_generated_plumbing(const at::Tensor & self, const at::Tensor & other, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_sparse_mm_reduce_impl::call(self, other, reduce); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, reduce); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _sparse_mm_reduce_impl_backward_generated_plumbing(const at::Tensor & self, const at::Tensor & grad_out, const at::Tensor & weight, c10::string_view reduce, const at::Tensor & arg_out, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(arg_out, cur_level)) { + return at::_ops::_sparse_mm_reduce_impl_backward::call(self, grad_out, weight, reduce, arg_out, output_mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + Tensor arg_out_value; + optional arg_out_bdim; + std::tie(arg_out_value, arg_out_bdim) = unwrapTensorAtLevel(arg_out, cur_level); + auto results = batch_rule(self_value, self_bdim, grad_out_value, grad_out_bdim, weight_value, weight_bdim, reduce, arg_out_value, arg_out_bdim, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor addmm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::addmm::call(self, mat1, mat2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & addmm__generated_plumbing(at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::addmm_::call(self, mat1, mat2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha); + return self; +} +template +at::Tensor _addmm_activation_generated_plumbing(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::_addmm_activation::call(self, mat1, mat2, beta, alpha, use_gelu); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(self_value, self_bdim, mat1_value, mat1_bdim, mat2_value, mat2_bdim, beta, alpha, use_gelu); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _scaled_mm_generated_plumbing(const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias, c10::optional out_dtype, const c10::optional & scale_a, const c10::optional & scale_b, const c10::optional & scale_result, bool use_fast_accum) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mat2, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(scale_a, cur_level) && !isBatchedAtLevel(scale_b, cur_level) && !isBatchedAtLevel(scale_result, cur_level)) { + return at::_ops::_scaled_mm::call(self, mat2, bias, out_dtype, scale_a, scale_b, scale_result, use_fast_accum); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional scale_a_value; + optional scale_a_bdim; + if (scale_a) { + std::tie(scale_a_value, scale_a_bdim) = unwrapTensorAtLevel(scale_a.value(), cur_level); + } + optional scale_b_value; + optional scale_b_bdim; + if (scale_b) { + std::tie(scale_b_value, scale_b_bdim) = unwrapTensorAtLevel(scale_b.value(), cur_level); + } + optional scale_result_value; + optional scale_result_bdim; + if (scale_result) { + std::tie(scale_result_value, scale_result_bdim) = unwrapTensorAtLevel(scale_result.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, mat2_value, mat2_bdim, bias_value, bias_bdim, out_dtype, scale_a_value, scale_a_bdim, scale_b_value, scale_b_bdim, scale_result_value, scale_result_bdim, use_fast_accum); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor sparse_compressed_tensor_comp_plain_value_size_generated_plumbing(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_indices, cur_level) && !isBatchedAtLevel(plain_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_compressed_tensor_comp_plain_value_size::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor compressed_indices_value; + optional compressed_indices_bdim; + std::tie(compressed_indices_value, compressed_indices_bdim) = unwrapTensorAtLevel(compressed_indices, cur_level); + Tensor plain_indices_value; + optional plain_indices_bdim; + std::tie(plain_indices_value, plain_indices_bdim) = unwrapTensorAtLevel(plain_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(compressed_indices_value, compressed_indices_bdim, plain_indices_value, plain_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_csr_tensor_crow_col_value_size_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_csr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_csc_tensor_ccol_row_value_size_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_csc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_bsr_tensor_crow_col_value_size_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_bsr_tensor_crow_col_value_size::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_bsc_tensor_ccol_row_value_size_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_bsc_tensor_ccol_row_value_size::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_compressed_tensor_comp_plain_value_generated_plumbing(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_indices, cur_level) && !isBatchedAtLevel(plain_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_compressed_tensor_comp_plain_value::call(compressed_indices, plain_indices, values, dtype, layout, device, pin_memory); + } + Tensor compressed_indices_value; + optional compressed_indices_bdim; + std::tie(compressed_indices_value, compressed_indices_bdim) = unwrapTensorAtLevel(compressed_indices, cur_level); + Tensor plain_indices_value; + optional plain_indices_bdim; + std::tie(plain_indices_value, plain_indices_bdim) = unwrapTensorAtLevel(plain_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(compressed_indices_value, compressed_indices_bdim, plain_indices_value, plain_indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_csr_tensor_crow_col_value_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_csr_tensor_crow_col_value::call(crow_indices, col_indices, values, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_csc_tensor_ccol_row_value_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_csc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_bsr_tensor_crow_col_value_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_bsr_tensor_crow_col_value::call(crow_indices, col_indices, values, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_bsc_tensor_ccol_row_value_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_bsc_tensor_ccol_row_value::call(ccol_indices, row_indices, values, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_compressed_tensor_unsafe_generated_plumbing(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_indices, cur_level) && !isBatchedAtLevel(plain_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_compressed_tensor_unsafe::call(compressed_indices, plain_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor compressed_indices_value; + optional compressed_indices_bdim; + std::tie(compressed_indices_value, compressed_indices_bdim) = unwrapTensorAtLevel(compressed_indices, cur_level); + Tensor plain_indices_value; + optional plain_indices_bdim; + std::tie(plain_indices_value, plain_indices_bdim) = unwrapTensorAtLevel(plain_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(compressed_indices_value, compressed_indices_bdim, plain_indices_value, plain_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_csr_tensor_unsafe_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_csr_tensor_unsafe::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_csc_tensor_unsafe_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_csc_tensor_unsafe::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_bsr_tensor_unsafe_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_bsr_tensor_unsafe::call(crow_indices, col_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_bsc_tensor_unsafe_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_bsc_tensor_unsafe::call(ccol_indices, row_indices, values, size, dtype, layout, device, pin_memory); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_coo_tensor_indices_generated_plumbing(const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_coo_tensor_indices::call(indices, values, dtype, layout, device, pin_memory, is_coalesced); + } + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(indices_value, indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory, is_coalesced); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_coo_tensor_indices_size_generated_plumbing(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::sparse_coo_tensor_indices_size::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced); + } + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(indices_value, indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory, is_coalesced); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_coo_tensor_unsafe_generated_plumbing(const at::Tensor & indices, const at::Tensor & values, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_coo_tensor_unsafe::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced); + } + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(indices_value, indices_bdim, values_value, values_bdim, size, dtype, layout, device, pin_memory, is_coalesced); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _validate_sparse_coo_tensor_args_generated_plumbing(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional is_coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_coo_tensor_args::call(indices, values, size, is_coalesced); + } + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(indices_value, indices_bdim, values_value, values_bdim, size, is_coalesced); +} +template +void _validate_sparse_compressed_tensor_args_generated_plumbing(const at::Tensor & compressed_indices, const at::Tensor & plain_indices, const at::Tensor & values, at::IntArrayRef size, at::Layout layout) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(compressed_indices, cur_level) && !isBatchedAtLevel(plain_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_compressed_tensor_args::call(compressed_indices, plain_indices, values, size, layout); + } + Tensor compressed_indices_value; + optional compressed_indices_bdim; + std::tie(compressed_indices_value, compressed_indices_bdim) = unwrapTensorAtLevel(compressed_indices, cur_level); + Tensor plain_indices_value; + optional plain_indices_bdim; + std::tie(plain_indices_value, plain_indices_bdim) = unwrapTensorAtLevel(plain_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(compressed_indices_value, compressed_indices_bdim, plain_indices_value, plain_indices_bdim, values_value, values_bdim, size, layout); +} +template +void _validate_sparse_csr_tensor_args_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_csr_tensor_args::call(crow_indices, col_indices, values, size); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size); +} +template +void _validate_sparse_csc_tensor_args_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_csc_tensor_args::call(ccol_indices, row_indices, values, size); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size); +} +template +void _validate_sparse_bsr_tensor_args_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, const at::Tensor & values, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_bsr_tensor_args::call(crow_indices, col_indices, values, size); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, values_value, values_bdim, size); +} +template +void _validate_sparse_bsc_tensor_args_generated_plumbing(const at::Tensor & ccol_indices, const at::Tensor & row_indices, const at::Tensor & values, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(ccol_indices, cur_level) && !isBatchedAtLevel(row_indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_validate_sparse_bsc_tensor_args::call(ccol_indices, row_indices, values, size); + } + Tensor ccol_indices_value; + optional ccol_indices_bdim; + std::tie(ccol_indices_value, ccol_indices_bdim) = unwrapTensorAtLevel(ccol_indices, cur_level); + Tensor row_indices_value; + optional row_indices_bdim; + std::tie(row_indices_value, row_indices_bdim) = unwrapTensorAtLevel(row_indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + batch_rule(ccol_indices_value, ccol_indices_bdim, row_indices_value, row_indices_bdim, values_value, values_bdim, size); +} +template +at::Tensor _sparse_coo_tensor_with_dims_and_tensors_generated_plumbing(int64_t sparse_dim, int64_t dense_dim, c10::SymIntArrayRef size, const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_sparse_coo_tensor_with_dims_and_tensors::call(sparse_dim, dense_dim, size, indices, values, dtype, layout, device, pin_memory, is_coalesced); + } + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(sparse_dim, dense_dim, size, indices_value, indices_bdim, values_value, values_bdim, dtype, layout, device, pin_memory, is_coalesced); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +const at::Tensor & sparse_resize__generated_plumbing(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sparse_resize_::call(self, size, sparse_dim, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, size, sparse_dim, dense_dim); + return self; +} +template +const at::Tensor & sparse_resize_and_clear__generated_plumbing(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sparse_resize_and_clear_::call(self, size, sparse_dim, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, size, sparse_dim, dense_dim); + return self; +} +template +at::Tensor sparse_mask_generated_plumbing(const at::Tensor & self, const at::Tensor & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::sparse_mask::call(self, mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_mask_projection_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, bool accumulate_matches) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_sparse_mask_projection::call(self, mask, accumulate_matches); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, accumulate_matches); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _to_cpu_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::_to_cpu::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_dense_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional masked_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_dense::call(self, dtype, masked_grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, masked_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_dense_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional masked_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_dense::call(self, dtype, masked_grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, masked_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_dense_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & input, c10::optional masked_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(input, cur_level)) { + return at::_ops::to_dense_backward::call(grad, input, masked_grad); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_value, input_bdim, masked_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor coalesce_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::coalesce::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _coalesce_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_coalesce::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _values_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_values::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _coalesced__generated_plumbing(at::Tensor & self, bool coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_coalesced_::call(self, coalesced); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, coalesced); + return self; +} +template +at::Tensor indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor values_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::values::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor crow_indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::crow_indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor col_indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::col_indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ccol_indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ccol_indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor row_indices_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::row_indices::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hspmm_generated_plumbing(const at::Tensor & mat1, const at::Tensor & mat2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(mat1, cur_level) && !isBatchedAtLevel(mat2, cur_level)) { + return at::_ops::hspmm::call(mat1, mat2); + } + Tensor mat1_value; + optional mat1_bdim; + std::tie(mat1_value, mat1_bdim) = unwrapTensorAtLevel(mat1, cur_level); + Tensor mat2_value; + optional mat2_bdim; + std::tie(mat2_value, mat2_bdim) = unwrapTensorAtLevel(mat2, cur_level); + auto results = batch_rule(mat1_value, mat1_bdim, mat2_value, mat2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & copy_sparse_to_sparse__generated_plumbing(at::Tensor & self, const at::Tensor & src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::copy_sparse_to_sparse_::call(self, src, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, src_value, src_bdim, non_blocking); + return self; +} +template +::std::vector unbind_int_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unbind_int::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unbind_Dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unbind_Dimname::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_sparse_dim_generated_plumbing(const at::Tensor & self, int64_t sparse_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse_sparse_dim::call(self, sparse_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sparse_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_sparse_dim_generated_plumbing(const at::Tensor & self, int64_t sparse_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse_sparse_dim::call(self, sparse_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, sparse_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_generated_plumbing(const at::Tensor & self, c10::optional layout, at::OptionalIntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse::call(self, layout, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, layout, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_generated_plumbing(const at::Tensor & self, c10::optional layout, at::OptionalIntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse::call(self, layout, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, layout, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_csr_generated_plumbing(const at::Tensor & self, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse_csr::call(self, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_csr_generated_plumbing(const at::Tensor & self, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse_csr::call(self, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_csc_generated_plumbing(const at::Tensor & self, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse_csc::call(self, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_csc_generated_plumbing(const at::Tensor & self, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse_csc::call(self, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_bsr_generated_plumbing(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse_bsr::call(self, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_bsr_generated_plumbing(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse_bsr::call(self, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_sparse_bsc_generated_plumbing(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_sparse_bsc::call(self, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_sparse_bsc_generated_plumbing(const at::Tensor & self, at::IntArrayRef blocksize, c10::optional dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_sparse_bsc::call(self, blocksize, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, blocksize, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _to_sparse_semi_structured_generated_plumbing(const at::Tensor & dense) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dense, cur_level)) { + return at::_ops::_to_sparse_semi_structured::call(dense); + } + Tensor dense_value; + optional dense_bdim; + std::tie(dense_value, dense_bdim) = unwrapTensorAtLevel(dense, cur_level); + auto results = batch_rule(dense_value, dense_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor to_mkldnn_generated_plumbing(const at::Tensor & self, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_mkldnn::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_reorder_conv2d_weight_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_reorder_conv2d_weight::call(self, padding, stride, dilation, groups, input_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding, stride, dilation, groups, input_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_reorder_conv3d_weight_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_reorder_conv3d_weight::call(self, padding, stride, dilation, groups); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding, stride, dilation, groups); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_mkldnn_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & input) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(input, cur_level)) { + return at::_ops::to_mkldnn_backward::call(grad, input); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_value, input_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantize_per_tensor_dynamic_generated_plumbing(const at::Tensor & self, at::ScalarType dtype, bool reduce_range) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantize_per_tensor_dynamic::call(self, dtype, reduce_range); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, reduce_range); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantize_per_tensor_generated_plumbing(const at::Tensor & self, double scale, int64_t zero_point, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantize_per_tensor::call(self, scale, zero_point, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, scale, zero_point, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantize_per_tensor_tensor_qparams_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::quantize_per_tensor_tensor_qparams::call(self, scale, zero_point, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector quantize_per_tensor_tensors_generated_plumbing(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level) && !isBatchedAtLevel(scales, cur_level) && !isBatchedAtLevel(zero_points, cur_level)) { + return at::_ops::quantize_per_tensor_tensors::call(tensors, scales, zero_points, dtype); + } + Tensor scales_value; + optional scales_bdim; + std::tie(scales_value, scales_bdim) = unwrapTensorAtLevel(scales, cur_level); + Tensor zero_points_value; + optional zero_points_bdim; + std::tie(zero_points_value, zero_points_bdim) = unwrapTensorAtLevel(zero_points, cur_level); + auto results = batch_rule(tensors, scales_value, scales_bdim, zero_points_value, zero_points_bdim, dtype); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantize_per_channel_generated_plumbing(const at::Tensor & self, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scales, cur_level) && !isBatchedAtLevel(zero_points, cur_level)) { + return at::_ops::quantize_per_channel::call(self, scales, zero_points, axis, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scales_value; + optional scales_bdim; + std::tie(scales_value, scales_bdim) = unwrapTensorAtLevel(scales, cur_level); + Tensor zero_points_value; + optional zero_points_bdim; + std::tie(zero_points_value, zero_points_bdim) = unwrapTensorAtLevel(zero_points, cur_level); + auto results = batch_rule(self_value, self_bdim, scales_value, scales_bdim, zero_points_value, zero_points_bdim, axis, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor dequantize_self_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::dequantize_self::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector dequantize_tensors_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::dequantize_tensors::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor q_per_channel_scales_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::q_per_channel_scales::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor q_per_channel_zero_points_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::q_per_channel_zero_points::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor int_repr_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::int_repr::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _make_per_tensor_quantized_tensor_generated_plumbing(const at::Tensor & self, double scale, int64_t zero_point) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_make_per_tensor_quantized_tensor::call(self, scale, zero_point); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, scale, zero_point); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _make_per_channel_quantized_tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_make_per_channel_quantized_tensor::call(self, scale, zero_point, axis); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, axis); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fake_quantize_per_tensor_affine_generated_plumbing(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fake_quantize_per_tensor_affine::call(self, scale, zero_point, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, scale, zero_point, quant_min, quant_max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fake_quantize_per_tensor_affine_tensor_qparams_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::fake_quantize_per_tensor_affine_tensor_qparams::call(self, scale, zero_point, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, quant_min, quant_max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple fake_quantize_per_tensor_affine_cachemask_generated_plumbing(const at::Tensor & self, double scale, int64_t zero_point, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask::call(self, scale, zero_point, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, scale, zero_point, quant_min, quant_max); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _fake_quantize_per_tensor_affine_cachemask_tensor_qparams_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, const at::Tensor & fake_quant_enabled, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level) && !isBatchedAtLevel(fake_quant_enabled, cur_level)) { + return at::_ops::_fake_quantize_per_tensor_affine_cachemask_tensor_qparams::call(self, scale, zero_point, fake_quant_enabled, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + Tensor fake_quant_enabled_value; + optional fake_quant_enabled_bdim; + std::tie(fake_quant_enabled_value, fake_quant_enabled_bdim) = unwrapTensorAtLevel(fake_quant_enabled, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, fake_quant_enabled_value, fake_quant_enabled_bdim, quant_min, quant_max); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor fake_quantize_per_tensor_affine_cachemask_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::fake_quantize_per_tensor_affine_cachemask_backward::call(grad, mask); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_value, grad_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _fake_quantize_learnable_per_tensor_affine_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine::call(self, scale, zero_point, quant_min, quant_max, grad_factor); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, quant_min, quant_max, grad_factor); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _fake_quantize_learnable_per_tensor_affine_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t quant_min, int64_t quant_max, double grad_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_fake_quantize_learnable_per_tensor_affine_backward::call(grad, self, scale, zero_point, quant_min, quant_max, grad_factor); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, quant_min, quant_max, grad_factor); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor fake_quantize_per_channel_affine_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::fake_quantize_per_channel_affine::call(self, scale, zero_point, axis, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, axis, quant_min, quant_max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple fake_quantize_per_channel_affine_cachemask_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::fake_quantize_per_channel_affine_cachemask::call(self, scale, zero_point, axis, quant_min, quant_max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, axis, quant_min, quant_max); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor fake_quantize_per_channel_affine_cachemask_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::fake_quantize_per_channel_affine_cachemask_backward::call(grad, mask); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_value, grad_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _fake_quantize_learnable_per_channel_affine_generated_plumbing(const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_fake_quantize_learnable_per_channel_affine::call(self, scale, zero_point, axis, quant_min, quant_max, grad_factor); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, axis, quant_min, quant_max, grad_factor); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _fake_quantize_learnable_per_channel_affine_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, const at::Tensor & scale, const at::Tensor & zero_point, int64_t axis, int64_t quant_min, int64_t quant_max, double grad_factor) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_fake_quantize_learnable_per_channel_affine_backward::call(grad, self, scale, zero_point, axis, quant_min, quant_max, grad_factor); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, axis, quant_min, quant_max, grad_factor); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _saturate_weight_to_fp16_generated_plumbing(const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_saturate_weight_to_fp16::call(weight); + } + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(weight_value, weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple choose_qparams_optimized_generated_plumbing(const at::Tensor & input, int64_t numel, int64_t n_bins, double ratio, int64_t bit_width) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::choose_qparams_optimized::call(input, numel, n_bins, ratio, bit_width); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, numel, n_bins, ratio, bit_width); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _autocast_to_reduced_precision_generated_plumbing(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled, at::ScalarType cuda_dtype, at::ScalarType cpu_dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_autocast_to_reduced_precision::call(self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _autocast_to_full_precision_generated_plumbing(const at::Tensor & self, bool cuda_enabled, bool cpu_enabled) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_autocast_to_full_precision::call(self, cuda_enabled, cpu_enabled); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, cuda_enabled, cpu_enabled); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _to_copy_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, bool non_blocking, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_to_copy::call(self, dtype, layout, device, pin_memory, non_blocking, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, non_blocking, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_dtype_layout_generated_plumbing(const at::Tensor & self, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, bool non_blocking, bool copy, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_dtype_layout::call(self, dtype, layout, device, pin_memory, non_blocking, copy, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, layout, device, pin_memory, non_blocking, copy, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_device_generated_plumbing(const at::Tensor & self, at::Device device, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_device::call(self, device, dtype, non_blocking, copy, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, device, dtype, non_blocking, copy, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_dtype_generated_plumbing(const at::Tensor & self, at::ScalarType dtype, bool non_blocking, bool copy, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_dtype::call(self, dtype, non_blocking, copy, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype, non_blocking, copy, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_other_generated_plumbing(const at::Tensor & self, const at::Tensor & other, bool non_blocking, bool copy, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::to_other::call(self, other, non_blocking, copy, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, non_blocking, copy, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector meshgrid_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::meshgrid::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector meshgrid_indexing_generated_plumbing(at::TensorList tensors, c10::string_view indexing) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::meshgrid_indexing::call(tensors, indexing); + } + + auto results = batch_rule(tensors, indexing); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cartesian_prod_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::cartesian_prod::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor combinations_generated_plumbing(const at::Tensor & self, int64_t r, bool with_replacement) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::combinations::call(self, r, with_replacement); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, r, with_replacement); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _lstm_mps_generated_plumbing(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::_lstm_mps::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level), makeBatched(std::get<10>(results), std::get<11>(results), cur_level)); +} +template +::std::tuple,::std::vector> lstm_mps_backward_generated_plumbing(const c10::optional & grad_y, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_y, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(z_state, cur_level) && !isBatchedAtLevel(cell_state_fwd, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(layersOutputs, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::lstm_mps_backward::call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor z_state_value; + optional z_state_bdim; + std::tie(z_state_value, z_state_bdim) = unwrapTensorAtLevel(z_state, cur_level); + Tensor cell_state_fwd_value; + optional cell_state_fwd_bdim; + std::tie(cell_state_fwd_value, cell_state_fwd_bdim) = unwrapTensorAtLevel(cell_state_fwd, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor layersOutputs_value; + optional layersOutputs_bdim; + std::tie(layersOutputs_value, layersOutputs_bdim) = unwrapTensorAtLevel(layersOutputs, cur_level); + optional grad_y_value; + optional grad_y_bdim; + if (grad_y) { + std::tie(grad_y_value, grad_y_bdim) = unwrapTensorAtLevel(grad_y.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + auto results = batch_rule(grad_y_value, grad_y_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, z_state_value, z_state_bdim, cell_state_fwd_value, cell_state_fwd_bdim, input_value, input_bdim, layersOutputs_value, layersOutputs_bdim, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _thnn_fused_lstm_cell_generated_plumbing(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & cx, const c10::optional & input_bias, const c10::optional & hidden_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input_gates, cur_level) && !isBatchedAtLevel(hidden_gates, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(input_bias, cur_level) && !isBatchedAtLevel(hidden_bias, cur_level)) { + return at::_ops::_thnn_fused_lstm_cell::call(input_gates, hidden_gates, cx, input_bias, hidden_bias); + } + Tensor input_gates_value; + optional input_gates_bdim; + std::tie(input_gates_value, input_gates_bdim) = unwrapTensorAtLevel(input_gates, cur_level); + Tensor hidden_gates_value; + optional hidden_gates_bdim; + std::tie(hidden_gates_value, hidden_gates_bdim) = unwrapTensorAtLevel(hidden_gates, cur_level); + Tensor cx_value; + optional cx_bdim; + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx, cur_level); + optional input_bias_value; + optional input_bias_bdim; + if (input_bias) { + std::tie(input_bias_value, input_bias_bdim) = unwrapTensorAtLevel(input_bias.value(), cur_level); + } + optional hidden_bias_value; + optional hidden_bias_bdim; + if (hidden_bias) { + std::tie(hidden_bias_value, hidden_bias_bdim) = unwrapTensorAtLevel(hidden_bias.value(), cur_level); + } + auto results = batch_rule(input_gates_value, input_gates_bdim, hidden_gates_value, hidden_gates_bdim, cx_value, cx_bdim, input_bias_value, input_bias_bdim, hidden_bias_value, hidden_bias_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _thnn_fused_lstm_cell_backward_impl_generated_plumbing(const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(cy, cur_level) && !isBatchedAtLevel(workspace, cur_level)) { + return at::_ops::_thnn_fused_lstm_cell_backward_impl::call(grad_hy, grad_cy, cx, cy, workspace, has_bias); + } + Tensor cx_value; + optional cx_bdim; + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx, cur_level); + Tensor cy_value; + optional cy_bdim; + std::tie(cy_value, cy_bdim) = unwrapTensorAtLevel(cy, cur_level); + Tensor workspace_value; + optional workspace_bdim; + std::tie(workspace_value, workspace_bdim) = unwrapTensorAtLevel(workspace, cur_level); + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + auto results = batch_rule(grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, cx_value, cx_bdim, cy_value, cy_bdim, workspace_value, workspace_bdim, has_bias); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _thnn_fused_lstm_cell_backward_generated_plumbing(const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & cx, const at::Tensor & cy, const at::Tensor & workspace, bool has_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(cy, cur_level) && !isBatchedAtLevel(workspace, cur_level)) { + return at::_ops::_thnn_fused_lstm_cell_backward::call(grad_hy, grad_cy, cx, cy, workspace, has_bias); + } + Tensor cx_value; + optional cx_bdim; + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx, cur_level); + Tensor cy_value; + optional cy_bdim; + std::tie(cy_value, cy_bdim) = unwrapTensorAtLevel(cy, cur_level); + Tensor workspace_value; + optional workspace_bdim; + std::tie(workspace_value, workspace_bdim) = unwrapTensorAtLevel(workspace, cur_level); + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + auto results = batch_rule(grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, cx_value, cx_bdim, cy_value, cy_bdim, workspace_value, workspace_bdim, has_bias); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple _thnn_differentiable_lstm_cell_backward_generated_plumbing(const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const c10::optional & input_bias, const c10::optional & hidden_bias, const at::Tensor & cx, const at::Tensor & cy) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(input_gates, cur_level) && !isBatchedAtLevel(hidden_gates, cur_level) && !isBatchedAtLevel(input_bias, cur_level) && !isBatchedAtLevel(hidden_bias, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(cy, cur_level)) { + return at::_ops::_thnn_differentiable_lstm_cell_backward::call(grad_hy, grad_cy, input_gates, hidden_gates, input_bias, hidden_bias, cx, cy); + } + Tensor input_gates_value; + optional input_gates_bdim; + std::tie(input_gates_value, input_gates_bdim) = unwrapTensorAtLevel(input_gates, cur_level); + Tensor hidden_gates_value; + optional hidden_gates_bdim; + std::tie(hidden_gates_value, hidden_gates_bdim) = unwrapTensorAtLevel(hidden_gates, cur_level); + Tensor cx_value; + optional cx_bdim; + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx, cur_level); + Tensor cy_value; + optional cy_bdim; + std::tie(cy_value, cy_bdim) = unwrapTensorAtLevel(cy, cur_level); + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + optional input_bias_value; + optional input_bias_bdim; + if (input_bias) { + std::tie(input_bias_value, input_bias_bdim) = unwrapTensorAtLevel(input_bias.value(), cur_level); + } + optional hidden_bias_value; + optional hidden_bias_bdim; + if (hidden_bias) { + std::tie(hidden_bias_value, hidden_bias_bdim) = unwrapTensorAtLevel(hidden_bias.value(), cur_level); + } + auto results = batch_rule(grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, input_gates_value, input_gates_bdim, hidden_gates_value, hidden_gates_bdim, input_bias_value, input_bias_bdim, hidden_bias_value, hidden_bias_bdim, cx_value, cx_bdim, cy_value, cy_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple _thnn_fused_gru_cell_generated_plumbing(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input_gates, cur_level) && !isBatchedAtLevel(hidden_gates, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(input_bias, cur_level) && !isBatchedAtLevel(hidden_bias, cur_level)) { + return at::_ops::_thnn_fused_gru_cell::call(input_gates, hidden_gates, hx, input_bias, hidden_bias); + } + Tensor input_gates_value; + optional input_gates_bdim; + std::tie(input_gates_value, input_gates_bdim) = unwrapTensorAtLevel(input_gates, cur_level); + Tensor hidden_gates_value; + optional hidden_gates_bdim; + std::tie(hidden_gates_value, hidden_gates_bdim) = unwrapTensorAtLevel(hidden_gates, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + optional input_bias_value; + optional input_bias_bdim; + if (input_bias) { + std::tie(input_bias_value, input_bias_bdim) = unwrapTensorAtLevel(input_bias.value(), cur_level); + } + optional hidden_bias_value; + optional hidden_bias_bdim; + if (hidden_bias) { + std::tie(hidden_bias_value, hidden_bias_bdim) = unwrapTensorAtLevel(hidden_bias.value(), cur_level); + } + auto results = batch_rule(input_gates_value, input_gates_bdim, hidden_gates_value, hidden_gates_bdim, hx_value, hx_bdim, input_bias_value, input_bias_bdim, hidden_bias_value, hidden_bias_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _thnn_fused_gru_cell_backward_generated_plumbing(const at::Tensor & grad_hy, const at::Tensor & workspace, bool has_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(workspace, cur_level)) { + return at::_ops::_thnn_fused_gru_cell_backward::call(grad_hy, workspace, has_bias); + } + Tensor grad_hy_value; + optional grad_hy_bdim; + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy, cur_level); + Tensor workspace_value; + optional workspace_bdim; + std::tie(workspace_value, workspace_bdim) = unwrapTensorAtLevel(workspace, cur_level); + auto results = batch_rule(grad_hy_value, grad_hy_bdim, workspace_value, workspace_bdim, has_bias); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple _thnn_differentiable_gru_cell_backward_generated_plumbing(const at::Tensor & grad_hy, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(input_gates, cur_level) && !isBatchedAtLevel(hidden_gates, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(input_bias, cur_level) && !isBatchedAtLevel(hidden_bias, cur_level)) { + return at::_ops::_thnn_differentiable_gru_cell_backward::call(grad_hy, input_gates, hidden_gates, hx, input_bias, hidden_bias); + } + Tensor grad_hy_value; + optional grad_hy_bdim; + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy, cur_level); + Tensor input_gates_value; + optional input_gates_bdim; + std::tie(input_gates_value, input_gates_bdim) = unwrapTensorAtLevel(input_gates, cur_level); + Tensor hidden_gates_value; + optional hidden_gates_bdim; + std::tie(hidden_gates_value, hidden_gates_bdim) = unwrapTensorAtLevel(hidden_gates, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + optional input_bias_value; + optional input_bias_bdim; + if (input_bias) { + std::tie(input_bias_value, input_bias_bdim) = unwrapTensorAtLevel(input_bias.value(), cur_level); + } + optional hidden_bias_value; + optional hidden_bias_bdim; + if (hidden_bias) { + std::tie(hidden_bias_value, hidden_bias_bdim) = unwrapTensorAtLevel(hidden_bias.value(), cur_level); + } + auto results = batch_rule(grad_hy_value, grad_hy_bdim, input_gates_value, input_gates_bdim, hidden_gates_value, hidden_gates_bdim, hx_value, hx_bdim, input_bias_value, input_bias_bdim, hidden_bias_value, hidden_bias_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple lstm_input_generated_plumbing(const at::Tensor & input, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::lstm_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple lstm_data_generated_plumbing(const at::Tensor & data, const at::Tensor & batch_sizes, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::lstm_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + auto results = batch_rule(data_value, data_bdim, batch_sizes_value, batch_sizes_bdim, hx, params, has_biases, num_layers, dropout, train, bidirectional); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple gru_input_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::gru_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple gru_data_generated_plumbing(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::gru_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(data_value, data_bdim, batch_sizes_value, batch_sizes_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple rnn_tanh_input_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::rnn_tanh_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple rnn_tanh_data_generated_plumbing(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::rnn_tanh_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(data_value, data_bdim, batch_sizes_value, batch_sizes_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple rnn_relu_input_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::rnn_relu_input::call(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple rnn_relu_data_generated_plumbing(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level)) { + return at::_ops::rnn_relu_data::call(data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + auto results = batch_rule(data_value, data_bdim, batch_sizes_value, batch_sizes_bdim, hx_value, hx_bdim, params, has_biases, num_layers, dropout, train, bidirectional); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple lstm_cell_generated_plumbing(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih, const c10::optional & b_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level)) { + return at::_ops::lstm_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + optional b_ih_value; + optional b_ih_bdim; + if (b_ih) { + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih.value(), cur_level); + } + optional b_hh_value; + optional b_hh_bdim; + if (b_hh) { + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, hx, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor gru_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih, const c10::optional & b_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level)) { + return at::_ops::gru_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + optional b_ih_value; + optional b_ih_bdim; + if (b_ih) { + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih.value(), cur_level); + } + optional b_hh_value; + optional b_hh_bdim; + if (b_hh) { + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rnn_tanh_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih, const c10::optional & b_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level)) { + return at::_ops::rnn_tanh_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + optional b_ih_value; + optional b_ih_bdim; + if (b_ih) { + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih.value(), cur_level); + } + optional b_hh_value; + optional b_hh_bdim; + if (b_hh) { + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rnn_relu_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const c10::optional & b_ih, const c10::optional & b_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level)) { + return at::_ops::rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + optional b_ih_value; + optional b_ih_bdim; + if (b_ih) { + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih.value(), cur_level); + } + optional b_hh_value; + optional b_hh_bdim; + if (b_hh) { + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple quantized_lstm_cell_generated_plumbing(const at::Tensor & input, at::TensorList hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level) && !isBatchedAtLevel(packed_ih, cur_level) && !isBatchedAtLevel(packed_hh, cur_level) && !isBatchedAtLevel(col_offsets_ih, cur_level) && !isBatchedAtLevel(col_offsets_hh, cur_level)) { + return at::_ops::quantized_lstm_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + Tensor b_ih_value; + optional b_ih_bdim; + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih, cur_level); + Tensor b_hh_value; + optional b_hh_bdim; + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh, cur_level); + Tensor packed_ih_value; + optional packed_ih_bdim; + std::tie(packed_ih_value, packed_ih_bdim) = unwrapTensorAtLevel(packed_ih, cur_level); + Tensor packed_hh_value; + optional packed_hh_bdim; + std::tie(packed_hh_value, packed_hh_bdim) = unwrapTensorAtLevel(packed_hh, cur_level); + Tensor col_offsets_ih_value; + optional col_offsets_ih_bdim; + std::tie(col_offsets_ih_value, col_offsets_ih_bdim) = unwrapTensorAtLevel(col_offsets_ih, cur_level); + Tensor col_offsets_hh_value; + optional col_offsets_hh_bdim; + std::tie(col_offsets_hh_value, col_offsets_hh_bdim) = unwrapTensorAtLevel(col_offsets_hh, cur_level); + auto results = batch_rule(input_value, input_bdim, hx, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim, packed_ih_value, packed_ih_bdim, packed_hh_value, packed_hh_bdim, col_offsets_ih_value, col_offsets_ih_bdim, col_offsets_hh_value, col_offsets_hh_bdim, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor quantized_gru_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level) && !isBatchedAtLevel(packed_ih, cur_level) && !isBatchedAtLevel(packed_hh, cur_level) && !isBatchedAtLevel(col_offsets_ih, cur_level) && !isBatchedAtLevel(col_offsets_hh, cur_level)) { + return at::_ops::quantized_gru_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + Tensor b_ih_value; + optional b_ih_bdim; + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih, cur_level); + Tensor b_hh_value; + optional b_hh_bdim; + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh, cur_level); + Tensor packed_ih_value; + optional packed_ih_bdim; + std::tie(packed_ih_value, packed_ih_bdim) = unwrapTensorAtLevel(packed_ih, cur_level); + Tensor packed_hh_value; + optional packed_hh_bdim; + std::tie(packed_hh_value, packed_hh_bdim) = unwrapTensorAtLevel(packed_hh, cur_level); + Tensor col_offsets_ih_value; + optional col_offsets_ih_bdim; + std::tie(col_offsets_ih_value, col_offsets_ih_bdim) = unwrapTensorAtLevel(col_offsets_ih, cur_level); + Tensor col_offsets_hh_value; + optional col_offsets_hh_bdim; + std::tie(col_offsets_hh_value, col_offsets_hh_bdim) = unwrapTensorAtLevel(col_offsets_hh, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim, packed_ih_value, packed_ih_bdim, packed_hh_value, packed_hh_bdim, col_offsets_ih_value, col_offsets_ih_bdim, col_offsets_hh_value, col_offsets_hh_bdim, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_rnn_relu_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level) && !isBatchedAtLevel(packed_ih, cur_level) && !isBatchedAtLevel(packed_hh, cur_level) && !isBatchedAtLevel(col_offsets_ih, cur_level) && !isBatchedAtLevel(col_offsets_hh, cur_level)) { + return at::_ops::quantized_rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + Tensor b_ih_value; + optional b_ih_bdim; + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih, cur_level); + Tensor b_hh_value; + optional b_hh_bdim; + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh, cur_level); + Tensor packed_ih_value; + optional packed_ih_bdim; + std::tie(packed_ih_value, packed_ih_bdim) = unwrapTensorAtLevel(packed_ih, cur_level); + Tensor packed_hh_value; + optional packed_hh_bdim; + std::tie(packed_hh_value, packed_hh_bdim) = unwrapTensorAtLevel(packed_hh, cur_level); + Tensor col_offsets_ih_value; + optional col_offsets_ih_bdim; + std::tie(col_offsets_ih_value, col_offsets_ih_bdim) = unwrapTensorAtLevel(col_offsets_ih, cur_level); + Tensor col_offsets_hh_value; + optional col_offsets_hh_bdim; + std::tie(col_offsets_hh_value, col_offsets_hh_bdim) = unwrapTensorAtLevel(col_offsets_hh, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim, packed_ih_value, packed_ih_bdim, packed_hh_value, packed_hh_bdim, col_offsets_ih_value, col_offsets_ih_bdim, col_offsets_hh_value, col_offsets_hh_bdim, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantized_rnn_tanh_cell_generated_plumbing(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(w_ih, cur_level) && !isBatchedAtLevel(w_hh, cur_level) && !isBatchedAtLevel(b_ih, cur_level) && !isBatchedAtLevel(b_hh, cur_level) && !isBatchedAtLevel(packed_ih, cur_level) && !isBatchedAtLevel(packed_hh, cur_level) && !isBatchedAtLevel(col_offsets_ih, cur_level) && !isBatchedAtLevel(col_offsets_hh, cur_level)) { + return at::_ops::quantized_rnn_tanh_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor w_ih_value; + optional w_ih_bdim; + std::tie(w_ih_value, w_ih_bdim) = unwrapTensorAtLevel(w_ih, cur_level); + Tensor w_hh_value; + optional w_hh_bdim; + std::tie(w_hh_value, w_hh_bdim) = unwrapTensorAtLevel(w_hh, cur_level); + Tensor b_ih_value; + optional b_ih_bdim; + std::tie(b_ih_value, b_ih_bdim) = unwrapTensorAtLevel(b_ih, cur_level); + Tensor b_hh_value; + optional b_hh_bdim; + std::tie(b_hh_value, b_hh_bdim) = unwrapTensorAtLevel(b_hh, cur_level); + Tensor packed_ih_value; + optional packed_ih_bdim; + std::tie(packed_ih_value, packed_ih_bdim) = unwrapTensorAtLevel(packed_ih, cur_level); + Tensor packed_hh_value; + optional packed_hh_bdim; + std::tie(packed_hh_value, packed_hh_bdim) = unwrapTensorAtLevel(packed_hh, cur_level); + Tensor col_offsets_ih_value; + optional col_offsets_ih_bdim; + std::tie(col_offsets_ih_value, col_offsets_ih_bdim) = unwrapTensorAtLevel(col_offsets_ih, cur_level); + Tensor col_offsets_hh_value; + optional col_offsets_hh_bdim; + std::tie(col_offsets_hh_value, col_offsets_hh_bdim) = unwrapTensorAtLevel(col_offsets_hh, cur_level); + auto results = batch_rule(input_value, input_bdim, hx_value, hx_bdim, w_ih_value, w_ih_bdim, w_hh_value, w_hh_bdim, b_ih_value, b_ih_bdim, b_hh_value, b_hh_bdim, packed_ih_value, packed_ih_bdim, packed_hh_value, packed_hh_bdim, col_offsets_ih_value, col_offsets_ih_bdim, col_offsets_hh_value, col_offsets_hh_bdim, scale_ih, scale_hh, zero_point_ih, zero_point_hh); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _pack_padded_sequence_generated_plumbing(const at::Tensor & input, const at::Tensor & lengths, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(lengths, cur_level)) { + return at::_ops::_pack_padded_sequence::call(input, lengths, batch_first); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor lengths_value; + optional lengths_bdim; + std::tie(lengths_value, lengths_bdim) = unwrapTensorAtLevel(lengths, cur_level); + auto results = batch_rule(input_value, input_bdim, lengths_value, lengths_bdim, batch_first); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _pack_padded_sequence_backward_generated_plumbing(const at::Tensor & grad, c10::SymIntArrayRef input_size, const at::Tensor & batch_sizes, bool batch_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level)) { + return at::_ops::_pack_padded_sequence_backward::call(grad, input_size, batch_sizes, batch_first); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_size, batch_sizes_value, batch_sizes_bdim, batch_first); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _pad_packed_sequence_generated_plumbing(const at::Tensor & data, const at::Tensor & batch_sizes, bool batch_first, const at::Scalar & padding_value, int64_t total_length) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(batch_sizes, cur_level)) { + return at::_ops::_pad_packed_sequence::call(data, batch_sizes, batch_first, padding_value, total_length); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + Tensor batch_sizes_value; + optional batch_sizes_bdim; + std::tie(batch_sizes_value, batch_sizes_bdim) = unwrapTensorAtLevel(batch_sizes, cur_level); + auto results = batch_rule(data_value, data_bdim, batch_sizes_value, batch_sizes_bdim, batch_first, padding_value, total_length); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor lift_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lift::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor lift_fresh_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lift_fresh::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor lift_fresh_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lift_fresh_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & masked_fill__Scalar_generated_plumbing(at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::masked_fill__Scalar::call(self, mask, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + batch_rule(self_value, self_bdim, mask_value, mask_bdim, value); + return self; +} +template +at::Tensor masked_fill_Scalar_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::masked_fill_Scalar::call(self, mask, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & masked_fill__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::masked_fill__Tensor::call(self, mask, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + batch_rule(self_value, self_bdim, mask_value, mask_bdim, value_value, value_bdim); + return self; +} +template +at::Tensor masked_fill_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::masked_fill_Tensor::call(self, mask, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, value_value, value_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & masked_scatter__generated_plumbing(at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::masked_scatter_::call(self, mask, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, mask_value, mask_bdim, source_value, source_bdim); + return self; +} +template +at::Tensor masked_scatter_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::masked_scatter::call(self, mask, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, source_value, source_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor masked_scatter_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & mask, c10::SymIntArrayRef sizes) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::masked_scatter_backward::call(grad_output, mask, sizes); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, mask_value, mask_bdim, sizes); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _masked_softmax_generated_plumbing(const at::Tensor & self, const at::Tensor & mask, c10::optional dim, c10::optional mask_type) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_masked_softmax::call(self, mask, dim, mask_type); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim, dim, mask_type); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _masked_softmax_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output, const at::Tensor & mask, c10::optional dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_masked_softmax_backward::call(grad_output, output, mask, dim); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim, mask_value, mask_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_dtype_generated_plumbing(const at::Tensor & self, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_dtype::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & put__generated_plumbing(at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::put_::call(self, index, source, accumulate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, index_value, index_bdim, source_value, source_bdim, accumulate); + return self; +} +template +at::Tensor put_generated_plumbing(const at::Tensor & self, const at::Tensor & index, const at::Tensor & source, bool accumulate) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::put::call(self, index, source, accumulate); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, index_value, index_bdim, source_value, source_bdim, accumulate); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_add__generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_add_::call(self, dim, index, source, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim, alpha); + return self; +} +template +at::Tensor index_add_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_add::call(self, dim, index, source, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor index_add_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_add_dimname::call(self, dim, index, source, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_reduce__generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_reduce_::call(self, dim, index, source, reduce, include_self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim, reduce, include_self); + return self; +} +template +at::Tensor index_reduce_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, c10::string_view reduce, bool include_self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::index_reduce::call(self, dim, index, source, reduce, include_self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, source_value, source_bdim, reduce, include_self); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_fill__int_Scalar_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_fill__int_Scalar::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return self; +} +template +at::Tensor index_fill_int_Scalar_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_fill_int_Scalar::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_fill__int_Tensor_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::index_fill__int_Tensor::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value_value, value_bdim); + return self; +} +template +at::Tensor index_fill_int_Tensor_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::index_fill_int_Tensor::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value_value, value_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & index_fill__Dimname_Scalar_generated_plumbing(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_fill__Dimname_Scalar::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return self; +} +template +at::Tensor & index_fill__Dimname_Tensor_generated_plumbing(at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::index_fill__Dimname_Tensor::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value_value, value_bdim); + return self; +} +template +at::Tensor index_fill_Dimname_Scalar_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_fill_Dimname_Scalar::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor index_fill_Dimname_Tensor_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::index_fill_Dimname_Tensor::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value_value, value_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor scatter_src_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_src::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter__src_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter__src::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return self; +} +template +at::Tensor scatter_value_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::scatter_value::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter__value_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::scatter__value::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return self; +} +template +at::Tensor scatter_reduce_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_reduce::call(self, dim, index, src, reduce); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim, reduce); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter__reduce_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter__reduce::call(self, dim, index, src, reduce); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim, reduce); + return self; +} +template +at::Tensor scatter_value_reduce_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::scatter_value_reduce::call(self, dim, index, value, reduce); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value, reduce); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter__value_reduce_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::scatter__value_reduce::call(self, dim, index, value, reduce); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value, reduce); + return self; +} +template +at::Tensor scatter_dimname_src_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_dimname_src::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor scatter_dimname_value_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::scatter_dimname_value::call(self, dim, index, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor scatter_add_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_add::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter_add__generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_add_::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return self; +} +template +at::Tensor scatter_add_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_add_dimname::call(self, dim, index, src); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor scatter_reduce_two_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_reduce_two::call(self, dim, index, src, reduce, include_self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim, reduce, include_self); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & scatter_reduce__two_generated_plumbing(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, bool include_self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::scatter_reduce__two::call(self, dim, index, src, reduce, include_self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + batch_rule(self_value, self_bdim, dim, index_value, index_bdim, src_value, src_bdim, reduce, include_self); + return self; +} +template +at::Tensor & eq__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::eq__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & eq__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::eq__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_and_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_and_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_and_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_and_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_and_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_and_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_and__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_and__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & bitwise_and__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_and__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor __and___Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__and___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __and___Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__and___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & __iand___Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__iand___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & __iand___Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__iand___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_or_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_or_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_or_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_or_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_or_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_or_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_or__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_or__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & bitwise_or__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_or__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor __or___Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__or___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __or___Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__or___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & __ior___Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__ior___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & __ior___Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__ior___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_xor_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_xor_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_xor_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_xor_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bitwise_xor_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_xor_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_xor__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_xor__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & bitwise_xor__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_xor__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor __xor___Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__xor___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __xor___Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__xor___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & __ixor___Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__ixor___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & __ixor___Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__ixor___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor __lshift___Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__lshift___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __lshift___Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__lshift___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & __ilshift___Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__ilshift___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & __ilshift___Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__ilshift___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_left_shift_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_left_shift_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_left_shift__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_left_shift__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_left_shift_Tensor_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_left_shift_Tensor_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_left_shift__Tensor_Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_left_shift__Tensor_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor bitwise_left_shift_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_left_shift_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __rshift___Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__rshift___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor __rshift___Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__rshift___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & __irshift___Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::__irshift___Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & __irshift___Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::__irshift___Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_right_shift_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_right_shift_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_right_shift__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_right_shift__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor bitwise_right_shift_Tensor_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_right_shift_Tensor_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & bitwise_right_shift__Tensor_Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::bitwise_right_shift__Tensor_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor bitwise_right_shift_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::bitwise_right_shift_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & tril__generated_plumbing(at::Tensor & self, int64_t diagonal) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tril_::call(self, diagonal); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, diagonal); + return self; +} +template +at::Tensor & triu__generated_plumbing(at::Tensor & self, int64_t diagonal) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::triu_::call(self, diagonal); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, diagonal); + return self; +} +template +at::Tensor & digamma__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::digamma_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor & lerp__Scalar_generated_plumbing(at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(end, cur_level)) { + return at::_ops::lerp__Scalar::call(self, end, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + batch_rule(self_value, self_bdim, end_value, end_bdim, weight); + return self; +} +template +at::Tensor & lerp__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(end, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::lerp__Tensor::call(self, end, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + batch_rule(self_value, self_bdim, end_value, end_bdim, weight_value, weight_bdim); + return self; +} +template +at::Tensor & addbmm__generated_plumbing(at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(batch1, cur_level) && !isBatchedAtLevel(batch2, cur_level)) { + return at::_ops::addbmm_::call(self, batch1, batch2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor batch1_value; + optional batch1_bdim; + std::tie(batch1_value, batch1_bdim) = unwrapTensorAtLevel(batch1, cur_level); + Tensor batch2_value; + optional batch2_bdim; + std::tie(batch2_value, batch2_bdim) = unwrapTensorAtLevel(batch2, cur_level); + batch_rule(self_value, self_bdim, batch1_value, batch1_bdim, batch2_value, batch2_bdim, beta, alpha); + return self; +} +template +at::Tensor addbmm_generated_plumbing(const at::Tensor & self, const at::Tensor & batch1, const at::Tensor & batch2, const at::Scalar & beta, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(batch1, cur_level) && !isBatchedAtLevel(batch2, cur_level)) { + return at::_ops::addbmm::call(self, batch1, batch2, beta, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor batch1_value; + optional batch1_bdim; + std::tie(batch1_value, batch1_bdim) = unwrapTensorAtLevel(batch1, cur_level); + Tensor batch2_value; + optional batch2_bdim; + std::tie(batch2_value, batch2_bdim) = unwrapTensorAtLevel(batch2, cur_level); + auto results = batch_rule(self_value, self_bdim, batch1_value, batch1_bdim, batch2_value, batch2_bdim, beta, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & random__from_generated_plumbing(at::Tensor & self, int64_t from, c10::optional to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random__from::call(self, from, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, from, to, generator); + return self; +} +template +at::Tensor & random__to_generated_plumbing(at::Tensor & self, int64_t to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random__to::call(self, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, to, generator); + return self; +} +template +at::Tensor & random__generated_plumbing(at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random_::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, generator); + return self; +} +template +at::Tensor & uniform__generated_plumbing(at::Tensor & self, double from, double to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::uniform_::call(self, from, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, from, to, generator); + return self; +} +template +at::Tensor & cauchy__generated_plumbing(at::Tensor & self, double median, double sigma, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cauchy_::call(self, median, sigma, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, median, sigma, generator); + return self; +} +template +at::Tensor & log_normal__generated_plumbing(at::Tensor & self, double mean, double std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_normal_::call(self, mean, std, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, mean, std, generator); + return self; +} +template +at::Tensor & exponential__generated_plumbing(at::Tensor & self, double lambd, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exponential_::call(self, lambd, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, lambd, generator); + return self; +} +template +at::Tensor & geometric__generated_plumbing(at::Tensor & self, double p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::geometric_::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, generator); + return self; +} +template +at::Tensor diag_generated_plumbing(const at::Tensor & self, int64_t diagonal) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diag::call(self, diagonal); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, diagonal); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cross_generated_plumbing(const at::Tensor & self, const at::Tensor & other, c10::optional dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::cross::call(self, other, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor triu_generated_plumbing(const at::Tensor & self, int64_t diagonal) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::triu::call(self, diagonal); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, diagonal); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor tril_generated_plumbing(const at::Tensor & self, int64_t diagonal) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::tril::call(self, diagonal); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, diagonal); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trace_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::trace::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor trace_backward_generated_plumbing(const at::Tensor & grad, c10::SymIntArrayRef sizes) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level)) { + return at::_ops::trace_backward::call(grad, sizes); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + auto results = batch_rule(grad_value, grad_bdim, sizes); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ne_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ne_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ne_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ne_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & ne__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ne__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & ne__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ne__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor not_equal_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::not_equal_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor not_equal_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::not_equal_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & not_equal__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::not_equal__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & not_equal__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::not_equal__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor eq_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::eq_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor eq_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::eq_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ge_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ge_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ge_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ge_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & ge__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ge__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & ge__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::ge__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor greater_equal_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::greater_equal_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor greater_equal_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::greater_equal_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & greater_equal__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::greater_equal__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & greater_equal__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::greater_equal__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor le_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::le_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor le_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::le_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & le__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::le__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & le__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::le__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor less_equal_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::less_equal_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor less_equal_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::less_equal_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & less_equal__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::less_equal__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & less_equal__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::less_equal__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor gt_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gt_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gt_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::gt_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & gt__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::gt__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & gt__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::gt__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor greater_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::greater_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor greater_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::greater_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & greater__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::greater__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & greater__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::greater__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor lt_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lt_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor lt_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::lt_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & lt__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lt__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & lt__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::lt__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor less_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::less_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor less_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::less_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & less__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::less__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor & less__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::less__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor take_generated_plumbing(const at::Tensor & self, const at::Tensor & index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::take::call(self, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, index_value, index_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor take_along_dim_generated_plumbing(const at::Tensor & self, const at::Tensor & indices, c10::optional dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::take_along_dim::call(self, indices, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(self_value, self_bdim, indices_value, indices_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor index_select_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_select::call(self, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor index_select_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_select_dimname::call(self, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor index_select_backward_generated_plumbing(const at::Tensor & grad, c10::SymIntArrayRef self_sizes, int64_t dim, const at::Tensor & index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::index_select_backward::call(grad, self_sizes, dim, index); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_sizes, dim, index_value, index_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor masked_select_generated_plumbing(const at::Tensor & self, const at::Tensor & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::masked_select::call(self, mask); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(self_value, self_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor masked_select_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & input, const at::Tensor & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::masked_select_backward::call(grad, input, mask); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor mask_value; + optional mask_bdim; + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask, cur_level); + auto results = batch_rule(grad_value, grad_bdim, input_value, input_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nonzero_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nonzero::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nonzero_static_generated_plumbing(const at::Tensor & self, int64_t size, int64_t fill_value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nonzero_static::call(self, size, fill_value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, fill_value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector nonzero_numpy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nonzero_numpy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argwhere_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argwhere::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gather_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::gather::call(self, dim, index, sparse_grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, sparse_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gather_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & self, int64_t dim, const at::Tensor & index, bool sparse_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::gather_backward::call(grad, self, dim, index, sparse_grad); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(grad_value, grad_bdim, self_value, self_bdim, dim, index_value, index_bdim, sparse_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor gather_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, bool sparse_grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level)) { + return at::_ops::gather_dimname::call(self, dim, index, sparse_grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, sparse_grad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _gather_sparse_backward_generated_plumbing(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & grad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(index, cur_level) && !isBatchedAtLevel(grad, cur_level)) { + return at::_ops::_gather_sparse_backward::call(self, dim, index, grad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor index_value; + optional index_bdim; + std::tie(index_value, index_bdim) = unwrapTensorAtLevel(index, cur_level); + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index_value, index_bdim, grad_value, grad_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor addcmul_generated_plumbing(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::addcmul::call(self, tensor1, tensor2, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor1_value; + optional tensor1_bdim; + std::tie(tensor1_value, tensor1_bdim) = unwrapTensorAtLevel(tensor1, cur_level); + Tensor tensor2_value; + optional tensor2_bdim; + std::tie(tensor2_value, tensor2_bdim) = unwrapTensorAtLevel(tensor2, cur_level); + auto results = batch_rule(self_value, self_bdim, tensor1_value, tensor1_bdim, tensor2_value, tensor2_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & addcmul__generated_plumbing(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::addcmul_::call(self, tensor1, tensor2, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor1_value; + optional tensor1_bdim; + std::tie(tensor1_value, tensor1_bdim) = unwrapTensorAtLevel(tensor1, cur_level); + Tensor tensor2_value; + optional tensor2_bdim; + std::tie(tensor2_value, tensor2_bdim) = unwrapTensorAtLevel(tensor2, cur_level); + batch_rule(self_value, self_bdim, tensor1_value, tensor1_bdim, tensor2_value, tensor2_bdim, value); + return self; +} +template +at::Tensor addcdiv_generated_plumbing(const at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::addcdiv::call(self, tensor1, tensor2, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor1_value; + optional tensor1_bdim; + std::tie(tensor1_value, tensor1_bdim) = unwrapTensorAtLevel(tensor1, cur_level); + Tensor tensor2_value; + optional tensor2_bdim; + std::tie(tensor2_value, tensor2_bdim) = unwrapTensorAtLevel(tensor2, cur_level); + auto results = batch_rule(self_value, self_bdim, tensor1_value, tensor1_bdim, tensor2_value, tensor2_bdim, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & addcdiv__generated_plumbing(at::Tensor & self, const at::Tensor & tensor1, const at::Tensor & tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::addcdiv_::call(self, tensor1, tensor2, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor tensor1_value; + optional tensor1_bdim; + std::tie(tensor1_value, tensor1_bdim) = unwrapTensorAtLevel(tensor1, cur_level); + Tensor tensor2_value; + optional tensor2_bdim; + std::tie(tensor2_value, tensor2_bdim) = unwrapTensorAtLevel(tensor2, cur_level); + batch_rule(self_value, self_bdim, tensor1_value, tensor1_bdim, tensor2_value, tensor2_bdim, value); + return self; +} +template +at::Tensor cross_entropy_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, double label_smoothing) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::cross_entropy_loss::call(self, target, weight, reduction, ignore_index, label_smoothing); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index, label_smoothing); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple triangular_solve_generated_plumbing(const at::Tensor & self, const at::Tensor & A, bool upper, bool transpose, bool unitriangular) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(A, cur_level)) { + return at::_ops::triangular_solve::call(self, A, upper, transpose, unitriangular); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(self_value, self_bdim, A_value, A_bdim, upper, transpose, unitriangular); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +void _linalg_check_errors_generated_plumbing(const at::Tensor & info, c10::string_view api_name, bool is_matrix) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(info, cur_level)) { + return at::_ops::_linalg_check_errors::call(info, api_name, is_matrix); + } + Tensor info_value; + optional info_bdim; + std::tie(info_value, info_bdim) = unwrapTensorAtLevel(info, cur_level); + batch_rule(info_value, info_bdim, api_name, is_matrix); +} +template +at::Tensor linalg_solve_triangular_generated_plumbing(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::linalg_solve_triangular::call(self, B, upper, left, unitriangular); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(self_value, self_bdim, B_value, B_bdim, upper, left, unitriangular); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_vander_generated_plumbing(const at::Tensor & x, c10::optional N) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::linalg_vander::call(x, N); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, N); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple svd_generated_plumbing(const at::Tensor & self, bool some, bool compute_uv) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::svd::call(self, some, compute_uv); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, some, compute_uv); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor swapaxes_generated_plumbing(const at::Tensor & self, int64_t axis0, int64_t axis1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::swapaxes::call(self, axis0, axis1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, axis0, axis1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor swapdims_generated_plumbing(const at::Tensor & self, int64_t dim0, int64_t dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::swapdims::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim0, dim1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cholesky_generated_plumbing(const at::Tensor & self, bool upper) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cholesky::call(self, upper); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, upper); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cholesky_solve_generated_plumbing(const at::Tensor & self, const at::Tensor & input2, bool upper) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(input2, cur_level)) { + return at::_ops::cholesky_solve::call(self, input2, upper); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + auto results = batch_rule(self_value, self_bdim, input2_value, input2_bdim, upper); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _cholesky_solve_helper_generated_plumbing(const at::Tensor & self, const at::Tensor & A, bool upper) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(A, cur_level)) { + return at::_ops::_cholesky_solve_helper::call(self, A, upper); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(self_value, self_bdim, A_value, A_bdim, upper); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cholesky_inverse_generated_plumbing(const at::Tensor & self, bool upper) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cholesky_inverse::call(self, upper); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, upper); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple qr_generated_plumbing(const at::Tensor & self, bool some) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::qr::call(self, some); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, some); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple geqrf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::geqrf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor orgqr_generated_plumbing(const at::Tensor & self, const at::Tensor & input2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(input2, cur_level)) { + return at::_ops::orgqr::call(self, input2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + auto results = batch_rule(self_value, self_bdim, input2_value, input2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ormqr_generated_plumbing(const at::Tensor & self, const at::Tensor & input2, const at::Tensor & input3, bool left, bool transpose) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(input2, cur_level) && !isBatchedAtLevel(input3, cur_level)) { + return at::_ops::ormqr::call(self, input2, input3, left, transpose); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor input2_value; + optional input2_bdim; + std::tie(input2_value, input2_bdim) = unwrapTensorAtLevel(input2, cur_level); + Tensor input3_value; + optional input3_bdim; + std::tie(input3_value, input3_bdim) = unwrapTensorAtLevel(input3, cur_level); + auto results = batch_rule(self_value, self_bdim, input2_value, input2_bdim, input3_value, input3_bdim, left, transpose); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _lu_with_info_generated_plumbing(const at::Tensor & self, bool pivot, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_lu_with_info::call(self, pivot, check_errors); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, pivot, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor lu_solve_generated_plumbing(const at::Tensor & self, const at::Tensor & LU_data, const at::Tensor & LU_pivots) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(LU_data, cur_level) && !isBatchedAtLevel(LU_pivots, cur_level)) { + return at::_ops::lu_solve::call(self, LU_data, LU_pivots); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor LU_data_value; + optional LU_data_bdim; + std::tie(LU_data_value, LU_data_bdim) = unwrapTensorAtLevel(LU_data, cur_level); + Tensor LU_pivots_value; + optional LU_pivots_bdim; + std::tie(LU_pivots_value, LU_pivots_bdim) = unwrapTensorAtLevel(LU_pivots, cur_level); + auto results = batch_rule(self_value, self_bdim, LU_data_value, LU_data_bdim, LU_pivots_value, LU_pivots_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple lu_unpack_generated_plumbing(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(LU_data, cur_level) && !isBatchedAtLevel(LU_pivots, cur_level)) { + return at::_ops::lu_unpack::call(LU_data, LU_pivots, unpack_data, unpack_pivots); + } + Tensor LU_data_value; + optional LU_data_bdim; + std::tie(LU_data_value, LU_data_bdim) = unwrapTensorAtLevel(LU_data, cur_level); + Tensor LU_pivots_value; + optional LU_pivots_bdim; + std::tie(LU_pivots_value, LU_pivots_bdim) = unwrapTensorAtLevel(LU_pivots, cur_level); + auto results = batch_rule(LU_data_value, LU_data_bdim, LU_pivots_value, LU_pivots_bdim, unpack_data, unpack_pivots); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor multinomial_generated_plumbing(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::multinomial::call(self, num_samples, replacement, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, num_samples, replacement, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & lgamma__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lgamma_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor lgamma_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::lgamma::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor digamma_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::digamma::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor polygamma_generated_plumbing(int64_t n, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::polygamma::call(n, self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(n, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & polygamma__generated_plumbing(at::Tensor & self, int64_t n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::polygamma_::call(self, n); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, n); + return self; +} +template +at::Tensor erfinv_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erfinv::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & erfinv__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::erfinv_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor i0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::i0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & i0__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::i0_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor sign_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sign::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & sign__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sign_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor signbit_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::signbit::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor dist_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::dist::call(self, other, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & atan2__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::atan2_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor atan2_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::atan2::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor arctan2_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::arctan2::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & arctan2__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::arctan2_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor lerp_Scalar_generated_plumbing(const at::Tensor & self, const at::Tensor & end, const at::Scalar & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(end, cur_level)) { + return at::_ops::lerp_Scalar::call(self, end, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + auto results = batch_rule(self_value, self_bdim, end_value, end_bdim, weight); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor lerp_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & end, const at::Tensor & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(end, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::lerp_Tensor::call(self, end, weight); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor end_value; + optional end_bdim; + std::tie(end_value, end_bdim) = unwrapTensorAtLevel(end, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(self_value, self_bdim, end_value, end_bdim, weight_value, weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor histc_generated_plumbing(const at::Tensor & self, int64_t bins, const at::Scalar & min, const at::Scalar & max) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::histc::call(self, bins, min, max); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, bins, min, max); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple histogram_bins_tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & bins, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(bins, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::histogram_bins_tensor::call(self, bins, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor bins_value; + optional bins_bdim; + std::tie(bins_value, bins_bdim) = unwrapTensorAtLevel(bins, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins_value, bins_bdim, weight_value, weight_bdim, density); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple histogram_bin_ct_generated_plumbing(const at::Tensor & self, int64_t bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::histogram_bin_ct::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::vector _histogramdd_bin_edges_generated_plumbing(const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_histogramdd_bin_edges::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _histogramdd_from_bin_cts_generated_plumbing(const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_histogramdd_from_bin_cts::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _histogramdd_from_bin_tensors_generated_plumbing(const at::Tensor & self, at::TensorList bins, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(bins, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_histogramdd_from_bin_tensors::call(self, bins, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, weight_value, weight_bdim, density); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple> histogramdd_generated_plumbing(const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::histogramdd::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple> histogramdd_int_bins_generated_plumbing(const at::Tensor & self, int64_t bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::histogramdd_int_bins::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple> histogramdd_TensorList_bins_generated_plumbing(const at::Tensor & self, at::TensorList bins, c10::optional> range, const c10::optional & weight, bool density) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(bins, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::histogramdd_TensorList_bins::call(self, bins, range, weight, density); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor fmod_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fmod_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & fmod__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fmod__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor fmod_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::fmod_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & fmod__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::fmod__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor hypot_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::hypot::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & hypot__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::hypot_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor igamma_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::igamma::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & igamma__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::igamma_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor igammac_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::igammac::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & igammac__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::igammac_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor nextafter_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::nextafter::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & nextafter__generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::nextafter_::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor remainder_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::remainder_Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & remainder__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::remainder__Scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, other); + return self; +} +template +at::Tensor remainder_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::remainder_Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & remainder__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::remainder__Tensor::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self_value, self_bdim, other_value, other_bdim); + return self; +} +template +at::Tensor remainder_Scalar_Tensor_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::remainder_Scalar_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor min_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::min::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fmin_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::fmin::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fmax_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::fmax::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor maximum_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::maximum::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_other_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::max_other::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor minimum_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::minimum::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor min_other_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::min_other::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantile_generated_plumbing(const at::Tensor & self, const at::Tensor & q, c10::optional dim, bool keepdim, c10::string_view interpolation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(q, cur_level)) { + return at::_ops::quantile::call(self, q, dim, keepdim, interpolation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor q_value; + optional q_bdim; + std::tie(q_value, q_bdim) = unwrapTensorAtLevel(q, cur_level); + auto results = batch_rule(self_value, self_bdim, q_value, q_bdim, dim, keepdim, interpolation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor quantile_scalar_generated_plumbing(const at::Tensor & self, double q, c10::optional dim, bool keepdim, c10::string_view interpolation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::quantile_scalar::call(self, q, dim, keepdim, interpolation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, q, dim, keepdim, interpolation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nanquantile_generated_plumbing(const at::Tensor & self, const at::Tensor & q, c10::optional dim, bool keepdim, c10::string_view interpolation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(q, cur_level)) { + return at::_ops::nanquantile::call(self, q, dim, keepdim, interpolation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor q_value; + optional q_bdim; + std::tie(q_value, q_bdim) = unwrapTensorAtLevel(q, cur_level); + auto results = batch_rule(self_value, self_bdim, q_value, q_bdim, dim, keepdim, interpolation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nanquantile_scalar_generated_plumbing(const at::Tensor & self, double q, c10::optional dim, bool keepdim, c10::string_view interpolation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nanquantile_scalar::call(self, q, dim, keepdim, interpolation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, q, dim, keepdim, interpolation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple sort_generated_plumbing(const at::Tensor & self, int64_t dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sort::call(self, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, descending); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple sort_stable_generated_plumbing(const at::Tensor & self, c10::optional stable, int64_t dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sort_stable::call(self, stable, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, stable, dim, descending); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple sort_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sort_dimname::call(self, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, descending); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple sort_dimname_stable_generated_plumbing(const at::Tensor & self, c10::optional stable, at::Dimname dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sort_dimname_stable::call(self, stable, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, stable, dim, descending); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor msort_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::msort::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argsort_generated_plumbing(const at::Tensor & self, int64_t dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argsort::call(self, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, descending); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argsort_stable_generated_plumbing(const at::Tensor & self, bool stable, int64_t dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argsort_stable::call(self, stable, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, stable, dim, descending); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor argsort_dimname_generated_plumbing(const at::Tensor & self, at::Dimname dim, bool descending) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::argsort_dimname::call(self, dim, descending); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, descending); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple topk_generated_plumbing(const at::Tensor & self, c10::SymInt k, int64_t dim, bool largest, bool sorted) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::topk::call(self, k, dim, largest, sorted); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, k, dim, largest, sorted); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor all_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::all::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor any_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::any::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor renorm_generated_plumbing(const at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::renorm::call(self, p, dim, maxnorm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, dim, maxnorm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & renorm__generated_plumbing(at::Tensor & self, const at::Scalar & p, int64_t dim, const at::Scalar & maxnorm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::renorm_::call(self, p, dim, maxnorm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, p, dim, maxnorm); + return self; +} +template +at::Tensor unfold_generated_plumbing(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unfold::call(self, dimension, size, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dimension, size, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unfold_backward_generated_plumbing(const at::Tensor & grad_in, c10::SymIntArrayRef input_sizes, int64_t dim, int64_t size, int64_t step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_in, cur_level)) { + return at::_ops::unfold_backward::call(grad_in, input_sizes, dim, size, step); + } + Tensor grad_in_value; + optional grad_in_bdim; + std::tie(grad_in_value, grad_in_bdim) = unwrapTensorAtLevel(grad_in, cur_level); + auto results = batch_rule(grad_in_value, grad_in_bdim, input_sizes, dim, size, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pow_Tensor_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::pow_Tensor_Tensor::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + auto results = batch_rule(self_value, self_bdim, exponent_value, exponent_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pow_Scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::pow_Scalar::call(self, exponent); + } + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + auto results = batch_rule(self, exponent_value, exponent_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pow_Tensor_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pow_Tensor_Scalar::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, exponent); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & pow__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pow__Scalar::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, exponent); + return self; +} +template +at::Tensor & pow__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::pow__Tensor::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + batch_rule(self_value, self_bdim, exponent_value, exponent_bdim); + return self; +} +template +at::Tensor float_power_Tensor_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::float_power_Tensor_Tensor::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + auto results = batch_rule(self_value, self_bdim, exponent_value, exponent_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor float_power_Scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::float_power_Scalar::call(self, exponent); + } + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + auto results = batch_rule(self, exponent_value, exponent_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor float_power_Tensor_Scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::float_power_Tensor_Scalar::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, exponent); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & float_power__Scalar_generated_plumbing(at::Tensor & self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::float_power__Scalar::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, exponent); + return self; +} +template +at::Tensor & float_power__Tensor_generated_plumbing(at::Tensor & self, const at::Tensor & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::float_power__Tensor::call(self, exponent); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor exponent_value; + optional exponent_bdim; + std::tie(exponent_value, exponent_bdim) = unwrapTensorAtLevel(exponent, cur_level); + batch_rule(self_value, self_bdim, exponent_value, exponent_bdim); + return self; +} +template +at::Tensor & normal__generated_plumbing(at::Tensor & self, double mean, double std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::normal_::call(self, mean, std, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, mean, std, generator); + return self; +} +template +at::Tensor normal_functional_generated_plumbing(const at::Tensor & self, double mean, double std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::normal_functional::call(self, mean, std, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, mean, std, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor normal_Tensor_float_generated_plumbing(const at::Tensor & mean, double std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(mean, cur_level)) { + return at::_ops::normal_Tensor_float::call(mean, std, generator); + } + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + auto results = batch_rule(mean_value, mean_bdim, std, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor normal_float_Tensor_generated_plumbing(double mean, const at::Tensor & std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(std, cur_level)) { + return at::_ops::normal_float_Tensor::call(mean, std, generator); + } + Tensor std_value; + optional std_bdim; + std::tie(std_value, std_bdim) = unwrapTensorAtLevel(std, cur_level); + auto results = batch_rule(mean, std_value, std_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor normal_Tensor_Tensor_generated_plumbing(const at::Tensor & mean, const at::Tensor & std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(mean, cur_level) && !isBatchedAtLevel(std, cur_level)) { + return at::_ops::normal_Tensor_Tensor::call(mean, std, generator); + } + Tensor mean_value; + optional mean_bdim; + std::tie(mean_value, mean_bdim) = unwrapTensorAtLevel(mean, cur_level); + Tensor std_value; + optional std_bdim; + std::tie(std_value, std_bdim) = unwrapTensorAtLevel(std, cur_level); + auto results = batch_rule(mean_value, mean_bdim, std_value, std_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor alias_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::alias::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _amp_foreach_non_finite_check_and_unscale__generated_plumbing(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(inv_scale, cur_level)) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale_::call(self, found_inf, inv_scale); + } + Tensor found_inf_value; + optional found_inf_bdim; + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf, cur_level); + Tensor inv_scale_value; + optional inv_scale_bdim; + std::tie(inv_scale_value, inv_scale_bdim) = unwrapTensorAtLevel(inv_scale, cur_level); + batch_rule(self, found_inf_value, found_inf_bdim, inv_scale_value, inv_scale_bdim); +} +template +::std::vector _foreach_add_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_add_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_add__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_add__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_add_List_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_add_List::call(self, other, alpha); + } + + auto results = batch_rule(self, other, alpha); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_add__List_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_add__List::call(self, other, alpha); + } + + batch_rule(self, other, alpha); +} +template +::std::vector _foreach_add_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_add_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_add__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_add__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_add_Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_add_Tensor::call(self, other, alpha); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim, alpha); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_add__Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_add__Tensor::call(self, other, alpha); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim, alpha); +} +template +::std::vector _foreach_sub_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sub_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sub__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sub__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_sub_List_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_sub_List::call(self, other, alpha); + } + + auto results = batch_rule(self, other, alpha); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sub__List_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_sub__List::call(self, other, alpha); + } + + batch_rule(self, other, alpha); +} +template +::std::vector _foreach_sub_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sub_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sub__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sub__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_mul_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_mul_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_mul__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_mul__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_mul_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_mul_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_mul__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_mul__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_mul_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_mul_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_mul__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_mul__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_mul_Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_mul_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_mul__Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_mul__Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim); +} +template +::std::vector _foreach_div_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_div_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_div__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_div__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_div_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_div_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_div__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_div__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_div_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_div_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_div__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_div__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_div_Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_div_Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_div__Tensor_generated_plumbing(at::TensorList self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_div__Tensor::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim); +} +template +::std::vector _foreach_clamp_max_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_max_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_max__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_max__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_clamp_max_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_clamp_max_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_max__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_clamp_max__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_clamp_max_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_max_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_max__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_max__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_clamp_min_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_min_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_min__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_min__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_clamp_min_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_clamp_min_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_min__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_clamp_min__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_clamp_min_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_min_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_clamp_min__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_clamp_min__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_maximum_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_maximum_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_maximum__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_maximum__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_maximum_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_maximum_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_maximum__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_maximum__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_maximum_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_maximum_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_maximum__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_maximum__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_minimum_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_minimum_Scalar::call(self, scalar); + } + + auto results = batch_rule(self, scalar); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_minimum__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & scalar) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_minimum__Scalar::call(self, scalar); + } + + batch_rule(self, scalar); +} +template +::std::vector _foreach_minimum_List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_minimum_List::call(self, other); + } + + auto results = batch_rule(self, other); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_minimum__List_generated_plumbing(at::TensorList self, at::TensorList other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_foreach_minimum__List::call(self, other); + } + + batch_rule(self, other); +} +template +::std::vector _foreach_minimum_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_minimum_ScalarList::call(self, scalars); + } + + auto results = batch_rule(self, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_minimum__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_minimum__ScalarList::call(self, scalars); + } + + batch_rule(self, scalars); +} +template +::std::vector _foreach_addcdiv_Scalar_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcdiv_Scalar::call(self, tensor1, tensor2, value); + } + + auto results = batch_rule(self, tensor1, tensor2, value); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_addcdiv_ScalarList_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcdiv_ScalarList::call(self, tensor1, tensor2, scalars); + } + + auto results = batch_rule(self, tensor1, tensor2, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_addcdiv_Tensor_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level)) { + return at::_ops::_foreach_addcdiv_Tensor::call(self, tensor1, tensor2, scalars); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + auto results = batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_addcdiv__Scalar_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcdiv__Scalar::call(self, tensor1, tensor2, value); + } + + batch_rule(self, tensor1, tensor2, value); +} +template +void _foreach_addcdiv__ScalarList_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcdiv__ScalarList::call(self, tensor1, tensor2, scalars); + } + + batch_rule(self, tensor1, tensor2, scalars); +} +template +void _foreach_addcdiv__Tensor_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level)) { + return at::_ops::_foreach_addcdiv__Tensor::call(self, tensor1, tensor2, scalars); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim); +} +template +::std::vector _foreach_addcmul_Scalar_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcmul_Scalar::call(self, tensor1, tensor2, value); + } + + auto results = batch_rule(self, tensor1, tensor2, value); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_addcmul_ScalarList_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcmul_ScalarList::call(self, tensor1, tensor2, scalars); + } + + auto results = batch_rule(self, tensor1, tensor2, scalars); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_addcmul_Tensor_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level)) { + return at::_ops::_foreach_addcmul_Tensor::call(self, tensor1, tensor2, scalars); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + auto results = batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_addcmul__Scalar_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcmul__Scalar::call(self, tensor1, tensor2, value); + } + + batch_rule(self, tensor1, tensor2, value); +} +template +void _foreach_addcmul__ScalarList_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level)) { + return at::_ops::_foreach_addcmul__ScalarList::call(self, tensor1, tensor2, scalars); + } + + batch_rule(self, tensor1, tensor2, scalars); +} +template +void _foreach_addcmul__Tensor_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level)) { + return at::_ops::_foreach_addcmul__Tensor::call(self, tensor1, tensor2, scalars); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim); +} +template +::std::vector _foreach_abs_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_abs::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_abs__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_abs_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_acos_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_acos::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_acos__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_acos_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_asin_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_asin::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_asin__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_asin_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_atan_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_atan::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_atan__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_atan_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_ceil_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_ceil::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_ceil__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_ceil_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_cos_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_cos::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_cos__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_cos_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_cosh_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_cosh::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_cosh__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_cosh_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_erf_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_erf::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_erf__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_erf_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_erfc_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_erfc::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_erfc__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_erfc_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_exp_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_exp::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_exp__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_exp_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_expm1_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_expm1::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_expm1__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_expm1_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_floor_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_floor::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_floor__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_floor_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_frac_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_frac::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_frac__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_frac_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_lerp_List_generated_plumbing(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level) && !isBatchedAtLevel(weights, cur_level)) { + return at::_ops::_foreach_lerp_List::call(self, tensors1, weights); + } + + auto results = batch_rule(self, tensors1, weights); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_lerp__List_generated_plumbing(at::TensorList self, at::TensorList tensors1, at::TensorList weights) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level) && !isBatchedAtLevel(weights, cur_level)) { + return at::_ops::_foreach_lerp__List::call(self, tensors1, weights); + } + + batch_rule(self, tensors1, weights); +} +template +::std::vector _foreach_lerp_Scalar_generated_plumbing(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level)) { + return at::_ops::_foreach_lerp_Scalar::call(self, tensors1, weight); + } + + auto results = batch_rule(self, tensors1, weight); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_lerp__Scalar_generated_plumbing(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level)) { + return at::_ops::_foreach_lerp__Scalar::call(self, tensors1, weight); + } + + batch_rule(self, tensors1, weight); +} +template +::std::vector _foreach_lgamma_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_lgamma::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_lgamma__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_lgamma_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_log_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_log__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_log10_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log10::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_log10__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log10_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_log1p_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log1p::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_log1p__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log1p_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_log2_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log2::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_log2__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_log2_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_neg_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_neg::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_neg__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_neg_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_norm_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & ord) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_norm_Scalar::call(self, ord); + } + + auto results = batch_rule(self, ord); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_pow_List_generated_plumbing(at::TensorList self, at::TensorList exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::_foreach_pow_List::call(self, exponent); + } + + auto results = batch_rule(self, exponent); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_pow_Scalar_generated_plumbing(at::TensorList self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_pow_Scalar::call(self, exponent); + } + + auto results = batch_rule(self, exponent); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_pow_ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_pow_ScalarList::call(self, exponent); + } + + auto results = batch_rule(self, exponent); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector _foreach_pow_ScalarAndTensor_generated_plumbing(const at::Scalar & self, at::TensorList exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::_foreach_pow_ScalarAndTensor::call(self, exponent); + } + + auto results = batch_rule(self, exponent); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_pow__List_generated_plumbing(at::TensorList self, at::TensorList exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level)) { + return at::_ops::_foreach_pow__List::call(self, exponent); + } + + batch_rule(self, exponent); +} +template +void _foreach_pow__Scalar_generated_plumbing(at::TensorList self, const at::Scalar & exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_pow__Scalar::call(self, exponent); + } + + batch_rule(self, exponent); +} +template +void _foreach_pow__ScalarList_generated_plumbing(at::TensorList self, at::ArrayRef exponent) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_pow__ScalarList::call(self, exponent); + } + + batch_rule(self, exponent); +} +template +::std::vector _foreach_reciprocal_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_reciprocal::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_reciprocal__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_reciprocal_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_round_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_round::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_round__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_round_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_sigmoid_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sigmoid::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sigmoid__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sigmoid_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_sign_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sign::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sign__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sign_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_sin_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sin::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sin__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sin_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_sinh_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sinh::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sinh__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sinh_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_sqrt_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sqrt::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_sqrt__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_sqrt_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_tan_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_tan::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_tan__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_tan_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_tanh_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_tanh::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_tanh__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_tanh_::call(self); + } + + batch_rule(self); +} +template +::std::vector _foreach_trunc_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_trunc::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_trunc__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_trunc_::call(self); + } + + batch_rule(self); +} +template +void _foreach_zero__generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_zero_::call(self); + } + + batch_rule(self); +} +template +void _foreach_copy__generated_plumbing(at::TensorList self, at::TensorList src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::_foreach_copy_::call(self, src, non_blocking); + } + + batch_rule(self, src, non_blocking); +} +template +at::Tensor bucketize_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & boundaries, bool out_int32, bool right) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(boundaries, cur_level)) { + return at::_ops::bucketize_Tensor::call(self, boundaries, out_int32, right); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor boundaries_value; + optional boundaries_bdim; + std::tie(boundaries_value, boundaries_bdim) = unwrapTensorAtLevel(boundaries, cur_level); + auto results = batch_rule(self_value, self_bdim, boundaries_value, boundaries_bdim, out_int32, right); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor bucketize_Scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & boundaries, bool out_int32, bool right) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(boundaries, cur_level)) { + return at::_ops::bucketize_Scalar::call(self, boundaries, out_int32, right); + } + Tensor boundaries_value; + optional boundaries_bdim; + std::tie(boundaries_value, boundaries_bdim) = unwrapTensorAtLevel(boundaries, cur_level); + auto results = batch_rule(self, boundaries_value, boundaries_bdim, out_int32, right); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor searchsorted_Tensor_generated_plumbing(const at::Tensor & sorted_sequence, const at::Tensor & self, bool out_int32, bool right, c10::optional side, const c10::optional & sorter) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(sorted_sequence, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(sorter, cur_level)) { + return at::_ops::searchsorted_Tensor::call(sorted_sequence, self, out_int32, right, side, sorter); + } + Tensor sorted_sequence_value; + optional sorted_sequence_bdim; + std::tie(sorted_sequence_value, sorted_sequence_bdim) = unwrapTensorAtLevel(sorted_sequence, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional sorter_value; + optional sorter_bdim; + if (sorter) { + std::tie(sorter_value, sorter_bdim) = unwrapTensorAtLevel(sorter.value(), cur_level); + } + auto results = batch_rule(sorted_sequence_value, sorted_sequence_bdim, self_value, self_bdim, out_int32, right, side, sorter_value, sorter_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor searchsorted_Scalar_generated_plumbing(const at::Tensor & sorted_sequence, const at::Scalar & self, bool out_int32, bool right, c10::optional side, const c10::optional & sorter) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(sorted_sequence, cur_level) && !isBatchedAtLevel(sorter, cur_level)) { + return at::_ops::searchsorted_Scalar::call(sorted_sequence, self, out_int32, right, side, sorter); + } + Tensor sorted_sequence_value; + optional sorted_sequence_bdim; + std::tie(sorted_sequence_value, sorted_sequence_bdim) = unwrapTensorAtLevel(sorted_sequence, cur_level); + optional sorter_value; + optional sorter_bdim; + if (sorter) { + std::tie(sorter_value, sorter_bdim) = unwrapTensorAtLevel(sorter.value(), cur_level); + } + auto results = batch_rule(sorted_sequence_value, sorted_sequence_bdim, self, out_int32, right, side, sorter_value, sorter_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _convert_indices_from_coo_to_csr_generated_plumbing(const at::Tensor & self, int64_t size, bool out_int32) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_convert_indices_from_coo_to_csr::call(self, size, out_int32); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, out_int32); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _convert_indices_from_csr_to_coo_generated_plumbing(const at::Tensor & crow_indices, const at::Tensor & col_indices, bool out_int32, bool transpose) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(crow_indices, cur_level) && !isBatchedAtLevel(col_indices, cur_level)) { + return at::_ops::_convert_indices_from_csr_to_coo::call(crow_indices, col_indices, out_int32, transpose); + } + Tensor crow_indices_value; + optional crow_indices_bdim; + std::tie(crow_indices_value, crow_indices_bdim) = unwrapTensorAtLevel(crow_indices, cur_level); + Tensor col_indices_value; + optional col_indices_bdim; + std::tie(col_indices_value, col_indices_bdim) = unwrapTensorAtLevel(col_indices, cur_level); + auto results = batch_rule(crow_indices_value, crow_indices_bdim, col_indices_value, col_indices_bdim, out_int32, transpose); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mse_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::mse_loss::call(self, target, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mse_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::mse_loss_backward::call(grad_output, self, target, reduction); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor l1_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::l1_loss::call(self, target, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor multi_margin_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::multi_margin_loss::call(self, target, p, margin, weight, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, p, margin, weight_value, weight_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor multi_margin_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const at::Scalar & p, const at::Scalar & margin, const c10::optional & weight, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::multi_margin_loss_backward::call(grad_output, self, target, p, margin, weight, reduction); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, p, margin, weight_value, weight_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor multilabel_margin_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::multilabel_margin_loss::call(self, target, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple multilabel_margin_loss_forward_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::multilabel_margin_loss_forward::call(self, target, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor multilabel_margin_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(is_target, cur_level)) { + return at::_ops::multilabel_margin_loss_backward::call(grad_output, self, target, reduction, is_target); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + Tensor is_target_value; + optional is_target_bdim; + std::tie(is_target_value, is_target_bdim) = unwrapTensorAtLevel(is_target, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, reduction, is_target_value, is_target_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nll_loss_nd_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::nll_loss_nd::call(self, target, weight, reduction, ignore_index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nll_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::nll_loss::call(self, target, weight, reduction, ignore_index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple nll_loss_forward_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::nll_loss_forward::call(self, target, weight, reduction, ignore_index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor nll_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(total_weight, cur_level)) { + return at::_ops::nll_loss_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + Tensor total_weight_value; + optional total_weight_bdim; + std::tie(total_weight_value, total_weight_bdim) = unwrapTensorAtLevel(total_weight, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index, total_weight_value, total_weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nll_loss2d_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::nll_loss2d::call(self, target, weight, reduction, ignore_index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple nll_loss2d_forward_generated_plumbing(const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::nll_loss2d_forward::call(self, target, weight, reduction, ignore_index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor nll_loss2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(total_weight, cur_level)) { + return at::_ops::nll_loss2d_backward::call(grad_output, self, target, weight, reduction, ignore_index, total_weight); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + Tensor total_weight_value; + optional total_weight_bdim; + std::tie(total_weight_value, total_weight_bdim) = unwrapTensorAtLevel(total_weight, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, weight_value, weight_bdim, reduction, ignore_index, total_weight_value, total_weight_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor smooth_l1_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::smooth_l1_loss::call(self, target, reduction, beta); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction, beta); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor smooth_l1_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double beta) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::smooth_l1_loss_backward::call(grad_output, self, target, reduction, beta); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, reduction, beta); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor huber_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::huber_loss::call(self, target, reduction, delta); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction, delta); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor huber_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, double delta) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::huber_loss_backward::call(grad_output, self, target, reduction, delta); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, reduction, delta); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor soft_margin_loss_generated_plumbing(const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::soft_margin_loss::call(self, target, reduction); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor soft_margin_loss_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(target, cur_level)) { + return at::_ops::soft_margin_loss_backward::call(grad_output, self, target, reduction); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor target_value; + optional target_bdim; + std::tie(target_value, target_bdim) = unwrapTensorAtLevel(target, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, target_value, target_bdim, reduction); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor elu_generated_plumbing(const at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::elu::call(self, alpha, scale, input_scale); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, alpha, scale, input_scale); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor elu_backward_generated_plumbing(const at::Tensor & grad_output, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale, bool is_result, const at::Tensor & self_or_result) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self_or_result, cur_level)) { + return at::_ops::elu_backward::call(grad_output, alpha, scale, input_scale, is_result, self_or_result); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_or_result_value; + optional self_or_result_bdim; + std::tie(self_or_result_value, self_or_result_bdim) = unwrapTensorAtLevel(self_or_result, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, alpha, scale, input_scale, is_result, self_or_result_value, self_or_result_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & elu__generated_plumbing(at::Tensor & self, const at::Scalar & alpha, const at::Scalar & scale, const at::Scalar & input_scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::elu_::call(self, alpha, scale, input_scale); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, alpha, scale, input_scale); + return self; +} +template +at::Tensor glu_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::glu::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor glu_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::glu_backward::call(grad_output, self, dim); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor glu_jvp_generated_plumbing(const at::Tensor & glu, const at::Tensor & x, const at::Tensor & dx, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(glu, cur_level) && !isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(dx, cur_level)) { + return at::_ops::glu_jvp::call(glu, x, dx, dim); + } + Tensor glu_value; + optional glu_bdim; + std::tie(glu_value, glu_bdim) = unwrapTensorAtLevel(glu, cur_level); + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor dx_value; + optional dx_bdim; + std::tie(dx_value, dx_bdim) = unwrapTensorAtLevel(dx, cur_level); + auto results = batch_rule(glu_value, glu_bdim, x_value, x_bdim, dx_value, dx_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor glu_backward_jvp_generated_plumbing(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_x, cur_level) && !isBatchedAtLevel(grad_glu, cur_level) && !isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(dgrad_glu, cur_level) && !isBatchedAtLevel(dx, cur_level)) { + return at::_ops::glu_backward_jvp::call(grad_x, grad_glu, x, dgrad_glu, dx, dim); + } + Tensor grad_x_value; + optional grad_x_bdim; + std::tie(grad_x_value, grad_x_bdim) = unwrapTensorAtLevel(grad_x, cur_level); + Tensor grad_glu_value; + optional grad_glu_bdim; + std::tie(grad_glu_value, grad_glu_bdim) = unwrapTensorAtLevel(grad_glu, cur_level); + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor dgrad_glu_value; + optional dgrad_glu_bdim; + std::tie(dgrad_glu_value, dgrad_glu_bdim) = unwrapTensorAtLevel(dgrad_glu, cur_level); + Tensor dx_value; + optional dx_bdim; + std::tie(dx_value, dx_bdim) = unwrapTensorAtLevel(dx, cur_level); + auto results = batch_rule(grad_x_value, grad_x_bdim, grad_glu_value, grad_glu_bdim, x_value, x_bdim, dgrad_glu_value, dgrad_glu_bdim, dx_value, dx_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hardsigmoid_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardsigmoid::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & hardsigmoid__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardsigmoid_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor hardsigmoid_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardsigmoid_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hardtanh_generated_plumbing(const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardtanh::call(self, min_val, max_val); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, min_val, max_val); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor hardtanh_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardtanh_backward::call(grad_output, self, min_val, max_val); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, min_val, max_val); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & hardtanh__generated_plumbing(at::Tensor & self, const at::Scalar & min_val, const at::Scalar & max_val) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardtanh_::call(self, min_val, max_val); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, min_val, max_val); + return self; +} +template +at::Tensor hardswish_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardswish::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & hardswish__generated_plumbing(at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardswish_::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim); + return self; +} +template +at::Tensor hardswish_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::hardswish_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor leaky_relu_generated_plumbing(const at::Tensor & self, const at::Scalar & negative_slope) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::leaky_relu::call(self, negative_slope); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, negative_slope); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor leaky_relu_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & negative_slope, bool self_is_result) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::leaky_relu_backward::call(grad_output, self, negative_slope, self_is_result); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, negative_slope, self_is_result); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & leaky_relu__generated_plumbing(at::Tensor & self, const at::Scalar & negative_slope) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::leaky_relu_::call(self, negative_slope); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, negative_slope); + return self; +} +template +at::Tensor log_sigmoid_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_sigmoid::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple log_sigmoid_forward_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_sigmoid_forward::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor log_sigmoid_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & buffer) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(buffer, cur_level)) { + return at::_ops::log_sigmoid_backward::call(grad_output, self, buffer); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor buffer_value; + optional buffer_bdim; + std::tie(buffer_value, buffer_bdim) = unwrapTensorAtLevel(buffer, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, buffer_value, buffer_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rrelu_with_noise_generated_plumbing(const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(noise, cur_level)) { + return at::_ops::rrelu_with_noise::call(self, noise, lower, upper, training, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor noise_value; + optional noise_bdim; + std::tie(noise_value, noise_bdim) = unwrapTensorAtLevel(noise, cur_level); + auto results = batch_rule(self_value, self_bdim, noise_value, noise_bdim, lower, upper, training, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor rrelu_with_noise_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, bool self_is_result) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(noise, cur_level)) { + return at::_ops::rrelu_with_noise_backward::call(grad_output, self, noise, lower, upper, training, self_is_result); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor noise_value; + optional noise_bdim; + std::tie(noise_value, noise_bdim) = unwrapTensorAtLevel(noise, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, noise_value, noise_bdim, lower, upper, training, self_is_result); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & rrelu_with_noise__generated_plumbing(at::Tensor & self, const at::Tensor & noise, const at::Scalar & lower, const at::Scalar & upper, bool training, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(noise, cur_level)) { + return at::_ops::rrelu_with_noise_::call(self, noise, lower, upper, training, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor noise_value; + optional noise_bdim; + std::tie(noise_value, noise_bdim) = unwrapTensorAtLevel(noise, cur_level); + batch_rule(self_value, self_bdim, noise_value, noise_bdim, lower, upper, training, generator); + return self; +} +template +at::Tensor softplus_generated_plumbing(const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::softplus::call(self, beta, threshold); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, beta, threshold); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor softplus_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & beta, const at::Scalar & threshold) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::softplus_backward::call(grad_output, self, beta, threshold); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, beta, threshold); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor softshrink_generated_plumbing(const at::Tensor & self, const at::Scalar & lambd) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::softshrink::call(self, lambd); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, lambd); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor softshrink_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Scalar & lambd) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::softshrink_backward::call(grad_output, self, lambd); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, lambd); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor adaptive_avg_pool2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_avg_pool2d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_adaptive_avg_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_adaptive_avg_pool2d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor mkldnn_adaptive_avg_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::mkldnn_adaptive_avg_pool2d_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _adaptive_avg_pool2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_adaptive_avg_pool2d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _adaptive_avg_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_adaptive_avg_pool2d_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor adaptive_avg_pool3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_avg_pool3d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _adaptive_avg_pool3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_adaptive_avg_pool3d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _adaptive_avg_pool3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::_adaptive_avg_pool3d_backward::call(grad_output, self); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple adaptive_max_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_max_pool2d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor adaptive_max_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::adaptive_max_pool2d_backward::call(grad_output, self, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple adaptive_max_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::adaptive_max_pool3d::call(self, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor adaptive_max_pool3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::adaptive_max_pool3d_backward::call(grad_output, self, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor avg_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::avg_pool2d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor avg_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::avg_pool2d_backward::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor avg_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::avg_pool3d::call(self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor avg_pool3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, bool ceil_mode, bool count_include_pad, c10::optional divisor_override) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::avg_pool3d_backward::call(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple fractional_max_pool2d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(random_samples, cur_level)) { + return at::_ops::fractional_max_pool2d::call(self, kernel_size, output_size, random_samples); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor random_samples_value; + optional random_samples_bdim; + std::tie(random_samples_value, random_samples_bdim) = unwrapTensorAtLevel(random_samples, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, output_size, random_samples_value, random_samples_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor fractional_max_pool2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::fractional_max_pool2d_backward::call(grad_output, self, kernel_size, output_size, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, output_size, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple fractional_max_pool3d_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & random_samples) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(random_samples, cur_level)) { + return at::_ops::fractional_max_pool3d::call(self, kernel_size, output_size, random_samples); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor random_samples_value; + optional random_samples_bdim; + std::tie(random_samples_value, random_samples_bdim) = unwrapTensorAtLevel(random_samples, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, output_size, random_samples_value, random_samples_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor fractional_max_pool3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef output_size, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::fractional_max_pool3d_backward::call(grad_output, self, kernel_size, output_size, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, output_size, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple max_pool2d_with_indices_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool2d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor max_pool2d_with_indices_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::max_pool2d_with_indices_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple max_pool3d_with_indices_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::max_pool3d_with_indices::call(self, kernel_size, stride, padding, dilation, ceil_mode); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor max_pool3d_with_indices_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, bool ceil_mode, const at::Tensor & indices) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::max_pool3d_with_indices_backward::call(grad_output, self, kernel_size, stride, padding, dilation, ceil_mode, indices); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, kernel_size, stride, padding, dilation, ceil_mode, indices_value, indices_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_unpool2d_generated_plumbing(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::max_unpool2d::call(self, indices, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(self_value, self_bdim, indices_value, indices_bdim, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor max_unpool3d_generated_plumbing(const at::Tensor & self, const at::Tensor & indices, c10::SymIntArrayRef output_size, at::IntArrayRef stride, at::IntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::max_unpool3d::call(self, indices, output_size, stride, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(self_value, self_bdim, indices_value, indices_bdim, output_size, stride, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad1d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad1d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad1d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad1d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad2d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad2d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad3d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor reflection_pad3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::reflection_pad3d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad1d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad1d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad1d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad1d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad2d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad2d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad2d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad3d::call(self, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor replication_pad3d_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::replication_pad3d_backward::call(grad_output, self, padding); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _pad_circular_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef pad) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_pad_circular::call(self, pad); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, pad); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _pad_enum_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef pad, int64_t mode, c10::optional value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_pad_enum::call(self, pad, mode, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, pad, mode, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pad_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::pad::call(self, pad, mode, value); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, pad, mode, value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_linear1d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_linear1d_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bilinear2d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_bilinear2d_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bilinear2d_aa_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_upsample_bilinear2d_aa_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_trilinear3d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_trilinear3d_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bicubic2d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_bicubic2d_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bicubic2d_aa_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, bool align_corners, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_upsample_bicubic2d_aa_vec::call(input, output_size, align_corners, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, align_corners, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest1d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_nearest1d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact1d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_upsample_nearest_exact1d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest2d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_nearest2d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact2d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_upsample_nearest_exact2d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest3d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::upsample_nearest3d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact3d_vec_generated_plumbing(const at::Tensor & input, at::OptionalSymIntArrayRef output_size, c10::optional> scale_factors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level)) { + return at::_ops::_upsample_nearest_exact3d_vec::call(input, output_size, scale_factors); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + auto results = batch_rule(input_value, input_bdim, output_size, scale_factors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_linear1d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_linear1d::call(self, output_size, align_corners, scales); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_linear1d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_linear1d_backward::call(grad_output, output_size, input_size, align_corners, scales); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bilinear2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_bilinear2d::call(self, output_size, align_corners, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bilinear2d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_bilinear2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bilinear2d_aa_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_upsample_bilinear2d_aa::call(self, output_size, align_corners, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bilinear2d_aa_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::_upsample_bilinear2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bicubic2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_bicubic2d::call(self, output_size, align_corners, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_bicubic2d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_bicubic2d_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bicubic2d_aa_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_upsample_bicubic2d_aa::call(self, output_size, align_corners, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_bicubic2d_aa_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::_upsample_bicubic2d_aa_backward::call(grad_output, output_size, input_size, align_corners, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_trilinear3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_trilinear3d::call(self, output_size, align_corners, scales_d, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, align_corners, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_trilinear3d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_trilinear3d_backward::call(grad_output, output_size, input_size, align_corners, scales_d, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, align_corners, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest1d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_nearest1d::call(self, output_size, scales); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact1d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_upsample_nearest_exact1d::call(self, output_size, scales); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest1d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_nearest1d_backward::call(grad_output, output_size, input_size, scales); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact1d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::_upsample_nearest_exact1d_backward::call(grad_output, output_size, input_size, scales); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_nearest2d::call(self, output_size, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact2d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_upsample_nearest_exact2d::call(self, output_size, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest2d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_nearest2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact2d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::_upsample_nearest_exact2d_backward::call(grad_output, output_size, input_size, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::upsample_nearest3d::call(self, output_size, scales_d, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact3d_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_upsample_nearest_exact3d::call(self, output_size, scales_d, scales_h, scales_w); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor upsample_nearest3d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::upsample_nearest3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _upsample_nearest_exact3d_backward_generated_plumbing(const at::Tensor & grad_output, c10::SymIntArrayRef output_size, c10::SymIntArrayRef input_size, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level)) { + return at::_ops::_upsample_nearest_exact3d_backward::call(grad_output, output_size, input_size, scales_d, scales_h, scales_w); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_size, input_size, scales_d, scales_h, scales_w); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sigmoid_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::sigmoid_backward::call(grad_output, output); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor logit_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level)) { + return at::_ops::logit_backward::call(grad_output, self, eps); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, eps); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor tanh_backward_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::tanh_backward::call(grad_output, output); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, output_value, output_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv_transpose2d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv_transpose2d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, output_padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv_transpose3d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv_transpose3d::call(self, weight, kernel_size, bias, stride, padding, output_padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, output_padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor thnn_conv2d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::thnn_conv2d::call(self, weight, kernel_size, bias, stride, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _slow_conv2d_forward_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_slow_conv2d_forward::call(self, weight, kernel_size, bias, stride, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _slow_conv2d_backward_output_mask_generated_plumbing(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, ::std::array output_mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level)) { + return at::_ops::_slow_conv2d_backward_output_mask::call(grad_output, self, weight, kernel_size, stride, padding, output_mask); + } + Tensor grad_output_value; + optional grad_output_bdim; + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output, cur_level); + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + auto results = batch_rule(grad_output_value, grad_output_bdim, self_value, self_bdim, weight_value, weight_bdim, kernel_size, stride, padding, output_mask); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor _conv_depthwise2d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::_conv_depthwise2d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor conv_depthwise3d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::conv_depthwise3d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv3d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv3d::call(self, weight, kernel_size, bias, stride, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv3d_forward_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv3d_forward::call(self, weight, kernel_size, bias, stride, padding); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv_dilated2d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv_dilated2d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slow_conv_dilated3d_generated_plumbing(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level)) { + return at::_ops::slow_conv_dilated3d::call(self, weight, kernel_size, bias, stride, padding, dilation); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor weight_value; + optional weight_bdim; + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, weight_value, weight_bdim, kernel_size, bias_value, bias_bdim, stride, padding, dilation); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor col2im_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef output_size, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::col2im::call(self, output_size, kernel_size, dilation, padding, stride); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, output_size, kernel_size, dilation, padding, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor column_stack_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::column_stack::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor im2col_generated_plumbing(const at::Tensor & self, at::IntArrayRef kernel_size, at::IntArrayRef dilation, at::IntArrayRef padding, at::IntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::im2col::call(self, kernel_size, dilation, padding, stride); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, kernel_size, dilation, padding, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isfinite_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isfinite::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isinf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isinf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void record_stream_generated_plumbing(at::Tensor & self, at::Stream s) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::record_stream::call(self, s); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, s); +} +template +at::Tensor isposinf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isposinf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor isneginf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::isneginf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _add_batch_dim_generated_plumbing(const at::Tensor & self, int64_t batch_dim, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_add_batch_dim::call(self, batch_dim, level); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, batch_dim, level); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _remove_batch_dim_generated_plumbing(const at::Tensor & self, int64_t level, int64_t batch_size, int64_t out_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_remove_batch_dim::call(self, level, batch_size, out_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, level, batch_size, out_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_entr_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_entr::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_ndtri_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_ndtri::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_log_ndtr_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_log_ndtr::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_expm1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_expm1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_exp2_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_exp2::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_psi_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_psi::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_digamma_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_digamma::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_gammaln_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_gammaln::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_erf_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_erf::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_erfc_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_erfc::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_erfcx_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_erfcx::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_erfinv_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_erfinv::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_ndtr_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_ndtr::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlog1py_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_xlog1py::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlog1py_self_scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_xlog1py_self_scalar::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlog1py_other_scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_xlog1py_other_scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlogy_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_xlogy::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlogy_self_scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_xlogy_self_scalar::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_xlogy_other_scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_xlogy_other_scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_zeta_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_zeta::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_zeta_self_scalar_generated_plumbing(const at::Scalar & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_zeta_self_scalar::call(self, other); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_zeta_other_scalar_generated_plumbing(const at::Tensor & self, const at::Scalar & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_zeta_other_scalar::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, other); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_i0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_i0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_i0e_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_i0e::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_i1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_i1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_i1e_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_i1e::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_logit_generated_plumbing(const at::Tensor & self, c10::optional eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_logit::call(self, eps); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, eps); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_polygamma_generated_plumbing(int64_t n, const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_polygamma::call(n, self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(n, self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_logsumexp_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim, bool keepdim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_logsumexp::call(self, dim, keepdim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, keepdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_expit_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_expit::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_sinc_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_sinc::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_round_generated_plumbing(const at::Tensor & self, int64_t decimals) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_round::call(self, decimals); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, decimals); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_log1p_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_log1p::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_log_softmax_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_log_softmax::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_gammainc_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_gammainc::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_gammaincc_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::special_gammaincc::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_multigammaln_generated_plumbing(const at::Tensor & self, int64_t p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_multigammaln::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_softmax_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_softmax::call(self, dim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_fft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_fft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ifft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ifft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_rfft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_rfft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_irfft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_irfft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_hfft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_hfft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ihfft_generated_plumbing(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ihfft::call(self, n, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_fft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_fft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ifft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ifft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_rfft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_rfft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_irfft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_irfft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_hfft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_hfft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ihfft2_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::IntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ihfft2::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_fftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_fftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ifftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ifftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_rfftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_rfftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_irfftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_irfftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_hfftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_hfftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ihfftn_generated_plumbing(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ihfftn::call(self, s, dim, norm); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, s, dim, norm); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_fftshift_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_fftshift::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor fft_ifftshift_generated_plumbing(const at::Tensor & self, at::OptionalIntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::fft_ifftshift::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_cholesky_ex_generated_plumbing(const at::Tensor & self, bool upper, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_cholesky_ex::call(self, upper, check_errors); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, upper, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_cholesky_generated_plumbing(const at::Tensor & self, bool upper) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_cholesky::call(self, upper); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, upper); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_cross_generated_plumbing(const at::Tensor & self, const at::Tensor & other, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::linalg_cross::call(self, other, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_lu_factor_generated_plumbing(const at::Tensor & A, bool pivot) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_lu_factor::call(A, pivot); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, pivot); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple linalg_lu_factor_ex_generated_plumbing(const at::Tensor & A, bool pivot, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_lu_factor_ex::call(A, pivot, check_errors); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, pivot, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple linalg_lu_generated_plumbing(const at::Tensor & A, bool pivot) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_lu::call(A, pivot); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, pivot); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor linalg_lu_solve_generated_plumbing(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(LU, cur_level) && !isBatchedAtLevel(pivots, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::linalg_lu_solve::call(LU, pivots, B, left, adjoint); + } + Tensor LU_value; + optional LU_bdim; + std::tie(LU_value, LU_bdim) = unwrapTensorAtLevel(LU, cur_level); + Tensor pivots_value; + optional pivots_bdim; + std::tie(pivots_value, pivots_bdim) = unwrapTensorAtLevel(pivots, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(LU_value, LU_bdim, pivots_value, pivots_bdim, B_value, B_bdim, left, adjoint); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _linalg_det_generated_plumbing(const at::Tensor & A) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::_linalg_det::call(A); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor linalg_det_generated_plumbing(const at::Tensor & A) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_det::call(A); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor det_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::det::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_ldl_factor_ex_generated_plumbing(const at::Tensor & self, bool hermitian, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_ldl_factor_ex::call(self, hermitian, check_errors); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, hermitian, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple linalg_ldl_factor_generated_plumbing(const at::Tensor & self, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_ldl_factor::call(self, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, hermitian); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_ldl_solve_generated_plumbing(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(LD, cur_level) && !isBatchedAtLevel(pivots, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::linalg_ldl_solve::call(LD, pivots, B, hermitian); + } + Tensor LD_value; + optional LD_bdim; + std::tie(LD_value, LD_bdim) = unwrapTensorAtLevel(LD, cur_level); + Tensor pivots_value; + optional pivots_bdim; + std::tie(pivots_value, pivots_bdim) = unwrapTensorAtLevel(pivots, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(LD_value, LD_bdim, pivots_value, pivots_bdim, B_value, B_bdim, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_lstsq_generated_plumbing(const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(b, cur_level)) { + return at::_ops::linalg_lstsq::call(self, b, rcond, driver); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor b_value; + optional b_bdim; + std::tie(b_value, b_bdim) = unwrapTensorAtLevel(b, cur_level); + auto results = batch_rule(self_value, self_bdim, b_value, b_bdim, rcond, driver); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +at::Tensor linalg_matmul_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::linalg_matmul::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_vecdot_generated_plumbing(const at::Tensor & x, const at::Tensor & y, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(y, cur_level)) { + return at::_ops::linalg_vecdot::call(x, y, dim); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor y_value; + optional y_bdim; + std::tie(y_value, y_bdim) = unwrapTensorAtLevel(y, cur_level); + auto results = batch_rule(x_value, x_bdim, y_value, y_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_exp_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_exp::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _linalg_slogdet_generated_plumbing(const at::Tensor & A) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::_linalg_slogdet::call(A); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple linalg_slogdet_generated_plumbing(const at::Tensor & A) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_slogdet::call(A); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple slogdet_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::slogdet::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor logdet_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::logdet::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_eig_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_eig::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor _linalg_eigvals_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_linalg_eigvals::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_eigvals_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_eigvals::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _linalg_eigh_generated_plumbing(const at::Tensor & A, c10::string_view UPLO, bool compute_v) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::_linalg_eigh::call(A, UPLO, compute_v); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, UPLO, compute_v); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple linalg_eigh_generated_plumbing(const at::Tensor & self, c10::string_view UPLO) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_eigh::call(self, UPLO); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, UPLO); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_eigvalsh_generated_plumbing(const at::Tensor & self, c10::string_view UPLO) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_eigvalsh::call(self, UPLO); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, UPLO); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_householder_product_generated_plumbing(const at::Tensor & input, const at::Tensor & tau) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(tau, cur_level)) { + return at::_ops::linalg_householder_product::call(input, tau); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor tau_value; + optional tau_bdim; + std::tie(tau_value, tau_bdim) = unwrapTensorAtLevel(tau, cur_level); + auto results = batch_rule(input_value, input_bdim, tau_value, tau_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_inv_ex_generated_plumbing(const at::Tensor & A, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_inv_ex::call(A, check_errors); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_inv_generated_plumbing(const at::Tensor & A) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_inv::call(A); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor inverse_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::inverse::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor inner_generated_plumbing(const at::Tensor & self, const at::Tensor & other) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::inner::call(self, other); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor outer_generated_plumbing(const at::Tensor & self, const at::Tensor & vec2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(vec2, cur_level)) { + return at::_ops::outer::call(self, vec2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor vec2_value; + optional vec2_bdim; + std::tie(vec2_value, vec2_bdim) = unwrapTensorAtLevel(vec2, cur_level); + auto results = batch_rule(self_value, self_bdim, vec2_value, vec2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ger_generated_plumbing(const at::Tensor & self, const at::Tensor & vec2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(vec2, cur_level)) { + return at::_ops::ger::call(self, vec2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor vec2_value; + optional vec2_bdim; + std::tie(vec2_value, vec2_bdim) = unwrapTensorAtLevel(vec2, cur_level); + auto results = batch_rule(self_value, self_bdim, vec2_value, vec2_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_norm_generated_plumbing(const at::Tensor & self, const c10::optional & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_norm::call(self, ord, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ord, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_norm_ord_str_generated_plumbing(const at::Tensor & self, c10::string_view ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_norm_ord_str::call(self, ord, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ord, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_vector_norm_generated_plumbing(const at::Tensor & self, const at::Scalar & ord, at::OptionalIntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_vector_norm::call(self, ord, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ord, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_norm_generated_plumbing(const at::Tensor & self, const at::Scalar & ord, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_norm::call(self, ord, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ord, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_norm_str_ord_generated_plumbing(const at::Tensor & self, c10::string_view ord, at::IntArrayRef dim, bool keepdim, c10::optional dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_norm_str_ord::call(self, ord, dim, keepdim, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ord, dim, keepdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _linalg_svd_generated_plumbing(const at::Tensor & A, bool full_matrices, bool compute_uv, c10::optional driver) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::_linalg_svd::call(A, full_matrices, compute_uv, driver); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, full_matrices, compute_uv, driver); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple linalg_svd_generated_plumbing(const at::Tensor & A, bool full_matrices, c10::optional driver) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_svd::call(A, full_matrices, driver); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, full_matrices, driver); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +at::Tensor linalg_svdvals_generated_plumbing(const at::Tensor & A, c10::optional driver) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_svdvals::call(A, driver); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, driver); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_cond_generated_plumbing(const at::Tensor & self, const c10::optional & p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_cond::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_cond_p_str_generated_plumbing(const at::Tensor & self, c10::string_view p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_cond_p_str::call(self, p); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_pinv_atol_rtol_tensor_generated_plumbing(const at::Tensor & self, const c10::optional & atol, const c10::optional & rtol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(atol, cur_level) && !isBatchedAtLevel(rtol, cur_level)) { + return at::_ops::linalg_pinv_atol_rtol_tensor::call(self, atol, rtol, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional atol_value; + optional atol_bdim; + if (atol) { + std::tie(atol_value, atol_bdim) = unwrapTensorAtLevel(atol.value(), cur_level); + } + optional rtol_value; + optional rtol_bdim; + if (rtol) { + std::tie(rtol_value, rtol_bdim) = unwrapTensorAtLevel(rtol.value(), cur_level); + } + auto results = batch_rule(self_value, self_bdim, atol_value, atol_bdim, rtol_value, rtol_bdim, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_pinv_atol_rtol_float_generated_plumbing(const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_pinv_atol_rtol_float::call(self, atol, rtol, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, atol, rtol, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_pinv_generated_plumbing(const at::Tensor & self, double rcond, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_pinv::call(self, rcond, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, rcond, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_pinv_rcond_tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & rcond, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(rcond, cur_level)) { + return at::_ops::linalg_pinv_rcond_tensor::call(self, rcond, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor rcond_value; + optional rcond_bdim; + std::tie(rcond_value, rcond_bdim) = unwrapTensorAtLevel(rcond, cur_level); + auto results = batch_rule(self_value, self_bdim, rcond_value, rcond_bdim, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _linalg_solve_ex_generated_plumbing(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::_linalg_solve_ex::call(A, B, left, check_errors); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(A_value, A_bdim, B_value, B_bdim, left, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple linalg_solve_ex_generated_plumbing(const at::Tensor & A, const at::Tensor & B, bool left, bool check_errors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::linalg_solve_ex::call(A, B, left, check_errors); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(A_value, A_bdim, B_value, B_bdim, left, check_errors); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_solve_generated_plumbing(const at::Tensor & A, const at::Tensor & B, bool left) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level) && !isBatchedAtLevel(B, cur_level)) { + return at::_ops::linalg_solve::call(A, B, left); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + Tensor B_value; + optional B_bdim; + std::tie(B_value, B_bdim) = unwrapTensorAtLevel(B, cur_level); + auto results = batch_rule(A_value, A_bdim, B_value, B_bdim, left); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_tensorinv_generated_plumbing(const at::Tensor & self, int64_t ind) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_tensorinv::call(self, ind); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, ind); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_tensorsolve_generated_plumbing(const at::Tensor & self, const at::Tensor & other, at::OptionalIntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::linalg_tensorsolve::call(self, other, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple linalg_qr_generated_plumbing(const at::Tensor & A, c10::string_view mode) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(A, cur_level)) { + return at::_ops::linalg_qr::call(A, mode); + } + Tensor A_value; + optional A_bdim; + std::tie(A_value, A_bdim) = unwrapTensorAtLevel(A, cur_level); + auto results = batch_rule(A_value, A_bdim, mode); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor linalg_matrix_power_generated_plumbing(const at::Tensor & self, int64_t n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_power::call(self, n); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_rank_atol_rtol_tensor_generated_plumbing(const at::Tensor & input, const c10::optional & atol, const c10::optional & rtol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(atol, cur_level) && !isBatchedAtLevel(rtol, cur_level)) { + return at::_ops::linalg_matrix_rank_atol_rtol_tensor::call(input, atol, rtol, hermitian); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + optional atol_value; + optional atol_bdim; + if (atol) { + std::tie(atol_value, atol_bdim) = unwrapTensorAtLevel(atol.value(), cur_level); + } + optional rtol_value; + optional rtol_bdim; + if (rtol) { + std::tie(rtol_value, rtol_bdim) = unwrapTensorAtLevel(rtol.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, atol_value, atol_bdim, rtol_value, rtol_bdim, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_rank_atol_rtol_float_generated_plumbing(const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_rank_atol_rtol_float::call(self, atol, rtol, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, atol, rtol, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_rank_generated_plumbing(const at::Tensor & self, double tol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::linalg_matrix_rank::call(self, tol, hermitian); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, tol, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_matrix_rank_tol_tensor_generated_plumbing(const at::Tensor & input, const at::Tensor & tol, bool hermitian) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(tol, cur_level)) { + return at::_ops::linalg_matrix_rank_tol_tensor::call(input, tol, hermitian); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor tol_value; + optional tol_bdim; + std::tie(tol_value, tol_bdim) = unwrapTensorAtLevel(tol, cur_level); + auto results = batch_rule(input_value, input_bdim, tol_value, tol_bdim, hermitian); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor linalg_multi_dot_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::linalg_multi_dot::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor nested_to_padded_tensor_generated_plumbing(const at::Tensor & self, double padding, at::OptionalIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::nested_to_padded_tensor::call(self, padding, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_serialization_subcmul_generated_plumbing(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level)) { + return at::_ops::_test_serialization_subcmul::call(self, other, alpha); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + auto results = batch_rule(self_value, self_bdim, other_value, other_bdim, alpha); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_parallel_materialize_generated_plumbing(const at::Tensor & self, int64_t num_parallel, bool skip_first) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_parallel_materialize::call(self, num_parallel, skip_first); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, num_parallel, skip_first); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_optional_intlist_generated_plumbing(const at::Tensor & values, at::OptionalIntArrayRef addends) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(values, cur_level)) { + return at::_ops::_test_optional_intlist::call(values, addends); + } + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(values_value, values_bdim, addends); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_optional_filled_intlist_generated_plumbing(const at::Tensor & values, at::OptionalIntArrayRef addends) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(values, cur_level)) { + return at::_ops::_test_optional_filled_intlist::call(values, addends); + } + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(values_value, values_bdim, addends); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_optional_floatlist_generated_plumbing(const at::Tensor & values, c10::optional> addends) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(values, cur_level)) { + return at::_ops::_test_optional_floatlist::call(values, addends); + } + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(values_value, values_bdim, addends); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_string_default_generated_plumbing(const at::Tensor & dummy, c10::string_view a, c10::string_view b) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dummy, cur_level)) { + return at::_ops::_test_string_default::call(dummy, a, b); + } + Tensor dummy_value; + optional dummy_bdim; + std::tie(dummy_value, dummy_bdim) = unwrapTensorAtLevel(dummy, cur_level); + auto results = batch_rule(dummy_value, dummy_bdim, a, b); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_ambiguous_defaults_a_generated_plumbing(const at::Tensor & dummy, int64_t a, int64_t b) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dummy, cur_level)) { + return at::_ops::_test_ambiguous_defaults_a::call(dummy, a, b); + } + Tensor dummy_value; + optional dummy_bdim; + std::tie(dummy_value, dummy_bdim) = unwrapTensorAtLevel(dummy, cur_level); + auto results = batch_rule(dummy_value, dummy_bdim, a, b); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_ambiguous_defaults_b_generated_plumbing(const at::Tensor & dummy, int64_t a, c10::string_view b) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(dummy, cur_level)) { + return at::_ops::_test_ambiguous_defaults_b::call(dummy, a, b); + } + Tensor dummy_value; + optional dummy_bdim; + std::tie(dummy_value, dummy_bdim) = unwrapTensorAtLevel(dummy, cur_level); + auto results = batch_rule(dummy_value, dummy_bdim, a, b); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_warn_in_autograd_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_warn_in_autograd::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_autograd_multiple_dispatch_fullcoverage_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_autograd_multiple_dispatch_fullcoverage::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_autograd_multiple_dispatch_ntonly_generated_plumbing(const at::Tensor & self, bool b) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_autograd_multiple_dispatch_ntonly::call(self, b); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, b); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_autograd_multiple_dispatch_view_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_autograd_multiple_dispatch_view::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _test_autograd_multiple_dispatch_view_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_test_autograd_multiple_dispatch_view_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor segment_reduce_generated_plumbing(const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths, const c10::optional & indices, const c10::optional & offsets, int64_t axis, bool unsafe, const c10::optional & initial) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(lengths, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(offsets, cur_level)) { + return at::_ops::segment_reduce::call(data, reduce, lengths, indices, offsets, axis, unsafe, initial); + } + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + optional lengths_value; + optional lengths_bdim; + if (lengths) { + std::tie(lengths_value, lengths_bdim) = unwrapTensorAtLevel(lengths.value(), cur_level); + } + optional indices_value; + optional indices_bdim; + if (indices) { + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices.value(), cur_level); + } + optional offsets_value; + optional offsets_bdim; + if (offsets) { + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets.value(), cur_level); + } + auto results = batch_rule(data_value, data_bdim, reduce, lengths_value, lengths_bdim, indices_value, indices_bdim, offsets_value, offsets_bdim, axis, unsafe, initial); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _segment_reduce_backward_generated_plumbing(const at::Tensor & grad, const at::Tensor & output, const at::Tensor & data, c10::string_view reduce, const c10::optional & lengths, const c10::optional & offsets, int64_t axis, const c10::optional & initial) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(data, cur_level) && !isBatchedAtLevel(lengths, cur_level) && !isBatchedAtLevel(offsets, cur_level)) { + return at::_ops::_segment_reduce_backward::call(grad, output, data, reduce, lengths, offsets, axis, initial); + } + Tensor grad_value; + optional grad_bdim; + std::tie(grad_value, grad_bdim) = unwrapTensorAtLevel(grad, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor data_value; + optional data_bdim; + std::tie(data_value, data_bdim) = unwrapTensorAtLevel(data, cur_level); + optional lengths_value; + optional lengths_bdim; + if (lengths) { + std::tie(lengths_value, lengths_bdim) = unwrapTensorAtLevel(lengths.value(), cur_level); + } + optional offsets_value; + optional offsets_bdim; + if (offsets) { + std::tie(offsets_value, offsets_bdim) = unwrapTensorAtLevel(offsets.value(), cur_level); + } + auto results = batch_rule(grad_value, grad_bdim, output_value, output_bdim, data_value, data_bdim, reduce, lengths_value, lengths_bdim, offsets_value, offsets_bdim, axis, initial); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor pad_sequence_generated_plumbing(at::TensorList sequences, bool batch_first, double padding_value) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(sequences, cur_level)) { + return at::_ops::pad_sequence::call(sequences, batch_first, padding_value); + } + + auto results = batch_rule(sequences, batch_first, padding_value); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor flatten_dense_tensors_generated_plumbing(at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::flatten_dense_tensors::call(tensors); + } + + auto results = batch_rule(tensors); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unflatten_dense_tensors_generated_plumbing(const at::Tensor & flat, at::TensorList tensors) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(flat, cur_level) && !isBatchedAtLevel(tensors, cur_level)) { + return at::_ops::unflatten_dense_tensors::call(flat, tensors); + } + Tensor flat_value; + optional flat_bdim; + std::tie(flat_value, flat_bdim) = unwrapTensorAtLevel(flat, cur_level); + auto results = batch_rule(flat_value, flat_bdim, tensors); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_tensor_from_tensor_list_generated_plumbing(at::TensorList list, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(list, cur_level)) { + return at::_ops::_nested_tensor_from_tensor_list::call(list, dtype, layout, device, pin_memory); + } + + auto results = batch_rule(list, dtype, layout, device, pin_memory); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _fw_primal_copy_generated_plumbing(const at::Tensor & self, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fw_primal_copy::call(self, level); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, level); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _make_dual_copy_generated_plumbing(const at::Tensor & primal, const at::Tensor & tangent, int64_t level) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(primal, cur_level) && !isBatchedAtLevel(tangent, cur_level)) { + return at::_ops::_make_dual_copy::call(primal, tangent, level); + } + Tensor primal_value; + optional primal_bdim; + std::tie(primal_value, primal_bdim) = unwrapTensorAtLevel(primal, cur_level); + Tensor tangent_value; + optional tangent_bdim; + std::tie(tangent_value, tangent_bdim) = unwrapTensorAtLevel(tangent, cur_level); + auto results = batch_rule(primal_value, primal_bdim, tangent_value, tangent_bdim, level); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_as_real_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_as_real_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_as_complex_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_as_complex_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _conj_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_conj_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _neg_view_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_neg_view_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor as_strided_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, c10::optional storage_offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::as_strided_copy::call(self, size, stride, storage_offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, stride, storage_offset); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _sparse_broadcast_to_copy_generated_plumbing(const at::Tensor & self, at::IntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_sparse_broadcast_to_copy::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor diagonal_copy_generated_plumbing(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::diagonal_copy::call(self, offset, dim1, dim2); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, offset, dim1, dim2); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor expand_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, bool implicit) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::expand_copy::call(self, size, implicit); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, implicit); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor permute_copy_generated_plumbing(const at::Tensor & self, at::IntArrayRef dims) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::permute_copy::call(self, dims); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dims); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _reshape_alias_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_reshape_alias_copy::call(self, size, stride); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor select_copy_int_generated_plumbing(const at::Tensor & self, int64_t dim, c10::SymInt index) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::select_copy_int::call(self, dim, index); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, index); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor detach_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::detach_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor slice_copy_Tensor_generated_plumbing(const at::Tensor & self, int64_t dim, c10::optional start, c10::optional end, c10::SymInt step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::slice_copy_Tensor::call(self, dim, start, end, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim, start, end, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector split_copy_Tensor_generated_plumbing(const at::Tensor & self, c10::SymInt split_size, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::split_copy_Tensor::call(self, split_size, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_size, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector split_with_sizes_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::split_with_sizes_copy::call(self, split_sizes, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, split_sizes, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_copy_dim_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_copy_dim::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor squeeze_copy_dims_generated_plumbing(const at::Tensor & self, at::IntArrayRef dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::squeeze_copy_dims::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor t_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::t_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor transpose_copy_int_generated_plumbing(const at::Tensor & self, int64_t dim0, int64_t dim1) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::transpose_copy_int::call(self, dim0, dim1); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim0, dim1); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unsqueeze_copy_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unsqueeze_copy::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _values_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_values_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor values_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::values_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor crow_indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::crow_indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor col_indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::col_indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor ccol_indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::ccol_indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor row_indices_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::row_indices_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::vector unbind_copy_int_generated_plumbing(const at::Tensor & self, int64_t dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unbind_copy_int::call(self, dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dim); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void unbind_copy_int_out_generated_plumbing(const at::Tensor & self, int64_t dim, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::unbind_copy_int_out::call(self, dim, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dim, out); +} +template +void split_copy_Tensor_out_generated_plumbing(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::split_copy_Tensor_out::call(self, split_size, dim, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, split_size, dim, out); +} +template +void split_with_sizes_copy_out_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::split_with_sizes_copy_out::call(self, split_sizes, dim, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, split_sizes, dim, out); +} +template +at::Tensor view_copy_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_copy::call(self, size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor view_copy_dtype_generated_plumbing(const at::Tensor & self, at::ScalarType dtype) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::view_copy_dtype::call(self, dtype); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dtype); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor unfold_copy_generated_plumbing(const at::Tensor & self, int64_t dimension, int64_t size, int64_t step) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::unfold_copy::call(self, dimension, size, step); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, dimension, size, step); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor alias_copy_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::alias_copy::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor to_padded_tensor_generated_plumbing(const at::Tensor & self, double padding, at::OptionalSymIntArrayRef output_size) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::to_padded_tensor::call(self, padding, output_size); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, padding, output_size); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _nested_tensor_softmax_with_shape_generated_plumbing(const at::Tensor & self, const at::Tensor & query) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(query, cur_level)) { + return at::_ops::_nested_tensor_softmax_with_shape::call(self, query); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + auto results = batch_rule(self_value, self_bdim, query_value, query_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _transformer_encoder_layer_fwd_generated_plumbing(const at::Tensor & src, int64_t embed_dim, int64_t num_heads, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, bool use_gelu, bool norm_first, double eps, const at::Tensor & norm_weight_1, const at::Tensor & norm_bias_1, const at::Tensor & norm_weight_2, const at::Tensor & norm_bias_2, const at::Tensor & ffn_weight_1, const at::Tensor & ffn_bias_1, const at::Tensor & ffn_weight_2, const at::Tensor & ffn_bias_2, const c10::optional & mask, c10::optional mask_type) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(src, cur_level) && !isBatchedAtLevel(qkv_weight, cur_level) && !isBatchedAtLevel(qkv_bias, cur_level) && !isBatchedAtLevel(proj_weight, cur_level) && !isBatchedAtLevel(proj_bias, cur_level) && !isBatchedAtLevel(norm_weight_1, cur_level) && !isBatchedAtLevel(norm_bias_1, cur_level) && !isBatchedAtLevel(norm_weight_2, cur_level) && !isBatchedAtLevel(norm_bias_2, cur_level) && !isBatchedAtLevel(ffn_weight_1, cur_level) && !isBatchedAtLevel(ffn_bias_1, cur_level) && !isBatchedAtLevel(ffn_weight_2, cur_level) && !isBatchedAtLevel(ffn_bias_2, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_transformer_encoder_layer_fwd::call(src, embed_dim, num_heads, qkv_weight, qkv_bias, proj_weight, proj_bias, use_gelu, norm_first, eps, norm_weight_1, norm_bias_1, norm_weight_2, norm_bias_2, ffn_weight_1, ffn_bias_1, ffn_weight_2, ffn_bias_2, mask, mask_type); + } + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + Tensor qkv_weight_value; + optional qkv_weight_bdim; + std::tie(qkv_weight_value, qkv_weight_bdim) = unwrapTensorAtLevel(qkv_weight, cur_level); + Tensor qkv_bias_value; + optional qkv_bias_bdim; + std::tie(qkv_bias_value, qkv_bias_bdim) = unwrapTensorAtLevel(qkv_bias, cur_level); + Tensor proj_weight_value; + optional proj_weight_bdim; + std::tie(proj_weight_value, proj_weight_bdim) = unwrapTensorAtLevel(proj_weight, cur_level); + Tensor proj_bias_value; + optional proj_bias_bdim; + std::tie(proj_bias_value, proj_bias_bdim) = unwrapTensorAtLevel(proj_bias, cur_level); + Tensor norm_weight_1_value; + optional norm_weight_1_bdim; + std::tie(norm_weight_1_value, norm_weight_1_bdim) = unwrapTensorAtLevel(norm_weight_1, cur_level); + Tensor norm_bias_1_value; + optional norm_bias_1_bdim; + std::tie(norm_bias_1_value, norm_bias_1_bdim) = unwrapTensorAtLevel(norm_bias_1, cur_level); + Tensor norm_weight_2_value; + optional norm_weight_2_bdim; + std::tie(norm_weight_2_value, norm_weight_2_bdim) = unwrapTensorAtLevel(norm_weight_2, cur_level); + Tensor norm_bias_2_value; + optional norm_bias_2_bdim; + std::tie(norm_bias_2_value, norm_bias_2_bdim) = unwrapTensorAtLevel(norm_bias_2, cur_level); + Tensor ffn_weight_1_value; + optional ffn_weight_1_bdim; + std::tie(ffn_weight_1_value, ffn_weight_1_bdim) = unwrapTensorAtLevel(ffn_weight_1, cur_level); + Tensor ffn_bias_1_value; + optional ffn_bias_1_bdim; + std::tie(ffn_bias_1_value, ffn_bias_1_bdim) = unwrapTensorAtLevel(ffn_bias_1, cur_level); + Tensor ffn_weight_2_value; + optional ffn_weight_2_bdim; + std::tie(ffn_weight_2_value, ffn_weight_2_bdim) = unwrapTensorAtLevel(ffn_weight_2, cur_level); + Tensor ffn_bias_2_value; + optional ffn_bias_2_bdim; + std::tie(ffn_bias_2_value, ffn_bias_2_bdim) = unwrapTensorAtLevel(ffn_bias_2, cur_level); + optional mask_value; + optional mask_bdim; + if (mask) { + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask.value(), cur_level); + } + auto results = batch_rule(src_value, src_bdim, embed_dim, num_heads, qkv_weight_value, qkv_weight_bdim, qkv_bias_value, qkv_bias_bdim, proj_weight_value, proj_weight_bdim, proj_bias_value, proj_bias_bdim, use_gelu, norm_first, eps, norm_weight_1_value, norm_weight_1_bdim, norm_bias_1_value, norm_bias_1_bdim, norm_weight_2_value, norm_weight_2_bdim, norm_bias_2_value, norm_bias_2_bdim, ffn_weight_1_value, ffn_weight_1_bdim, ffn_bias_1_value, ffn_bias_1_bdim, ffn_weight_2_value, ffn_weight_2_bdim, ffn_bias_2_value, ffn_bias_2_bdim, mask_value, mask_bdim, mask_type); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _native_multi_head_attention_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask, bool need_weights, bool average_attn_weights, c10::optional mask_type) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(qkv_weight, cur_level) && !isBatchedAtLevel(qkv_bias, cur_level) && !isBatchedAtLevel(proj_weight, cur_level) && !isBatchedAtLevel(proj_bias, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_native_multi_head_attention::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask, need_weights, average_attn_weights, mask_type); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor qkv_weight_value; + optional qkv_weight_bdim; + std::tie(qkv_weight_value, qkv_weight_bdim) = unwrapTensorAtLevel(qkv_weight, cur_level); + Tensor qkv_bias_value; + optional qkv_bias_bdim; + std::tie(qkv_bias_value, qkv_bias_bdim) = unwrapTensorAtLevel(qkv_bias, cur_level); + Tensor proj_weight_value; + optional proj_weight_bdim; + std::tie(proj_weight_value, proj_weight_bdim) = unwrapTensorAtLevel(proj_weight, cur_level); + Tensor proj_bias_value; + optional proj_bias_bdim; + std::tie(proj_bias_value, proj_bias_bdim) = unwrapTensorAtLevel(proj_bias, cur_level); + optional mask_value; + optional mask_bdim; + if (mask) { + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, embed_dim, num_head, qkv_weight_value, qkv_weight_bdim, qkv_bias_value, qkv_bias_bdim, proj_weight_value, proj_weight_bdim, proj_bias_value, proj_bias_bdim, mask_value, mask_bdim, need_weights, average_attn_weights, mask_type); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +at::Tensor scaled_dot_product_attention_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask, double dropout_p, bool is_causal, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(attn_mask, cur_level)) { + return at::_ops::scaled_dot_product_attention::call(query, key, value, attn_mask, dropout_p, is_causal, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + optional attn_mask_value; + optional attn_mask_bdim; + if (attn_mask) { + std::tie(attn_mask_value, attn_mask_bdim) = unwrapTensorAtLevel(attn_mask.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, attn_mask_value, attn_mask_bdim, dropout_p, is_causal, scale); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +::std::tuple _scaled_dot_product_attention_math_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_mask, double dropout_p, bool is_causal, const c10::optional & dropout_mask, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(attn_mask, cur_level) && !isBatchedAtLevel(dropout_mask, cur_level)) { + return at::_ops::_scaled_dot_product_attention_math::call(query, key, value, attn_mask, dropout_p, is_causal, dropout_mask, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + optional attn_mask_value; + optional attn_mask_bdim; + if (attn_mask) { + std::tie(attn_mask_value, attn_mask_bdim) = unwrapTensorAtLevel(attn_mask.value(), cur_level); + } + optional dropout_mask_value; + optional dropout_mask_bdim; + if (dropout_mask) { + std::tie(dropout_mask_value, dropout_mask_bdim) = unwrapTensorAtLevel(dropout_mask.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, attn_mask_value, attn_mask_bdim, dropout_p, is_causal, dropout_mask_value, dropout_mask_bdim, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_flash_attention_for_cpu_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, const c10::optional & attn_mask, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(attn_mask, cur_level)) { + return at::_ops::_scaled_dot_product_flash_attention_for_cpu::call(query, key, value, dropout_p, is_causal, attn_mask, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + optional attn_mask_value; + optional attn_mask_bdim; + if (attn_mask) { + std::tie(attn_mask_value, attn_mask_bdim) = unwrapTensorAtLevel(attn_mask.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, dropout_p, is_causal, attn_mask_value, attn_mask_bdim, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_flash_attention_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(out, cur_level) && !isBatchedAtLevel(logsumexp, cur_level) && !isBatchedAtLevel(cum_seq_q, cur_level) && !isBatchedAtLevel(cum_seq_k, cur_level) && !isBatchedAtLevel(philox_seed, cur_level) && !isBatchedAtLevel(philox_offset, cur_level)) { + return at::_ops::_scaled_dot_product_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor out_value; + optional out_bdim; + std::tie(out_value, out_bdim) = unwrapTensorAtLevel(out, cur_level); + Tensor logsumexp_value; + optional logsumexp_bdim; + std::tie(logsumexp_value, logsumexp_bdim) = unwrapTensorAtLevel(logsumexp, cur_level); + Tensor cum_seq_q_value; + optional cum_seq_q_bdim; + std::tie(cum_seq_q_value, cum_seq_q_bdim) = unwrapTensorAtLevel(cum_seq_q, cur_level); + Tensor cum_seq_k_value; + optional cum_seq_k_bdim; + std::tie(cum_seq_k_value, cum_seq_k_bdim) = unwrapTensorAtLevel(cum_seq_k, cur_level); + Tensor philox_seed_value; + optional philox_seed_bdim; + std::tie(philox_seed_value, philox_seed_bdim) = unwrapTensorAtLevel(philox_seed, cur_level); + Tensor philox_offset_value; + optional philox_offset_bdim; + std::tie(philox_offset_value, philox_offset_bdim) = unwrapTensorAtLevel(philox_offset, cur_level); + auto results = batch_rule(grad_out_value, grad_out_bdim, query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, out_value, out_bdim, logsumexp_value, logsumexp_bdim, cum_seq_q_value, cum_seq_q_bdim, cum_seq_k_value, cum_seq_k_bdim, max_q, max_k, dropout_p, is_causal, philox_seed_value, philox_seed_bdim, philox_offset_value, philox_offset_bdim, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_flash_attention_for_cpu_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, double dropout_p, bool is_causal, const c10::optional & attn_mask, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(out, cur_level) && !isBatchedAtLevel(logsumexp, cur_level) && !isBatchedAtLevel(attn_mask, cur_level)) { + return at::_ops::_scaled_dot_product_flash_attention_for_cpu_backward::call(grad_out, query, key, value, out, logsumexp, dropout_p, is_causal, attn_mask, scale); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor out_value; + optional out_bdim; + std::tie(out_value, out_bdim) = unwrapTensorAtLevel(out, cur_level); + Tensor logsumexp_value; + optional logsumexp_bdim; + std::tie(logsumexp_value, logsumexp_bdim) = unwrapTensorAtLevel(logsumexp, cur_level); + optional attn_mask_value; + optional attn_mask_bdim; + if (attn_mask) { + std::tie(attn_mask_value, attn_mask_bdim) = unwrapTensorAtLevel(attn_mask.value(), cur_level); + } + auto results = batch_rule(grad_out_value, grad_out_bdim, query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, out_value, out_bdim, logsumexp_value, logsumexp_bdim, dropout_p, is_causal, attn_mask_value, attn_mask_bdim, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_efficient_attention_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & attn_bias, bool compute_log_sumexp, double dropout_p, bool is_causal, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(attn_bias, cur_level)) { + return at::_ops::_scaled_dot_product_efficient_attention::call(query, key, value, attn_bias, compute_log_sumexp, dropout_p, is_causal, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + optional attn_bias_value; + optional attn_bias_bdim; + if (attn_bias) { + std::tie(attn_bias_value, attn_bias_bdim) = unwrapTensorAtLevel(attn_bias.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, attn_bias_value, attn_bias_bdim, compute_log_sumexp, dropout_p, is_causal, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_efficient_attention_backward_generated_plumbing(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & attn_bias, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & philox_seed, const at::Tensor & philox_offset, double dropout_p, ::std::array grad_input_mask, bool is_causal, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out_, cur_level) && !isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(attn_bias, cur_level) && !isBatchedAtLevel(out, cur_level) && !isBatchedAtLevel(logsumexp, cur_level) && !isBatchedAtLevel(philox_seed, cur_level) && !isBatchedAtLevel(philox_offset, cur_level)) { + return at::_ops::_scaled_dot_product_efficient_attention_backward::call(grad_out_, query, key, value, attn_bias, out, logsumexp, philox_seed, philox_offset, dropout_p, grad_input_mask, is_causal, scale); + } + Tensor grad_out__value; + optional grad_out__bdim; + std::tie(grad_out__value, grad_out__bdim) = unwrapTensorAtLevel(grad_out_, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor attn_bias_value; + optional attn_bias_bdim; + std::tie(attn_bias_value, attn_bias_bdim) = unwrapTensorAtLevel(attn_bias, cur_level); + Tensor out_value; + optional out_bdim; + std::tie(out_value, out_bdim) = unwrapTensorAtLevel(out, cur_level); + Tensor logsumexp_value; + optional logsumexp_bdim; + std::tie(logsumexp_value, logsumexp_bdim) = unwrapTensorAtLevel(logsumexp, cur_level); + Tensor philox_seed_value; + optional philox_seed_bdim; + std::tie(philox_seed_value, philox_seed_bdim) = unwrapTensorAtLevel(philox_seed, cur_level); + Tensor philox_offset_value; + optional philox_offset_bdim; + std::tie(philox_offset_value, philox_offset_bdim) = unwrapTensorAtLevel(philox_offset, cur_level); + auto results = batch_rule(grad_out__value, grad_out__bdim, query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, attn_bias_value, attn_bias_bdim, out_value, out_bdim, logsumexp_value, logsumexp_bdim, philox_seed_value, philox_seed_bdim, philox_offset_value, philox_offset_bdim, dropout_p, grad_input_mask, is_causal, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _scaled_dot_product_cudnn_attention_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p, bool is_causal, bool return_debug_mask, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level)) { + return at::_ops::_scaled_dot_product_cudnn_attention::call(query, key, value, dropout_p, is_causal, return_debug_mask, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, dropout_p, is_causal, return_debug_mask, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +::std::tuple _flash_attention_forward_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & cum_seq_q, const c10::optional & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, bool return_debug_mask, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(cum_seq_q, cur_level) && !isBatchedAtLevel(cum_seq_k, cur_level)) { + return at::_ops::_flash_attention_forward::call(query, key, value, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + optional cum_seq_q_value; + optional cum_seq_q_bdim; + if (cum_seq_q) { + std::tie(cum_seq_q_value, cum_seq_q_bdim) = unwrapTensorAtLevel(cum_seq_q.value(), cur_level); + } + optional cum_seq_k_value; + optional cum_seq_k_bdim; + if (cum_seq_k) { + std::tie(cum_seq_k_value, cum_seq_k_bdim) = unwrapTensorAtLevel(cum_seq_k.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, cum_seq_q_value, cum_seq_q_bdim, cum_seq_k_value, cum_seq_k_bdim, max_q, max_k, dropout_p, is_causal, return_debug_mask, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +::std::tuple _flash_attention_backward_generated_plumbing(const at::Tensor & grad_out, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const at::Tensor & out, const at::Tensor & logsumexp, const at::Tensor & cum_seq_q, const at::Tensor & cum_seq_k, c10::SymInt max_q, c10::SymInt max_k, double dropout_p, bool is_causal, const at::Tensor & philox_seed, const at::Tensor & philox_offset, c10::optional scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out, cur_level) && !isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(out, cur_level) && !isBatchedAtLevel(logsumexp, cur_level) && !isBatchedAtLevel(cum_seq_q, cur_level) && !isBatchedAtLevel(cum_seq_k, cur_level) && !isBatchedAtLevel(philox_seed, cur_level) && !isBatchedAtLevel(philox_offset, cur_level)) { + return at::_ops::_flash_attention_backward::call(grad_out, query, key, value, out, logsumexp, cum_seq_q, cum_seq_k, max_q, max_k, dropout_p, is_causal, philox_seed, philox_offset, scale); + } + Tensor grad_out_value; + optional grad_out_bdim; + std::tie(grad_out_value, grad_out_bdim) = unwrapTensorAtLevel(grad_out, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor out_value; + optional out_bdim; + std::tie(out_value, out_bdim) = unwrapTensorAtLevel(out, cur_level); + Tensor logsumexp_value; + optional logsumexp_bdim; + std::tie(logsumexp_value, logsumexp_bdim) = unwrapTensorAtLevel(logsumexp, cur_level); + Tensor cum_seq_q_value; + optional cum_seq_q_bdim; + std::tie(cum_seq_q_value, cum_seq_q_bdim) = unwrapTensorAtLevel(cum_seq_q, cur_level); + Tensor cum_seq_k_value; + optional cum_seq_k_bdim; + std::tie(cum_seq_k_value, cum_seq_k_bdim) = unwrapTensorAtLevel(cum_seq_k, cur_level); + Tensor philox_seed_value; + optional philox_seed_bdim; + std::tie(philox_seed_value, philox_seed_bdim) = unwrapTensorAtLevel(philox_seed, cur_level); + Tensor philox_offset_value; + optional philox_offset_bdim; + std::tie(philox_offset_value, philox_offset_bdim) = unwrapTensorAtLevel(philox_offset, cur_level); + auto results = batch_rule(grad_out_value, grad_out_bdim, query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, out_value, out_bdim, logsumexp_value, logsumexp_bdim, cum_seq_q_value, cum_seq_q_bdim, cum_seq_k_value, cum_seq_k_bdim, max_q, max_k, dropout_p, is_causal, philox_seed_value, philox_seed_bdim, philox_offset_value, philox_offset_bdim, scale); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +::std::tuple _efficient_attention_backward_generated_plumbing(const at::Tensor & grad_out_, const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, const c10::optional & bias, const at::Tensor & out, const c10::optional & cu_seqlens_q, const c10::optional & cu_seqlens_k, c10::SymInt max_seqlen_q, c10::SymInt max_seqlen_k, const at::Tensor & logsumexp, double dropout_p, const at::Tensor & philox_seed, const at::Tensor & philox_offset, int64_t custom_mask_type, bool bias_requires_grad, c10::optional scale, c10::optional num_splits_key) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_out_, cur_level) && !isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(out, cur_level) && !isBatchedAtLevel(cu_seqlens_q, cur_level) && !isBatchedAtLevel(cu_seqlens_k, cur_level) && !isBatchedAtLevel(logsumexp, cur_level) && !isBatchedAtLevel(philox_seed, cur_level) && !isBatchedAtLevel(philox_offset, cur_level)) { + return at::_ops::_efficient_attention_backward::call(grad_out_, query, key, value, bias, out, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, logsumexp, dropout_p, philox_seed, philox_offset, custom_mask_type, bias_requires_grad, scale, num_splits_key); + } + Tensor grad_out__value; + optional grad_out__bdim; + std::tie(grad_out__value, grad_out__bdim) = unwrapTensorAtLevel(grad_out_, cur_level); + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor out_value; + optional out_bdim; + std::tie(out_value, out_bdim) = unwrapTensorAtLevel(out, cur_level); + Tensor logsumexp_value; + optional logsumexp_bdim; + std::tie(logsumexp_value, logsumexp_bdim) = unwrapTensorAtLevel(logsumexp, cur_level); + Tensor philox_seed_value; + optional philox_seed_bdim; + std::tie(philox_seed_value, philox_seed_bdim) = unwrapTensorAtLevel(philox_seed, cur_level); + Tensor philox_offset_value; + optional philox_offset_bdim; + std::tie(philox_offset_value, philox_offset_bdim) = unwrapTensorAtLevel(philox_offset, cur_level); + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + optional cu_seqlens_q_value; + optional cu_seqlens_q_bdim; + if (cu_seqlens_q) { + std::tie(cu_seqlens_q_value, cu_seqlens_q_bdim) = unwrapTensorAtLevel(cu_seqlens_q.value(), cur_level); + } + optional cu_seqlens_k_value; + optional cu_seqlens_k_bdim; + if (cu_seqlens_k) { + std::tie(cu_seqlens_k_value, cu_seqlens_k_bdim) = unwrapTensorAtLevel(cu_seqlens_k.value(), cur_level); + } + auto results = batch_rule(grad_out__value, grad_out__bdim, query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, bias_value, bias_bdim, out_value, out_bdim, cu_seqlens_q_value, cu_seqlens_q_bdim, cu_seqlens_k_value, cu_seqlens_k_bdim, max_seqlen_q, max_seqlen_k, logsumexp_value, logsumexp_bdim, dropout_p, philox_seed_value, philox_seed_bdim, philox_offset_value, philox_offset_bdim, custom_mask_type, bias_requires_grad, scale, num_splits_key); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level)); +} +template +at::Tensor _triton_scaled_dot_attention_generated_plumbing(const at::Tensor & q, const at::Tensor & k, const at::Tensor & v, double dropout_p) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(q, cur_level) && !isBatchedAtLevel(k, cur_level) && !isBatchedAtLevel(v, cur_level)) { + return at::_ops::_triton_scaled_dot_attention::call(q, k, v, dropout_p); + } + Tensor q_value; + optional q_bdim; + std::tie(q_value, q_bdim) = unwrapTensorAtLevel(q, cur_level); + Tensor k_value; + optional k_bdim; + std::tie(k_value, k_bdim) = unwrapTensorAtLevel(k, cur_level); + Tensor v_value; + optional v_bdim; + std::tie(v_value, v_bdim) = unwrapTensorAtLevel(v, cur_level); + auto results = batch_rule(q_value, q_bdim, k_value, k_bdim, v_value, v_bdim, dropout_p); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor & _fill_mem_eff_dropout_mask__generated_plumbing(at::Tensor & self, double dropout_p, int64_t seed, int64_t offset) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_inplace_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_fill_mem_eff_dropout_mask_::call(self, dropout_p, seed, offset); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, dropout_p, seed, offset); + return self; +} +template +at::Tensor _triton_multi_head_attention_generated_plumbing(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, int64_t embed_dim, int64_t num_head, const at::Tensor & qkv_weight, const at::Tensor & qkv_bias, const at::Tensor & proj_weight, const at::Tensor & proj_bias, const c10::optional & mask) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(query, cur_level) && !isBatchedAtLevel(key, cur_level) && !isBatchedAtLevel(value, cur_level) && !isBatchedAtLevel(qkv_weight, cur_level) && !isBatchedAtLevel(qkv_bias, cur_level) && !isBatchedAtLevel(proj_weight, cur_level) && !isBatchedAtLevel(proj_bias, cur_level) && !isBatchedAtLevel(mask, cur_level)) { + return at::_ops::_triton_multi_head_attention::call(query, key, value, embed_dim, num_head, qkv_weight, qkv_bias, proj_weight, proj_bias, mask); + } + Tensor query_value; + optional query_bdim; + std::tie(query_value, query_bdim) = unwrapTensorAtLevel(query, cur_level); + Tensor key_value; + optional key_bdim; + std::tie(key_value, key_bdim) = unwrapTensorAtLevel(key, cur_level); + Tensor value_value; + optional value_bdim; + std::tie(value_value, value_bdim) = unwrapTensorAtLevel(value, cur_level); + Tensor qkv_weight_value; + optional qkv_weight_bdim; + std::tie(qkv_weight_value, qkv_weight_bdim) = unwrapTensorAtLevel(qkv_weight, cur_level); + Tensor qkv_bias_value; + optional qkv_bias_bdim; + std::tie(qkv_bias_value, qkv_bias_bdim) = unwrapTensorAtLevel(qkv_bias, cur_level); + Tensor proj_weight_value; + optional proj_weight_bdim; + std::tie(proj_weight_value, proj_weight_bdim) = unwrapTensorAtLevel(proj_weight, cur_level); + Tensor proj_bias_value; + optional proj_bias_bdim; + std::tie(proj_bias_value, proj_bias_bdim) = unwrapTensorAtLevel(proj_bias, cur_level); + optional mask_value; + optional mask_bdim; + if (mask) { + std::tie(mask_value, mask_bdim) = unwrapTensorAtLevel(mask.value(), cur_level); + } + auto results = batch_rule(query_value, query_bdim, key_value, key_bdim, value_value, value_bdim, embed_dim, num_head, qkv_weight_value, qkv_weight_bdim, qkv_bias_value, qkv_bias_bdim, proj_weight_value, proj_weight_bdim, proj_bias_value, proj_bias_bdim, mask_value, mask_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_airy_ai_generated_plumbing(const at::Tensor & x) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_airy_ai::call(x); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_bessel_j0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_bessel_j0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_bessel_j1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_bessel_j1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_bessel_y0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_bessel_y0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_bessel_y1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_bessel_y1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_t_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_t::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_t_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_t_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_t_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_chebyshev_polynomial_t_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_u_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_u::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_u_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_u_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_u_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_chebyshev_polynomial_u_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_v_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_v::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_v_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_v_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_v_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_chebyshev_polynomial_v_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_w_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_w::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_w_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_chebyshev_polynomial_w_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_chebyshev_polynomial_w_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_chebyshev_polynomial_w_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_h_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_hermite_polynomial_h::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_h_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_hermite_polynomial_h_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_h_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_hermite_polynomial_h_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_he_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_hermite_polynomial_he::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_he_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_hermite_polynomial_he_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_hermite_polynomial_he_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_hermite_polynomial_he_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_laguerre_polynomial_l_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_laguerre_polynomial_l::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_laguerre_polynomial_l_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_laguerre_polynomial_l_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_laguerre_polynomial_l_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_laguerre_polynomial_l_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_legendre_polynomial_p_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_legendre_polynomial_p::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_legendre_polynomial_p_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_legendre_polynomial_p_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_legendre_polynomial_p_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_legendre_polynomial_p_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_modified_bessel_i0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_modified_bessel_i0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_modified_bessel_i1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_modified_bessel_i1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_modified_bessel_k0_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_modified_bessel_k0::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_modified_bessel_k1_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::special_modified_bessel_k1::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_scaled_modified_bessel_k0_generated_plumbing(const at::Tensor & x) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_scaled_modified_bessel_k0::call(x); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_scaled_modified_bessel_k1_generated_plumbing(const at::Tensor & x) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_scaled_modified_bessel_k1::call(x); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_t_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_t::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_t_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_t_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_t_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_t_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_u_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_u::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_u_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_u_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_u_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_u_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_v_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_v::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_v_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_v_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_v_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_v_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_w_generated_plumbing(const at::Tensor & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level) && !isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_w::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x_value, x_bdim, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_w_x_scalar_generated_plumbing(const at::Scalar & x, const at::Tensor & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(n, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_w_x_scalar::call(x, n); + } + Tensor n_value; + optional n_bdim; + std::tie(n_value, n_bdim) = unwrapTensorAtLevel(n, cur_level); + auto results = batch_rule(x, n_value, n_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_shifted_chebyshev_polynomial_w_n_scalar_generated_plumbing(const at::Tensor & x, const at::Scalar & n) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_shifted_chebyshev_polynomial_w_n_scalar::call(x, n); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim, n); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor special_spherical_bessel_j0_generated_plumbing(const at::Tensor & x) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(x, cur_level)) { + return at::_ops::special_spherical_bessel_j0::call(x); + } + Tensor x_value; + optional x_bdim; + std::tie(x_value, x_bdim) = unwrapTensorAtLevel(x, cur_level); + auto results = batch_rule(x_value, x_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _foobar_generated_plumbing(const at::Tensor & self, bool arg1, bool arg2, bool arg3) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foobar::call(self, arg1, arg2, arg3); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, arg1, arg2, arg3); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _fused_adam__generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adam_::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _fused_adam__tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adam__tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _fused_adamw__generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adamw_::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _fused_adamw__tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adamw__tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _fused_sgd__generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_sgd_::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _fused_sgd__tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_sgd__tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr_value, lr_bdim, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); +} +template +void _propagate_xla_data_generated_plumbing(const at::Tensor & input, const at::Tensor & output) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(output, cur_level)) { + return at::_ops::_propagate_xla_data::call(input, output); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + batch_rule(input_value, input_bdim, output_value, output_bdim); +} +template +void _cudnn_rnn_backward_out_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, c10::SymIntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(weight_buf, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(dropout_state, cur_level) && !isBatchedAtLevel(reserve, cur_level) && !isBatchedAtLevel(out0, cur_level) && !isBatchedAtLevel(out1, cur_level) && !isBatchedAtLevel(out2, cur_level) && !isBatchedAtLevel(out3, cur_level)) { + return at::_ops::_cudnn_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_buf_value; + optional weight_buf_bdim; + std::tie(weight_buf_value, weight_buf_bdim) = unwrapTensorAtLevel(weight_buf, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor reserve_value; + optional reserve_bdim; + std::tie(reserve_value, reserve_bdim) = unwrapTensorAtLevel(reserve, cur_level); + Tensor out0_value; + optional out0_bdim; + std::tie(out0_value, out0_bdim) = unwrapTensorAtLevel(out0, cur_level); + Tensor out1_value; + optional out1_bdim; + std::tie(out1_value, out1_bdim) = unwrapTensorAtLevel(out1, cur_level); + Tensor out2_value; + optional out2_bdim; + std::tie(out2_value, out2_bdim) = unwrapTensorAtLevel(out2, cur_level); + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional grad_output_value; + optional grad_output_bdim; + if (grad_output) { + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + batch_rule(input_value, input_bdim, weight, weight_stride0, weight_buf_value, weight_buf_bdim, hx_value, hx_bdim, cx_value, cx_bdim, output_value, output_bdim, grad_output_value, grad_output_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim, reserve_value, reserve_bdim, output_mask, out0_value, out0_bdim, out1_value, out1_bdim, out2_value, out2_bdim, out3); +} +template +at::Tensor bernoulli_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(p, cur_level)) { + return at::_ops::bernoulli_Tensor::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor p_value; + optional p_bdim; + std::tie(p_value, p_bdim) = unwrapTensorAtLevel(p, cur_level); + auto results = batch_rule(self_value, self_bdim, p_value, p_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor embedding_renorm_generated_plumbing(const at::Tensor & self, const at::Tensor & indices, double max_norm, double norm_type) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level)) { + return at::_ops::embedding_renorm::call(self, indices, max_norm, norm_type); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor indices_value; + optional indices_bdim; + std::tie(indices_value, indices_bdim) = unwrapTensorAtLevel(indices, cur_level); + auto results = batch_rule(self_value, self_bdim, indices_value, indices_bdim, max_norm, norm_type); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor resize_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::resize::call(self, size, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _resize_output_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef size, at::Device device) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_resize_output::call(self, size, device); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, device); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _index_put_impl_generated_plumbing(const at::Tensor & self, const c10::List> & indices, const at::Tensor & values, bool accumulate, bool unsafe) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(indices, cur_level) && !isBatchedAtLevel(values, cur_level)) { + return at::_ops::_index_put_impl::call(self, indices, values, accumulate, unsafe); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor values_value; + optional values_bdim; + std::tie(values_value, values_bdim) = unwrapTensorAtLevel(values, cur_level); + auto results = batch_rule(self_value, self_bdim, indices, values_value, values_bdim, accumulate, unsafe); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void miopen_rnn_backward_out_generated_plumbing(const at::Tensor & input, at::TensorList weight, int64_t weight_stride0, const at::Tensor & weight_buf, const at::Tensor & hx, const c10::optional & cx, const at::Tensor & output, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first, double dropout, bool train, bool bidirectional, at::IntArrayRef batch_sizes, const c10::optional & dropout_state, const at::Tensor & reserve, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::TensorList out3) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(weight_buf, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(cx, cur_level) && !isBatchedAtLevel(output, cur_level) && !isBatchedAtLevel(grad_output, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(dropout_state, cur_level) && !isBatchedAtLevel(reserve, cur_level) && !isBatchedAtLevel(out0, cur_level) && !isBatchedAtLevel(out1, cur_level) && !isBatchedAtLevel(out2, cur_level) && !isBatchedAtLevel(out3, cur_level)) { + return at::_ops::miopen_rnn_backward_out::call(input, weight, weight_stride0, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, output_mask, out0, out1, out2, out3); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor weight_buf_value; + optional weight_buf_bdim; + std::tie(weight_buf_value, weight_buf_bdim) = unwrapTensorAtLevel(weight_buf, cur_level); + Tensor hx_value; + optional hx_bdim; + std::tie(hx_value, hx_bdim) = unwrapTensorAtLevel(hx, cur_level); + Tensor output_value; + optional output_bdim; + std::tie(output_value, output_bdim) = unwrapTensorAtLevel(output, cur_level); + Tensor reserve_value; + optional reserve_bdim; + std::tie(reserve_value, reserve_bdim) = unwrapTensorAtLevel(reserve, cur_level); + Tensor out0_value; + optional out0_bdim; + std::tie(out0_value, out0_bdim) = unwrapTensorAtLevel(out0, cur_level); + Tensor out1_value; + optional out1_bdim; + std::tie(out1_value, out1_bdim) = unwrapTensorAtLevel(out1, cur_level); + Tensor out2_value; + optional out2_bdim; + std::tie(out2_value, out2_bdim) = unwrapTensorAtLevel(out2, cur_level); + optional cx_value; + optional cx_bdim; + if (cx) { + std::tie(cx_value, cx_bdim) = unwrapTensorAtLevel(cx.value(), cur_level); + } + optional grad_output_value; + optional grad_output_bdim; + if (grad_output) { + std::tie(grad_output_value, grad_output_bdim) = unwrapTensorAtLevel(grad_output.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + optional dropout_state_value; + optional dropout_state_bdim; + if (dropout_state) { + std::tie(dropout_state_value, dropout_state_bdim) = unwrapTensorAtLevel(dropout_state.value(), cur_level); + } + batch_rule(input_value, input_bdim, weight, weight_stride0, weight_buf_value, weight_buf_bdim, hx_value, hx_bdim, cx_value, cx_bdim, output_value, output_bdim, grad_output_value, grad_output_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state_value, dropout_state_bdim, reserve_value, reserve_bdim, output_mask, out0_value, out0_bdim, out1_value, out1_bdim, out2_value, out2_bdim, out3); +} +template +::std::tuple _native_batch_norm_legit_functional_generated_plumbing(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & running_mean, const at::Tensor & running_var, bool training, double momentum, double eps) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(bias, cur_level) && !isBatchedAtLevel(running_mean, cur_level) && !isBatchedAtLevel(running_var, cur_level)) { + return at::_ops::_native_batch_norm_legit_functional::call(input, weight, bias, running_mean, running_var, training, momentum, eps); + } + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor running_mean_value; + optional running_mean_bdim; + std::tie(running_mean_value, running_mean_bdim) = unwrapTensorAtLevel(running_mean, cur_level); + Tensor running_var_value; + optional running_var_bdim; + std::tie(running_var_value, running_var_bdim) = unwrapTensorAtLevel(running_var, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + optional bias_value; + optional bias_bdim; + if (bias) { + std::tie(bias_value, bias_bdim) = unwrapTensorAtLevel(bias.value(), cur_level); + } + auto results = batch_rule(input_value, input_bdim, weight_value, weight_bdim, bias_value, bias_bdim, running_mean_value, running_mean_bdim, running_var_value, running_var_bdim, training, momentum, eps); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +void unsafe_split_Tensor_out_generated_plumbing(const at::Tensor & self, c10::SymInt split_size, int64_t dim, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::unsafe_split_Tensor_out::call(self, split_size, dim, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, split_size, dim, out); +} +template +void unsafe_split_with_sizes_out_generated_plumbing(const at::Tensor & self, c10::SymIntArrayRef split_sizes, int64_t dim, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::unsafe_split_with_sizes_out::call(self, split_sizes, dim, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + batch_rule(self_value, self_bdim, split_sizes, dim, out); +} +template +at::Tensor resize_as_generated_plumbing(const at::Tensor & self, const at::Tensor & the_template, c10::optional memory_format) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(the_template, cur_level)) { + return at::_ops::resize_as::call(self, the_template, memory_format); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor the_template_value; + optional the_template_bdim; + std::tie(the_template_value, the_template_bdim) = unwrapTensorAtLevel(the_template, cur_level); + auto results = batch_rule(self_value, self_bdim, the_template_value, the_template_bdim, memory_format); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor resize_as_sparse_generated_plumbing(const at::Tensor & self, const at::Tensor & the_template) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(the_template, cur_level)) { + return at::_ops::resize_as_sparse::call(self, the_template); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor the_template_value; + optional the_template_bdim; + std::tie(the_template_value, the_template_bdim) = unwrapTensorAtLevel(the_template, cur_level); + auto results = batch_rule(self_value, self_bdim, the_template_value, the_template_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor zero_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::zero::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_resize_generated_plumbing(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sparse_resize::call(self, size, sparse_dim, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, sparse_dim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor sparse_resize_and_clear_generated_plumbing(const at::Tensor & self, at::IntArrayRef size, int64_t sparse_dim, int64_t dense_dim) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::sparse_resize_and_clear::call(self, size, sparse_dim, dense_dim); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, size, sparse_dim, dense_dim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor _coalesced_generated_plumbing(const at::Tensor & self, bool coalesced) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_coalesced::call(self, coalesced); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, coalesced); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor copy_sparse_to_sparse_generated_plumbing(const at::Tensor & self, const at::Tensor & src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::copy_sparse_to_sparse::call(self, src, non_blocking); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor src_value; + optional src_bdim; + std::tie(src_value, src_bdim) = unwrapTensorAtLevel(src, cur_level); + auto results = batch_rule(self_value, self_bdim, src_value, src_bdim, non_blocking); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void quantize_per_tensor_tensors_out_generated_plumbing(at::TensorList tensors, const at::Tensor & scales, const at::Tensor & zero_points, at::ScalarType dtype, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level) && !isBatchedAtLevel(scales, cur_level) && !isBatchedAtLevel(zero_points, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::quantize_per_tensor_tensors_out::call(tensors, scales, zero_points, dtype, out); + } + Tensor scales_value; + optional scales_bdim; + std::tie(scales_value, scales_bdim) = unwrapTensorAtLevel(scales, cur_level); + Tensor zero_points_value; + optional zero_points_bdim; + std::tie(zero_points_value, zero_points_bdim) = unwrapTensorAtLevel(zero_points, cur_level); + batch_rule(tensors, scales_value, scales_bdim, zero_points_value, zero_points_bdim, dtype, out); +} +template +void dequantize_tensors_out_generated_plumbing(at::TensorList tensors, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(tensors, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::dequantize_tensors_out::call(tensors, out); + } + + batch_rule(tensors, out); +} +template +::std::tuple _fused_moving_avg_obs_fq_helper_functional_generated_plumbing(const at::Tensor & self, const at::Tensor & observer_on, const at::Tensor & fake_quant_on, const at::Tensor & running_min, const at::Tensor & running_max, const at::Tensor & scale, const at::Tensor & zero_point, double averaging_const, int64_t quant_min, int64_t quant_max, int64_t ch_axis, bool per_row_fake_quant, bool symmetric_quant) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(observer_on, cur_level) && !isBatchedAtLevel(fake_quant_on, cur_level) && !isBatchedAtLevel(running_min, cur_level) && !isBatchedAtLevel(running_max, cur_level) && !isBatchedAtLevel(scale, cur_level) && !isBatchedAtLevel(zero_point, cur_level)) { + return at::_ops::_fused_moving_avg_obs_fq_helper_functional::call(self, observer_on, fake_quant_on, running_min, running_max, scale, zero_point, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor observer_on_value; + optional observer_on_bdim; + std::tie(observer_on_value, observer_on_bdim) = unwrapTensorAtLevel(observer_on, cur_level); + Tensor fake_quant_on_value; + optional fake_quant_on_bdim; + std::tie(fake_quant_on_value, fake_quant_on_bdim) = unwrapTensorAtLevel(fake_quant_on, cur_level); + Tensor running_min_value; + optional running_min_bdim; + std::tie(running_min_value, running_min_bdim) = unwrapTensorAtLevel(running_min, cur_level); + Tensor running_max_value; + optional running_max_bdim; + std::tie(running_max_value, running_max_bdim) = unwrapTensorAtLevel(running_max, cur_level); + Tensor scale_value; + optional scale_bdim; + std::tie(scale_value, scale_bdim) = unwrapTensorAtLevel(scale, cur_level); + Tensor zero_point_value; + optional zero_point_bdim; + std::tie(zero_point_value, zero_point_bdim) = unwrapTensorAtLevel(zero_point, cur_level); + auto results = batch_rule(self_value, self_bdim, observer_on_value, observer_on_bdim, fake_quant_on_value, fake_quant_on_bdim, running_min_value, running_min_bdim, running_max_value, running_max_bdim, scale_value, scale_bdim, zero_point_value, zero_point_bdim, averaging_const, quant_min, quant_max, ch_axis, per_row_fake_quant, symmetric_quant); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level), makeBatched(std::get<4>(results), std::get<5>(results), cur_level), makeBatched(std::get<6>(results), std::get<7>(results), cur_level), makeBatched(std::get<8>(results), std::get<9>(results), cur_level), makeBatched(std::get<10>(results), std::get<11>(results), cur_level)); +} +template +void lstm_mps_backward_out_generated_plumbing(const c10::optional & grad_y, const c10::optional & grad_hy, const c10::optional & grad_cy, const at::Tensor & z_state, const at::Tensor & cell_state_fwd, const at::Tensor & input, const at::Tensor & layersOutputs, at::TensorList hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first, at::Tensor & out0, at::TensorList out1, at::TensorList out2) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(grad_y, cur_level) && !isBatchedAtLevel(grad_hy, cur_level) && !isBatchedAtLevel(grad_cy, cur_level) && !isBatchedAtLevel(z_state, cur_level) && !isBatchedAtLevel(cell_state_fwd, cur_level) && !isBatchedAtLevel(input, cur_level) && !isBatchedAtLevel(layersOutputs, cur_level) && !isBatchedAtLevel(hx, cur_level) && !isBatchedAtLevel(params, cur_level) && !isBatchedAtLevel(out0, cur_level) && !isBatchedAtLevel(out1, cur_level) && !isBatchedAtLevel(out2, cur_level)) { + return at::_ops::lstm_mps_backward_out::call(grad_y, grad_hy, grad_cy, z_state, cell_state_fwd, input, layersOutputs, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0, out1, out2); + } + Tensor z_state_value; + optional z_state_bdim; + std::tie(z_state_value, z_state_bdim) = unwrapTensorAtLevel(z_state, cur_level); + Tensor cell_state_fwd_value; + optional cell_state_fwd_bdim; + std::tie(cell_state_fwd_value, cell_state_fwd_bdim) = unwrapTensorAtLevel(cell_state_fwd, cur_level); + Tensor input_value; + optional input_bdim; + std::tie(input_value, input_bdim) = unwrapTensorAtLevel(input, cur_level); + Tensor layersOutputs_value; + optional layersOutputs_bdim; + std::tie(layersOutputs_value, layersOutputs_bdim) = unwrapTensorAtLevel(layersOutputs, cur_level); + Tensor out0_value; + optional out0_bdim; + std::tie(out0_value, out0_bdim) = unwrapTensorAtLevel(out0, cur_level); + optional grad_y_value; + optional grad_y_bdim; + if (grad_y) { + std::tie(grad_y_value, grad_y_bdim) = unwrapTensorAtLevel(grad_y.value(), cur_level); + } + optional grad_hy_value; + optional grad_hy_bdim; + if (grad_hy) { + std::tie(grad_hy_value, grad_hy_bdim) = unwrapTensorAtLevel(grad_hy.value(), cur_level); + } + optional grad_cy_value; + optional grad_cy_bdim; + if (grad_cy) { + std::tie(grad_cy_value, grad_cy_bdim) = unwrapTensorAtLevel(grad_cy.value(), cur_level); + } + batch_rule(grad_y_value, grad_y_bdim, grad_hy_value, grad_hy_bdim, grad_cy_value, grad_cy_bdim, z_state_value, z_state_bdim, cell_state_fwd_value, cell_state_fwd_bdim, input_value, input_bdim, layersOutputs_value, layersOutputs_bdim, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, out0_value, out0_bdim, out1, out2); +} +template +at::Tensor set_source_Storage_generated_plumbing(const at::Tensor & self, at::Storage source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::set_source_Storage::call(self, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor set_source_Storage_storage_offset_generated_plumbing(const at::Tensor & self, at::Storage source, c10::SymInt storage_offset, c10::SymIntArrayRef size, c10::SymIntArrayRef stride) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::set_source_Storage_storage_offset::call(self, source, storage_offset, size, stride); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, source, storage_offset, size, stride); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor set_source_Tensor_generated_plumbing(const at::Tensor & self, const at::Tensor & source) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(source, cur_level)) { + return at::_ops::set_source_Tensor::call(self, source); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor source_value; + optional source_bdim; + std::tie(source_value, source_bdim) = unwrapTensorAtLevel(source, cur_level); + auto results = batch_rule(self_value, self_bdim, source_value, source_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor set_generated_plumbing(const at::Tensor & self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::set::call(self); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor random_from_generated_plumbing(const at::Tensor & self, int64_t from, c10::optional to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random_from::call(self, from, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, from, to, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor random_to_generated_plumbing(const at::Tensor & self, int64_t to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random_to::call(self, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, to, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor random_generated_plumbing(const at::Tensor & self, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::random::call(self, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor uniform_generated_plumbing(const at::Tensor & self, double from, double to, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::uniform::call(self, from, to, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, from, to, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor cauchy_generated_plumbing(const at::Tensor & self, double median, double sigma, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::cauchy::call(self, median, sigma, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, median, sigma, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor log_normal_generated_plumbing(const at::Tensor & self, double mean, double std, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::log_normal::call(self, mean, std, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, mean, std, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor exponential_generated_plumbing(const at::Tensor & self, double lambd, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::exponential::call(self, lambd, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, lambd, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +at::Tensor geometric_generated_plumbing(const at::Tensor & self, double p, c10::optional generator) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::geometric::call(self, p, generator); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + auto results = batch_rule(self_value, self_bdim, p, generator); + return makeBatched(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _histogramdd_bin_edges_out_generated_plumbing(const at::Tensor & self, at::IntArrayRef bins, c10::optional> range, const c10::optional & weight, bool density, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(weight, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_histogramdd_bin_edges_out::call(self, bins, range, weight, density, out); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + optional weight_value; + optional weight_bdim; + if (weight) { + std::tie(weight_value, weight_bdim) = unwrapTensorAtLevel(weight.value(), cur_level); + } + batch_rule(self_value, self_bdim, bins, range, weight_value, weight_bdim, density, out); +} +template +void _amp_foreach_non_finite_check_and_unscale_out_generated_plumbing(at::TensorList self, at::Tensor & found_inf, const at::Tensor & inv_scale, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(inv_scale, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale_out::call(self, found_inf, inv_scale, out); + } + Tensor found_inf_value; + optional found_inf_bdim; + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf, cur_level); + Tensor inv_scale_value; + optional inv_scale_bdim; + std::tie(inv_scale_value, inv_scale_bdim) = unwrapTensorAtLevel(inv_scale, cur_level); + batch_rule(self, found_inf_value, found_inf_bdim, inv_scale_value, inv_scale_bdim, out); +} +template +::std::tuple<::std::vector,at::Tensor> _amp_foreach_non_finite_check_and_unscale_generated_plumbing(at::TensorList self, const at::Tensor & found_inf, const at::Tensor & inv_scale) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(inv_scale, cur_level)) { + return at::_ops::_amp_foreach_non_finite_check_and_unscale::call(self, found_inf, inv_scale); + } + Tensor found_inf_value; + optional found_inf_bdim; + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf, cur_level); + Tensor inv_scale_value; + optional inv_scale_bdim; + std::tie(inv_scale_value, inv_scale_bdim) = unwrapTensorAtLevel(inv_scale, cur_level); + auto results = batch_rule(self, found_inf_value, found_inf_bdim, inv_scale_value, inv_scale_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +::std::tuple _amp_update_scale_generated_plumbing(const at::Tensor & self, const at::Tensor & growth_tracker, const at::Tensor & found_inf, double scale_growth_factor, double scale_backoff_factor, int64_t growth_interval) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(growth_tracker, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_amp_update_scale::call(self, growth_tracker, found_inf, scale_growth_factor, scale_backoff_factor, growth_interval); + } + Tensor self_value; + optional self_bdim; + std::tie(self_value, self_bdim) = unwrapTensorAtLevel(self, cur_level); + Tensor growth_tracker_value; + optional growth_tracker_bdim; + std::tie(growth_tracker_value, growth_tracker_bdim) = unwrapTensorAtLevel(growth_tracker, cur_level); + Tensor found_inf_value; + optional found_inf_bdim; + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf, cur_level); + auto results = batch_rule(self_value, self_bdim, growth_tracker_value, growth_tracker_bdim, found_inf_value, found_inf_bdim, scale_growth_factor, scale_backoff_factor, growth_interval); + return std::make_tuple(makeBatched(std::get<0>(results), std::get<1>(results), cur_level), makeBatched(std::get<2>(results), std::get<3>(results), cur_level)); +} +template +void _foreach_add_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_add_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_add_List_out_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_add_List_out::call(self, other, alpha, out); + } + + batch_rule(self, other, alpha, out); +} +template +void _foreach_add_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_add_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_add_Tensor_out_generated_plumbing(at::TensorList self, const at::Tensor & other, const at::Scalar & alpha, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_add_Tensor_out::call(self, other, alpha, out); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim, alpha, out); +} +template +void _foreach_sub_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sub_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_sub_List_out_generated_plumbing(at::TensorList self, at::TensorList other, const at::Scalar & alpha, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sub_List_out::call(self, other, alpha, out); + } + + batch_rule(self, other, alpha, out); +} +template +void _foreach_sub_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sub_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_mul_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_mul_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_mul_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_mul_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_mul_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_mul_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_mul_Tensor_out_generated_plumbing(at::TensorList self, const at::Tensor & other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_mul_Tensor_out::call(self, other, out); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim, out); +} +template +void _foreach_div_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_div_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_div_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_div_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_div_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_div_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_div_Tensor_out_generated_plumbing(at::TensorList self, const at::Tensor & other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_div_Tensor_out::call(self, other, out); + } + Tensor other_value; + optional other_bdim; + std::tie(other_value, other_bdim) = unwrapTensorAtLevel(other, cur_level); + batch_rule(self, other_value, other_bdim, out); +} +template +void _foreach_clamp_max_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_max_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_clamp_max_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_max_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_clamp_max_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_max_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_clamp_min_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_min_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_clamp_min_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_min_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_clamp_min_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_clamp_min_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_maximum_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_maximum_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_maximum_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_maximum_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_maximum_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_maximum_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_minimum_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & scalar, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_minimum_Scalar_out::call(self, scalar, out); + } + + batch_rule(self, scalar, out); +} +template +void _foreach_minimum_List_out_generated_plumbing(at::TensorList self, at::TensorList other, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(other, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_minimum_List_out::call(self, other, out); + } + + batch_rule(self, other, out); +} +template +void _foreach_minimum_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_minimum_ScalarList_out::call(self, scalars, out); + } + + batch_rule(self, scalars, out); +} +template +void _foreach_addcdiv_Scalar_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcdiv_Scalar_out::call(self, tensor1, tensor2, value, out); + } + + batch_rule(self, tensor1, tensor2, value, out); +} +template +void _foreach_addcdiv_ScalarList_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcdiv_ScalarList_out::call(self, tensor1, tensor2, scalars, out); + } + + batch_rule(self, tensor1, tensor2, scalars, out); +} +template +void _foreach_addcdiv_Tensor_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcdiv_Tensor_out::call(self, tensor1, tensor2, scalars, out); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim, out); +} +template +void _foreach_addcmul_Scalar_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Scalar & value, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcmul_Scalar_out::call(self, tensor1, tensor2, value, out); + } + + batch_rule(self, tensor1, tensor2, value, out); +} +template +void _foreach_addcmul_ScalarList_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, at::ArrayRef scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcmul_ScalarList_out::call(self, tensor1, tensor2, scalars, out); + } + + batch_rule(self, tensor1, tensor2, scalars, out); +} +template +void _foreach_addcmul_Tensor_out_generated_plumbing(at::TensorList self, at::TensorList tensor1, at::TensorList tensor2, const at::Tensor & scalars, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensor1, cur_level) && !isBatchedAtLevel(tensor2, cur_level) && !isBatchedAtLevel(scalars, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_addcmul_Tensor_out::call(self, tensor1, tensor2, scalars, out); + } + Tensor scalars_value; + optional scalars_bdim; + std::tie(scalars_value, scalars_bdim) = unwrapTensorAtLevel(scalars, cur_level); + batch_rule(self, tensor1, tensor2, scalars_value, scalars_bdim, out); +} +template +void _foreach_abs_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_abs_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_acos_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_acos_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_asin_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_asin_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_atan_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_atan_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_ceil_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_ceil_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_cos_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_cos_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_cosh_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_cosh_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_erf_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_erf_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_erfc_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_erfc_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_exp_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_exp_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_expm1_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_expm1_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_floor_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_floor_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_frac_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_frac_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_lerp_List_out_generated_plumbing(at::TensorList self, at::TensorList tensors1, at::TensorList weights, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level) && !isBatchedAtLevel(weights, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_lerp_List_out::call(self, tensors1, weights, out); + } + + batch_rule(self, tensors1, weights, out); +} +template +void _foreach_lerp_Scalar_out_generated_plumbing(at::TensorList self, at::TensorList tensors1, const at::Scalar & weight, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(tensors1, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_lerp_Scalar_out::call(self, tensors1, weight, out); + } + + batch_rule(self, tensors1, weight, out); +} +template +void _foreach_lgamma_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_lgamma_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_log_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_log_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_log10_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_log10_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_log1p_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_log1p_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_log2_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_log2_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_neg_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_neg_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_norm_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & ord, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_norm_Scalar_out::call(self, ord, out); + } + + batch_rule(self, ord, out); +} +template +void _foreach_pow_List_out_generated_plumbing(at::TensorList self, at::TensorList exponent, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(exponent, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_pow_List_out::call(self, exponent, out); + } + + batch_rule(self, exponent, out); +} +template +void _foreach_pow_Scalar_out_generated_plumbing(at::TensorList self, const at::Scalar & exponent, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_pow_Scalar_out::call(self, exponent, out); + } + + batch_rule(self, exponent, out); +} +template +void _foreach_pow_ScalarList_out_generated_plumbing(at::TensorList self, at::ArrayRef exponent, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_pow_ScalarList_out::call(self, exponent, out); + } + + batch_rule(self, exponent, out); +} +template +void _foreach_reciprocal_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_reciprocal_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_round_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_round_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_sigmoid_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sigmoid_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_sign_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sign_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_sin_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sin_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_sinh_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sinh_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_sqrt_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_sqrt_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_tan_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_tan_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_tanh_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_tanh_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_trunc_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_trunc_out::call(self, out); + } + + batch_rule(self, out); +} +template +void _foreach_zero_out_generated_plumbing(at::TensorList self, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_zero_out::call(self, out); + } + + batch_rule(self, out); +} +template +::std::vector _foreach_zero_generated_plumbing(at::TensorList self) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level)) { + return at::_ops::_foreach_zero::call(self); + } + + auto results = batch_rule(self); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _foreach_copy_out_generated_plumbing(at::TensorList self, at::TensorList src, bool non_blocking, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_foreach_copy_out::call(self, src, non_blocking, out); + } + + batch_rule(self, src, non_blocking, out); +} +template +::std::vector _foreach_copy_generated_plumbing(at::TensorList self, at::TensorList src, bool non_blocking) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(src, cur_level)) { + return at::_ops::_foreach_copy::call(self, src, non_blocking); + } + + auto results = batch_rule(self, src, non_blocking); + return makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level); +} +template +void _fused_adam_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_adam_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adam_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adam::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level), makeBatchedVector(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +void _fused_adam_tensor_lr_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_adam_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adam_tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adam_tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level), makeBatchedVector(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +void _fused_adamw_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_adamw_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adamw_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, double lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adamw::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level), makeBatchedVector(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +void _fused_adamw_tensor_lr_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_adamw_tensor_lr_out::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf, out); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector,::std::vector,::std::vector> _fused_adamw_tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList exp_avgs, at::TensorList exp_avg_sqs, at::TensorList max_exp_avg_sqs, at::TensorList state_steps, const at::Tensor & lr, double beta1, double beta2, double weight_decay, double eps, bool amsgrad, bool maximize, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(exp_avgs, cur_level) && !isBatchedAtLevel(exp_avg_sqs, cur_level) && !isBatchedAtLevel(max_exp_avg_sqs, cur_level) && !isBatchedAtLevel(state_steps, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_adamw_tensor_lr::call(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, lr_value, lr_bdim, beta1, beta2, weight_decay, eps, amsgrad, maximize, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level), makeBatchedVector(std::get<6>(results), std::get<7>(results), cur_level), makeBatchedVector(std::get<8>(results), std::get<9>(results), cur_level)); +} +template +void _fused_sgd_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_sgd_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector> _fused_sgd_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, double lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_sgd::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level)); +} +template +void _fused_sgd_tensor_lr_out_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf, at::TensorList out) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing_no_returns"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level) && !isBatchedAtLevel(out, cur_level)) { + return at::_ops::_fused_sgd_tensor_lr_out::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf, out); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr_value, lr_bdim, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim, out); +} +template +::std::tuple<::std::vector,::std::vector,::std::vector> _fused_sgd_tensor_lr_generated_plumbing(at::TensorList self, at::TensorList grads, at::TensorList momentum_buffer_list, double weight_decay, double momentum, const at::Tensor & lr, double dampening, bool nesterov, bool maximize, bool is_first_step, const c10::optional & grad_scale, const c10::optional & found_inf) { + c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchBatched); + auto maybe_layer = maybeCurrentDynamicLayer(); + vmap_check_escaped(maybe_layer, "gen_vmap_plumbing"); + int64_t cur_level = maybe_layer->layerId(); + if (!isBatchedAtLevel(self, cur_level) && !isBatchedAtLevel(grads, cur_level) && !isBatchedAtLevel(momentum_buffer_list, cur_level) && !isBatchedAtLevel(lr, cur_level) && !isBatchedAtLevel(grad_scale, cur_level) && !isBatchedAtLevel(found_inf, cur_level)) { + return at::_ops::_fused_sgd_tensor_lr::call(self, grads, momentum_buffer_list, weight_decay, momentum, lr, dampening, nesterov, maximize, is_first_step, grad_scale, found_inf); + } + Tensor lr_value; + optional lr_bdim; + std::tie(lr_value, lr_bdim) = unwrapTensorAtLevel(lr, cur_level); + optional grad_scale_value; + optional grad_scale_bdim; + if (grad_scale) { + std::tie(grad_scale_value, grad_scale_bdim) = unwrapTensorAtLevel(grad_scale.value(), cur_level); + } + optional found_inf_value; + optional found_inf_bdim; + if (found_inf) { + std::tie(found_inf_value, found_inf_bdim) = unwrapTensorAtLevel(found_inf.value(), cur_level); + } + auto results = batch_rule(self, grads, momentum_buffer_list, weight_decay, momentum, lr_value, lr_bdim, dampening, nesterov, maximize, is_first_step, grad_scale_value, grad_scale_bdim, found_inf_value, found_inf_bdim); + return std::make_tuple(makeBatchedVector(std::get<0>(results), std::get<1>(results), cur_level), makeBatchedVector(std::get<2>(results), std::get<3>(results), cur_level), makeBatchedVector(std::get<4>(results), std::get<5>(results), cur_level)); +} + +}} // namespace at::functorch diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtils.h b/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..142665b7c8b27b64be1ca7b3b44aaca132141c77 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtils.h @@ -0,0 +1,153 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace at { + +// if dim_post_expr is 0 and wrap_scalar is true, then dim must be in the +// range [-1, 0]. This is a special case for scalar tensors and manifests in +// e.g. torch.sum(scalar_tensor, 0) Otherwise, dim should be in the range +// [-dim_post_expr, dim_post_expr-1]. +using c10::maybe_wrap_dim; + +inline int64_t maybe_wrap_dim(int64_t dim, TensorImpl* tensor) { + return maybe_wrap_dim(dim, tensor->dim()); +} + +inline int64_t maybe_wrap_dim(int64_t dim, TensorList tensors) { + if (tensors.empty()) { + // can't wrap empty TensorList; rely on underlying implementation to throw + // error if necessary. + return dim; + } + return maybe_wrap_dim(dim, tensors[0].dim()); +} + +inline int64_t maybe_wrap_dim( + int64_t dim, + const std::vector>& tensor_sizes) { + if (tensor_sizes.empty()) { + // can't wrap empty list; rely on underlying implementation to throw error + // if necessary + return dim; + } + return maybe_wrap_dim(dim, tensor_sizes[0].size()); +} + +// Given an array of dimensions `dims` of length `ndims`, this function "Wraps" +// each dim in-place for a tensor of rank `dim_post_expr`, allowing dims to be +// specified using negative indices. +// +// Additionally, if `wrap_scalar` is true then scalar tensors with rank 0, will +// allow dimensions in the range [-1, 0]. Otherwise, an IndexError is raised for +// dimensions not in the range [-dim_post_expr, dim_post_expr). +inline void maybe_wrap_dims_n( + int64_t* dims, + int64_t ndims, + int64_t dim_post_expr, + bool wrap_scalars = true) { + if (dim_post_expr <= 0) { + if (wrap_scalars) { + dim_post_expr = 1; // this will make range [-1, 0] + } else { + TORCH_CHECK_INDEX( + ndims == 0, + "Dimension specified as ", + dims[0], + " but tensor has no dimensions"); + return; + } + } + int64_t min = -dim_post_expr; + int64_t max = dim_post_expr - 1; + for (const auto i : c10::irange(ndims)) { + auto& dim = dims[i]; + if (dim < min || dim > max) { + TORCH_CHECK_INDEX( + false, + "Dimension out of range (expected to be in range of [", + min, + ", ", + max, + "], but got ", + dim, + ")"); + } + if (dim < 0) + dim += dim_post_expr; + } +} + +// Given a contiguous container of dimensions `dims`, this function "Wraps" +// each dim in-place for a tensor of rank `dim_post_expr`, allowing dims to be +// specified using negative indices. +// +// Additionally, if `wrap_scalar` is true then scalar tensors with rank 0, will +// allow dimensions in the range [-1, 0]. Otherwise, an IndexError is raised for +// dimensions not in the range [-dim_post_expr, dim_post_expr). +template +inline void maybe_wrap_dims( + Container& dims, + int64_t dim_post_expr, + bool wrap_scalars = true) { + return maybe_wrap_dims_n( + dims.data(), dims.size(), dim_post_expr, wrap_scalars); +} + +// previously, size [0] tensors were the only possible empty tensors; thus, it +// wasn't possible to cat empty tensors unless all the other tensors were +// 1-dimensional, so we allowed these tensors to be "skipped" (both for wrap +// dimension behavior and dimension size checking). We maintain this behavior +// for backwards compatibility, but only for this specific size (i.e. other +// empty sizes are not skipped). +template +inline int64_t _legacy_cat_wrap_dim( + int64_t dim, + const std::vector>& tensor_sizes) { + for (auto& sizes : tensor_sizes) { + if (sizes.size() == 1 && sizes[0] == 0) { + continue; + } + return maybe_wrap_dim(dim, sizes.size()); + } + return dim; +} + +inline int64_t legacy_cat_wrap_dim( + int64_t dim, + const std::vector>& tensor_sizes) { + return _legacy_cat_wrap_dim(dim, tensor_sizes); +} + +inline int64_t legacy_cat_wrap_dim_symint( + int64_t dim, + const std::vector>& tensor_sizes) { + return _legacy_cat_wrap_dim(dim, tensor_sizes); +} + +inline int64_t legacy_cat_wrap_dim( + int64_t dim, + const MaterializedITensorListRef& tensors) { + for (const Tensor& tensor : tensors) { + if (tensor.dim() == 1 && tensor.sizes()[0] == 0) { + continue; + } + return maybe_wrap_dim(dim, tensor.dim()); + } + return dim; +} + +// wrap negative dims in a vector +inline void wrap_all_dims( + std::vector& dims_to_wrap, + int64_t tensor_total_dims) { + for (const auto i : c10::irange(dims_to_wrap.size())) { + dims_to_wrap[i] = maybe_wrap_dim(dims_to_wrap[i], tensor_total_dims); + } +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtilsMulti.h b/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtilsMulti.h new file mode 100644 index 0000000000000000000000000000000000000000..58a23f95c0d2c39d4450e0e70adda091278b9079 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtilsMulti.h @@ -0,0 +1,44 @@ +#pragma once + +#include +#include +#include +#include +#include + +namespace at { + +// This is in an extra file to work around strange interaction of +// bitset on Windows with operator overloading + +constexpr size_t dim_bitset_size = 64; + +static inline std::bitset dim_list_to_bitset( + OptionalIntArrayRef opt_dims, + size_t ndims) { + TORCH_CHECK( + ndims <= dim_bitset_size, + "only tensors with up to ", + dim_bitset_size, + " dims are supported"); + std::bitset seen; + if (opt_dims.has_value()) { + auto dims = opt_dims.value(); + for (const auto i : c10::irange(dims.size())) { + size_t dim = maybe_wrap_dim(dims[i], static_cast(ndims)); + TORCH_CHECK( + !seen[dim], + "dim ", + dim, + " appears multiple times in the list of dims"); + seen[dim] = true; + } + } else { + for (size_t dim = 0; dim < ndims; dim++) { + seen[dim] = true; + } + } + return seen; +} + +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/autocast_mode.h b/venv/lib/python3.10/site-packages/torch/include/ATen/autocast_mode.h new file mode 100644 index 0000000000000000000000000000000000000000..b3f2fcd511ff65ad8f78fc60759da354558858c6 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/autocast_mode.h @@ -0,0 +1,647 @@ +#pragma once + +#include +#include +#include +#include + +#include +#include + +namespace at::autocast { + +TORCH_API bool is_enabled(); +TORCH_API void set_enabled(bool enabled); +TORCH_API void clear_cache(); +TORCH_API int increment_nesting(); +TORCH_API int decrement_nesting(); +TORCH_API bool is_cpu_enabled(); +TORCH_API void set_cpu_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_gpu_dtype(); +TORCH_API at::ScalarType get_autocast_cpu_dtype(); +TORCH_API void set_autocast_gpu_dtype(at::ScalarType dtype); +TORCH_API void set_autocast_cpu_dtype(at::ScalarType dtype); +TORCH_API bool is_xpu_enabled(); +TORCH_API void set_xpu_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_xpu_dtype(); +TORCH_API void set_autocast_xpu_dtype(at::ScalarType dtype); +TORCH_API bool is_ipu_enabled(); +TORCH_API void set_ipu_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_ipu_dtype(); +TORCH_API void set_autocast_ipu_dtype(at::ScalarType dtype); +TORCH_API bool is_hpu_enabled(); +TORCH_API void set_hpu_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_hpu_dtype(); +TORCH_API void set_autocast_hpu_dtype(at::ScalarType dtype); +TORCH_API bool is_xla_enabled(); +TORCH_API void set_xla_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_xla_dtype(); +TORCH_API void set_autocast_xla_dtype(at::ScalarType dtype); +TORCH_API bool is_privateuseone_enabled(); +TORCH_API void set_privateuseone_enabled(bool enabled); +TORCH_API at::ScalarType get_autocast_privateuseone_dtype(); +TORCH_API void set_autocast_privateuseone_dtype(at::ScalarType dtype); +TORCH_API bool is_autocast_cache_enabled(); +TORCH_API void set_autocast_cache_enabled(bool enabled); + +namespace { +inline bool is_autocast_eligible( + const Tensor& tensor, + c10::DeviceType device_type) { + switch (device_type) { + case c10::DeviceType::CUDA: + return (tensor.is_cuda() || tensor.is_xla()) && + tensor.is_floating_point(); + case c10::DeviceType::CPU: + return (tensor.is_cpu() || tensor.is_mkldnn()) && + tensor.is_floating_point(); + case c10::DeviceType::XPU: + return tensor.is_xpu() && tensor.is_floating_point(); + case c10::DeviceType::IPU: + return tensor.is_ipu() && tensor.is_floating_point(); + case c10::DeviceType::HPU: + return tensor.is_hpu() && tensor.is_floating_point(); + case c10::DeviceType::XLA: + return tensor.is_xla() && tensor.is_floating_point(); + case c10::DeviceType::PrivateUse1: + return tensor.is_privateuseone() && tensor.is_floating_point(); + default: + return false; + } +} +} // namespace + +inline DispatchKey get_autocast_dispatch_key_from_device_type( + c10::DeviceType device_type) { + switch (device_type) { + case c10::DeviceType::CUDA: + return DispatchKey::Autocast; + case c10::DeviceType::CPU: + return DispatchKey::AutocastCPU; + case c10::DeviceType::XPU: + return DispatchKey::AutocastXPU; + case c10::DeviceType::IPU: + return DispatchKey::AutocastIPU; + case c10::DeviceType::HPU: + return DispatchKey::AutocastHPU; + case c10::DeviceType::XLA: + return DispatchKey::AutocastXLA; + case c10::DeviceType::PrivateUse1: + return DispatchKey::AutocastPrivateUse1; + default: + throw std::runtime_error( + "unknown device type for autocast in get_autocast_dispatch_key_from_device_type"); + } +} + +inline at::ScalarType get_lower_precision_fp_from_device_type( + c10::DeviceType device_type) { + switch (device_type) { + case c10::DeviceType::CUDA: + return get_autocast_gpu_dtype(); + case c10::DeviceType::CPU: + return get_autocast_cpu_dtype(); + case c10::DeviceType::XPU: + return get_autocast_xpu_dtype(); + case c10::DeviceType::IPU: + return get_autocast_ipu_dtype(); + case c10::DeviceType::HPU: + return get_autocast_hpu_dtype(); + case c10::DeviceType::XLA: + return get_autocast_xla_dtype(); + case c10::DeviceType::PrivateUse1: + return get_autocast_privateuseone_dtype(); + default: + throw std::runtime_error( + "unknown device type for autocast in get_lower_precision_fp_from_device_type"); + } +} + +/******************************************************************** +Logic to extract the promote type from any Tensor or TensorList args. +********************************************************************/ + +// Overload to catch Tensor args. +// If nextArg is floating-point, compare its scalar_type with our +// current best guess for the promote type, and update if necessary. +inline at::ScalarType prioritize( + at::ScalarType current, + const Tensor& nextArg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + if (current == at::kDouble) { + AT_ERROR("promote type is double in at::autocast::prioritize"); + return current; + } + at::ScalarType lower_precision_fp = + get_lower_precision_fp_from_device_type(device_type); + if (is_autocast_eligible(nextArg, device_type)) { + auto next = nextArg.scalar_type(); + if (next == at::kDouble) { + return current; // ignores double tensors + } else if (current == at::kFloat || next == at::kFloat) { + return at::kFloat; // prioritizes float over lower_precision_fp + } else if (current == lower_precision_fp && next == lower_precision_fp) { + return lower_precision_fp; + } else { + AT_ERROR("Unexpected floating ScalarType in at::autocast::prioritize"); + return current; + } + } else { + return current; + } +} + +// Overload to catch TensorList args (for e.g. cat, stack). +// Reuses the overload above to process each Tensor in the list. +inline at::ScalarType prioritize( + at::ScalarType current, + const TensorList& list, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + for (const auto& tensor : list) { + current = prioritize(current, tensor, device_type); + } + return current; +} + +inline at::ScalarType prioritize( + at::ScalarType current, + const ITensorListRef& list, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + for (const auto& tensor : list) { + current = prioritize(current, tensor, device_type); + } + return current; +} + +// Template to catch non-Tensor args (no-op that returns current best guess) +template +inline at::ScalarType prioritize( + at::ScalarType current, + T nextArg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + return current; +} + +// Overload for the tail case. +inline at::ScalarType promote_type( + at::ScalarType current, + c10::DeviceType device_type) { + return current; +} + +// Unpack args and determine if incoming lower_precision_fp tensors need to be +// promoted to float32. Non-Tensor arguments are ignored. +template +inline at::ScalarType promote_type( + at::ScalarType current, + c10::DeviceType device_type, + Arg0 arg0, + Args... args) { + auto new_current = prioritize(current, arg0, device_type); + return promote_type(new_current, device_type, args...); +} + +/**************************************************** +Logic to apply cached casting to any Tensor argument. +****************************************************/ +inline bool is_eligible( + const Tensor& arg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + return ( + arg.defined() && is_autocast_eligible(arg, device_type) && + (arg.scalar_type() != at::kDouble)); +} + +// Overload to catch Tensor args +TORCH_API Tensor cached_cast( + at::ScalarType to_type, + const Tensor& arg, + c10::DeviceType device_type = c10::DeviceType::CUDA); + +// Overload to process optional +inline c10::optional cached_cast( + at::ScalarType to_type, + const c10::optional& arg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + if (arg.has_value()) { + return cached_cast(to_type, *arg, device_type); + } else { + return c10::nullopt; + } +} + +// Overload to process TensorLists +inline std::vector cached_cast( + at::ScalarType to_type, + const TensorList& arg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + std::vector vec; + vec.reserve(arg.size()); + for (const auto& t : arg) { + vec.emplace_back(cached_cast(to_type, t, device_type)); + } + return vec; +} + +inline std::vector cached_cast( + at::ScalarType to_type, + const ITensorListRef& arg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + std::vector vec; + vec.reserve(arg.size()); + for (const auto& t : arg) { + vec.emplace_back(cached_cast(to_type, t, device_type)); + } + return vec; +} + +// Template to catch non-Tensor args. +template +inline T cached_cast( + at::ScalarType to_type, + T arg, + c10::DeviceType device_type = c10::DeviceType::CUDA) { + return arg; +} + +/******************************************************* +Logic to flip an output dtype flag. +Keep it simple for now by assuming only one such flag is +present in the argument list. If I ever need a function +with more than flag I'll figure out something else. +The policy is: +If the user has explicity specified a dtype, respect it. +Otherwise, set it to the autocast type. +********************************************************/ + +// Overload to catch dtype flags +c10::optional inline set_opt_dtype( + at::ScalarType to_type, + const c10::optional& dtype) { + return dtype.has_value() ? dtype : to_type; +} + +// Template to catch other args +template +inline T set_opt_dtype(at::ScalarType to_type, T arg) { + return arg; +} + +template +inline bool firstarg_is_eligible( + c10::DeviceType device_type, + const Tensor& arg, + Args... args) { + return is_eligible(arg, device_type); +} + +template +inline at::ScalarType type_from_firstarg( + c10::DeviceType device_type, + at::ScalarType to_type, + const Tensor& arg, + Args... args) { + return (is_eligible(arg, device_type) ? to_type : arg.scalar_type()); +} + +// Policies correspond to op categories that need code-divergent handling. +// Wrapper templates below are specialized based on a policy template parameter. +enum class CastPolicy : uint8_t { + lower_precision_fp = 0, // Cast all inputs to lower_precision_fp before + // running the op. Currently, lower_precision_fp is + // fp16 for AutocastCUDA, and is defined by user + // (default bf16) for AutocastCPU or other device. + fp32, // Cast all inputs to at::kFloat before running the op. + fp32_set_opt_dtype, // Treats functions (like softmax) that + // 1. we'd like to run in fp32 and + // 2. have a c10::optional arg that controls + // the output type. + // fp32_set_opt_dtype wrappers' policy is: if the output + // type is already set, don't touch it, otherwise, set + // it to at::kFloat. + fp32_append_dtype, // Treats functions (like norm) that + // 1. we'd like to run in fp32 and + // 2. have some overloads that accept an output type and + // other overloads that don't. + // fp32_append_dtype wrappers wrap the overloads that don't + // have an output dtype. + // The wrapper policy is: append at::kFloat to the args, + // and redispatch to the type-aware overload. + promote, // Run in the widest dtype among several args. +}; + +/******************************************************************************************************** +Templates to provide wrapper functions + +I'm copying the pattern used in core/boxing/impl/WrapFunctionIntoFunctor.h to +extract args and return type. (see also +https://stackoverflow.com/questions/46533698/how-to-deduce-argument-list-from-function-pointer) + +This strategy uses an exterior "WrapFunction" that extracts arguments on behalf +of (in my case several specializations of) an interior "WrapFunction_". +Interior WrapFunction_ specializations are defined for each CastPolicy. +********************************************************************************************************/ + +// Base template for WrapFunction_, which is specialized to contain a "call" +// method each CastPolicy +template < + CastPolicy policy, + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class ArgList> +struct WrapFunction_ {}; + +// CastPolicy::lower_precision_fp General_DeviceType +template < + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class... Args> +struct WrapFunction_< + CastPolicy::lower_precision_fp, + device_type, + Redispatch, + F, + Ret, + guts::typelist::typelist> { + static Ret call(Args... args) { + c10::impl::ExcludeDispatchKeyGuard no_autocast( + get_autocast_dispatch_key_from_device_type(device_type)); + return (*F)(cached_cast( + get_lower_precision_fp_from_device_type(device_type), + args, + device_type)...); + } +}; + +// CastPolicy::fp32 General_DeviceType +template < + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class... Args> +struct WrapFunction_< + CastPolicy::fp32, + device_type, + Redispatch, + F, + Ret, + guts::typelist::typelist> { + static Ret call(Args... args) { + c10::impl::ExcludeDispatchKeyGuard no_autocast( + get_autocast_dispatch_key_from_device_type(device_type)); + return (*F)(cached_cast(at::kFloat, args, device_type)...); + } +}; + +// CastPolicy::fp32_set_opt_dtype General_DeviceType +template < + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class... Args> +struct WrapFunction_< + CastPolicy::fp32_set_opt_dtype, + device_type, + Redispatch, + F, + Ret, + guts::typelist::typelist> { + static Ret call(Args... args) { + c10::impl::ExcludeDispatchKeyGuard no_autocast( + get_autocast_dispatch_key_from_device_type(device_type)); + if (firstarg_is_eligible(device_type, args...)) { + return (*F)(set_opt_dtype(at::kFloat, args)...); + } else { + // If ineligible, calls F with unaltered args. Does not set opt dtype, + // because setting opt dtype explicitly may interfere with internal + // implicit promotion decisions. + return (*F)(args...); + } + } +}; + +// CastPolicy::fp32_append_dtype General_DeviceType +template < + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class... Args> +struct WrapFunction_< + CastPolicy::fp32_append_dtype, + device_type, + Redispatch, + F, + Ret, + guts::typelist::typelist> { + static Ret call(Args... args) { + c10::impl::ExcludeDispatchKeyGuard no_autocast( + get_autocast_dispatch_key_from_device_type(device_type)); + at::ScalarType out_type = + type_from_firstarg(device_type, at::kFloat, args...); + return (*F)(args..., out_type); + } +}; + +// CastPolicy::promote General_DeviceType +template < + c10::DeviceType device_type, + class Redispatch, + Redispatch* F, + class Ret, + class... Args> +struct WrapFunction_< + CastPolicy::promote, + device_type, + Redispatch, + F, + Ret, + guts::typelist::typelist> { + static Ret call(Args... args) { + c10::impl::ExcludeDispatchKeyGuard no_autocast( + get_autocast_dispatch_key_from_device_type(device_type)); + auto to_type = promote_type( + get_lower_precision_fp_from_device_type(device_type), + device_type, + args...); + return (*F)(cached_cast(to_type, args, device_type)...); + } +}; + +// Wrapper to infer return_type and parameter_types for WrapFunction_ (imitating +// core/boxing/impl/WrapFunctionIntoFunctor.h) +template < + CastPolicy policy, + c10::DeviceType device_type, + class Registered, // The signature for which we're registering. The + // dispatcher's calling code invokes our registered + // functions with arguments matching Registered, so we + // register WrapFunction_::call methods with a matching + // signature to properly field those arguments. + // guts::function_traits below extracts return_type and + // parameter_types from Registered, which WrapFunction_ + // templates above use to declare their call methods. + class Redispatch, // The signature for the function we're redispatching to. + // In most cases this is the same as Registered, but for + // some ops (for example, ops where we append a dtype) + // it's useful to redispatch to a function with a + // different signature. + Redispatch* F> // The actual function we're redispatching to. +struct WrapFunction final { + using type = WrapFunction_< + policy, + device_type, + Redispatch, + F, + typename guts::function_traits::return_type, + typename guts::function_traits::parameter_types>; +}; + +/***************************************************************************************************************** +This section performs load-time registration for autocast wrappers. + +It's debatable at what level operations should be patched. We'd like casts to +be autograd-exposed and precede autograd history recording, so that for +lower_precision_fp ops, input tensors are saved for backward in +lower_precision_fp rather than fp32. Saving inputs in lower_precision_fp +can significantly reduce a model's memory footprint. + +Option 1 (strawman): Patch only at the level of explicit calls into +cudnn/cublas (cudnn_convolution, etc), because those are the code paths that are +guaranteed to use Tensor Cores, therefore they're the ones that will benefit +most from lower_precision_fp. Potential pitfall: convolutions (and other ops) +are wrapped in several layers of at::* calls. If one of those happens to record +autograd history, then we've lost the opportunity to save inputs in +lower_precision_fp. + +Option 2: Patch the Python-exposed surface of calls, to make 100% sure autograd +history recording can't sneak in ahead of autocast. This mirrors Apex most +closely. + +I think Option 2 is the right answer for all ops, not just convolutions. Option +2 is what I implement here. +*****************************************************************************************************************/ + +/******************************************************************************************************************** +Explicit registration for out-of-place ops + +The stuff below could be codegenned. Ed said +> you are going to have to write the function definition at some point, I +wouldn't try to get clever about it Therefore, for the moment, this is all +copy pasted in from VariableTypeEverything.cpp with appropriate substitutions. +********************************************************************************************************************/ + +} // namespace at::autocast + +#define ADD_NS(RAW_OP) at::RAW_OP + +// Common cases where registration signature matches redispatch signature +// (that's why SIGNATURE is repeated in the WrapFunction instantiation) +#define KERNEL(DISPATCHKEY, OP, POLICY) \ + m.impl( \ + TORCH_SELECTIVE_NAME("aten::" #OP), \ + &::at::autocast::WrapFunction< \ + ::at::autocast::CastPolicy::POLICY, \ + DISPATCHKEY, \ + decltype(ATEN_FN(OP)), \ + decltype(ATEN_FN(OP)), \ + &ATEN_FN(OP)>::type::call); + +#define KERNEL2(DISPATCHKEY, OP, OVERLOAD, POLICY) \ + m.impl( \ + TORCH_SELECTIVE_NAME("aten::" #OP "." #OVERLOAD), \ + &::at::autocast::WrapFunction< \ + ::at::autocast::CastPolicy::POLICY, \ + DISPATCHKEY, \ + decltype(ATEN_FN2(OP, OVERLOAD)), \ + decltype(ATEN_FN2(OP, OVERLOAD)), \ + &ATEN_FN2(OP, OVERLOAD)>::type::call); + +// Less-common but still useful case: redispatching to a function +// with a new signature (e.g. appending a dtype) +#define KERNEL_DIFFERENT_REDISPATCH_SIGNATURE( \ + DISPATCHKEY, \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) \ + m.impl( \ + TORCH_SELECTIVE_NAME("aten::" REGISTER_NAME), \ + &::at::autocast::WrapFunction< \ + ::at::autocast::CastPolicy::POLICY, \ + DISPATCHKEY, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + &REDISPATCH_FUNC>::type::call); + +// KERNEL_CPU/KERNEL_CPU2/KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_CPU +// registration for AutocastCPU +#define KERNEL_CPU(OP, POLICY) KERNEL(c10::DeviceType::CPU, OP, POLICY) + +#define KERNEL_CPU2(OP, OVERLOAD, POLICY) \ + KERNEL2(c10::DeviceType::CPU, OP, OVERLOAD, POLICY) + +#define KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_CPU( \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) \ + KERNEL_DIFFERENT_REDISPATCH_SIGNATURE( \ + c10::DeviceType::CPU, \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) + +// KERNEL_CUDA/KERNEL_CUDA2/KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_CUDA +// registration for AutocastCUDA +#define KERNEL_CUDA(OP, POLICY) KERNEL(c10::DeviceType::CUDA, OP, POLICY) + +#define KERNEL_CUDA2(OP, OVERLOAD, POLICY) \ + KERNEL2(c10::DeviceType::CUDA, OP, OVERLOAD, POLICY) + +#define KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_CUDA( \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) \ + KERNEL_DIFFERENT_REDISPATCH_SIGNATURE( \ + c10::DeviceType::CUDA, \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) + +// KERNEL_PRIVATEUSEONE/KERNEL_PRIVATEUSEONE2/ +// KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_PRIVATEUSEONE +// registration for AutocastPrivateUse1 +#define KERNEL_PRIVATEUSEONE(OP, POLICY) \ + KERNEL(c10::DeviceType::PrivateUse1, OP, POLICY) + +#define KERNEL_PRIVATEUSEONE2(OP, OVERLOAD, POLICY) \ + KERNEL2(c10::DeviceType::PrivateUse1, OP, OVERLOAD, POLICY) + +#define KERNEL_DIFFERENT_REDISPATCH_SIGNATURE_PRIVATEUSEONE( \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) \ + KERNEL_DIFFERENT_REDISPATCH_SIGNATURE( \ + c10::DeviceType::PrivateUse1, \ + REDISPATCH_FUNC, \ + REGISTER_NAME, \ + REGISTER_SIGNATURE, \ + REDISPATCH_SIGNATURE, \ + POLICY) diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/jit_macros.h b/venv/lib/python3.10/site-packages/torch/include/ATen/jit_macros.h new file mode 100644 index 0000000000000000000000000000000000000000..9af826549021a0853beb83c74b6ac695728ab054 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/jit_macros.h @@ -0,0 +1,7 @@ +#pragma once +#include +#include + +// AT_USE_JITERATOR(), controls whether we jit some elementwise kernels +#define AT_USE_JITERATOR() true +#define jiterator_stringify(...) std::string(#__VA_ARGS__); diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/jiterator_macros.h b/venv/lib/python3.10/site-packages/torch/include/ATen/jiterator_macros.h new file mode 100644 index 0000000000000000000000000000000000000000..3aa4c7ebb0af07fd65012d9d531aaa140dd6c212 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/jiterator_macros.h @@ -0,0 +1,38 @@ +#pragma once +#include +#include + +#define JITERATOR_HOST_DEVICE C10_HOST_DEVICE +#if defined(_MSC_VER) && defined(__CUDACC__) +// NVRTC on Windows errors if __host__ __device__ attribute is +// present on kernel. +// error: attribute "__host__" does not apply here +// error: attribute "__device__" does not apply here +#define JITERATOR_HOST_DEVICE +#endif + +// jiterator_also_stringify_as macro is used to define code (for CPU/ROCm) +// and generate code string for `jiterator` (only when compiling for CUDA). +// Usage : +// jiterator_also_stringify_as( +// jiterator_code(template T identity(T x) { return x; }), +// identity_string); +// This will define the template `identity` as present in code and +// also define `std::string identity_string` with the code as the string +// if this is being compiled for CUDA. + +// `jiterator_code` macro is to deal with `,` in the kernel code. +// These `,`s confuse the preprocessor into thinking we are passing +// multiple arguments to the macro. +#define jiterator_code(...) __VA_ARGS__ +#if defined(__CUDACC__) || defined(__HIPCC__) +// CPU and CUDA and ROCm case +#define stringify_code(...) #__VA_ARGS__ +#define jiterator_also_stringify_as(code, str_name) \ + code /* define the function */ \ + const std::string str_name = std::string(stringify_code(code)); +#else +// CPU only or CPU and ROCm case +// Only needs the function +#define jiterator_also_stringify_as(code, str_name) code +#endif diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_batch_norm_impl_index_backward.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_batch_norm_impl_index_backward.h new file mode 100644 index 0000000000000000000000000000000000000000..5e2829eaa3dfe255d555694ab36f29dbc5243296 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_batch_norm_impl_index_backward.h @@ -0,0 +1,30 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::_batch_norm_impl_index_backward(int impl_index, Tensor input, Tensor grad_output, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_var_transform, bool train, float eps, bool[3] output_mask, Tensor reservedSpace) -> (Tensor, Tensor, Tensor) +inline ::std::tuple _batch_norm_impl_index_backward(int64_t impl_index, const at::Tensor & input, const at::Tensor & grad_output, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_var_transform, bool train, double eps, ::std::array output_mask, const at::Tensor & reservedSpace) { + return at::_ops::_batch_norm_impl_index_backward::call(impl_index, input, grad_output, weight, running_mean, running_var, save_mean, save_var_transform, train, eps, output_mask, reservedSpace); +} + +} diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_log_cuda_dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_log_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..721482794ec6a38430cf440d75b8f52597b4113b --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_foreach_log_cuda_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API ::std::vector _foreach_log(at::TensorList self); +TORCH_API void _foreach_log_(at::TensorList self); + +} // namespace cuda +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_forward_ops.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_forward_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..c457a5bf1cc1f57d1d182621aa68a67b0556fe87 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/_slow_conv2d_forward_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API _slow_conv2d_forward_output { + using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, c10::SymIntArrayRef, const c10::optional &, c10::SymIntArrayRef, c10::SymIntArrayRef, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_slow_conv2d_forward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "output") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_slow_conv2d_forward.output(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding, *, Tensor(a!) output) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, at::Tensor & output); +}; + +struct TORCH_API _slow_conv2d_forward { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, c10::SymIntArrayRef, const c10::optional &, c10::SymIntArrayRef, c10::SymIntArrayRef); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_slow_conv2d_forward") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_slow_conv2d_forward(Tensor self, Tensor weight, SymInt[2] kernel_size, Tensor? bias, SymInt[2] stride, SymInt[2] padding) -> Tensor") + static at::Tensor call(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding); +}; + +}} // namespace at::_ops diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/deg2rad_compositeexplicitautograd_dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/deg2rad_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..26458e6b05ed233ed19076ce3b5f7909070903a7 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/deg2rad_compositeexplicitautograd_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor deg2rad(const at::Tensor & self); +TORCH_API at::Tensor & deg2rad_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & deg2rad_outf(const at::Tensor & self, at::Tensor & out); +TORCH_API at::Tensor & deg2rad_(at::Tensor & self); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/dropout_compositeimplicitautograd_dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/dropout_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..03d108f257bd00a9d7c2126e9fdd6b65d9a93102 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/dropout_compositeimplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API at::Tensor dropout(const at::Tensor & input, double p, bool train); +TORCH_API at::Tensor & dropout_(at::Tensor & self, double p, bool train); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/heaviside_native.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/heaviside_native.h new file mode 100644 index 0000000000000000000000000000000000000000..ee73a2a725a38f2bfcf4425f06b802bac4dae677 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/heaviside_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace native { +struct TORCH_API structured_heaviside_out : public at::meta::structured_heaviside { +void impl(const at::Tensor & self, const at::Tensor & values, const at::Tensor & out); +}; +} // namespace native +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_solve_compositeimplicitautograd_dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_solve_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..b56d85b93666fca706e614c4f58f609d728e8311 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/linalg_solve_compositeimplicitautograd_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API at::Tensor linalg_solve(const at::Tensor & A, const at::Tensor & B, bool left=true); +TORCH_API at::Tensor & linalg_solve_out(at::Tensor & out, const at::Tensor & A, const at::Tensor & B, bool left=true); +TORCH_API at::Tensor & linalg_solve_outf(const at::Tensor & A, const at::Tensor & B, bool left, at::Tensor & out); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/mm.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/mm.h new file mode 100644 index 0000000000000000000000000000000000000000..96058f8f09f9c31b5448ee8ee32db0c8c6b12c55 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/mm.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::mm(Tensor self, Tensor mat2) -> Tensor +inline at::Tensor mm(const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::mm::call(self, mat2); +} + +// aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & mm_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & mat2) { + return at::_ops::mm_out::call(self, mat2, out); +} +// aten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & mm_outf(const at::Tensor & self, const at::Tensor & mat2, at::Tensor & out) { + return at::_ops::mm_out::call(self, mat2, out); +} + +} diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/pad_sequence_native.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/pad_sequence_native.h new file mode 100644 index 0000000000000000000000000000000000000000..ea46e18e84d79fddb4273506f23daeeecc575d56 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/pad_sequence_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor pad_sequence(at::TensorList sequences, bool batch_first=false, double padding_value=0.0); +} // namespace native +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/ops/special_bessel_y1_compositeexplicitautogradnonfunctional_dispatch.h b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/special_bessel_y1_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..44c3ad7577e1dc7ace380f933624fcee90afd961 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/ops/special_bessel_y1_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor special_bessel_y1(const at::Tensor & self); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/venv/lib/python3.10/site-packages/torch/include/ATen/record_function.h b/venv/lib/python3.10/site-packages/torch/include/ATen/record_function.h new file mode 100644 index 0000000000000000000000000000000000000000..431ff64cf553f08f92157a0f5f17a8dd8d20acf2 --- /dev/null +++ b/venv/lib/python3.10/site-packages/torch/include/ATen/record_function.h @@ -0,0 +1,740 @@ +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace c10 { +class TORCH_API OperatorHandle; +} + +namespace at { + +// Function name to record NCCL metadata +extern TORCH_API const std::string kParamCommsCallName; + +// Kind of record function scope; +enum class C10_API_ENUM RecordScope : uint8_t { + // c10/ATen ops, autograd nodes + FUNCTION = 0, + // Functions/nodes called from the autograd + BACKWARD_FUNCTION, + // TorchScript functions, methods + TORCHSCRIPT_FUNCTION, + // Kernel Function dtype Tag + KERNEL_FUNCTION_DTYPE, + // Torchbind custom class, + CUSTOM_CLASS, + // Generic Build Feature + BUILD_FEATURE, + // Kernel Function dtype Tag + LITE_INTERPRETER, + // User defined scope (e.g. with record_function()) + USER_SCOPE, + // Scopes for static runtime, a specialized TorchScript interpreter + STATIC_RUNTIME_OP, + STATIC_RUNTIME_MODEL, + NUM_SCOPES, // must be the last in the list +}; + +} // namespace at + +namespace std { +template <> +struct hash { + size_t operator()(const at::RecordScope& sc) const { + return static_cast(sc); + } +}; +} // namespace std + +namespace at { + +struct TORCH_API StringView { + StringView() : StringView(nullptr) {} + explicit StringView(const char* str_ptr) + : owned_str_ptr_(nullptr), str_ptr_(str_ptr) {} + explicit StringView(std::string str) + : owned_str_ptr_(std::make_shared(std::move(str))), + str_ptr_(owned_str_ptr_->c_str()) {} + + const char* str() const { + return str_ptr_; + } + + friend std::ostream& operator<<(std::ostream& os, const StringView& dt) { + os << dt.str(); + return os; + } + + friend bool operator==(const StringView& lhs, const StringView& rhs) { + return strcmp(lhs.str(), rhs.str()) == 0; + } + + friend bool operator!=(const StringView& lhs, const StringView& rhs) { + return !(lhs == rhs); + } + + private: + std::shared_ptr owned_str_ptr_; + const char* str_ptr_; +}; + +// Soft limit on the number of callbacks to use; +constexpr std::size_t kSoftLimitCallbacks = 4; + +// An abstract base class for various observer contexts that can be attached to +// the RecordFunction. +struct ObserverContext { + virtual ~ObserverContext() = default; + + protected: + ObserverContext() = default; +}; + +typedef c10::SmallVector CallbackHandles; +typedef c10::SmallVector, kSoftLimitCallbacks> + ObserverContextList; +typedef uint64_t RecordFunctionHandle; +struct RecordFunction; + +// +// PyTorch callbacks/observers API: +// + +/** + * RecordFunctionCallback represents a pair of callbacks to be used with + * RecordFunction, members: + * start, end - the callbacks to run when entering and exiting the scope; + * optionally, the start callback may return an ObserverContext which will + * be passed to the end callback, use appropriate constructor accordingly. + * needs_inputs - whether the callbacks need the inputs passed from the + * observed function/range; NOTE: passing the inputs incurs an additional + * overhead; sampling_probability - if not 1.0, then the callback is + * probabilistically sampled to run; NOTE: start and end callbacks always run as + * a pair and are sampled together; scopes - types of scopes to execute the + * callbacks on (see RecordScope); passing empty set means the callbacks will be + * executed for all possible scope types should_run - optional function that + * returns whether this callback should run; overwrites the effect of setting + * sampling_probability + */ +class TORCH_API RecordFunctionCallback { + public: + using StartCallback = + std::unique_ptr (*)(const RecordFunction&); + using EndCallback = void (*)(const RecordFunction&, ObserverContext*); + + // This interface supports observers that require passing an ObserverContext + // between start and end callbacks. + explicit RecordFunctionCallback( + StartCallback start, + EndCallback end = nullptr) + : start_(start), end_(end) { + scopes_.fill(true); + } + + RecordFunctionCallback& needsInputs(bool needs_inputs) { + needs_inputs_ = needs_inputs; + return *this; + } + + RecordFunctionCallback& needsOutputs(bool needs_outputs) { + needs_outputs_ = needs_outputs; + return *this; + } + + RecordFunctionCallback& needsIds(bool needs_ids) { + needs_ids_ = needs_ids; + return *this; + } + + RecordFunctionCallback& samplingProb(double sampling_prob) { + TORCH_CHECK( + sampling_prob >= 0.0 && sampling_prob <= 1.0, + "Invalid sampling probability"); + sampling_prob_ = sampling_prob; + return *this; + } + + RecordFunctionCallback& scopes( + const std::unordered_set>& scopes) { + if (!scopes.empty()) { + scopes_.fill(false); + for (auto sc : scopes) { + scopes_[static_cast(sc)] = true; + } + } else { + scopes_.fill(true); + } + return *this; + } + + bool needsInputs() const { + return needs_inputs_; + } + + bool needsOutputs() const { + return needs_outputs_; + } + + bool needsIds() const { + return needs_ids_; + } + + double samplingProb() const { + return sampling_prob_; + } + + bool checkScope(RecordScope sc) const { + return scopes_[(size_t)sc]; + } + + StartCallback start() const { + return start_; + } + + EndCallback end() const { + return end_; + } + + private: + StartCallback start_; + EndCallback end_; + double sampling_prob_ = 1.0; + std::array(RecordScope::NUM_SCOPES)> scopes_ = {}; + bool needs_inputs_ = false; + bool needs_outputs_ = false; + bool needs_ids_ = false; +}; + +// Notes: +// - two types of callbacks are provided: thread local and global +// - thread local callbacks are added/removed only for the given thread +// and are stored locally for each thread and separately from the list +// of the global callbacks +// - global callbacks are stored in a single per process list and are +// invoked by every RecordFunction, in addition to the thread local +// callbacks specific to the given thread +// - we allow the added callbacks to be sampled, by specifying a sampling +// probability for each callback pair, if the start callback is +// not picked to run, the corresponding end callback won't be called +// - a typical use case for the global callbacks is passive monitoring +// in the background (e.g. fleet-wide monitoring), without focusing on +// the specific piece of code +// - in contrast, thread local callbacks are enabled locally, on demand, +// for the specific piece of code (range) and are not sampled +// - a typical use case for thread local callbacks is profiler and code +// execution tracer +// - note, thread local callbacks are automatically propagated with +// ThreadLocalState across JIT continuations and async tasks (at::launch) + +typedef uint64_t CallbackHandle; + +constexpr CallbackHandle INVALID_CALLBACK_HANDLE{0}; + +// It is unnecessary to use atomic operations for enabling +// thread-local function callbacks. Moreover, it prevents saving to +// ThreadLocalState because std::atomic is non-copyable. +struct RecordFunctionCallbacksEntry { + RecordFunctionCallbacksEntry(RecordFunctionCallback cb, CallbackHandle h) + : callback_(cb), handle_(h) {} + + RecordFunctionCallback callback_; + bool enabled_{true}; + CallbackHandle handle_; +}; + +// Holds pairs (callbacks, unique_id) +using RecordFunctionCallbacks = std::vector; + +// Generated by the callback managers to determine which functions to run. +struct StepCallbacks { + StepCallbacks() = default; + StepCallbacks(uint64_t thread_id, RecordScope scope) + : thread_id_{thread_id}, scope_{scope} {} + + bool empty() const { + return callbacks_.empty(); + } + + struct StartEndPair { + RecordFunctionCallback::StartCallback start_; + RecordFunctionCallback::EndCallback end_; + }; + + using StartEndPairs = c10::SmallVector; + + StartEndPairs callbacks_; + uint64_t thread_id_{0}; + RecordScope scope_{RecordScope::FUNCTION}; + bool needs_inputs_{false}; + bool needs_outputs_{false}; + bool needs_ids_{false}; +}; + +struct TORCH_API RecordFunction { + // Default constructor is used with before function called afterwards: + // scope - record scope that this function tracks + // pre_sampled - whether this RecordFunction was already pre-sampled with + // kLowProb probability + explicit RecordFunction(RecordScope scope = RecordScope::FUNCTION); + explicit RecordFunction(StepCallbacks&& step_callbacks); + + template + void before( + F fn, + c10::ArrayRef args, + int64_t current_sequence_nr = -1) { + if (!isActive()) { + return; + } + inputs_ = args; + before(fn, current_sequence_nr); + } + + template + void before( + F fn, + const std::vector* args, + int64_t current_sequence_nr = -1) { + before( + std::move(fn), + c10::ArrayRef(args->data(), args->size()), + current_sequence_nr); + } + + // Destructor calls end callbacks + virtual ~RecordFunction(); + + RecordFunction(const RecordFunction&) = delete; + RecordFunction& operator=(const RecordFunction&) = delete; + + const char* name() const; + + int64_t seqNr() const { + return sequence_nr_; + } + + c10::ArrayRef inputs() const { +#ifndef NDEBUG + TORCH_INTERNAL_ASSERT_DEBUG_ONLY( + inputs_valid_, "Called inputs() outside RecordFunction start callback"); +#endif + return inputs_; + } + + const std::vector& outputs() const { + return outputs_; + } + + void setOutputs(std::vector&& outputs) { + outputs_ = std::move(outputs); + } + + void setOutputs(c10::ArrayRef outputs) { + outputs_ = outputs.vec(); + } + + size_t num_inputs() const; + size_t num_outputs() const; + + // Retrieves the thread_id that this RecordFunction ran start callbacks with. + // Useful for writing thread safe end callbacks that may be potentially + // executed in a different thread (async ops) + uint64_t threadId() const { + return step_callbacks_.thread_id_; + } + + // For backward functions - thread id of the corresponding forward function, + // or zero otherwise; + // used alongside with sequence number to correlate backward functions with + // the forward ones + uint64_t forwardThreadId() const { + return fwd_thread_id_; + } + + void setForwardThreadId(uint64_t thread_id) { + fwd_thread_id_ = thread_id; + } + + RecordScope scope() const { + return step_callbacks_.scope_; + } + + // Returns logical thread_id for the current thread + static uint64_t currentThreadId(); + + // Internal functions, do not use directly; + // used in python's context manager + + // before functions initialize RecordFunction members and call + // start callbacks + using schema_ref_t = std::reference_wrapper; + void before(const char* name, int64_t sequence_nr = -1); + void before(std::string name, int64_t sequence_nr = -1); + void before(schema_ref_t schema, int64_t sequence_nr = -1); + + // Sets node ID for distributed profiling + static void setDefaultNodeId(int64_t defaultNodeId); + // Gets node ID for distributed profiling + static int64_t getDefaultNodeId(); + + // Calls end callbacks. After end(), accessors will no longer provide useful + // results. + void end(); + + // Internal-only, used only force async event for distributed events + // profiling. + void _setAsync(); + + // Returns whether this RecordFunction corresponds to an async event or not. + bool isAsync() const; + + // Returns whether this RecordFunction corresponds to NCCL metadata collection + // or not. + bool isNcclMeta() const { + return is_nccl_meta_; + } + + // Internal-only, used to denote out variant used for Static Runtime execution + void _setStaticRuntimeOutVariant(); + bool isStaticRuntimeOutVariant() const; + + RecordFunctionHandle handle() const { + return handle_; + } + + c10::optional operator_name() const; + + // This method returns a copy of the FunctionSchema and can be expensive. + c10::optional operator_schema() const; + + void setHandle(RecordFunctionHandle handle) { + handle_ = handle; + } + + // Whether this RecordFunction runs any callbacks. + bool isActive() const { + return !step_callbacks_.empty(); + } + + bool needsInputs() const { + return step_callbacks_.needs_inputs_; + } + + bool needsOutputs() const { + return step_callbacks_.needs_outputs_; + } + + int64_t debugHandle() const { + return debug_handle_; + } + + void setDebugHandle(int64_t debug_handle) { + debug_handle_ = debug_handle; + } + + void invalidateInputs() { +#ifndef NDEBUG + inputs_valid_ = false; +#endif + } + + private: + void runStartCallbacks(); + + StepCallbacks step_callbacks_; + + // In cases when RecordFunction might be active but we chose not to + // use the observers (e.g. operator is not observed), this boolean + // flag is used to check whether the start callbacks were called + bool called_start_callbacks_ = false; + +#ifndef NDEBUG + bool inputs_valid_ = false; +#endif + + // Stores various ObserverContext objects with event metadata for callbacks. + ObserverContextList ctx_; + + std::variant fn_; + + int64_t sequence_nr_ = -1; + c10::ArrayRef inputs_; + std::vector outputs_; + + // For backward functions - thread id of the forward function + uint64_t fwd_thread_id_ = 0; + + // Unique id for this RecordFunction, used in callbacks to track start + // and end of ranges + RecordFunctionHandle handle_{0}; + + // Whether this record_function corresponds to an async event or not. Async + // events can complete in different threads or follow a future-like pattern + // of use. + bool is_async_{false}; + + // Debug handles are used for lazy annotation of module hierarchy + // and callstack. + // This is specifically is useful for mobile runtime, where generated + // debug handles can be lazily symbolicated using debug information + int64_t debug_handle_{-1}; + + // Whether this RecordFunction is used for an out variant run with + // Static Runtime + bool is_static_runtime_out_variant_{false}; + + // Whether this RecordFunction is used for NCCL metadata collection + bool is_nccl_meta_{false}; +}; + +TORCH_API StepCallbacks getStepCallbacks(RecordScope scope); + +TORCH_API c10::optional getStepCallbacksUnlessEmpty( + RecordScope scope); + +namespace detail { +template +void record_function_with_scope( + RecordFunction& guard, + F fn, + const Inputs& inputs, + Args&&... args) { + if (guard.needsInputs()) { + guard.before( + fn, + c10::ArrayRef(inputs.data(), inputs.size()), + std::forward(args)...); + } else { + guard.before(fn, std::forward(args)...); + } +} + +template +void record_function_with_scope_and_debug_handle( + RecordFunction& guard, + F fn, + int64_t debug_handle, + const Inputs& inputs, + Args&&... args) { + guard.setDebugHandle(debug_handle); + if (guard.needsInputs()) { + guard.before( + fn, + c10::ArrayRef(inputs.data(), inputs.size()), + std::forward(args)...); + } else { + guard.before(fn, std::forward(args)...); + } +} + +template +void record_function_with_scope( + RecordFunction& guard, + F fn, + c10::ArrayRef inputs, + Args&&... args) { + return record_function_with_scope< + c10::ArrayRef, + F, + Args...>(guard, std::move(fn), inputs, std::forward(args)...); +} + +template +void record_function_with_scope_and_debug_handle( + RecordFunction& guard, + F fn, + int64_t debug_handle, + c10::ArrayRef inputs, + Args&&... args) { + return record_function_with_scope_and_debug_handle< + c10::ArrayRef, + F, + Args...>( + guard, std::move(fn), debug_handle, inputs, std::forward(args)...); +} + +} // namespace detail + +// optional argument - function's seq_no +#define RECORD_FUNCTION_WITH_SCOPE(scope, fn, inputs, ...) \ + at::RecordFunction guard(scope); \ + if (guard.isActive()) { \ + ::at::detail::record_function_with_scope( \ + guard, fn, inputs, ##__VA_ARGS__); \ + } + +#define RECORD_FUNCTION_WITH_SCOPE_INPUTS_OUTPUTS( \ + scope, fn, inputs, outputs, ...) \ + at::RecordFunction guard(scope); \ + if (guard.isActive()) { \ + if (guard.needsInputs()) { \ + guard.before(fn, inputs, ##__VA_ARGS__); \ + } else { \ + guard.before(fn, ##__VA_ARGS__); \ + } \ + if (guard.needsOutputs()) { \ + guard.setOutputs(outputs); \ + } \ + } + +#define RECORD_FUNCTION(fn, inputs, ...) \ + RECORD_FUNCTION_WITH_SCOPE( \ + at::RecordScope::FUNCTION, fn, inputs, ##__VA_ARGS__) + +#define RECORD_TORCHSCRIPT_FUNCTION(mn, inputs) \ + RECORD_FUNCTION_WITH_SCOPE(at::RecordScope::TORCHSCRIPT_FUNCTION, mn, inputs) + +#define RECORD_FUNCTION_WITH_INPUTS_OUTPUTS(fn, inputs, outputs, ...) \ + RECORD_FUNCTION_WITH_SCOPE_INPUTS_OUTPUTS( \ + at::RecordScope::FUNCTION, fn, inputs, outputs, ##__VA_ARGS__) + +// Custom user scopes in C++; similar to Python's 'with record_function("..."):' +#define RECORD_USER_SCOPE(fn) \ + RECORD_FUNCTION_WITH_SCOPE( \ + at::RecordScope::USER_SCOPE, fn, c10::ArrayRef{}) + +// RECORD_USER_SCOPE with inputs +#define RECORD_USER_SCOPE_WITH_INPUTS(fn, inputs) \ + RECORD_FUNCTION_WITH_SCOPE(at::RecordScope::USER_SCOPE, fn, inputs) + +// Helper macro to pass in debug handle that is used to +// post process events +#define RECORD_WITH_SCOPE_DEBUG_HANDLE_AND_INPUTS( \ + scope, fn, debug_handle, inputs, ...) \ + at::RecordFunction guard(scope); \ + if (guard.isActive()) { \ + ::at::detail::record_function_with_scope_and_debug_handle( \ + guard, fn, debug_handle, inputs, ##__VA_ARGS__); \ + } + +// Helper macros to record LITE INTERPETER scope events with debug handles +#define RECORD_EDGE_SCOPE_WITH_DEBUG_HANDLE_AND_INPUTS( \ + fn, debug_handle, inputs) \ + RECORD_WITH_SCOPE_DEBUG_HANDLE_AND_INPUTS( \ + at::RecordScope::LITE_INTERPRETER, fn, debug_handle, inputs) + +// Bookend to the RECORD_FUNCTION macros. Use this after the kernel +// launch to let the profiler bind the outputs to the op that produced +// them. Note that guard is declared by RECORD_FUNCTION so this macro +// needs to be called from the same scope as RECORD_FUNCTION +#define RECORD_OUTPUTS(outputs) \ + if (guard.needsOutputs()) { \ + guard.setOutputs( \ + std::vector(outputs.begin(), outputs.end())); \ + } + +/** + * addThreadLocalCallback adds a thread local callback to run with + * RecordFunction, returns handle to use with removeThreadLocalCallback + */ +TORCH_API CallbackHandle addThreadLocalCallback(RecordFunctionCallback cb); + +/** + * hasThreadLocalCallbacks returns whether there're callbacks registered + * with addThreadLocalCallback + */ +TORCH_API bool hasThreadLocalCallbacks(); + +/** + * clearThreadLocalCallbacks removes all thread local callbacks + */ +TORCH_API void clearThreadLocalCallbacks(); + +/** + * addGlobalCallback adds a global callback to run with RecordFunction: + * + * only during the program initialization + */ +TORCH_API CallbackHandle addGlobalCallback(RecordFunctionCallback cb); + +/** + * removeCallback removes a callback given the handle returned by + * addThreadLocalCallback or addGlobalCallback; + * + * no other code can run simultaneously + */ +TORCH_API void removeCallback(CallbackHandle handle); + +/** + * Prevent the given callback from executing. If handle is invalid, + * does nothing. + */ +TORCH_API void disableCallback(CallbackHandle handle); + +/** + * Allow the given callback, previously disabled with disableCallback, to + * execute again. If handle is invalid, does nothing. + */ +TORCH_API void reenableCallback(CallbackHandle handle); + +/** + * hasGlobalCallbacks returns whether there're global callbacks + * registered with pushGlobalCallback + */ +TORCH_API bool hasGlobalCallbacks(); + +/** + * clearGlobalCallbacks removes all global callbacks + */ +TORCH_API void clearGlobalCallbacks(); + +// for both thread local and global callbacks +TORCH_API bool hasCallbacks(); +TORCH_API void clearCallbacks(); + +/** + * enableRecordFunction enables RecordFunction thread locally + */ +TORCH_API void enableRecordFunction(bool enable = true); + +/** + * isRecordFunctionEnabled returns whether RecordFunction + * is enabled thread locally + */ +TORCH_API bool isRecordFunctionEnabled(); + +class TORCH_API RecordFunctionGuard { + public: + explicit RecordFunctionGuard(bool is_enabled = true) + : prev_value_(isRecordFunctionEnabled()) { + enableRecordFunction(is_enabled); + } + + virtual ~RecordFunctionGuard() { + enableRecordFunction(prev_value_); + } + + private: + bool prev_value_ = false; +}; + +class TORCH_API DisableRecordFunctionGuard : public RecordFunctionGuard { + public: + DisableRecordFunctionGuard() : RecordFunctionGuard(false) {} + ~DisableRecordFunctionGuard() override = default; +}; + +struct TORCH_API RecordFunctionTLS { + // Thread local vector of callbacks, holds pairs (callbacks, unique_id); + // must be sorted in increasing handles order + RecordFunctionCallbacks sorted_tls_callbacks_; + + bool tls_record_function_enabled_ = true; +}; + +TORCH_API const RecordFunctionTLS& get_record_function_tls_(); + +TORCH_API void set_record_function_tls_(const RecordFunctionTLS& tls); + +TORCH_API void set_record_function_seed_for_testing(uint32_t seed); + +} // namespace at