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@@ -203,3 +203,4 @@ llmeval-env/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_cnn_infer.so.
 env-llmeval/lib/python3.10/site-packages/torch/bin/protoc filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/torch/bin/protoc-3.13.0.0 filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/torch/lib/libc10.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/torch/lib/libtorch_python.so filter=lfs diff=lfs merge=lfs -text

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/ArrayRef.h

@@ -0,0 +1,2 @@
+#pragma once
+#include <c10/util/ArrayRef.h>

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/CPUGeneratorImpl.h

@@ -0,0 +1,49 @@
+#pragma once
+
+#include <ATen/core/Generator.h>
+#include <ATen/core/MT19937RNGEngine.h>
+#include <c10/core/GeneratorImpl.h>
+#include <c10/util/Optional.h>
+
+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<CPUGeneratorImpl> 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<c10::TensorImpl> get_state() const override;
+  static c10::DeviceType device_type();
+  uint32_t random();
+  uint64_t random64();
+  c10::optional<float> next_float_normal_sample();
+  c10::optional<double> next_double_normal_sample();
+  void set_next_float_normal_sample(c10::optional<float> randn);
+  void set_next_double_normal_sample(c10::optional<double> randn);
+  at::mt19937 engine();
+  void set_engine(at::mt19937 engine);
+
+ private:
+  CPUGeneratorImpl* clone_impl() const override;
+  at::mt19937 engine_;
+  c10::optional<float> next_float_normal_sample_;
+  c10::optional<double> 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

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/Context.h

@@ -0,0 +1,518 @@
+#pragma once
+
+#include <ATen/CPUGeneratorImpl.h>
+#include <ATen/LinalgBackend.h>
+#include <ATen/core/ATenGeneral.h>
+#include <ATen/core/DeprecatedTypeProperties.h>
+#include <ATen/core/Generator.h>
+#include <ATen/core/LegacyTypeDispatch.h>
+#include <ATen/detail/CUDAHooksInterface.h>
+#include <ATen/detail/HIPHooksInterface.h>
+#include <ATen/detail/IPUHooksInterface.h>
+#include <ATen/detail/MPSHooksInterface.h>
+#include <ATen/detail/MTIAHooksInterface.h>
+#include <ATen/detail/ORTHooksInterface.h>
+#include <ATen/detail/PrivateUse1HooksInterface.h>
+#include <ATen/detail/XPUHooksInterface.h>
+#include <c10/core/QEngine.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/util/CallOnce.h>
+#include <c10/util/Exception.h>
+#include <c10/util/env.h>
+#include <c10/util/irange.h>
+
+#include <cstdint>
+#include <memory>
+#include <mutex>
+
+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.");
+    }
+  }
+  Device getDeviceFromPtr(void* data, c10::DeviceType device_type) {
+    initCUDAIfNeeded(device_type);
+    initHIPIfNeeded(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::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(); });
+  }
+  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);
+
+  // 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;
+
+  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 <reason>
+  //      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#cublasApi_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<at::QEngine>& 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();
+
+ private:
+  void initCUDAIfNeeded(c10::DeviceType p) {
+    if (p == c10::DeviceType::CUDA) {
+      lazyInitCUDA();
+    }
+  }
+  void initHIPIfNeeded(c10::DeviceType p) {
+    if (p == c10::DeviceType::HIP) {
+      lazyInitHIP();
+    }
+  }
+  static bool checkCuBLASConfigDeterministic();
+  c10::once_flag thc_init;
+  c10::once_flag thh_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;
+#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;
+  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<at::QEngine> quantized_engine = c10::nullopt;
+  bool enable_sparse_tensor_invariant_checks = 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<std::mutex> 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<c10::DeviceIndex>(i)));
+      {
+        // See Note [Acquire lock when using random generators]
+        std::lock_guard<std::mutex> lock(cuda_gen.mutex());
+        cuda_gen.set_current_seed(seed);
+      }
+    }
+  }
+
+  const auto xpu_num_gpus = detail::getXPUHooks().getNumGPUs();
+  if (hasXPU() && xpu_num_gpus > 0) {
+    for (const auto i : c10::irange(xpu_num_gpus)) {
+      auto xpu_gen = globalContext().defaultGenerator(
+          Device(at::kXPU, static_cast<c10::DeviceIndex>(i)));
+      {
+        // See Note [Acquire lock when using random generators]
+        std::lock_guard<std::mutex> 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<std::mutex> 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;
+};
+
+#ifdef USE_ROCM
+struct TORCH_API ROCmBackwardPassGuard {
+  ROCmBackwardPassGuard();
+  ~ROCmBackwardPassGuard();
+  static bool is_backward_pass();
+
+ private:
+  static thread_local bool is_backward_pass_;
+};
+#endif
+
+} // namespace at

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/ExpandUtils.h

@@ -0,0 +1,527 @@
+#pragma once
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/view.h>
+#include <ATen/ops/view_copy.h>
+#endif
+
+#include <ATen/Tensor.h>
+#include <ATen/core/DimVector.h>
+#include <c10/util/Exception.h>
+#include <c10/util/MaybeOwned.h>
+#include <c10/util/irange.h>
+
+#include <functional>
+#include <sstream>
+#include <tuple>
+#include <utility>
+
+namespace at {
+
+TORCH_API std::vector<int64_t> infer_size(IntArrayRef a, IntArrayRef b);
+TORCH_API std::vector<SymInt> infer_size_symint(
+    SymIntArrayRef a,
+    SymIntArrayRef b);
+TORCH_API DimVector infer_size_dimvector(IntArrayRef a, IntArrayRef b);
+TORCH_API SymDimVector
+infer_size_symdimvector(SymIntArrayRef a, SymIntArrayRef b);
+
+// Named type instead of a pair/tuple so that we can be sure to
+// construct the vectors in place and get NRVO.
+template <typename Container>
+struct InferExpandGeometryResult {
+  Container sizes;
+  Container strides;
+  explicit InferExpandGeometryResult(size_t ndim)
+      : sizes(ndim), strides(ndim) {}
+  explicit InferExpandGeometryResult(IntArrayRef sizes_, size_t ndim)
+      : sizes(sizes_.begin(), sizes_.end()), strides(ndim) {}
+};
+
+TORCH_API std::tuple<std::vector<int64_t>, std::vector<int64_t>>
+inferExpandGeometry(
+    IntArrayRef tensor_sizes,
+    IntArrayRef tensor_strides,
+    IntArrayRef sizes);
+
+TORCH_API InferExpandGeometryResult<DimVector> inferExpandGeometry_dimvector(
+    IntArrayRef tensor_sizes,
+    IntArrayRef tensor_strides,
+    IntArrayRef sizes);
+
+TORCH_API std::vector<int64_t> infer_dense_strides(
+    IntArrayRef tensor_sizes,
+    IntArrayRef tensor_strides);
+
+// True if input shapes are expandable
+// NOTE: infer_size did a similar check, please keep them sync if change is
+// needed
+inline bool are_expandable(IntArrayRef shape1, IntArrayRef shape2) {
+  size_t ndim1 = shape1.size();
+  size_t ndim2 = shape2.size();
+  size_t ndim = ndim1 < ndim2 ? ndim1 : ndim2;
+
+  for (int64_t i = static_cast<int64_t>(ndim) - 1; i >= 0; --i) {
+    if (shape1[--ndim1] == shape2[--ndim2] || shape1[ndim1] == 1 ||
+        shape2[ndim2] == 1) {
+      continue;
+    }
+    return false;
+  }
+  return true;
+}
+
+// avoid copy-construction of Tensor by using a reference_wrapper.
+inline void check_defined(
+    std::initializer_list<std::reference_wrapper<const Tensor>> tensors,
+    const char* api_name) {
+  for (auto& t : tensors) {
+    if (!t.get().defined()) {
+      AT_ERROR(api_name, "(...) called with an undefined Tensor");
+    }
+  }
+}
+
+// NOTE [ ExpandUtils Borrowing ]
+//
+// Functions in ExpandUtils return `c10::MaybeOwned<Tensor>` because
+// expansion may not actually be needed, in which case we can improve
+// efficiency by returning
+// `c10::MaybeOwned<Tensor>::borrowed(to_expand)`. However, this means
+// that you need to be careful: the returned `c10::MaybeOwned<Tensor>`
+// must not outlive the original `Tensor` object that `to_expand`
+// referred to! The deleted rvalue reference overloads of these
+// functions help with this by preventing trivial use of a temporary
+// resulting from a function call, but it is still possible to make a
+// mistake.
+
+inline c10::MaybeOwned<Tensor> expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand) {
+  if (tensor.sym_sizes().equals(to_expand.sym_sizes())) {
+    return c10::MaybeOwned<Tensor>::borrowed(to_expand);
+  }
+  return c10::MaybeOwned<Tensor>::owned(
+      to_expand.expand_symint(tensor.sym_sizes()));
+}
+
+inline c10::MaybeOwned<Tensor> expand_inplace(
+    const Tensor& tensor,
+    Tensor&& to_expand) = delete;
+
+inline c10::MaybeOwned<Tensor> expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand,
+    const char* api_name) {
+  check_defined({tensor, to_expand}, api_name);
+  return expand_inplace(tensor, to_expand);
+}
+
+inline c10::MaybeOwned<Tensor> expand_inplace(
+    const Tensor& tensor,
+    Tensor&& to_expand,
+    const char* api_name) = delete;
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand1,
+    const Tensor& to_expand2) {
+  if (tensor.sizes().equals(to_expand1.sizes()) &&
+      tensor.sizes().equals((to_expand2.sizes()))) {
+    return std::make_tuple(
+        c10::MaybeOwned<Tensor>::borrowed(to_expand1),
+        c10::MaybeOwned<Tensor>::borrowed(to_expand2));
+  }
+
+  return std::make_tuple(
+      c10::MaybeOwned<Tensor>::owned(to_expand1.expand(tensor.sizes())),
+      c10::MaybeOwned<Tensor>::owned(to_expand2.expand(tensor.sizes())));
+}
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    Tensor&& to_expand1,
+    const Tensor& to_expand2) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand1,
+    Tensor&& to_expand2) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(const Tensor& tensor, Tensor&& to_expand1, Tensor&& to_expand2) =
+    delete;
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    const char* api_name) {
+  check_defined({tensor, to_expand1, to_expand2}, api_name);
+  return expand_inplace(tensor, to_expand1, to_expand2);
+}
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    const char* api_name) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    const char* api_name) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_inplace(
+    const Tensor& tensor,
+    Tensor&& to_expand1,
+    Tensor&& to_expand2,
+    const char* api_name) = delete;
+
+// See NOTE [ ExpandUtils Borrowing ] above for `MaybeOwned` explanation.
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(const Tensor& to_expand1, const Tensor& to_expand2) {
+  auto s1 = to_expand1.sym_sizes();
+  auto s2 = to_expand2.sym_sizes();
+  if (s1.equals(s2)) {
+    return std::make_tuple(
+        c10::MaybeOwned<Tensor>::borrowed(to_expand1),
+        c10::MaybeOwned<Tensor>::borrowed(to_expand2));
+  }
+
+  auto expanded_size = infer_size_symdimvector(s1, s2);
+  return std::make_tuple(
+      c10::MaybeOwned<Tensor>::owned(to_expand1.expand_symint(expanded_size)),
+      c10::MaybeOwned<Tensor>::owned(to_expand2.expand_symint(expanded_size)));
+}
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(Tensor&& to_expand1, const Tensor& to_expand2) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(const Tensor& to_expand1, Tensor&& to_expand2) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(Tensor&& to_expand1, Tensor&& to_expand2) = delete;
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    const char* api_name) {
+  check_defined({to_expand1, to_expand2}, api_name);
+  return expand_outplace(to_expand1, to_expand2);
+}
+
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    const char* api_name) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    const char* api_name) = delete;
+inline std::tuple<c10::MaybeOwned<Tensor>, c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    Tensor&& to_expand2,
+    const char* api_name) = delete;
+
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    const Tensor& to_expand3) {
+  if (to_expand1.sizes().equals(to_expand2.sizes()) &&
+      to_expand1.sizes().equals(to_expand3.sizes())) {
+    return std::make_tuple(
+        c10::MaybeOwned<Tensor>::borrowed(to_expand1),
+        c10::MaybeOwned<Tensor>::borrowed(to_expand2),
+        c10::MaybeOwned<Tensor>::borrowed(to_expand3));
+  }
+
+  auto expanded_size12 =
+      infer_size_dimvector(to_expand1.sizes(), to_expand2.sizes());
+  auto expanded_size =
+      infer_size_dimvector(expanded_size12, to_expand3.sizes());
+  return std::make_tuple(
+      c10::MaybeOwned<Tensor>::owned(to_expand1.expand(expanded_size)),
+      c10::MaybeOwned<Tensor>::owned(to_expand2.expand(expanded_size)),
+      c10::MaybeOwned<Tensor>::owned(to_expand3.expand(expanded_size)));
+}
+
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    const Tensor& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    const Tensor& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    Tensor&& to_expand2,
+    const Tensor& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    Tensor&& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    Tensor&& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    Tensor&& to_expand3) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(Tensor&& to_expand1, Tensor&& to_expand2, Tensor&& to_expand3) =
+    delete;
+
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    const Tensor& to_expand3,
+    const char* api_name) {
+  check_defined({to_expand1, to_expand2, to_expand3}, api_name);
+  return expand_outplace(to_expand1, to_expand2, to_expand3);
+}
+
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    const Tensor& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    const Tensor& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    Tensor&& to_expand2,
+    const Tensor& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    const Tensor& to_expand2,
+    Tensor&& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    const Tensor& to_expand2,
+    Tensor&& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    const Tensor& to_expand1,
+    Tensor&& to_expand2,
+    Tensor&& to_expand3,
+    const char* api_name) = delete;
+inline std::tuple<
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>,
+    c10::MaybeOwned<Tensor>>
+expand_outplace(
+    Tensor&& to_expand1,
+    Tensor&& to_expand2,
+    Tensor&& to_expand3,
+    const char* api_name) = delete;
+
+inline c10::MaybeOwned<Tensor> expand_size(
+    const Tensor& to_expand,
+    IntArrayRef sizes) {
+  if (to_expand.sizes().equals(sizes)) {
+    return c10::MaybeOwned<Tensor>::borrowed(to_expand);
+  }
+
+  return c10::MaybeOwned<Tensor>::owned(to_expand.expand(sizes));
+}
+
+inline c10::MaybeOwned<Tensor> expand_size(
+    Tensor&& to_expand,
+    IntArrayRef sizes) = delete;
+
+inline c10::MaybeOwned<Tensor> expand_size(
+    const Tensor& to_expand,
+    IntArrayRef sizes,
+    const char* api_name) {
+  check_defined({to_expand}, api_name);
+  return expand_size(to_expand, sizes);
+}
+
+inline c10::MaybeOwned<Tensor> expand_size(
+    Tensor&& to_expand,
+    IntArrayRef sizes,
+    const char* api_name) = delete;
+
+inline std::vector<Tensor> expand_outplace(TensorList to_expand) {
+  // expands a list of Tensors; ignores undefined (null) tensors
+  bool first = true;
+  DimVector sizes;
+  for (const auto i : c10::irange(to_expand.size())) {
+    if (!to_expand[i].defined()) {
+      continue;
+    } else if (first) {
+      sizes = to_expand[i].sizes();
+      first = false;
+    } else {
+      sizes = infer_size_dimvector(sizes, to_expand[i].sizes());
+    }
+  }
+
+  std::vector<Tensor> result(to_expand.size());
+  for (const auto i : c10::irange(to_expand.size())) {
+    if (!to_expand[i].defined()) {
+      continue;
+    } else if (to_expand[i].sizes().equals(sizes)) {
+      result[i] = to_expand[i];
+    } else {
+      result[i] = to_expand[i].expand(sizes);
+    }
+  }
+  return result;
+}
+
+template <typename T>
+inline Tensor _sum_to(
+    Tensor tensor,
+    const c10::ArrayRef<T> shape,
+    bool always_return_non_view = false) {
+  if (shape.size() == 0) {
+    return tensor.sum();
+  }
+
+  auto sizes = at::symint::sizes<T>(tensor);
+  c10::SmallVector<int64_t, 8> reduce_dims;
+  const int64_t leading_dims = sizes.size() - shape.size();
+  for (const auto i : c10::irange(leading_dims)) {
+    reduce_dims.push_back(i);
+  }
+  for (int64_t i = leading_dims; i < static_cast<int64_t>(sizes.size()); ++i) {
+    if (shape[i - leading_dims] == 1 && sizes[i] != 1) {
+      reduce_dims.push_back(i);
+    }
+  }
+
+  if (!reduce_dims.empty()) {
+    tensor = tensor.sum(reduce_dims, /*keepdim=*/true);
+  }
+
+  if (always_return_non_view) {
+    // This is only actually used by the functionalization pass.
+    // We want to be able to guarantee that this function doesn't return a view
+    // of the input.
+    return leading_dims > 0 ? at::symint::view_copy<T>(tensor, shape)
+                            : tensor.clone();
+  } else {
+    return leading_dims > 0 ? at::symint::view<T>(tensor, shape) : tensor;
+  }
+}
+
+inline Tensor sum_to(
+    Tensor tensor,
+    const c10::SymIntArrayRef shape,
+    bool always_return_non_view = false) {
+  return _sum_to(std::move(tensor), shape, always_return_non_view);
+}
+
+// Sums `tensor` repeatedly to produce a tensor of shape `shape`.
+// Precondition: is_expandable_to(shape, tensor.sizes()) must be true
+inline Tensor sum_to(
+    Tensor tensor,
+    const IntArrayRef shape,
+    bool always_return_non_view = false) {
+  return _sum_to(std::move(tensor), shape, always_return_non_view);
+}
+
+static inline bool is_expandable_to(
+    SymIntArrayRef shape,
+    c10::SymIntArrayRef desired) {
+  size_t ndim = shape.size();
+  size_t target_dim = desired.size();
+  if (ndim > target_dim) {
+    return false;
+  }
+  for (const auto i : c10::irange(ndim)) {
+    const auto& size = shape[ndim - i - 1];
+    const auto& target = desired[target_dim - i - 1];
+    if (size != target && size != 1) {
+      return false;
+    }
+  }
+  return true;
+}
+
+static inline bool is_expandable_to(IntArrayRef shape, IntArrayRef desired) {
+  auto sym_shape = c10::SymIntArrayRef(
+      reinterpret_cast<const c10::SymInt*>(shape.data()), shape.size());
+  auto sym_desired = c10::SymIntArrayRef(
+      reinterpret_cast<const c10::SymInt*>(desired.data()), desired.size());
+  return is_expandable_to(sym_shape, sym_desired);
+}
+
+} // namespace at

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/LinalgBackend.h

@@ -0,0 +1,31 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+
+#include <ostream>
+#include <string>
+
+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

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/NativeFunctions.h

@@ -0,0 +1,1295 @@
+#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 <ATen/ops/{my_operator}_native.h> \
+  and see NOTE [TORCH_ASSERT_ONLY_METHOD_OPERATORS].
+#endif
+
+#include <c10/core/Scalar.h>
+#include <c10/core/Storage.h>
+#include <c10/core/TensorOptions.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Optional.h>
+#include <c10/core/QScheme.h>
+#include <ATen/core/Reduction.h>
+#include <ATen/core/Tensor.h>
+#include <tuple>
+#include <vector>
+
+#include <ATen/ops/_adaptive_avg_pool2d_native.h>
+#include <ATen/ops/_adaptive_avg_pool2d_backward_native.h>
+#include <ATen/ops/_adaptive_avg_pool3d_native.h>
+#include <ATen/ops/_adaptive_avg_pool3d_backward_native.h>
+#include <ATen/ops/_add_batch_dim_native.h>
+#include <ATen/ops/_add_relu_native.h>
+#include <ATen/ops/_addmm_activation_native.h>
+#include <ATen/ops/_aminmax_native.h>
+#include <ATen/ops/_amp_foreach_non_finite_check_and_unscale_native.h>
+#include <ATen/ops/_amp_update_scale_native.h>
+#include <ATen/ops/_assert_async_native.h>
+#include <ATen/ops/_assert_tensor_metadata_native.h>
+#include <ATen/ops/_autocast_to_full_precision_native.h>
+#include <ATen/ops/_autocast_to_reduced_precision_native.h>
+#include <ATen/ops/_backward_native.h>
+#include <ATen/ops/_batch_norm_impl_index_native.h>
+#include <ATen/ops/_batch_norm_impl_index_backward_native.h>
+#include <ATen/ops/_cast_Byte_native.h>
+#include <ATen/ops/_cast_Char_native.h>
+#include <ATen/ops/_cast_Double_native.h>
+#include <ATen/ops/_cast_Float_native.h>
+#include <ATen/ops/_cast_Half_native.h>
+#include <ATen/ops/_cast_Int_native.h>
+#include <ATen/ops/_cast_Long_native.h>
+#include <ATen/ops/_cast_Short_native.h>
+#include <ATen/ops/_cdist_backward_native.h>
+#include <ATen/ops/_cdist_forward_native.h>
+#include <ATen/ops/_cholesky_solve_helper_native.h>
+#include <ATen/ops/_choose_qparams_per_tensor_native.h>
+#include <ATen/ops/_coalesce_native.h>
+#include <ATen/ops/_coalesced_native.h>
+#include <ATen/ops/_compute_linear_combination_native.h>
+#include <ATen/ops/_conj_native.h>
+#include <ATen/ops/_conj_copy_native.h>
+#include <ATen/ops/_conj_physical_native.h>
+#include <ATen/ops/_conv_depthwise2d_native.h>
+#include <ATen/ops/_convert_indices_from_coo_to_csr_native.h>
+#include <ATen/ops/_convert_indices_from_csr_to_coo_native.h>
+#include <ATen/ops/_convert_weight_to_int4pack_native.h>
+#include <ATen/ops/_convolution_native.h>
+#include <ATen/ops/_convolution_double_backward_native.h>
+#include <ATen/ops/_convolution_mode_native.h>
+#include <ATen/ops/_copy_from_native.h>
+#include <ATen/ops/_copy_from_and_resize_native.h>
+#include <ATen/ops/_cslt_compress_native.h>
+#include <ATen/ops/_cslt_sparse_mm_native.h>
+#include <ATen/ops/_ctc_loss_native.h>
+#include <ATen/ops/_ctc_loss_backward_native.h>
+#include <ATen/ops/_cudnn_ctc_loss_native.h>
+#include <ATen/ops/_cudnn_init_dropout_state_native.h>
+#include <ATen/ops/_cudnn_rnn_native.h>
+#include <ATen/ops/_cudnn_rnn_backward_native.h>
+#include <ATen/ops/_cudnn_rnn_flatten_weight_native.h>
+#include <ATen/ops/_cufft_clear_plan_cache_native.h>
+#include <ATen/ops/_cufft_get_plan_cache_max_size_native.h>
+#include <ATen/ops/_cufft_get_plan_cache_size_native.h>
+#include <ATen/ops/_cufft_set_plan_cache_max_size_native.h>
+#include <ATen/ops/_cummax_helper_native.h>
+#include <ATen/ops/_cummin_helper_native.h>
+#include <ATen/ops/_debug_has_internal_overlap_native.h>
+#include <ATen/ops/_dimI_native.h>
+#include <ATen/ops/_dimV_native.h>
+#include <ATen/ops/_dim_arange_native.h>
+#include <ATen/ops/_dirichlet_grad_native.h>
+#include <ATen/ops/_efficient_attention_backward_native.h>
+#include <ATen/ops/_efficient_attention_forward_native.h>
+#include <ATen/ops/_efficientzerotensor_native.h>
+#include <ATen/ops/_embedding_bag_native.h>
+#include <ATen/ops/_embedding_bag_backward_native.h>
+#include <ATen/ops/_embedding_bag_dense_backward_native.h>
+#include <ATen/ops/_embedding_bag_forward_only_native.h>
+#include <ATen/ops/_embedding_bag_per_sample_weights_backward_native.h>
+#include <ATen/ops/_embedding_bag_sparse_backward_native.h>
+#include <ATen/ops/_empty_affine_quantized_native.h>
+#include <ATen/ops/_empty_per_channel_affine_quantized_native.h>
+#include <ATen/ops/_euclidean_dist_native.h>
+#include <ATen/ops/_fake_quantize_learnable_per_channel_affine_native.h>
+#include <ATen/ops/_fake_quantize_learnable_per_channel_affine_backward_native.h>
+#include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_native.h>
+#include <ATen/ops/_fake_quantize_learnable_per_tensor_affine_backward_native.h>
+#include <ATen/ops/_fake_quantize_per_tensor_affine_cachemask_tensor_qparams_native.h>
+#include <ATen/ops/_fft_c2c_native.h>
+#include <ATen/ops/_fft_c2r_native.h>
+#include <ATen/ops/_fft_r2c_native.h>
+#include <ATen/ops/_fill_mem_eff_dropout_mask_native.h>
+#include <ATen/ops/_flash_attention_backward_native.h>
+#include <ATen/ops/_flash_attention_forward_native.h>
+#include <ATen/ops/_foobar_native.h>
+#include <ATen/ops/_foreach_abs_native.h>
+#include <ATen/ops/_foreach_acos_native.h>
+#include <ATen/ops/_foreach_add_native.h>
+#include <ATen/ops/_foreach_addcdiv_native.h>
+#include <ATen/ops/_foreach_addcmul_native.h>
+#include <ATen/ops/_foreach_asin_native.h>
+#include <ATen/ops/_foreach_atan_native.h>
+#include <ATen/ops/_foreach_ceil_native.h>
+#include <ATen/ops/_foreach_clamp_max_native.h>
+#include <ATen/ops/_foreach_clamp_min_native.h>
+#include <ATen/ops/_foreach_copy_native.h>
+#include <ATen/ops/_foreach_cos_native.h>
+#include <ATen/ops/_foreach_cosh_native.h>
+#include <ATen/ops/_foreach_div_native.h>
+#include <ATen/ops/_foreach_erf_native.h>
+#include <ATen/ops/_foreach_erfc_native.h>
+#include <ATen/ops/_foreach_exp_native.h>
+#include <ATen/ops/_foreach_expm1_native.h>
+#include <ATen/ops/_foreach_floor_native.h>
+#include <ATen/ops/_foreach_frac_native.h>
+#include <ATen/ops/_foreach_lerp_native.h>
+#include <ATen/ops/_foreach_lgamma_native.h>
+#include <ATen/ops/_foreach_log_native.h>
+#include <ATen/ops/_foreach_log10_native.h>
+#include <ATen/ops/_foreach_log1p_native.h>
+#include <ATen/ops/_foreach_log2_native.h>
+#include <ATen/ops/_foreach_maximum_native.h>
+#include <ATen/ops/_foreach_minimum_native.h>
+#include <ATen/ops/_foreach_mul_native.h>
+#include <ATen/ops/_foreach_neg_native.h>
+#include <ATen/ops/_foreach_norm_native.h>
+#include <ATen/ops/_foreach_pow_native.h>
+#include <ATen/ops/_foreach_reciprocal_native.h>
+#include <ATen/ops/_foreach_round_native.h>
+#include <ATen/ops/_foreach_sigmoid_native.h>
+#include <ATen/ops/_foreach_sign_native.h>
+#include <ATen/ops/_foreach_sin_native.h>
+#include <ATen/ops/_foreach_sinh_native.h>
+#include <ATen/ops/_foreach_sqrt_native.h>
+#include <ATen/ops/_foreach_sub_native.h>
+#include <ATen/ops/_foreach_tan_native.h>
+#include <ATen/ops/_foreach_tanh_native.h>
+#include <ATen/ops/_foreach_trunc_native.h>
+#include <ATen/ops/_foreach_zero_native.h>
+#include <ATen/ops/_functional_assert_async_native.h>
+#include <ATen/ops/_functional_sym_constrain_range_native.h>
+#include <ATen/ops/_functional_sym_constrain_range_for_size_native.h>
+#include <ATen/ops/_fused_adam_native.h>
+#include <ATen/ops/_fused_adamw_native.h>
+#include <ATen/ops/_fused_dropout_native.h>
+#include <ATen/ops/_fused_moving_avg_obs_fq_helper_native.h>
+#include <ATen/ops/_fused_sdp_choice_native.h>
+#include <ATen/ops/_fw_primal_native.h>
+#include <ATen/ops/_fw_primal_copy_native.h>
+#include <ATen/ops/_gather_sparse_backward_native.h>
+#include <ATen/ops/_grid_sampler_2d_cpu_fallback_native.h>
+#include <ATen/ops/_grid_sampler_2d_cpu_fallback_backward_native.h>
+#include <ATen/ops/_has_compatible_shallow_copy_type_native.h>
+#include <ATen/ops/_has_same_storage_numel_native.h>
+#include <ATen/ops/_histogramdd_bin_edges_native.h>
+#include <ATen/ops/_histogramdd_from_bin_cts_native.h>
+#include <ATen/ops/_histogramdd_from_bin_tensors_native.h>
+#include <ATen/ops/_index_put_impl_native.h>
+#include <ATen/ops/_indices_native.h>
+#include <ATen/ops/_indices_copy_native.h>
+#include <ATen/ops/_int_mm_native.h>
+#include <ATen/ops/_is_all_true_native.h>
+#include <ATen/ops/_is_any_true_native.h>
+#include <ATen/ops/_is_zerotensor_native.h>
+#include <ATen/ops/_linalg_check_errors_native.h>
+#include <ATen/ops/_linalg_det_native.h>
+#include <ATen/ops/_linalg_eigh_native.h>
+#include <ATen/ops/_linalg_slogdet_native.h>
+#include <ATen/ops/_linalg_solve_ex_native.h>
+#include <ATen/ops/_linalg_svd_native.h>
+#include <ATen/ops/_local_scalar_dense_native.h>
+#include <ATen/ops/_log_softmax_native.h>
+#include <ATen/ops/_log_softmax_backward_data_native.h>
+#include <ATen/ops/_logcumsumexp_native.h>
+#include <ATen/ops/_lstm_mps_native.h>
+#include <ATen/ops/_lu_with_info_native.h>
+#include <ATen/ops/_make_dep_token_native.h>
+#include <ATen/ops/_make_dual_native.h>
+#include <ATen/ops/_make_dual_copy_native.h>
+#include <ATen/ops/_make_per_channel_quantized_tensor_native.h>
+#include <ATen/ops/_make_per_tensor_quantized_tensor_native.h>
+#include <ATen/ops/_masked_scale_native.h>
+#include <ATen/ops/_masked_softmax_native.h>
+#include <ATen/ops/_masked_softmax_backward_native.h>
+#include <ATen/ops/_mixed_dtypes_linear_native.h>
+#include <ATen/ops/_mkldnn_reshape_native.h>
+#include <ATen/ops/_mkldnn_transpose_native.h>
+#include <ATen/ops/_mps_convolution_native.h>
+#include <ATen/ops/_mps_convolution_transpose_native.h>
+#include <ATen/ops/_native_batch_norm_legit_native.h>
+#include <ATen/ops/_native_batch_norm_legit_no_training_native.h>
+#include <ATen/ops/_native_multi_head_attention_native.h>
+#include <ATen/ops/_neg_view_native.h>
+#include <ATen/ops/_neg_view_copy_native.h>
+#include <ATen/ops/_nested_from_padded_native.h>
+#include <ATen/ops/_nested_from_padded_and_nested_example_native.h>
+#include <ATen/ops/_nested_select_backward_native.h>
+#include <ATen/ops/_nested_sum_backward_native.h>
+#include <ATen/ops/_nested_tensor_from_mask_native.h>
+#include <ATen/ops/_nested_tensor_from_mask_left_aligned_native.h>
+#include <ATen/ops/_nested_tensor_from_tensor_list_native.h>
+#include <ATen/ops/_nested_tensor_size_native.h>
+#include <ATen/ops/_nested_tensor_softmax_with_shape_native.h>
+#include <ATen/ops/_nested_tensor_storage_offsets_native.h>
+#include <ATen/ops/_nested_tensor_strides_native.h>
+#include <ATen/ops/_nested_view_from_buffer_native.h>
+#include <ATen/ops/_nested_view_from_buffer_copy_native.h>
+#include <ATen/ops/_new_zeros_with_same_feature_meta_native.h>
+#include <ATen/ops/_nnpack_available_native.h>
+#include <ATen/ops/_nnpack_spatial_convolution_native.h>
+#include <ATen/ops/_nnz_native.h>
+#include <ATen/ops/_pack_padded_sequence_native.h>
+#include <ATen/ops/_pack_padded_sequence_backward_native.h>
+#include <ATen/ops/_pad_circular_native.h>
+#include <ATen/ops/_pad_enum_native.h>
+#include <ATen/ops/_pad_packed_sequence_native.h>
+#include <ATen/ops/_pdist_backward_native.h>
+#include <ATen/ops/_pdist_forward_native.h>
+#include <ATen/ops/_pin_memory_native.h>
+#include <ATen/ops/_prelu_kernel_native.h>
+#include <ATen/ops/_prelu_kernel_backward_native.h>
+#include <ATen/ops/_propagate_xla_data_native.h>
+#include <ATen/ops/_remove_batch_dim_native.h>
+#include <ATen/ops/_reshape_alias_native.h>
+#include <ATen/ops/_reshape_alias_copy_native.h>
+#include <ATen/ops/_reshape_copy_native.h>
+#include <ATen/ops/_reshape_from_tensor_native.h>
+#include <ATen/ops/_resize_output_native.h>
+#include <ATen/ops/_rowwise_prune_native.h>
+#include <ATen/ops/_sample_dirichlet_native.h>
+#include <ATen/ops/_saturate_weight_to_fp16_native.h>
+#include <ATen/ops/_scaled_dot_product_attention_math_native.h>
+#include <ATen/ops/_scaled_dot_product_efficient_attention_native.h>
+#include <ATen/ops/_scaled_dot_product_efficient_attention_backward_native.h>
+#include <ATen/ops/_scaled_dot_product_flash_attention_native.h>
+#include <ATen/ops/_scaled_dot_product_flash_attention_backward_native.h>
+#include <ATen/ops/_scaled_mm_native.h>
+#include <ATen/ops/_segment_reduce_backward_native.h>
+#include <ATen/ops/_shape_as_tensor_native.h>
+#include <ATen/ops/_slow_conv2d_backward_native.h>
+#include <ATen/ops/_slow_conv2d_forward_native.h>
+#include <ATen/ops/_sobol_engine_draw_native.h>
+#include <ATen/ops/_sobol_engine_ff_native.h>
+#include <ATen/ops/_sobol_engine_initialize_state_native.h>
+#include <ATen/ops/_sobol_engine_scramble_native.h>
+#include <ATen/ops/_softmax_native.h>
+#include <ATen/ops/_softmax_backward_data_native.h>
+#include <ATen/ops/_sparse_addmm_native.h>
+#include <ATen/ops/_sparse_broadcast_to_native.h>
+#include <ATen/ops/_sparse_broadcast_to_copy_native.h>
+#include <ATen/ops/_sparse_bsc_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_bsr_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_compressed_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_coo_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_coo_tensor_with_dims_native.h>
+#include <ATen/ops/_sparse_coo_tensor_with_dims_and_tensors_native.h>
+#include <ATen/ops/_sparse_csc_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_csr_prod_native.h>
+#include <ATen/ops/_sparse_csr_sum_native.h>
+#include <ATen/ops/_sparse_csr_tensor_unsafe_native.h>
+#include <ATen/ops/_sparse_log_softmax_native.h>
+#include <ATen/ops/_sparse_log_softmax_backward_data_native.h>
+#include <ATen/ops/_sparse_mask_projection_native.h>
+#include <ATen/ops/_sparse_mm_native.h>
+#include <ATen/ops/_sparse_mm_reduce_impl_native.h>
+#include <ATen/ops/_sparse_mm_reduce_impl_backward_native.h>
+#include <ATen/ops/_sparse_semi_structured_linear_native.h>
+#include <ATen/ops/_sparse_softmax_native.h>
+#include <ATen/ops/_sparse_softmax_backward_data_native.h>
+#include <ATen/ops/_sparse_sparse_matmul_native.h>
+#include <ATen/ops/_sparse_sum_native.h>
+#include <ATen/ops/_sparse_sum_backward_native.h>
+#include <ATen/ops/_spdiags_native.h>
+#include <ATen/ops/_stack_native.h>
+#include <ATen/ops/_standard_gamma_native.h>
+#include <ATen/ops/_standard_gamma_grad_native.h>
+#include <ATen/ops/_test_ambiguous_defaults_native.h>
+#include <ATen/ops/_test_autograd_multiple_dispatch_native.h>
+#include <ATen/ops/_test_autograd_multiple_dispatch_view_native.h>
+#include <ATen/ops/_test_autograd_multiple_dispatch_view_copy_native.h>
+#include <ATen/ops/_test_check_tensor_native.h>
+#include <ATen/ops/_test_functorch_fallback_native.h>
+#include <ATen/ops/_test_optional_filled_intlist_native.h>
+#include <ATen/ops/_test_optional_floatlist_native.h>
+#include <ATen/ops/_test_optional_intlist_native.h>
+#include <ATen/ops/_test_serialization_subcmul_native.h>
+#include <ATen/ops/_test_string_default_native.h>
+#include <ATen/ops/_test_warn_in_autograd_native.h>
+#include <ATen/ops/_thnn_differentiable_gru_cell_backward_native.h>
+#include <ATen/ops/_thnn_differentiable_lstm_cell_backward_native.h>
+#include <ATen/ops/_thnn_fused_gru_cell_native.h>
+#include <ATen/ops/_thnn_fused_gru_cell_backward_native.h>
+#include <ATen/ops/_thnn_fused_lstm_cell_native.h>
+#include <ATen/ops/_thnn_fused_lstm_cell_backward_native.h>
+#include <ATen/ops/_thnn_fused_lstm_cell_backward_impl_native.h>
+#include <ATen/ops/_to_copy_native.h>
+#include <ATen/ops/_to_cpu_native.h>
+#include <ATen/ops/_to_dense_native.h>
+#include <ATen/ops/_to_sparse_native.h>
+#include <ATen/ops/_to_sparse_bsc_native.h>
+#include <ATen/ops/_to_sparse_bsr_native.h>
+#include <ATen/ops/_to_sparse_csc_native.h>
+#include <ATen/ops/_to_sparse_csr_native.h>
+#include <ATen/ops/_to_sparse_semi_structured_native.h>
+#include <ATen/ops/_transform_bias_rescale_qkv_native.h>
+#include <ATen/ops/_transformer_encoder_layer_fwd_native.h>
+#include <ATen/ops/_trilinear_native.h>
+#include <ATen/ops/_triton_multi_head_attention_native.h>
+#include <ATen/ops/_triton_scaled_dot_attention_native.h>
+#include <ATen/ops/_unique_native.h>
+#include <ATen/ops/_unique2_native.h>
+#include <ATen/ops/_unpack_dual_native.h>
+#include <ATen/ops/_unsafe_index_native.h>
+#include <ATen/ops/_unsafe_index_put_native.h>
+#include <ATen/ops/_unsafe_view_native.h>
+#include <ATen/ops/_upsample_bicubic2d_aa_native.h>
+#include <ATen/ops/_upsample_bicubic2d_aa_backward_native.h>
+#include <ATen/ops/_upsample_bilinear2d_aa_native.h>
+#include <ATen/ops/_upsample_bilinear2d_aa_backward_native.h>
+#include <ATen/ops/_upsample_nearest_exact1d_native.h>
+#include <ATen/ops/_upsample_nearest_exact1d_backward_native.h>
+#include <ATen/ops/_upsample_nearest_exact2d_native.h>
+#include <ATen/ops/_upsample_nearest_exact2d_backward_native.h>
+#include <ATen/ops/_upsample_nearest_exact3d_native.h>
+#include <ATen/ops/_upsample_nearest_exact3d_backward_native.h>
+#include <ATen/ops/_use_cudnn_ctc_loss_native.h>
+#include <ATen/ops/_use_cudnn_rnn_flatten_weight_native.h>
+#include <ATen/ops/_validate_compressed_sparse_indices_native.h>
+#include <ATen/ops/_validate_sparse_bsc_tensor_args_native.h>
+#include <ATen/ops/_validate_sparse_bsr_tensor_args_native.h>
+#include <ATen/ops/_validate_sparse_compressed_tensor_args_native.h>
+#include <ATen/ops/_validate_sparse_coo_tensor_args_native.h>
+#include <ATen/ops/_validate_sparse_csc_tensor_args_native.h>
+#include <ATen/ops/_validate_sparse_csr_tensor_args_native.h>
+#include <ATen/ops/_values_native.h>
+#include <ATen/ops/_values_copy_native.h>
+#include <ATen/ops/_version_native.h>
+#include <ATen/ops/_weight_int4pack_mm_native.h>
+#include <ATen/ops/_weight_norm_native.h>
+#include <ATen/ops/_weight_norm_differentiable_backward_native.h>
+#include <ATen/ops/_weight_norm_interface_native.h>
+#include <ATen/ops/_weight_norm_interface_backward_native.h>
+#include <ATen/ops/abs_native.h>
+#include <ATen/ops/absolute_native.h>
+#include <ATen/ops/acos_native.h>
+#include <ATen/ops/acosh_native.h>
+#include <ATen/ops/adaptive_avg_pool1d_native.h>
+#include <ATen/ops/adaptive_avg_pool2d_native.h>
+#include <ATen/ops/adaptive_avg_pool3d_native.h>
+#include <ATen/ops/adaptive_avg_pool3d_backward_native.h>
+#include <ATen/ops/adaptive_max_pool1d_native.h>
+#include <ATen/ops/adaptive_max_pool2d_native.h>
+#include <ATen/ops/adaptive_max_pool2d_backward_native.h>
+#include <ATen/ops/adaptive_max_pool3d_native.h>
+#include <ATen/ops/adaptive_max_pool3d_backward_native.h>
+#include <ATen/ops/add_native.h>
+#include <ATen/ops/addbmm_native.h>
+#include <ATen/ops/addcdiv_native.h>
+#include <ATen/ops/addcmul_native.h>
+#include <ATen/ops/addmm_native.h>
+#include <ATen/ops/addmv_native.h>
+#include <ATen/ops/addr_native.h>
+#include <ATen/ops/adjoint_native.h>
+#include <ATen/ops/affine_grid_generator_native.h>
+#include <ATen/ops/affine_grid_generator_backward_native.h>
+#include <ATen/ops/alias_native.h>
+#include <ATen/ops/alias_copy_native.h>
+#include <ATen/ops/align_as_native.h>
+#include <ATen/ops/align_tensors_native.h>
+#include <ATen/ops/align_to_native.h>
+#include <ATen/ops/all_native.h>
+#include <ATen/ops/allclose_native.h>
+#include <ATen/ops/alpha_dropout_native.h>
+#include <ATen/ops/amax_native.h>
+#include <ATen/ops/amin_native.h>
+#include <ATen/ops/aminmax_native.h>
+#include <ATen/ops/and_native.h>
+#include <ATen/ops/angle_native.h>
+#include <ATen/ops/any_native.h>
+#include <ATen/ops/arange_native.h>
+#include <ATen/ops/arccos_native.h>
+#include <ATen/ops/arccosh_native.h>
+#include <ATen/ops/arcsin_native.h>
+#include <ATen/ops/arcsinh_native.h>
+#include <ATen/ops/arctan_native.h>
+#include <ATen/ops/arctan2_native.h>
+#include <ATen/ops/arctanh_native.h>
+#include <ATen/ops/argmax_native.h>
+#include <ATen/ops/argmin_native.h>
+#include <ATen/ops/argsort_native.h>
+#include <ATen/ops/argwhere_native.h>
+#include <ATen/ops/as_strided_native.h>
+#include <ATen/ops/as_strided_copy_native.h>
+#include <ATen/ops/as_strided_scatter_native.h>
+#include <ATen/ops/asin_native.h>
+#include <ATen/ops/asinh_native.h>
+#include <ATen/ops/atan_native.h>
+#include <ATen/ops/atan2_native.h>
+#include <ATen/ops/atanh_native.h>
+#include <ATen/ops/atleast_1d_native.h>
+#include <ATen/ops/atleast_2d_native.h>
+#include <ATen/ops/atleast_3d_native.h>
+#include <ATen/ops/avg_pool1d_native.h>
+#include <ATen/ops/avg_pool2d_native.h>
+#include <ATen/ops/avg_pool2d_backward_native.h>
+#include <ATen/ops/avg_pool3d_native.h>
+#include <ATen/ops/avg_pool3d_backward_native.h>
+#include <ATen/ops/baddbmm_native.h>
+#include <ATen/ops/bartlett_window_native.h>
+#include <ATen/ops/batch_norm_native.h>
+#include <ATen/ops/batch_norm_backward_elemt_native.h>
+#include <ATen/ops/batch_norm_backward_reduce_native.h>
+#include <ATen/ops/batch_norm_elemt_native.h>
+#include <ATen/ops/batch_norm_gather_stats_native.h>
+#include <ATen/ops/batch_norm_gather_stats_with_counts_native.h>
+#include <ATen/ops/batch_norm_stats_native.h>
+#include <ATen/ops/batch_norm_update_stats_native.h>
+#include <ATen/ops/bernoulli_native.h>
+#include <ATen/ops/bilinear_native.h>
+#include <ATen/ops/binary_cross_entropy_native.h>
+#include <ATen/ops/binary_cross_entropy_backward_native.h>
+#include <ATen/ops/binary_cross_entropy_with_logits_native.h>
+#include <ATen/ops/bincount_native.h>
+#include <ATen/ops/binomial_native.h>
+#include <ATen/ops/bitwise_and_native.h>
+#include <ATen/ops/bitwise_left_shift_native.h>
+#include <ATen/ops/bitwise_not_native.h>
+#include <ATen/ops/bitwise_or_native.h>
+#include <ATen/ops/bitwise_right_shift_native.h>
+#include <ATen/ops/bitwise_xor_native.h>
+#include <ATen/ops/blackman_window_native.h>
+#include <ATen/ops/block_diag_native.h>
+#include <ATen/ops/bmm_native.h>
+#include <ATen/ops/broadcast_tensors_native.h>
+#include <ATen/ops/broadcast_to_native.h>
+#include <ATen/ops/bucketize_native.h>
+#include <ATen/ops/can_cast_native.h>
+#include <ATen/ops/cartesian_prod_native.h>
+#include <ATen/ops/cat_native.h>
+#include <ATen/ops/cauchy_native.h>
+#include <ATen/ops/ccol_indices_native.h>
+#include <ATen/ops/ccol_indices_copy_native.h>
+#include <ATen/ops/cdist_native.h>
+#include <ATen/ops/ceil_native.h>
+#include <ATen/ops/celu_native.h>
+#include <ATen/ops/chain_matmul_native.h>
+#include <ATen/ops/chalf_native.h>
+#include <ATen/ops/channel_shuffle_native.h>
+#include <ATen/ops/cholesky_native.h>
+#include <ATen/ops/cholesky_inverse_native.h>
+#include <ATen/ops/cholesky_solve_native.h>
+#include <ATen/ops/choose_qparams_optimized_native.h>
+#include <ATen/ops/chunk_native.h>
+#include <ATen/ops/clamp_native.h>
+#include <ATen/ops/clamp_max_native.h>
+#include <ATen/ops/clamp_min_native.h>
+#include <ATen/ops/clip_native.h>
+#include <ATen/ops/clone_native.h>
+#include <ATen/ops/coalesce_native.h>
+#include <ATen/ops/col2im_native.h>
+#include <ATen/ops/col_indices_native.h>
+#include <ATen/ops/col_indices_copy_native.h>
+#include <ATen/ops/column_stack_native.h>
+#include <ATen/ops/combinations_native.h>
+#include <ATen/ops/complex_native.h>
+#include <ATen/ops/concat_native.h>
+#include <ATen/ops/concatenate_native.h>
+#include <ATen/ops/conj_native.h>
+#include <ATen/ops/conj_physical_native.h>
+#include <ATen/ops/constant_pad_nd_native.h>
+#include <ATen/ops/contiguous_native.h>
+#include <ATen/ops/conv1d_native.h>
+#include <ATen/ops/conv2d_native.h>
+#include <ATen/ops/conv3d_native.h>
+#include <ATen/ops/conv_depthwise3d_native.h>
+#include <ATen/ops/conv_tbc_native.h>
+#include <ATen/ops/conv_tbc_backward_native.h>
+#include <ATen/ops/conv_transpose1d_native.h>
+#include <ATen/ops/conv_transpose2d_native.h>
+#include <ATen/ops/conv_transpose3d_native.h>
+#include <ATen/ops/convolution_native.h>
+#include <ATen/ops/convolution_backward_native.h>
+#include <ATen/ops/convolution_backward_overrideable_native.h>
+#include <ATen/ops/convolution_overrideable_native.h>
+#include <ATen/ops/copy_native.h>
+#include <ATen/ops/copy_sparse_to_sparse_native.h>
+#include <ATen/ops/copysign_native.h>
+#include <ATen/ops/corrcoef_native.h>
+#include <ATen/ops/cos_native.h>
+#include <ATen/ops/cosh_native.h>
+#include <ATen/ops/cosine_embedding_loss_native.h>
+#include <ATen/ops/cosine_similarity_native.h>
+#include <ATen/ops/count_nonzero_native.h>
+#include <ATen/ops/cov_native.h>
+#include <ATen/ops/cross_native.h>
+#include <ATen/ops/cross_entropy_loss_native.h>
+#include <ATen/ops/crow_indices_native.h>
+#include <ATen/ops/crow_indices_copy_native.h>
+#include <ATen/ops/ctc_loss_native.h>
+#include <ATen/ops/cudnn_affine_grid_generator_native.h>
+#include <ATen/ops/cudnn_affine_grid_generator_backward_native.h>
+#include <ATen/ops/cudnn_batch_norm_native.h>
+#include <ATen/ops/cudnn_batch_norm_backward_native.h>
+#include <ATen/ops/cudnn_convolution_native.h>
+#include <ATen/ops/cudnn_convolution_add_relu_native.h>
+#include <ATen/ops/cudnn_convolution_relu_native.h>
+#include <ATen/ops/cudnn_convolution_transpose_native.h>
+#include <ATen/ops/cudnn_grid_sampler_native.h>
+#include <ATen/ops/cudnn_grid_sampler_backward_native.h>
+#include <ATen/ops/cudnn_is_acceptable_native.h>
+#include <ATen/ops/cummax_native.h>
+#include <ATen/ops/cummaxmin_backward_native.h>
+#include <ATen/ops/cummin_native.h>
+#include <ATen/ops/cumprod_native.h>
+#include <ATen/ops/cumprod_backward_native.h>
+#include <ATen/ops/cumsum_native.h>
+#include <ATen/ops/cumulative_trapezoid_native.h>
+#include <ATen/ops/data_native.h>
+#include <ATen/ops/deg2rad_native.h>
+#include <ATen/ops/dense_dim_native.h>
+#include <ATen/ops/dequantize_native.h>
+#include <ATen/ops/det_native.h>
+#include <ATen/ops/detach_native.h>
+#include <ATen/ops/detach_copy_native.h>
+#include <ATen/ops/diag_native.h>
+#include <ATen/ops/diag_embed_native.h>
+#include <ATen/ops/diagflat_native.h>
+#include <ATen/ops/diagonal_native.h>
+#include <ATen/ops/diagonal_backward_native.h>
+#include <ATen/ops/diagonal_copy_native.h>
+#include <ATen/ops/diagonal_scatter_native.h>
+#include <ATen/ops/diff_native.h>
+#include <ATen/ops/digamma_native.h>
+#include <ATen/ops/dist_native.h>
+#include <ATen/ops/div_native.h>
+#include <ATen/ops/divide_native.h>
+#include <ATen/ops/dot_native.h>
+#include <ATen/ops/dropout_native.h>
+#include <ATen/ops/dsplit_native.h>
+#include <ATen/ops/dstack_native.h>
+#include <ATen/ops/einsum_native.h>
+#include <ATen/ops/elu_native.h>
+#include <ATen/ops/elu_backward_native.h>
+#include <ATen/ops/embedding_native.h>
+#include <ATen/ops/embedding_backward_native.h>
+#include <ATen/ops/embedding_bag_native.h>
+#include <ATen/ops/embedding_dense_backward_native.h>
+#include <ATen/ops/embedding_renorm_native.h>
+#include <ATen/ops/embedding_sparse_backward_native.h>
+#include <ATen/ops/empty_native.h>
+#include <ATen/ops/empty_like_native.h>
+#include <ATen/ops/empty_permuted_native.h>
+#include <ATen/ops/empty_quantized_native.h>
+#include <ATen/ops/empty_strided_native.h>
+#include <ATen/ops/eq_native.h>
+#include <ATen/ops/equal_native.h>
+#include <ATen/ops/erf_native.h>
+#include <ATen/ops/erfc_native.h>
+#include <ATen/ops/erfinv_native.h>
+#include <ATen/ops/exp_native.h>
+#include <ATen/ops/exp2_native.h>
+#include <ATen/ops/expand_native.h>
+#include <ATen/ops/expand_as_native.h>
+#include <ATen/ops/expand_copy_native.h>
+#include <ATen/ops/expm1_native.h>
+#include <ATen/ops/exponential_native.h>
+#include <ATen/ops/eye_native.h>
+#include <ATen/ops/fake_quantize_per_channel_affine_native.h>
+#include <ATen/ops/fake_quantize_per_channel_affine_cachemask_native.h>
+#include <ATen/ops/fake_quantize_per_channel_affine_cachemask_backward_native.h>
+#include <ATen/ops/fake_quantize_per_tensor_affine_native.h>
+#include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_native.h>
+#include <ATen/ops/fake_quantize_per_tensor_affine_cachemask_backward_native.h>
+#include <ATen/ops/fbgemm_linear_fp16_weight_native.h>
+#include <ATen/ops/fbgemm_linear_fp16_weight_fp32_activation_native.h>
+#include <ATen/ops/fbgemm_linear_int8_weight_native.h>
+#include <ATen/ops/fbgemm_linear_int8_weight_fp32_activation_native.h>
+#include <ATen/ops/fbgemm_linear_quantize_weight_native.h>
+#include <ATen/ops/fbgemm_pack_gemm_matrix_fp16_native.h>
+#include <ATen/ops/fbgemm_pack_quantized_matrix_native.h>
+#include <ATen/ops/feature_alpha_dropout_native.h>
+#include <ATen/ops/feature_dropout_native.h>
+#include <ATen/ops/fft_fft_native.h>
+#include <ATen/ops/fft_fft2_native.h>
+#include <ATen/ops/fft_fftfreq_native.h>
+#include <ATen/ops/fft_fftn_native.h>
+#include <ATen/ops/fft_fftshift_native.h>
+#include <ATen/ops/fft_hfft_native.h>
+#include <ATen/ops/fft_hfft2_native.h>
+#include <ATen/ops/fft_hfftn_native.h>
+#include <ATen/ops/fft_ifft_native.h>
+#include <ATen/ops/fft_ifft2_native.h>
+#include <ATen/ops/fft_ifftn_native.h>
+#include <ATen/ops/fft_ifftshift_native.h>
+#include <ATen/ops/fft_ihfft_native.h>
+#include <ATen/ops/fft_ihfft2_native.h>
+#include <ATen/ops/fft_ihfftn_native.h>
+#include <ATen/ops/fft_irfft_native.h>
+#include <ATen/ops/fft_irfft2_native.h>
+#include <ATen/ops/fft_irfftn_native.h>
+#include <ATen/ops/fft_rfft_native.h>
+#include <ATen/ops/fft_rfft2_native.h>
+#include <ATen/ops/fft_rfftfreq_native.h>
+#include <ATen/ops/fft_rfftn_native.h>
+#include <ATen/ops/fill_native.h>
+#include <ATen/ops/fill_diagonal_native.h>
+#include <ATen/ops/fix_native.h>
+#include <ATen/ops/flatten_native.h>
+#include <ATen/ops/flatten_dense_tensors_native.h>
+#include <ATen/ops/flip_native.h>
+#include <ATen/ops/fliplr_native.h>
+#include <ATen/ops/flipud_native.h>
+#include <ATen/ops/float_power_native.h>
+#include <ATen/ops/floor_native.h>
+#include <ATen/ops/floor_divide_native.h>
+#include <ATen/ops/fmax_native.h>
+#include <ATen/ops/fmin_native.h>
+#include <ATen/ops/fmod_native.h>
+#include <ATen/ops/frac_native.h>
+#include <ATen/ops/fractional_max_pool2d_native.h>
+#include <ATen/ops/fractional_max_pool2d_backward_native.h>
+#include <ATen/ops/fractional_max_pool3d_native.h>
+#include <ATen/ops/fractional_max_pool3d_backward_native.h>
+#include <ATen/ops/frexp_native.h>
+#include <ATen/ops/frobenius_norm_native.h>
+#include <ATen/ops/from_file_native.h>
+#include <ATen/ops/full_native.h>
+#include <ATen/ops/full_like_native.h>
+#include <ATen/ops/fused_moving_avg_obs_fake_quant_native.h>
+#include <ATen/ops/gather_native.h>
+#include <ATen/ops/gather_backward_native.h>
+#include <ATen/ops/gcd_native.h>
+#include <ATen/ops/ge_native.h>
+#include <ATen/ops/gelu_native.h>
+#include <ATen/ops/gelu_backward_native.h>
+#include <ATen/ops/geometric_native.h>
+#include <ATen/ops/geqrf_native.h>
+#include <ATen/ops/ger_native.h>
+#include <ATen/ops/glu_native.h>
+#include <ATen/ops/glu_backward_native.h>
+#include <ATen/ops/glu_backward_jvp_native.h>
+#include <ATen/ops/glu_jvp_native.h>
+#include <ATen/ops/gradient_native.h>
+#include <ATen/ops/greater_native.h>
+#include <ATen/ops/greater_equal_native.h>
+#include <ATen/ops/grid_sampler_native.h>
+#include <ATen/ops/grid_sampler_2d_native.h>
+#include <ATen/ops/grid_sampler_2d_backward_native.h>
+#include <ATen/ops/grid_sampler_3d_native.h>
+#include <ATen/ops/grid_sampler_3d_backward_native.h>
+#include <ATen/ops/group_norm_native.h>
+#include <ATen/ops/gru_native.h>
+#include <ATen/ops/gru_cell_native.h>
+#include <ATen/ops/gt_native.h>
+#include <ATen/ops/hamming_window_native.h>
+#include <ATen/ops/hann_window_native.h>
+#include <ATen/ops/hardshrink_native.h>
+#include <ATen/ops/hardshrink_backward_native.h>
+#include <ATen/ops/hardsigmoid_native.h>
+#include <ATen/ops/hardsigmoid_backward_native.h>
+#include <ATen/ops/hardswish_native.h>
+#include <ATen/ops/hardswish_backward_native.h>
+#include <ATen/ops/hardtanh_native.h>
+#include <ATen/ops/hardtanh_backward_native.h>
+#include <ATen/ops/heaviside_native.h>
+#include <ATen/ops/hinge_embedding_loss_native.h>
+#include <ATen/ops/histc_native.h>
+#include <ATen/ops/histogram_native.h>
+#include <ATen/ops/histogramdd_native.h>
+#include <ATen/ops/hsplit_native.h>
+#include <ATen/ops/hspmm_native.h>
+#include <ATen/ops/hstack_native.h>
+#include <ATen/ops/huber_loss_native.h>
+#include <ATen/ops/huber_loss_backward_native.h>
+#include <ATen/ops/hypot_native.h>
+#include <ATen/ops/i0_native.h>
+#include <ATen/ops/igamma_native.h>
+#include <ATen/ops/igammac_native.h>
+#include <ATen/ops/im2col_native.h>
+#include <ATen/ops/imag_native.h>
+#include <ATen/ops/index_native.h>
+#include <ATen/ops/index_add_native.h>
+#include <ATen/ops/index_copy_native.h>
+#include <ATen/ops/index_fill_native.h>
+#include <ATen/ops/index_put_native.h>
+#include <ATen/ops/index_reduce_native.h>
+#include <ATen/ops/index_select_native.h>
+#include <ATen/ops/index_select_backward_native.h>
+#include <ATen/ops/indices_native.h>
+#include <ATen/ops/indices_copy_native.h>
+#include <ATen/ops/infinitely_differentiable_gelu_backward_native.h>
+#include <ATen/ops/inner_native.h>
+#include <ATen/ops/instance_norm_native.h>
+#include <ATen/ops/int_repr_native.h>
+#include <ATen/ops/inverse_native.h>
+#include <ATen/ops/is_coalesced_native.h>
+#include <ATen/ops/is_complex_native.h>
+#include <ATen/ops/is_conj_native.h>
+#include <ATen/ops/is_distributed_native.h>
+#include <ATen/ops/is_floating_point_native.h>
+#include <ATen/ops/is_inference_native.h>
+#include <ATen/ops/is_leaf_native.h>
+#include <ATen/ops/is_neg_native.h>
+#include <ATen/ops/is_nonzero_native.h>
+#include <ATen/ops/is_pinned_native.h>
+#include <ATen/ops/is_same_size_native.h>
+#include <ATen/ops/is_set_to_native.h>
+#include <ATen/ops/is_signed_native.h>
+#include <ATen/ops/is_vulkan_available_native.h>
+#include <ATen/ops/isclose_native.h>
+#include <ATen/ops/isfinite_native.h>
+#include <ATen/ops/isin_native.h>
+#include <ATen/ops/isinf_native.h>
+#include <ATen/ops/isnan_native.h>
+#include <ATen/ops/isneginf_native.h>
+#include <ATen/ops/isposinf_native.h>
+#include <ATen/ops/isreal_native.h>
+#include <ATen/ops/istft_native.h>
+#include <ATen/ops/item_native.h>
+#include <ATen/ops/kaiser_window_native.h>
+#include <ATen/ops/kl_div_native.h>
+#include <ATen/ops/kron_native.h>
+#include <ATen/ops/kthvalue_native.h>
+#include <ATen/ops/l1_loss_native.h>
+#include <ATen/ops/layer_norm_native.h>
+#include <ATen/ops/lcm_native.h>
+#include <ATen/ops/ldexp_native.h>
+#include <ATen/ops/le_native.h>
+#include <ATen/ops/leaky_relu_native.h>
+#include <ATen/ops/leaky_relu_backward_native.h>
+#include <ATen/ops/lerp_native.h>
+#include <ATen/ops/less_native.h>
+#include <ATen/ops/less_equal_native.h>
+#include <ATen/ops/lgamma_native.h>
+#include <ATen/ops/lift_native.h>
+#include <ATen/ops/lift_fresh_native.h>
+#include <ATen/ops/lift_fresh_copy_native.h>
+#include <ATen/ops/linalg_cholesky_native.h>
+#include <ATen/ops/linalg_cholesky_ex_native.h>
+#include <ATen/ops/linalg_cond_native.h>
+#include <ATen/ops/linalg_cross_native.h>
+#include <ATen/ops/linalg_det_native.h>
+#include <ATen/ops/linalg_diagonal_native.h>
+#include <ATen/ops/linalg_eig_native.h>
+#include <ATen/ops/linalg_eigh_native.h>
+#include <ATen/ops/linalg_eigvals_native.h>
+#include <ATen/ops/linalg_eigvalsh_native.h>
+#include <ATen/ops/linalg_householder_product_native.h>
+#include <ATen/ops/linalg_inv_native.h>
+#include <ATen/ops/linalg_inv_ex_native.h>
+#include <ATen/ops/linalg_ldl_factor_native.h>
+#include <ATen/ops/linalg_ldl_factor_ex_native.h>
+#include <ATen/ops/linalg_ldl_solve_native.h>
+#include <ATen/ops/linalg_lstsq_native.h>
+#include <ATen/ops/linalg_lu_native.h>
+#include <ATen/ops/linalg_lu_factor_native.h>
+#include <ATen/ops/linalg_lu_factor_ex_native.h>
+#include <ATen/ops/linalg_lu_solve_native.h>
+#include <ATen/ops/linalg_matmul_native.h>
+#include <ATen/ops/linalg_matrix_exp_native.h>
+#include <ATen/ops/linalg_matrix_norm_native.h>
+#include <ATen/ops/linalg_matrix_power_native.h>
+#include <ATen/ops/linalg_matrix_rank_native.h>
+#include <ATen/ops/linalg_multi_dot_native.h>
+#include <ATen/ops/linalg_norm_native.h>
+#include <ATen/ops/linalg_pinv_native.h>
+#include <ATen/ops/linalg_qr_native.h>
+#include <ATen/ops/linalg_slogdet_native.h>
+#include <ATen/ops/linalg_solve_native.h>
+#include <ATen/ops/linalg_solve_ex_native.h>
+#include <ATen/ops/linalg_solve_triangular_native.h>
+#include <ATen/ops/linalg_svd_native.h>
+#include <ATen/ops/linalg_svdvals_native.h>
+#include <ATen/ops/linalg_tensorinv_native.h>
+#include <ATen/ops/linalg_tensorsolve_native.h>
+#include <ATen/ops/linalg_vander_native.h>
+#include <ATen/ops/linalg_vecdot_native.h>
+#include <ATen/ops/linalg_vector_norm_native.h>
+#include <ATen/ops/linear_native.h>
+#include <ATen/ops/linear_backward_native.h>
+#include <ATen/ops/linspace_native.h>
+#include <ATen/ops/log_native.h>
+#include <ATen/ops/log10_native.h>
+#include <ATen/ops/log1p_native.h>
+#include <ATen/ops/log2_native.h>
+#include <ATen/ops/log_normal_native.h>
+#include <ATen/ops/log_sigmoid_native.h>
+#include <ATen/ops/log_sigmoid_backward_native.h>
+#include <ATen/ops/log_sigmoid_forward_native.h>
+#include <ATen/ops/log_softmax_native.h>
+#include <ATen/ops/logaddexp_native.h>
+#include <ATen/ops/logaddexp2_native.h>
+#include <ATen/ops/logcumsumexp_native.h>
+#include <ATen/ops/logdet_native.h>
+#include <ATen/ops/logical_and_native.h>
+#include <ATen/ops/logical_not_native.h>
+#include <ATen/ops/logical_or_native.h>
+#include <ATen/ops/logical_xor_native.h>
+#include <ATen/ops/logit_native.h>
+#include <ATen/ops/logit_backward_native.h>
+#include <ATen/ops/logspace_native.h>
+#include <ATen/ops/logsumexp_native.h>
+#include <ATen/ops/lshift_native.h>
+#include <ATen/ops/lstm_native.h>
+#include <ATen/ops/lstm_cell_native.h>
+#include <ATen/ops/lstm_mps_backward_native.h>
+#include <ATen/ops/lt_native.h>
+#include <ATen/ops/lu_solve_native.h>
+#include <ATen/ops/lu_unpack_native.h>
+#include <ATen/ops/mH_native.h>
+#include <ATen/ops/mT_native.h>
+#include <ATen/ops/margin_ranking_loss_native.h>
+#include <ATen/ops/masked_fill_native.h>
+#include <ATen/ops/masked_scatter_native.h>
+#include <ATen/ops/masked_scatter_backward_native.h>
+#include <ATen/ops/masked_select_native.h>
+#include <ATen/ops/masked_select_backward_native.h>
+#include <ATen/ops/matmul_native.h>
+#include <ATen/ops/matmul_backward_native.h>
+#include <ATen/ops/matrix_H_native.h>
+#include <ATen/ops/matrix_exp_native.h>
+#include <ATen/ops/matrix_exp_backward_native.h>
+#include <ATen/ops/matrix_power_native.h>
+#include <ATen/ops/max_native.h>
+#include <ATen/ops/max_pool1d_native.h>
+#include <ATen/ops/max_pool1d_with_indices_native.h>
+#include <ATen/ops/max_pool2d_native.h>
+#include <ATen/ops/max_pool2d_backward_native.h>
+#include <ATen/ops/max_pool2d_with_indices_native.h>
+#include <ATen/ops/max_pool2d_with_indices_backward_native.h>
+#include <ATen/ops/max_pool3d_native.h>
+#include <ATen/ops/max_pool3d_with_indices_native.h>
+#include <ATen/ops/max_pool3d_with_indices_backward_native.h>
+#include <ATen/ops/max_unpool2d_native.h>
+#include <ATen/ops/max_unpool3d_native.h>
+#include <ATen/ops/maximum_native.h>
+#include <ATen/ops/mean_native.h>
+#include <ATen/ops/median_native.h>
+#include <ATen/ops/meshgrid_native.h>
+#include <ATen/ops/min_native.h>
+#include <ATen/ops/minimum_native.h>
+#include <ATen/ops/miopen_batch_norm_native.h>
+#include <ATen/ops/miopen_batch_norm_backward_native.h>
+#include <ATen/ops/miopen_convolution_native.h>
+#include <ATen/ops/miopen_convolution_add_relu_native.h>
+#include <ATen/ops/miopen_convolution_relu_native.h>
+#include <ATen/ops/miopen_convolution_transpose_native.h>
+#include <ATen/ops/miopen_depthwise_convolution_native.h>
+#include <ATen/ops/miopen_rnn_native.h>
+#include <ATen/ops/miopen_rnn_backward_native.h>
+#include <ATen/ops/mish_native.h>
+#include <ATen/ops/mish_backward_native.h>
+#include <ATen/ops/mkldnn_adaptive_avg_pool2d_native.h>
+#include <ATen/ops/mkldnn_adaptive_avg_pool2d_backward_native.h>
+#include <ATen/ops/mkldnn_convolution_native.h>
+#include <ATen/ops/mkldnn_linear_native.h>
+#include <ATen/ops/mkldnn_linear_backward_native.h>
+#include <ATen/ops/mkldnn_linear_backward_input_native.h>
+#include <ATen/ops/mkldnn_linear_backward_weights_native.h>
+#include <ATen/ops/mkldnn_max_pool2d_native.h>
+#include <ATen/ops/mkldnn_max_pool2d_backward_native.h>
+#include <ATen/ops/mkldnn_max_pool3d_native.h>
+#include <ATen/ops/mkldnn_max_pool3d_backward_native.h>
+#include <ATen/ops/mkldnn_reorder_conv2d_weight_native.h>
+#include <ATen/ops/mkldnn_reorder_conv3d_weight_native.h>
+#include <ATen/ops/mkldnn_rnn_layer_native.h>
+#include <ATen/ops/mkldnn_rnn_layer_backward_native.h>
+#include <ATen/ops/mm_native.h>
+#include <ATen/ops/mode_native.h>
+#include <ATen/ops/moveaxis_native.h>
+#include <ATen/ops/movedim_native.h>
+#include <ATen/ops/mps_convolution_backward_native.h>
+#include <ATen/ops/mps_convolution_transpose_backward_native.h>
+#include <ATen/ops/mse_loss_native.h>
+#include <ATen/ops/mse_loss_backward_native.h>
+#include <ATen/ops/msort_native.h>
+#include <ATen/ops/mul_native.h>
+#include <ATen/ops/multi_margin_loss_native.h>
+#include <ATen/ops/multi_margin_loss_backward_native.h>
+#include <ATen/ops/multilabel_margin_loss_native.h>
+#include <ATen/ops/multilabel_margin_loss_backward_native.h>
+#include <ATen/ops/multilabel_margin_loss_forward_native.h>
+#include <ATen/ops/multinomial_native.h>
+#include <ATen/ops/multiply_native.h>
+#include <ATen/ops/mv_native.h>
+#include <ATen/ops/mvlgamma_native.h>
+#include <ATen/ops/nan_to_num_native.h>
+#include <ATen/ops/nanmean_native.h>
+#include <ATen/ops/nanmedian_native.h>
+#include <ATen/ops/nanquantile_native.h>
+#include <ATen/ops/nansum_native.h>
+#include <ATen/ops/narrow_native.h>
+#include <ATen/ops/narrow_copy_native.h>
+#include <ATen/ops/native_batch_norm_native.h>
+#include <ATen/ops/native_batch_norm_backward_native.h>
+#include <ATen/ops/native_channel_shuffle_native.h>
+#include <ATen/ops/native_dropout_native.h>
+#include <ATen/ops/native_dropout_backward_native.h>
+#include <ATen/ops/native_group_norm_native.h>
+#include <ATen/ops/native_group_norm_backward_native.h>
+#include <ATen/ops/native_layer_norm_native.h>
+#include <ATen/ops/native_layer_norm_backward_native.h>
+#include <ATen/ops/native_norm_native.h>
+#include <ATen/ops/ne_native.h>
+#include <ATen/ops/neg_native.h>
+#include <ATen/ops/negative_native.h>
+#include <ATen/ops/nested_to_padded_tensor_native.h>
+#include <ATen/ops/new_empty_native.h>
+#include <ATen/ops/new_empty_strided_native.h>
+#include <ATen/ops/new_full_native.h>
+#include <ATen/ops/new_ones_native.h>
+#include <ATen/ops/new_zeros_native.h>
+#include <ATen/ops/nextafter_native.h>
+#include <ATen/ops/nll_loss_native.h>
+#include <ATen/ops/nll_loss2d_native.h>
+#include <ATen/ops/nll_loss2d_backward_native.h>
+#include <ATen/ops/nll_loss2d_forward_native.h>
+#include <ATen/ops/nll_loss_backward_native.h>
+#include <ATen/ops/nll_loss_forward_native.h>
+#include <ATen/ops/nll_loss_nd_native.h>
+#include <ATen/ops/nonzero_native.h>
+#include <ATen/ops/nonzero_numpy_native.h>
+#include <ATen/ops/nonzero_static_native.h>
+#include <ATen/ops/norm_native.h>
+#include <ATen/ops/norm_except_dim_native.h>
+#include <ATen/ops/normal_native.h>
+#include <ATen/ops/not_equal_native.h>
+#include <ATen/ops/nuclear_norm_native.h>
+#include <ATen/ops/numpy_T_native.h>
+#include <ATen/ops/one_hot_native.h>
+#include <ATen/ops/ones_native.h>
+#include <ATen/ops/ones_like_native.h>
+#include <ATen/ops/or_native.h>
+#include <ATen/ops/orgqr_native.h>
+#include <ATen/ops/ormqr_native.h>
+#include <ATen/ops/outer_native.h>
+#include <ATen/ops/output_nr_native.h>
+#include <ATen/ops/pad_native.h>
+#include <ATen/ops/pad_sequence_native.h>
+#include <ATen/ops/pairwise_distance_native.h>
+#include <ATen/ops/pdist_native.h>
+#include <ATen/ops/permute_native.h>
+#include <ATen/ops/permute_copy_native.h>
+#include <ATen/ops/pin_memory_native.h>
+#include <ATen/ops/pinverse_native.h>
+#include <ATen/ops/pixel_shuffle_native.h>
+#include <ATen/ops/pixel_unshuffle_native.h>
+#include <ATen/ops/poisson_native.h>
+#include <ATen/ops/poisson_nll_loss_native.h>
+#include <ATen/ops/polar_native.h>
+#include <ATen/ops/polygamma_native.h>
+#include <ATen/ops/positive_native.h>
+#include <ATen/ops/pow_native.h>
+#include <ATen/ops/prelu_native.h>
+#include <ATen/ops/prod_native.h>
+#include <ATen/ops/promote_types_native.h>
+#include <ATen/ops/put_native.h>
+#include <ATen/ops/q_per_channel_axis_native.h>
+#include <ATen/ops/q_per_channel_scales_native.h>
+#include <ATen/ops/q_per_channel_zero_points_native.h>
+#include <ATen/ops/q_scale_native.h>
+#include <ATen/ops/q_zero_point_native.h>
+#include <ATen/ops/qr_native.h>
+#include <ATen/ops/qscheme_native.h>
+#include <ATen/ops/quantile_native.h>
+#include <ATen/ops/quantize_per_channel_native.h>
+#include <ATen/ops/quantize_per_tensor_native.h>
+#include <ATen/ops/quantize_per_tensor_dynamic_native.h>
+#include <ATen/ops/quantized_batch_norm_native.h>
+#include <ATen/ops/quantized_gru_cell_native.h>
+#include <ATen/ops/quantized_lstm_cell_native.h>
+#include <ATen/ops/quantized_max_pool1d_native.h>
+#include <ATen/ops/quantized_max_pool2d_native.h>
+#include <ATen/ops/quantized_max_pool3d_native.h>
+#include <ATen/ops/quantized_rnn_relu_cell_native.h>
+#include <ATen/ops/quantized_rnn_tanh_cell_native.h>
+#include <ATen/ops/rad2deg_native.h>
+#include <ATen/ops/rand_native.h>
+#include <ATen/ops/rand_like_native.h>
+#include <ATen/ops/randint_native.h>
+#include <ATen/ops/randint_like_native.h>
+#include <ATen/ops/randn_native.h>
+#include <ATen/ops/randn_like_native.h>
+#include <ATen/ops/random_native.h>
+#include <ATen/ops/randperm_native.h>
+#include <ATen/ops/range_native.h>
+#include <ATen/ops/ravel_native.h>
+#include <ATen/ops/real_native.h>
+#include <ATen/ops/reciprocal_native.h>
+#include <ATen/ops/record_stream_native.h>
+#include <ATen/ops/refine_names_native.h>
+#include <ATen/ops/reflection_pad1d_native.h>
+#include <ATen/ops/reflection_pad1d_backward_native.h>
+#include <ATen/ops/reflection_pad2d_native.h>
+#include <ATen/ops/reflection_pad2d_backward_native.h>
+#include <ATen/ops/reflection_pad3d_native.h>
+#include <ATen/ops/reflection_pad3d_backward_native.h>
+#include <ATen/ops/relu_native.h>
+#include <ATen/ops/relu6_native.h>
+#include <ATen/ops/remainder_native.h>
+#include <ATen/ops/rename_native.h>
+#include <ATen/ops/renorm_native.h>
+#include <ATen/ops/repeat_native.h>
+#include <ATen/ops/repeat_interleave_native.h>
+#include <ATen/ops/replication_pad1d_native.h>
+#include <ATen/ops/replication_pad1d_backward_native.h>
+#include <ATen/ops/replication_pad2d_native.h>
+#include <ATen/ops/replication_pad2d_backward_native.h>
+#include <ATen/ops/replication_pad3d_native.h>
+#include <ATen/ops/replication_pad3d_backward_native.h>
+#include <ATen/ops/requires_grad_native.h>
+#include <ATen/ops/reshape_native.h>
+#include <ATen/ops/reshape_as_native.h>
+#include <ATen/ops/resize_native.h>
+#include <ATen/ops/resize_as_native.h>
+#include <ATen/ops/resize_as_sparse_native.h>
+#include <ATen/ops/resolve_conj_native.h>
+#include <ATen/ops/resolve_neg_native.h>
+#include <ATen/ops/result_type_native.h>
+#include <ATen/ops/retain_grad_native.h>
+#include <ATen/ops/retains_grad_native.h>
+#include <ATen/ops/rnn_relu_native.h>
+#include <ATen/ops/rnn_relu_cell_native.h>
+#include <ATen/ops/rnn_tanh_native.h>
+#include <ATen/ops/rnn_tanh_cell_native.h>
+#include <ATen/ops/roll_native.h>
+#include <ATen/ops/rot90_native.h>
+#include <ATen/ops/round_native.h>
+#include <ATen/ops/row_indices_native.h>
+#include <ATen/ops/row_indices_copy_native.h>
+#include <ATen/ops/row_stack_native.h>
+#include <ATen/ops/rrelu_native.h>
+#include <ATen/ops/rrelu_with_noise_native.h>
+#include <ATen/ops/rrelu_with_noise_backward_native.h>
+#include <ATen/ops/rshift_native.h>
+#include <ATen/ops/rsqrt_native.h>
+#include <ATen/ops/rsub_native.h>
+#include <ATen/ops/scalar_tensor_native.h>
+#include <ATen/ops/scaled_dot_product_attention_native.h>
+#include <ATen/ops/scatter_native.h>
+#include <ATen/ops/scatter_add_native.h>
+#include <ATen/ops/scatter_reduce_native.h>
+#include <ATen/ops/searchsorted_native.h>
+#include <ATen/ops/segment_reduce_native.h>
+#include <ATen/ops/select_native.h>
+#include <ATen/ops/select_backward_native.h>
+#include <ATen/ops/select_copy_native.h>
+#include <ATen/ops/select_scatter_native.h>
+#include <ATen/ops/selu_native.h>
+#include <ATen/ops/set_native.h>
+#include <ATen/ops/set_data_native.h>
+#include <ATen/ops/sgn_native.h>
+#include <ATen/ops/sigmoid_native.h>
+#include <ATen/ops/sigmoid_backward_native.h>
+#include <ATen/ops/sign_native.h>
+#include <ATen/ops/signbit_native.h>
+#include <ATen/ops/silu_native.h>
+#include <ATen/ops/silu_backward_native.h>
+#include <ATen/ops/sin_native.h>
+#include <ATen/ops/sinc_native.h>
+#include <ATen/ops/sinh_native.h>
+#include <ATen/ops/size_native.h>
+#include <ATen/ops/slice_native.h>
+#include <ATen/ops/slice_backward_native.h>
+#include <ATen/ops/slice_copy_native.h>
+#include <ATen/ops/slice_scatter_native.h>
+#include <ATen/ops/slogdet_native.h>
+#include <ATen/ops/slow_conv3d_native.h>
+#include <ATen/ops/slow_conv3d_forward_native.h>
+#include <ATen/ops/slow_conv_dilated2d_native.h>
+#include <ATen/ops/slow_conv_dilated3d_native.h>
+#include <ATen/ops/slow_conv_transpose2d_native.h>
+#include <ATen/ops/slow_conv_transpose3d_native.h>
+#include <ATen/ops/smm_native.h>
+#include <ATen/ops/smooth_l1_loss_native.h>
+#include <ATen/ops/smooth_l1_loss_backward_native.h>
+#include <ATen/ops/soft_margin_loss_native.h>
+#include <ATen/ops/soft_margin_loss_backward_native.h>
+#include <ATen/ops/softmax_native.h>
+#include <ATen/ops/softplus_native.h>
+#include <ATen/ops/softplus_backward_native.h>
+#include <ATen/ops/softshrink_native.h>
+#include <ATen/ops/softshrink_backward_native.h>
+#include <ATen/ops/sort_native.h>
+#include <ATen/ops/sparse_bsc_tensor_native.h>
+#include <ATen/ops/sparse_bsr_tensor_native.h>
+#include <ATen/ops/sparse_compressed_tensor_native.h>
+#include <ATen/ops/sparse_coo_tensor_native.h>
+#include <ATen/ops/sparse_csc_tensor_native.h>
+#include <ATen/ops/sparse_csr_tensor_native.h>
+#include <ATen/ops/sparse_dim_native.h>
+#include <ATen/ops/sparse_mask_native.h>
+#include <ATen/ops/sparse_resize_native.h>
+#include <ATen/ops/sparse_resize_and_clear_native.h>
+#include <ATen/ops/sparse_sampled_addmm_native.h>
+#include <ATen/ops/special_airy_ai_native.h>
+#include <ATen/ops/special_bessel_j0_native.h>
+#include <ATen/ops/special_bessel_j1_native.h>
+#include <ATen/ops/special_bessel_y0_native.h>
+#include <ATen/ops/special_bessel_y1_native.h>
+#include <ATen/ops/special_chebyshev_polynomial_t_native.h>
+#include <ATen/ops/special_chebyshev_polynomial_u_native.h>
+#include <ATen/ops/special_chebyshev_polynomial_v_native.h>
+#include <ATen/ops/special_chebyshev_polynomial_w_native.h>
+#include <ATen/ops/special_digamma_native.h>
+#include <ATen/ops/special_entr_native.h>
+#include <ATen/ops/special_erf_native.h>
+#include <ATen/ops/special_erfc_native.h>
+#include <ATen/ops/special_erfcx_native.h>
+#include <ATen/ops/special_erfinv_native.h>
+#include <ATen/ops/special_exp2_native.h>
+#include <ATen/ops/special_expit_native.h>
+#include <ATen/ops/special_expm1_native.h>
+#include <ATen/ops/special_gammainc_native.h>
+#include <ATen/ops/special_gammaincc_native.h>
+#include <ATen/ops/special_gammaln_native.h>
+#include <ATen/ops/special_hermite_polynomial_h_native.h>
+#include <ATen/ops/special_hermite_polynomial_he_native.h>
+#include <ATen/ops/special_i0_native.h>
+#include <ATen/ops/special_i0e_native.h>
+#include <ATen/ops/special_i1_native.h>
+#include <ATen/ops/special_i1e_native.h>
+#include <ATen/ops/special_laguerre_polynomial_l_native.h>
+#include <ATen/ops/special_legendre_polynomial_p_native.h>
+#include <ATen/ops/special_log1p_native.h>
+#include <ATen/ops/special_log_ndtr_native.h>
+#include <ATen/ops/special_log_softmax_native.h>
+#include <ATen/ops/special_logit_native.h>
+#include <ATen/ops/special_logsumexp_native.h>
+#include <ATen/ops/special_modified_bessel_i0_native.h>
+#include <ATen/ops/special_modified_bessel_i1_native.h>
+#include <ATen/ops/special_modified_bessel_k0_native.h>
+#include <ATen/ops/special_modified_bessel_k1_native.h>
+#include <ATen/ops/special_multigammaln_native.h>
+#include <ATen/ops/special_ndtr_native.h>
+#include <ATen/ops/special_ndtri_native.h>
+#include <ATen/ops/special_polygamma_native.h>
+#include <ATen/ops/special_psi_native.h>
+#include <ATen/ops/special_round_native.h>
+#include <ATen/ops/special_scaled_modified_bessel_k0_native.h>
+#include <ATen/ops/special_scaled_modified_bessel_k1_native.h>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_t_native.h>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_u_native.h>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_v_native.h>
+#include <ATen/ops/special_shifted_chebyshev_polynomial_w_native.h>
+#include <ATen/ops/special_sinc_native.h>
+#include <ATen/ops/special_softmax_native.h>
+#include <ATen/ops/special_spherical_bessel_j0_native.h>
+#include <ATen/ops/special_xlog1py_native.h>
+#include <ATen/ops/special_xlogy_native.h>
+#include <ATen/ops/special_zeta_native.h>
+#include <ATen/ops/split_native.h>
+#include <ATen/ops/split_copy_native.h>
+#include <ATen/ops/split_with_sizes_native.h>
+#include <ATen/ops/split_with_sizes_copy_native.h>
+#include <ATen/ops/sqrt_native.h>
+#include <ATen/ops/square_native.h>
+#include <ATen/ops/squeeze_native.h>
+#include <ATen/ops/squeeze_copy_native.h>
+#include <ATen/ops/sspaddmm_native.h>
+#include <ATen/ops/stack_native.h>
+#include <ATen/ops/std_native.h>
+#include <ATen/ops/std_mean_native.h>
+#include <ATen/ops/stft_native.h>
+#include <ATen/ops/stride_native.h>
+#include <ATen/ops/sub_native.h>
+#include <ATen/ops/subtract_native.h>
+#include <ATen/ops/sum_native.h>
+#include <ATen/ops/sum_to_size_native.h>
+#include <ATen/ops/svd_native.h>
+#include <ATen/ops/swapaxes_native.h>
+#include <ATen/ops/swapdims_native.h>
+#include <ATen/ops/sym_constrain_range_native.h>
+#include <ATen/ops/sym_constrain_range_for_size_native.h>
+#include <ATen/ops/sym_numel_native.h>
+#include <ATen/ops/sym_size_native.h>
+#include <ATen/ops/sym_storage_offset_native.h>
+#include <ATen/ops/sym_stride_native.h>
+#include <ATen/ops/t_native.h>
+#include <ATen/ops/t_copy_native.h>
+#include <ATen/ops/take_native.h>
+#include <ATen/ops/take_along_dim_native.h>
+#include <ATen/ops/tan_native.h>
+#include <ATen/ops/tanh_native.h>
+#include <ATen/ops/tanh_backward_native.h>
+#include <ATen/ops/tensor_split_native.h>
+#include <ATen/ops/tensordot_native.h>
+#include <ATen/ops/thnn_conv2d_native.h>
+#include <ATen/ops/threshold_native.h>
+#include <ATen/ops/threshold_backward_native.h>
+#include <ATen/ops/tile_native.h>
+#include <ATen/ops/to_native.h>
+#include <ATen/ops/to_dense_native.h>
+#include <ATen/ops/to_dense_backward_native.h>
+#include <ATen/ops/to_mkldnn_native.h>
+#include <ATen/ops/to_mkldnn_backward_native.h>
+#include <ATen/ops/to_padded_tensor_native.h>
+#include <ATen/ops/to_sparse_native.h>
+#include <ATen/ops/to_sparse_bsc_native.h>
+#include <ATen/ops/to_sparse_bsr_native.h>
+#include <ATen/ops/to_sparse_csc_native.h>
+#include <ATen/ops/to_sparse_csr_native.h>
+#include <ATen/ops/topk_native.h>
+#include <ATen/ops/trace_native.h>
+#include <ATen/ops/trace_backward_native.h>
+#include <ATen/ops/transpose_native.h>
+#include <ATen/ops/transpose_copy_native.h>
+#include <ATen/ops/trapezoid_native.h>
+#include <ATen/ops/trapz_native.h>
+#include <ATen/ops/triangular_solve_native.h>
+#include <ATen/ops/tril_native.h>
+#include <ATen/ops/tril_indices_native.h>
+#include <ATen/ops/triplet_margin_loss_native.h>
+#include <ATen/ops/triu_native.h>
+#include <ATen/ops/triu_indices_native.h>
+#include <ATen/ops/true_divide_native.h>
+#include <ATen/ops/trunc_native.h>
+#include <ATen/ops/type_as_native.h>
+#include <ATen/ops/unbind_native.h>
+#include <ATen/ops/unbind_copy_native.h>
+#include <ATen/ops/unflatten_native.h>
+#include <ATen/ops/unflatten_dense_tensors_native.h>
+#include <ATen/ops/unfold_native.h>
+#include <ATen/ops/unfold_backward_native.h>
+#include <ATen/ops/unfold_copy_native.h>
+#include <ATen/ops/uniform_native.h>
+#include <ATen/ops/unique_consecutive_native.h>
+#include <ATen/ops/unique_dim_native.h>
+#include <ATen/ops/unique_dim_consecutive_native.h>
+#include <ATen/ops/unsafe_chunk_native.h>
+#include <ATen/ops/unsafe_split_native.h>
+#include <ATen/ops/unsafe_split_with_sizes_native.h>
+#include <ATen/ops/unsqueeze_native.h>
+#include <ATen/ops/unsqueeze_copy_native.h>
+#include <ATen/ops/upsample_bicubic2d_native.h>
+#include <ATen/ops/upsample_bicubic2d_backward_native.h>
+#include <ATen/ops/upsample_bilinear2d_native.h>
+#include <ATen/ops/upsample_bilinear2d_backward_native.h>
+#include <ATen/ops/upsample_linear1d_native.h>
+#include <ATen/ops/upsample_linear1d_backward_native.h>
+#include <ATen/ops/upsample_nearest1d_native.h>
+#include <ATen/ops/upsample_nearest1d_backward_native.h>
+#include <ATen/ops/upsample_nearest2d_native.h>
+#include <ATen/ops/upsample_nearest2d_backward_native.h>
+#include <ATen/ops/upsample_nearest3d_native.h>
+#include <ATen/ops/upsample_nearest3d_backward_native.h>
+#include <ATen/ops/upsample_trilinear3d_native.h>
+#include <ATen/ops/upsample_trilinear3d_backward_native.h>
+#include <ATen/ops/value_selecting_reduction_backward_native.h>
+#include <ATen/ops/values_native.h>
+#include <ATen/ops/values_copy_native.h>
+#include <ATen/ops/vander_native.h>
+#include <ATen/ops/var_native.h>
+#include <ATen/ops/var_mean_native.h>
+#include <ATen/ops/vdot_native.h>
+#include <ATen/ops/view_native.h>
+#include <ATen/ops/view_as_native.h>
+#include <ATen/ops/view_as_complex_native.h>
+#include <ATen/ops/view_as_complex_copy_native.h>
+#include <ATen/ops/view_as_real_native.h>
+#include <ATen/ops/view_as_real_copy_native.h>
+#include <ATen/ops/view_copy_native.h>
+#include <ATen/ops/vsplit_native.h>
+#include <ATen/ops/vstack_native.h>
+#include <ATen/ops/where_native.h>
+#include <ATen/ops/xlogy_native.h>
+#include <ATen/ops/xor_native.h>
+#include <ATen/ops/zero_native.h>
+#include <ATen/ops/zeros_native.h>
+#include <ATen/ops/zeros_like_native.h>
+
+

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/TensorIteratorInternal.h

@@ -0,0 +1,72 @@
+#pragma once
+#include <ATen/native/TensorIterator.h>
+#include <c10/util/SmallBuffer.h>
+#include <c10/util/irange.h>
+
+namespace at {
+
+struct DimCounter {
+  DimCounter(IntArrayRef shape, Range range);
+
+  void increment(const std::array<int64_t, 2>& step);
+  bool is_done() const;
+  std::array<int64_t, 2> max_2d_step() const;
+
+  IntArrayRef shape;
+  Range range;
+  c10::SmallBuffer<int64_t, 4> values;
+  int64_t offset;
+};
+
+namespace internal {
+
+inline void get_data_ptrs(
+    char** ptrs,
+    ArrayRef<char*> base,
+    IntArrayRef strides,
+    IntArrayRef counter) {
+  const int64_t ntensors = base.size();
+  const int64_t 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<char*, 4> ptrs(ntensors);
+      get_data_ptrs(ptrs.data(), {base_ptrs, ntensors}, strides, {range.begin});
+      loop(ptrs.data(), strides.data(), range.size(), 1);
+    }
+  } else {
+    c10::SmallBuffer<char*, 4> 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

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/Utils.h

@@ -0,0 +1,138 @@
+#pragma once
+
+#include <ATen/EmptyTensor.h>
+#include <ATen/Formatting.h>
+#include <ATen/core/ATenGeneral.h>
+#include <ATen/core/Generator.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/StorageImpl.h>
+#include <c10/core/UndefinedTensorImpl.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/accumulate.h>
+#include <c10/util/irange.h>
+
+#include <algorithm>
+#include <memory>
+#include <numeric>
+#include <sstream>
+#include <typeinfo>
+
+#define AT_DISALLOW_COPY_AND_ASSIGN(TypeName) \
+  TypeName(const TypeName&) = delete;         \
+  void operator=(const TypeName&) = delete
+
+namespace at {
+
+TORCH_API int _crash_if_asan(int);
+
+// Converts a TensorList (i.e. ArrayRef<Tensor> to vector of TensorImpl*)
+// NB: This is ONLY used by legacy TH bindings, and ONLY used by cat.
+// Once cat is ported entirely to ATen this can be deleted!
+static inline std::vector<TensorImpl*> checked_dense_tensor_list_unwrap(
+    ArrayRef<Tensor> tensors,
+    const char* name,
+    int pos,
+    c10::DeviceType device_type,
+    ScalarType scalar_type) {
+  std::vector<TensorImpl*> unwrapped;
+  unwrapped.reserve(tensors.size());
+  for (const auto i : c10::irange(tensors.size())) {
+    const auto& expr = tensors[i];
+    if (expr.layout() != Layout::Strided) {
+      AT_ERROR(
+          "Expected dense tensor but got ",
+          expr.layout(),
+          " for sequence element ",
+          i,
+          " in sequence argument at position #",
+          pos,
+          " '",
+          name,
+          "'");
+    }
+    if (expr.device().type() != device_type) {
+      AT_ERROR(
+          "Expected object of device type ",
+          device_type,
+          " but got device type ",
+          expr.device().type(),
+          " for sequence element ",
+          i,
+          " in sequence argument at position #",
+          pos,
+          " '",
+          name,
+          "'");
+    }
+    if (expr.scalar_type() != scalar_type) {
+      AT_ERROR(
+          "Expected object of scalar type ",
+          scalar_type,
+          " but got scalar type ",
+          expr.scalar_type(),
+          " for sequence element ",
+          i,
+          " in sequence argument at position #",
+          pos,
+          " '",
+          name,
+          "'");
+    }
+    unwrapped.emplace_back(expr.unsafeGetTensorImpl());
+  }
+  return unwrapped;
+}
+
+template <size_t N>
+std::array<int64_t, N> check_intlist(
+    ArrayRef<int64_t> list,
+    const char* name,
+    int pos) {
+  if (list.empty()) {
+    // TODO: is this necessary?  We used to treat nullptr-vs-not in IntList
+    // differently with strides as a way of faking optional.
+    list = {};
+  }
+  auto res = std::array<int64_t, N>();
+  if (list.size() == 1 && N > 1) {
+    res.fill(list[0]);
+    return res;
+  }
+  if (list.size() != N) {
+    AT_ERROR(
+        "Expected a list of ",
+        N,
+        " ints but got ",
+        list.size(),
+        " for argument #",
+        pos,
+        " '",
+        name,
+        "'");
+  }
+  std::copy_n(list.begin(), N, res.begin());
+  return res;
+}
+
+using at::detail::check_size_nonnegative;
+
+namespace detail {
+
+template <typename T>
+TORCH_API Tensor tensor_cpu(ArrayRef<T> values, const TensorOptions& options);
+
+template <typename T>
+TORCH_API Tensor
+tensor_backend(ArrayRef<T> values, const TensorOptions& options);
+
+template <typename T>
+TORCH_API Tensor
+tensor_complex_cpu(ArrayRef<T> values, const TensorOptions& options);
+
+template <typename T>
+TORCH_API Tensor
+tensor_complex_backend(ArrayRef<T> values, const TensorOptions& options);
+} // namespace detail
+
+} // namespace at

env-llmeval/lib/python3.10/site-packages/torch/include/ATen/WrapDimUtilsMulti.h

@@ -0,0 +1,44 @@
+#pragma once
+
+#include <ATen/WrapDimUtils.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/util/irange.h>
+#include <bitset>
+#include <sstream>
+
+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_bitset_size> 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<dim_bitset_size> 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<int64_t>(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

env-llmeval/lib/python3.10/site-packages/torch/include/c10/cuda/impl/CUDAGuardImpl.h

@@ -0,0 +1,211 @@
+#pragma once
+
+#include <c10/core/DeviceGuard.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/GPUTrace.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAException.h>
+#include <c10/cuda/CUDAFunctions.h>
+#include <c10/cuda/CUDAStream.h>
+
+#include <cuda_runtime_api.h>
+
+namespace c10 {
+namespace cuda {
+namespace impl {
+
+struct CUDAGuardImpl final : public c10::impl::DeviceGuardImplInterface {
+  static constexpr DeviceType static_type = DeviceType::CUDA;
+
+  CUDAGuardImpl() = default;
+  explicit CUDAGuardImpl(DeviceType t) {
+    TORCH_INTERNAL_ASSERT(t == DeviceType::CUDA);
+  }
+  DeviceType type() const override {
+    return DeviceType::CUDA;
+  }
+  Device exchangeDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    int old_device_index = c10::cuda::ExchangeDevice(d.index());
+    return Device(DeviceType::CUDA, old_device_index);
+  }
+  Device getDevice() const override {
+    int device;
+    C10_CUDA_CHECK(c10::cuda::GetDevice(&device));
+    return Device(DeviceType::CUDA, device);
+  }
+  c10::optional<Device> uncheckedGetDevice() const noexcept {
+    int device;
+    const auto err = C10_CUDA_ERROR_HANDLED(c10::cuda::GetDevice(&device));
+    C10_CUDA_CHECK_WARN(err);
+    if (err != cudaSuccess) {
+      return c10::nullopt;
+    }
+    return Device(DeviceType::CUDA, device);
+  }
+  void setDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    C10_CUDA_CHECK(c10::cuda::SetDevice(d.index()));
+  }
+  void uncheckedSetDevice(Device d) const noexcept override {
+    C10_CUDA_CHECK_WARN(c10::cuda::MaybeSetDevice(d.index()));
+  }
+  Stream getStream(Device d) const noexcept override {
+    return getCurrentCUDAStream(d.index()).unwrap();
+  }
+  Stream getDefaultStream(Device d) const override {
+    return getDefaultCUDAStream(d.index());
+  }
+  Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false)
+      const override {
+    return getStreamFromPool(isHighPriority, d.index());
+  }
+  // NB: These do NOT set the current device
+  Stream exchangeStream(Stream s) const noexcept override {
+    CUDAStream cs(s);
+    auto old_stream = getCurrentCUDAStream(s.device().index());
+    setCurrentCUDAStream(cs);
+    return old_stream.unwrap();
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return device_count();
+  }
+
+  // Event-related functions
+  void createEvent(cudaEvent_t* cuda_event, const EventFlag flag) const {
+    // Maps PyTorch's Event::Flag to CUDA flag
+    auto cuda_flag = cudaEventDefault;
+    switch (flag) {
+      case EventFlag::PYTORCH_DEFAULT:
+      case EventFlag::CUDA_EVENT_DISABLE_TIMING:
+        cuda_flag = cudaEventDisableTiming;
+        break;
+      case EventFlag::BACKEND_DEFAULT:
+      case EventFlag::CUDA_EVENT_DEFAULT:
+        cuda_flag = cudaEventDefault;
+        break;
+      default:
+        TORCH_CHECK(false, "CUDA event received unknown flag");
+    }
+
+    C10_CUDA_CHECK(cudaEventCreateWithFlags(cuda_event, cuda_flag));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_creation(
+          reinterpret_cast<uintptr_t>(cuda_event));
+    }
+  }
+
+  void destroyEvent(void* event, const DeviceIndex device_index)
+      const noexcept override {
+    if (!event)
+      return;
+    auto cuda_event = static_cast<cudaEvent_t>(event);
+    int orig_device;
+    C10_CUDA_CHECK_WARN(c10::cuda::GetDevice(&orig_device));
+    C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(device_index));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_deletion(
+          reinterpret_cast<uintptr_t>(cuda_event));
+    }
+    C10_CUDA_CHECK_WARN(cudaEventDestroy(cuda_event));
+    C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(orig_device));
+  }
+
+  void record(
+      void** event,
+      const Stream& stream,
+      const DeviceIndex device_index,
+      const EventFlag flag) const override {
+    TORCH_CHECK(
+        device_index == -1 || device_index == stream.device_index(),
+        "Event device index ",
+        device_index,
+        " does not match recording stream's device index ",
+        stream.device_index(),
+        ".");
+
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(*event);
+    CUDAStream cuda_stream{stream};
+
+    // Moves to stream's device to record
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+
+    // Creates the event (lazily)
+    if (!cuda_event)
+      createEvent(&cuda_event, flag);
+    C10_CUDA_CHECK(cudaEventRecord(cuda_event, cuda_stream));
+    // Makes the void* point to the (possibly just allocated) CUDA event
+    *event = cuda_event;
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_record(
+          reinterpret_cast<uintptr_t>(cuda_event),
+          reinterpret_cast<uintptr_t>(cuda_stream.stream()));
+    }
+
+    // Resets device
+    setDevice(orig_device);
+  }
+
+  void block(void* event, const Stream& stream) const override {
+    if (!event)
+      return;
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(event);
+    CUDAStream cuda_stream{stream};
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+    C10_CUDA_CHECK(cudaStreamWaitEvent(
+        cuda_stream,
+        cuda_event,
+        /*flags (must be zero)=*/0));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_wait(
+          reinterpret_cast<uintptr_t>(cuda_event),
+          reinterpret_cast<uintptr_t>(cuda_stream.stream()));
+    }
+    setDevice(orig_device);
+  }
+
+  // May be called from any device
+  bool queryEvent(void* event) const override {
+    if (!event)
+      return true;
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(event);
+    const cudaError_t err = C10_CUDA_ERROR_HANDLED(cudaEventQuery(cuda_event));
+    if (err != cudaErrorNotReady) {
+      C10_CUDA_CHECK(err);
+    } else {
+      // ignore and clear the error if not ready
+      (void)cudaGetLastError();
+    }
+    return (err == cudaSuccess);
+  }
+
+  // Stream-related functions
+  bool queryStream(const Stream& stream) const override {
+    CUDAStream cuda_stream{stream};
+    return cuda_stream.query();
+  }
+
+  void synchronizeStream(const Stream& stream) const override {
+    CUDAStream cuda_stream{stream};
+    cuda_stream.synchronize();
+  }
+
+  void recordDataPtrOnStream(const c10::DataPtr& data_ptr, const Stream& stream)
+      const override {
+    CUDAStream cuda_stream{stream};
+    CUDACachingAllocator::recordStream(data_ptr, cuda_stream);
+  }
+};
+
+} // namespace impl
+} // namespace cuda
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/cuda/impl/CUDATest.h

@@ -0,0 +1,13 @@
+#pragma once
+
+#include <c10/cuda/CUDAMacros.h>
+
+namespace c10 {
+namespace cuda {
+namespace impl {
+
+C10_CUDA_API int c10_cuda_test();
+
+}
+} // namespace cuda
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/cuda/impl/cuda_cmake_macros.h

@@ -0,0 +1,6 @@
+#pragma once
+
+// Automatically generated header file for the C10 CUDA library.  Do not
+// include this file directly.  Instead, include c10/cuda/CUDAMacros.h
+
+#define C10_CUDA_BUILD_SHARED_LIBS

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/AlignOf.h

@@ -0,0 +1,174 @@
+//===--- AlignOf.h - Portable calculation of type alignment -----*- C++ -*-===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+//
+// This file defines the AlignedCharArray and AlignedCharArrayUnion classes.
+//
+//===----------------------------------------------------------------------===//
+
+// ATen: modified from llvm::AlignOf
+// replaced LLVM_ALIGNAS with alignas
+
+#pragma once
+
+#include <cstddef>
+
+namespace c10 {
+
+/// \struct AlignedCharArray
+/// \brief Helper for building an aligned character array type.
+///
+/// This template is used to explicitly build up a collection of aligned
+/// character array types. We have to build these up using a macro and explicit
+/// specialization to cope with MSVC (at least till 2015) where only an
+/// integer literal can be used to specify an alignment constraint. Once built
+/// up here, we can then begin to indirect between these using normal C++
+/// template parameters.
+
+// MSVC requires special handling here.
+#ifndef _MSC_VER
+
+template <size_t Alignment, size_t Size>
+struct AlignedCharArray {
+  alignas(Alignment) char buffer[Size];
+};
+
+#else // _MSC_VER
+
+/// \brief Create a type with an aligned char buffer.
+template <size_t Alignment, size_t Size>
+struct AlignedCharArray;
+
+// We provide special variations of this template for the most common
+// alignments because __declspec(align(...)) doesn't actually work when it is
+// a member of a by-value function argument in MSVC, even if the alignment
+// request is something reasonably like 8-byte or 16-byte. Note that we can't
+// even include the declspec with the union that forces the alignment because
+// MSVC warns on the existence of the declspec despite the union member forcing
+// proper alignment.
+
+template <size_t Size>
+struct AlignedCharArray<1, Size> {
+  union {
+    char aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<2, Size> {
+  union {
+    short aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<4, Size> {
+  union {
+    int aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<8, Size> {
+  union {
+    double aligned;
+    char buffer[Size];
+  };
+};
+
+// The rest of these are provided with a __declspec(align(...)) and we simply
+// can't pass them by-value as function arguments on MSVC.
+
+#define AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(x) \
+  template <size_t Size>                          \
+  struct AlignedCharArray<x, Size> {              \
+    __declspec(align(x)) char buffer[Size];       \
+  };
+
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(16)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(32)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(64)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(128)
+
+#undef AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT
+
+#endif // _MSC_VER
+
+namespace detail {
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+class AlignerImpl {
+  T1 t1;
+  T2 t2;
+  T3 t3;
+  T4 t4;
+  T5 t5;
+  T6 t6;
+  T7 t7;
+  T8 t8;
+  T9 t9;
+  T10 t10;
+
+ public:
+  AlignerImpl() = delete;
+};
+
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+union SizerImpl {
+  char arr1[sizeof(T1)], arr2[sizeof(T2)], arr3[sizeof(T3)], arr4[sizeof(T4)],
+      arr5[sizeof(T5)], arr6[sizeof(T6)], arr7[sizeof(T7)], arr8[sizeof(T8)],
+      arr9[sizeof(T9)], arr10[sizeof(T10)];
+};
+} // end namespace detail
+
+/// \brief This union template exposes a suitably aligned and sized character
+/// array member which can hold elements of any of up to ten types.
+///
+/// These types may be arrays, structs, or any other types. The goal is to
+/// expose a char array buffer member which can be used as suitable storage for
+/// a placement new of any of these types. Support for more than ten types can
+/// be added at the cost of more boilerplate.
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+struct AlignedCharArrayUnion
+    : AlignedCharArray<
+          alignof(detail::AlignerImpl<T1, T2, T3, T4, T5, T6, T7, T8, T9, T10>),
+          sizeof(::c10::detail::
+                     SizerImpl<T1, T2, T3, T4, T5, T6, T7, T8, T9, T10>)> {};
+} // end namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/ApproximateClock.h

@@ -0,0 +1,121 @@
+// Copyright 2023-present Facebook. All Rights Reserved.
+
+#pragma once
+
+#include <chrono>
+#include <cstddef>
+#include <cstdint>
+#include <list>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Optional.h>
+#include <c10/util/hash.h>
+
+#ifndef _WIN32
+#include <ctime>
+#endif
+#if defined(C10_IOS) && defined(C10_MOBILE)
+#include <sys/time.h> // for gettimeofday()
+#endif
+
+#if defined(__i386__) || defined(__x86_64__) || defined(__amd64__)
+#define C10_RDTSC
+#if defined(_MSC_VER)
+#include <intrin.h>
+#elif defined(__CUDACC__) || defined(__HIPCC__)
+#undef C10_RDTSC
+#elif defined(__clang__)
+// `__rdtsc` is available by default.
+// NB: This has to be first, because Clang will also define `__GNUC__`
+#elif defined(__GNUC__)
+#include <x86intrin.h>
+#else
+#undef C10_RDTSC
+#endif
+#endif
+
+namespace c10 {
+
+using time_t = int64_t;
+using steady_clock_t = std::conditional<
+    std::chrono::high_resolution_clock::is_steady,
+    std::chrono::high_resolution_clock,
+    std::chrono::steady_clock>::type;
+
+inline time_t getTimeSinceEpoch() {
+  auto now = std::chrono::system_clock::now().time_since_epoch();
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
+}
+
+inline time_t getTime(bool allow_monotonic = false) {
+#if defined(C10_IOS) && defined(C10_MOBILE)
+  // clock_gettime is only available on iOS 10.0 or newer. Unlike OS X, iOS
+  // can't rely on CLOCK_REALTIME, as it is defined no matter if clock_gettime
+  // is implemented or not
+  struct timeval now;
+  gettimeofday(&now, NULL);
+  return static_cast<time_t>(now.tv_sec) * 1000000000 +
+      static_cast<time_t>(now.tv_usec) * 1000;
+#elif defined(_WIN32) || defined(__MACH__)
+  return std::chrono::duration_cast<std::chrono::nanoseconds>(
+             steady_clock_t::now().time_since_epoch())
+      .count();
+#else
+  // clock_gettime is *much* faster than std::chrono implementation on Linux
+  struct timespec t {};
+  auto mode = CLOCK_REALTIME;
+  if (allow_monotonic) {
+    mode = CLOCK_MONOTONIC;
+  }
+  clock_gettime(mode, &t);
+  return static_cast<time_t>(t.tv_sec) * 1000000000 +
+      static_cast<time_t>(t.tv_nsec);
+#endif
+}
+
+// We often do not need to capture true wall times. If a fast mechanism such
+// as TSC is available we can use that instead and convert back to epoch time
+// during post processing. This greatly reduce the clock's contribution to
+// profiling.
+//   http://btorpey.github.io/blog/2014/02/18/clock-sources-in-linux/
+//   https://quick-bench.com/q/r8opkkGZSJMu9wM_XTbDouq-0Io
+// TODO: We should use
+// `https://github.com/google/benchmark/blob/main/src/cycleclock.h`
+inline auto getApproximateTime() {
+#if defined(C10_RDTSC)
+  return static_cast<uint64_t>(__rdtsc());
+#else
+  return getTime();
+#endif
+}
+
+using approx_time_t = decltype(getApproximateTime());
+static_assert(
+    std::is_same<approx_time_t, int64_t>::value ||
+        std::is_same<approx_time_t, uint64_t>::value,
+    "Expected either int64_t (`getTime`) or uint64_t (some TSC reads).");
+
+// Convert `getCount` results to Nanoseconds since unix epoch.
+class C10_API ApproximateClockToUnixTimeConverter final {
+ public:
+  ApproximateClockToUnixTimeConverter();
+  std::function<time_t(approx_time_t)> makeConverter();
+
+  struct UnixAndApproximateTimePair {
+    time_t t_;
+    approx_time_t approx_t_;
+  };
+  static UnixAndApproximateTimePair measurePair();
+
+ private:
+  static constexpr size_t replicates = 1001;
+  using time_pairs = std::array<UnixAndApproximateTimePair, replicates>;
+  time_pairs measurePairs();
+
+  time_pairs start_times_;
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/BFloat16-inl.h

@@ -0,0 +1,343 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/bit_cast.h>
+
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#else
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+#include <ext/oneapi/bfloat16.hpp>
+#endif
+
+namespace c10 {
+
+/// Constructors
+inline C10_HOST_DEVICE BFloat16::BFloat16(float value)
+    :
+#if defined(__CUDACC__) && !defined(USE_ROCM) && defined(__CUDA_ARCH__) && \
+    __CUDA_ARCH__ >= 800
+      x(__bfloat16_as_ushort(__float2bfloat16(value)))
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+      x(c10::bit_cast<uint16_t>(sycl::ext::oneapi::bfloat16(value)))
+#else
+      // RNE by default
+      x(detail::round_to_nearest_even(value))
+#endif
+{
+}
+
+/// Implicit conversions
+inline C10_HOST_DEVICE BFloat16::operator float() const {
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+  return __bfloat162float(*reinterpret_cast<const __nv_bfloat16*>(&x));
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+  return float(*reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x));
+#else
+  return detail::f32_from_bits(x);
+#endif
+}
+
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+inline C10_HOST_DEVICE BFloat16::BFloat16(const __nv_bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator __nv_bfloat16() const {
+  return *reinterpret_cast<const __nv_bfloat16*>(&x);
+}
+#endif
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+inline C10_HOST_DEVICE BFloat16::BFloat16(
+    const sycl::ext::oneapi::bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator sycl::ext::oneapi::bfloat16() const {
+  return *reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x);
+}
+#endif
+
+// CUDA intrinsics
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+inline C10_DEVICE BFloat16 __ldg(const BFloat16* ptr) {
+#if !defined(USE_ROCM) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+  return __ldg(reinterpret_cast<const __nv_bfloat16*>(ptr));
+#else
+  return *ptr;
+#endif
+}
+#endif
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE BFloat16
+operator+(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator-(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator*(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator/(const BFloat16& a, const BFloat16& b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator-(const BFloat16& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE BFloat16& operator+=(BFloat16& a, const BFloat16& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator-=(BFloat16& a, const BFloat16& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator*=(BFloat16& a, const BFloat16& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator/=(BFloat16& a, const BFloat16& b) {
+  a = a / b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator|(BFloat16& a, const BFloat16& b) {
+  a.x = a.x | b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator^(BFloat16& a, const BFloat16& b) {
+  a.x = a.x ^ b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator&(BFloat16& a, const BFloat16& b) {
+  a.x = a.x & b.x;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(BFloat16 a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(BFloat16 a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(BFloat16 a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(BFloat16 a, float b) {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, BFloat16 b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, BFloat16 b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, BFloat16 b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, BFloat16 b) {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const BFloat16& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const BFloat16& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const BFloat16& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const BFloat16& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(BFloat16 a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(BFloat16 a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(BFloat16 a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(BFloat16 a, double b) {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, BFloat16 b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, BFloat16 b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, BFloat16 b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, BFloat16 b) {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int b) {
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int b) {
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int b) {
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int b) {
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int64_t b) {
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int64_t b) {
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int64_t b) {
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int64_t b) {
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) / b;
+}
+
+// Overloading < and > operators, because std::max and std::min use them.
+
+inline C10_HOST_DEVICE bool operator>(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) > float(rhs);
+}
+
+inline C10_HOST_DEVICE bool operator<(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) < float(rhs);
+}
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::BFloat16> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = true;
+  static constexpr auto has_denorm = numeric_limits<float>::has_denorm;
+  static constexpr auto has_denorm_loss =
+      numeric_limits<float>::has_denorm_loss;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 8;
+  static constexpr int digits10 = 2;
+  static constexpr int max_digits10 = 4;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -125;
+  static constexpr int min_exponent10 = -37;
+  static constexpr int max_exponent = 128;
+  static constexpr int max_exponent10 = 38;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::BFloat16 min() {
+    return c10::BFloat16(0x0080, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 lowest() {
+    return c10::BFloat16(0xFF7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 max() {
+    return c10::BFloat16(0x7F7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 epsilon() {
+    return c10::BFloat16(0x3C00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 round_error() {
+    return c10::BFloat16(0x3F00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 infinity() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 quiet_NaN() {
+    return c10::BFloat16(0x7FC0, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 signaling_NaN() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 denorm_min() {
+    return c10::BFloat16(0x0001, c10::BFloat16::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/CallOnce.h

@@ -0,0 +1,67 @@
+#pragma once
+
+#include <atomic>
+#include <mutex>
+#include <utility>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+
+namespace c10 {
+
+// custom c10 call_once implementation to avoid the deadlock in std::call_once.
+// The implementation here is a simplified version from folly and likely much
+// much higher memory footprint.
+template <typename Flag, typename F, typename... Args>
+inline void call_once(Flag& flag, F&& f, Args&&... args) {
+  if (C10_LIKELY(flag.test_once())) {
+    return;
+  }
+  flag.call_once_slow(std::forward<F>(f), std::forward<Args>(args)...);
+}
+
+class once_flag {
+ public:
+#ifndef _WIN32
+  // running into build error on MSVC. Can't seem to get a repro locally so I'm
+  // just avoiding constexpr
+  //
+  //   C:/actions-runner/_work/pytorch/pytorch\c10/util/CallOnce.h(26): error:
+  //   defaulted default constructor cannot be constexpr because the
+  //   corresponding implicitly declared default constructor would not be
+  //   constexpr 1 error detected in the compilation of
+  //   "C:/actions-runner/_work/pytorch/pytorch/aten/src/ATen/cuda/cub.cu".
+  constexpr
+#endif
+      once_flag() noexcept = default;
+  once_flag(const once_flag&) = delete;
+  once_flag& operator=(const once_flag&) = delete;
+
+ private:
+  template <typename Flag, typename F, typename... Args>
+  friend void call_once(Flag& flag, F&& f, Args&&... args);
+
+  template <typename F, typename... Args>
+  void call_once_slow(F&& f, Args&&... args) {
+    std::lock_guard<std::mutex> guard(mutex_);
+    if (init_.load(std::memory_order_relaxed)) {
+      return;
+    }
+    c10::guts::invoke(f, std::forward<Args>(args)...);
+    init_.store(true, std::memory_order_release);
+  }
+
+  bool test_once() {
+    return init_.load(std::memory_order_acquire);
+  }
+
+  void reset_once() {
+    init_.store(false, std::memory_order_release);
+  }
+
+ private:
+  std::mutex mutex_;
+  std::atomic<bool> init_{false};
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/DimVector.h

@@ -0,0 +1,16 @@
+#pragma once
+
+#include <c10/core/SymInt.h>
+#include <c10/core/impl/SizesAndStrides.h>
+#include <c10/util/SmallVector.h>
+#include <cstdint>
+
+namespace c10 {
+
+constexpr size_t kDimVectorStaticSize = C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE;
+
+/// A container for sizes or strides
+using DimVector = SmallVector<int64_t, kDimVectorStaticSize>;
+using SymDimVector = SmallVector<c10::SymInt, kDimVectorStaticSize>;
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Exception.h

@@ -0,0 +1,715 @@
+#ifndef C10_UTIL_EXCEPTION_H_
+#define C10_UTIL_EXCEPTION_H_
+
+#include <c10/macros/Macros.h>
+#include <c10/util/StringUtil.h>
+
+#include <cstddef>
+#include <exception>
+#include <string>
+#include <variant>
+#include <vector>
+
+#if defined(_MSC_VER) && _MSC_VER <= 1900
+#define __func__ __FUNCTION__
+#endif
+
+namespace c10 {
+
+/// The primary ATen error class.
+/// Provides a complete error message with source location information via
+/// `what()`, and a more concise message via `what_without_backtrace()`.
+/// Don't throw this directly; use TORCH_CHECK/TORCH_INTERNAL_ASSERT instead.
+///
+/// NB: c10::Error is handled specially by the default torch to suppress the
+/// backtrace, see torch/csrc/Exceptions.h
+class C10_API Error : public std::exception {
+  // The actual error message.
+  std::string msg_;
+
+  // Context for the message (in order of decreasing specificity).  Context will
+  // be automatically formatted appropriately, so it is not necessary to add
+  // extra leading/trailing newlines to strings inside this vector
+  std::vector<std::string> context_;
+
+  // The C++ backtrace at the point when this exception was raised.  This
+  // may be empty if there is no valid backtrace.  (We don't use optional
+  // here to reduce the dependencies this file has.)
+  std::string backtrace_;
+
+  // These two are derived fields from msg_stack_ and backtrace_, but we need
+  // fields for the strings so that we can return a const char* (as the
+  // signature of std::exception requires).  Currently, the invariant
+  // is that these fields are ALWAYS populated consistently with respect
+  // to msg_stack_ and backtrace_.
+  std::string what_;
+  std::string what_without_backtrace_;
+
+  // This is a little debugging trick: you can stash a relevant pointer
+  // in caller, and then when you catch the exception, you can compare
+  // against pointers you have on hand to get more information about
+  // where the exception came from.  In Caffe2, this is used to figure
+  // out which operator raised an exception.
+  const void* caller_;
+
+ public:
+  // PyTorch-style Error constructor.  NB: the implementation of this
+  // is actually in Logging.cpp
+  Error(SourceLocation source_location, std::string msg);
+
+  // Caffe2-style error message
+  Error(
+      const char* file,
+      const uint32_t line,
+      const char* condition,
+      const std::string& msg,
+      const std::string& backtrace,
+      const void* caller = nullptr);
+
+  // Base constructor
+  Error(std::string msg, std::string backtrace, const void* caller = nullptr);
+
+  // Add some new context to the message stack.  The last added context
+  // will be formatted at the end of the context list upon printing.
+  // WARNING: This method is O(n) in the size of the stack, so don't go
+  // wild adding a ridiculous amount of context to error messages.
+  void add_context(std::string msg);
+
+  const std::string& msg() const {
+    return msg_;
+  }
+
+  const std::vector<std::string>& context() const {
+    return context_;
+  }
+
+  const std::string& backtrace() const {
+    return backtrace_;
+  }
+
+  /// Returns the complete error message, including the source location.
+  /// The returned pointer is invalidated if you call add_context() on
+  /// this object.
+  const char* what() const noexcept override {
+    return what_.c_str();
+  }
+
+  const void* caller() const noexcept {
+    return caller_;
+  }
+
+  /// Returns only the error message string, without source location.
+  /// The returned pointer is invalidated if you call add_context() on
+  /// this object.
+  virtual const char* what_without_backtrace() const noexcept {
+    return what_without_backtrace_.c_str();
+  }
+
+ private:
+  void refresh_what();
+  std::string compute_what(bool include_backtrace) const;
+};
+
+class C10_API Warning {
+ public:
+  class C10_API UserWarning {};
+  class C10_API DeprecationWarning {};
+
+  using warning_variant_t = std::variant<UserWarning, DeprecationWarning>;
+
+  Warning(
+      warning_variant_t type,
+      const SourceLocation& source_location,
+      std::string msg,
+      bool verbatim);
+
+  Warning(
+      warning_variant_t type,
+      SourceLocation source_location,
+      const char* msg,
+      bool verbatim);
+
+  Warning(
+      warning_variant_t type,
+      SourceLocation source_location,
+      ::c10::detail::CompileTimeEmptyString msg,
+      bool verbatim);
+
+  // Getters for members
+  warning_variant_t type() const;
+  const SourceLocation& source_location() const;
+  const std::string& msg() const;
+  bool verbatim() const;
+
+ private:
+  // The type of warning
+  warning_variant_t type_;
+
+  // Where the warning happened.
+  SourceLocation source_location_;
+
+  // The actual warning message.
+  std::string msg_;
+
+  // See note: [Verbatim Warnings]
+  bool verbatim_;
+};
+
+using UserWarning = Warning::UserWarning;
+using DeprecationWarning = Warning::DeprecationWarning;
+
+// Issue a warning with a given message. Dispatched to the current
+// warning handler.
+void C10_API warn(const Warning& warning);
+
+class C10_API WarningHandler {
+ public:
+  virtual ~WarningHandler() = default;
+  /// The default warning handler. Prints the message to stderr.
+  virtual void process(const Warning& warning);
+};
+
+namespace WarningUtils {
+
+// Note: [Verbatim Warnings]
+// Warnings originating in C++ code can appear out-of-place to Python users:
+// a user runs a line in Python, but the warning references a line in C++.
+// Some parts of PyTorch, like the JIT, are cognizant of this mismatch
+// and take care to map warnings back to the user's program, but most
+// of PyTorch simply throws a context-free warning. To allow warning
+// handlers to add context where appropriate, warn takes the
+// "verbatim" flag. When this is false a warning handler might append
+// the C++ warning to a Python warning message that relates the warning
+// back to the user's program. Callers who have already accounted for
+// context in their warnings should set verbatim to true so their warnings
+// appear without modification.
+
+/// Sets the global warning handler. This is not thread-safe, so it should
+/// generally be called once during initialization or while holding the GIL
+/// for programs that use python.
+/// User is responsible for keeping the WarningHandler alive until
+/// it is not needed.
+C10_API void set_warning_handler(WarningHandler* handler) noexcept(true);
+/// Gets the global warning handler.
+C10_API WarningHandler* get_warning_handler() noexcept(true);
+
+class C10_API WarningHandlerGuard {
+  WarningHandler* prev_handler_;
+
+ public:
+  WarningHandlerGuard(WarningHandler* new_handler)
+      : prev_handler_(c10::WarningUtils::get_warning_handler()) {
+    c10::WarningUtils::set_warning_handler(new_handler);
+  }
+  ~WarningHandlerGuard() {
+    c10::WarningUtils::set_warning_handler(prev_handler_);
+  }
+};
+
+/// The TORCH_WARN_ONCE macro is difficult to test for. Use
+/// setWarnAlways(true) to turn it into TORCH_WARN, which can be
+/// tested for more easily.
+C10_API void set_warnAlways(bool) noexcept(true);
+C10_API bool get_warnAlways() noexcept(true);
+
+// A RAII guard that sets warn_always (not thread-local) on
+// construction, and sets it back to the original value upon destruction.
+struct C10_API WarnAlways {
+ public:
+  explicit WarnAlways(bool setting = true);
+  ~WarnAlways();
+
+ private:
+  bool prev_setting;
+};
+
+} // namespace WarningUtils
+
+// Like Error, but we always report the C++ backtrace, instead of only
+// reporting when TORCH_SHOW_CPP_STACKTRACES
+class C10_API ErrorAlwaysShowCppStacktrace : public Error {
+  using Error::Error;
+  const char* what_without_backtrace() const noexcept override {
+    return what();
+  }
+};
+
+// Used in ATen for out-of-bound indices that can reasonably only be detected
+// lazily inside a kernel (See: advanced indexing).  These turn into
+// IndexError when they cross to Python.
+class C10_API IndexError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for invalid values.  These turn into
+// ValueError when they cross to Python.
+class C10_API ValueError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for invalid types.  These turn into
+// TypeError when they cross to Python.
+class C10_API TypeError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for functionality that is not implemented.  These turn into
+// NotImplementedError when they cross to Python.
+class C10_API NotImplementedError : public Error {
+  using Error::Error;
+};
+
+// Used in ATen for non finite indices.  These turn into
+// ExitException when they cross to Python.
+class C10_API EnforceFiniteError : public Error {
+  using Error::Error;
+};
+
+// Used in Onnxifi backend lowering.  These turn into
+// ExitException when they cross to Python.
+class C10_API OnnxfiBackendSystemError : public Error {
+  using Error::Error;
+};
+
+// Used for numerical errors from the linalg module. These
+// turn into LinAlgError when they cross into Python.
+class C10_API LinAlgError : public Error {
+  using Error::Error;
+};
+
+class C10_API OutOfMemoryError : public Error {
+  using Error::Error;
+};
+
+// Base error type for all distributed errors.
+// These turn into DistError when they cross into Python.
+class C10_API DistError : public Error {
+  using Error::Error;
+};
+
+// Used for collective communication library errors from the distributed module.
+// These turn into DistBackendError when they cross into Python.
+class C10_API DistBackendError : public DistError {
+  using DistError::DistError;
+};
+
+// Used for errors originating from the store.
+// These turn into DistStoreError when they cross into Python.
+class C10_API DistStoreError : public DistError {
+  using DistError::DistError;
+};
+
+// Used for errors originating from the TCP/IP stack and not from collective
+// libraries. These turn into DistNetworkError when they cross into Python.
+class C10_API DistNetworkError : public DistError {
+  using DistError::DistError;
+};
+
+// A utility function to return an exception std::string by prepending its
+// exception type before its what() content
+C10_API std::string GetExceptionString(const std::exception& e);
+
+} // namespace c10
+
+// Private helper macro for implementing TORCH_INTERNAL_ASSERT and TORCH_CHECK
+//
+// Note: In the debug build With MSVC, __LINE__ might be of long type (a.k.a
+// int32_t), which is different from the definition of `SourceLocation` that
+// requires unsigned int (a.k.a uint32_t) and may cause a compile error with the
+// message: error C2397: conversion from 'long' to 'uint32_t' requires a
+// narrowing conversion Here the static cast is used to pass the build. if this
+// is used inside a lambda the __func__ macro expands to operator(), which isn't
+// very useful, but hard to fix in a macro so suppressing the warning.
+#define C10_THROW_ERROR(err_type, msg) \
+  throw ::c10::err_type(               \
+      {__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, msg)
+
+#define C10_BUILD_ERROR(err_type, msg) \
+  ::c10::err_type({__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, msg)
+
+// Private helper macro for workaround MSVC misexpansion of nested macro
+// invocations involving __VA_ARGS__.  See
+// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly
+#define C10_EXPAND_MSVC_WORKAROUND(x) x
+
+// On nvcc, C10_UNLIKELY thwarts missing return statement analysis.  In cases
+// where the unlikely expression may be a constant, use this macro to ensure
+// return statement analysis keeps working (at the cost of not getting the
+// likely/unlikely annotation on nvcc).
+// https://github.com/pytorch/pytorch/issues/21418
+//
+// Currently, this is only used in the error reporting macros below.  If you
+// want to use it more generally, move me to Macros.h
+//
+// TODO: Brian Vaughan observed that we might be able to get this to work on
+// nvcc by writing some sort of C++ overload that distinguishes constexpr inputs
+// from non-constexpr.  Since there isn't any evidence that losing C10_UNLIKELY
+// in nvcc is causing us perf problems, this is not yet implemented, but this
+// might be an interesting piece of C++ code for an intrepid bootcamper to
+// write.
+#if defined(__CUDACC__)
+#define C10_UNLIKELY_OR_CONST(e) e
+#else
+#define C10_UNLIKELY_OR_CONST(e) C10_UNLIKELY(e)
+#endif
+
+// ----------------------------------------------------------------------------
+// Error reporting macros
+// ----------------------------------------------------------------------------
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_RETHROW(e, ...) throw
+#else
+#define TORCH_RETHROW(e, ...)               \
+  do {                                      \
+    e.add_context(::c10::str(__VA_ARGS__)); \
+    throw;                                  \
+  } while (false)
+#endif
+
+// A utility macro to provide assert()-like functionality; that is, enforcement
+// of internal invariants in code.  It supports an arbitrary number of extra
+// arguments (evaluated only on failure), which will be printed in the assert
+// failure message using operator<< (this is useful to print some variables
+// which may be useful for debugging.)
+//
+// Usage:
+//    TORCH_INTERNAL_ASSERT(should_be_true);
+//    TORCH_INTERNAL_ASSERT(x == 0, "x = ", x);
+//
+// Assuming no bugs in PyTorch, the conditions tested by this macro should
+// always be true; e.g., it should be possible to disable all of these
+// conditions without changing observable user behavior.  If you would like to
+// do error reporting for user input, please use TORCH_CHECK instead.
+//
+// NOTE: It is SAFE to use this macro in production code; on failure, this
+// simply raises an exception, it does NOT unceremoniously quit the process
+// (unlike assert()).
+//
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_INTERNAL_ASSERT(cond, ...)                              \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                               \
+    ::c10::detail::torchCheckFail(                                    \
+        __func__,                                                     \
+        __FILE__,                                                     \
+        static_cast<uint32_t>(__LINE__),                              \
+        #cond " INTERNAL ASSERT FAILED at " C10_STRINGIZE(__FILE__)); \
+  }
+#else
+// It would be nice if we could build a combined string literal out of
+// the TORCH_INTERNAL_ASSERT prefix and a user-provided string literal
+// as the first argument, but there doesn't seem to be any good way to
+// do that while still supporting having a first argument that isn't a
+// string literal.
+#define TORCH_INTERNAL_ASSERT(cond, ...)                                         \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                                          \
+    ::c10::detail::torchInternalAssertFail(                                      \
+        __func__,                                                                \
+        __FILE__,                                                                \
+        static_cast<uint32_t>(__LINE__),                                         \
+        #cond                                                                    \
+        " INTERNAL ASSERT FAILED at " C10_STRINGIZE(__FILE__) ":" C10_STRINGIZE( \
+            __LINE__) ", please report a bug to PyTorch. ",                      \
+        c10::str(__VA_ARGS__));                                                  \
+  }
+#endif
+
+// A utility macro to make it easier to test for error conditions from user
+// input.  Like TORCH_INTERNAL_ASSERT, it supports an arbitrary number of extra
+// arguments (evaluated only on failure), which will be printed in the error
+// message using operator<< (e.g., you can pass any object which has
+// operator<< defined.  Most objects in PyTorch have these definitions!)
+//
+// Usage:
+//    TORCH_CHECK(should_be_true); // A default error message will be provided
+//                                 // in this case; but we recommend writing an
+//                                 // explicit error message, as it is more
+//                                 // user friendly.
+//    TORCH_CHECK(x == 0, "Expected x to be 0, but got ", x);
+//
+// On failure, this macro will raise an exception.  If this exception propagates
+// to Python, it will convert into a Python RuntimeError.
+//
+// NOTE: It is SAFE to use this macro in production code; on failure, this
+// simply raises an exception, it does NOT unceremoniously quit the process
+// (unlike CHECK() from glog.)
+//
+#define TORCH_CHECK_WITH(error_t, cond, ...) \
+  TORCH_CHECK_WITH_MSG(error_t, cond, "", __VA_ARGS__)
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_CHECK_MSG(cond, type, ...) \
+  (#cond #type " CHECK FAILED at " C10_STRINGIZE(__FILE__))
+#define TORCH_CHECK_WITH_MSG(error_t, cond, type, ...)                \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                               \
+    C10_THROW_ERROR(Error, TORCH_CHECK_MSG(cond, type, __VA_ARGS__)); \
+  }
+#else
+namespace c10 {
+namespace detail {
+template <typename... Args>
+decltype(auto) torchCheckMsgImpl(const char* /*msg*/, const Args&... args) {
+  return ::c10::str(args...);
+}
+inline C10_API const char* torchCheckMsgImpl(const char* msg) {
+  return msg;
+}
+// If there is just 1 user-provided C-string argument, use it.
+inline C10_API const char* torchCheckMsgImpl(
+    const char* /*msg*/,
+    const char* args) {
+  return args;
+}
+} // namespace detail
+} // namespace c10
+
+#define TORCH_CHECK_MSG(cond, type, ...)                   \
+  (::c10::detail::torchCheckMsgImpl(                       \
+      "Expected " #cond                                    \
+      " to be true, but got false.  "                      \
+      "(Could this error message be improved?  If so, "    \
+      "please report an enhancement request to PyTorch.)", \
+      ##__VA_ARGS__))
+#define TORCH_CHECK_WITH_MSG(error_t, cond, type, ...)                  \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {                                 \
+    C10_THROW_ERROR(error_t, TORCH_CHECK_MSG(cond, type, __VA_ARGS__)); \
+  }
+#endif
+
+namespace c10 {
+namespace detail {
+
+[[noreturn]] C10_API void torchCheckFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const std::string& msg);
+[[noreturn]] C10_API void torchCheckFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* msg);
+
+// The c10::str() call that creates userMsg can have 1 of 3 return
+// types depending on the number and types of arguments passed to
+// TORCH_INTERNAL_ASSERT.  0 arguments will get a
+// CompileTimeEmptyString, 1 const char * will be passed straight
+// through, and anything else will get converted to std::string.
+[[noreturn]] C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    const char* userMsg);
+[[noreturn]] inline C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    ::c10::detail::CompileTimeEmptyString /*userMsg*/) {
+  torchCheckFail(func, file, line, condMsg);
+}
+[[noreturn]] C10_API void torchInternalAssertFail(
+    const char* func,
+    const char* file,
+    uint32_t line,
+    const char* condMsg,
+    const std::string& userMsg);
+
+} // namespace detail
+} // namespace c10
+
+#ifdef STRIP_ERROR_MESSAGES
+#define TORCH_CHECK(cond, ...)                   \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {          \
+    ::c10::detail::torchCheckFail(               \
+        __func__,                                \
+        __FILE__,                                \
+        static_cast<uint32_t>(__LINE__),         \
+        TORCH_CHECK_MSG(cond, "", __VA_ARGS__)); \
+  }
+#else
+#define TORCH_CHECK(cond, ...)                     \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {            \
+    ::c10::detail::torchCheckFail(                 \
+        __func__,                                  \
+        __FILE__,                                  \
+        static_cast<uint32_t>(__LINE__),           \
+        TORCH_CHECK_MSG(cond, "", ##__VA_ARGS__)); \
+  }
+#endif
+
+// An utility macro that does what `TORCH_CHECK` does if compiled in the host
+// code, otherwise does nothing. Supposed to be used in the code shared between
+// host and device code as an alternative for `TORCH_CHECK`.
+#if defined(__CUDACC__) || defined(__HIPCC__)
+#define TORCH_CHECK_IF_NOT_ON_CUDA(cond, ...)
+#else
+#define TORCH_CHECK_IF_NOT_ON_CUDA(cond, ...) TORCH_CHECK(cond, ##__VA_ARGS__)
+#endif
+
+// Debug only version of TORCH_INTERNAL_ASSERT. This macro only checks in debug
+// build, and does nothing in release build.  It is appropriate to use
+// in situations where you want to add an assert to a hotpath, but it is
+// too expensive to run this assert on production builds.
+#ifdef NDEBUG
+// Optimized version - generates no code.
+#define TORCH_INTERNAL_ASSERT_DEBUG_ONLY(...) \
+  while (false)                               \
+  C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__))
+#else
+#define TORCH_INTERNAL_ASSERT_DEBUG_ONLY(...) \
+  C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__))
+#endif
+
+// TODO: We're going to get a lot of similar looking string literals
+// this way; check if this actually affects binary size.
+
+// Like TORCH_CHECK, but raises LinAlgError instead of Error.
+#define TORCH_CHECK_LINALG(cond, ...) \
+  TORCH_CHECK_WITH_MSG(LinAlgError, cond, "LINALG", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises IndexErrors instead of Errors.
+#define TORCH_CHECK_INDEX(cond, ...) \
+  TORCH_CHECK_WITH_MSG(IndexError, cond, "INDEX", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises ValueErrors instead of Errors.
+#define TORCH_CHECK_VALUE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(ValueError, cond, "VALUE", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises TypeErrors instead of Errors.
+#define TORCH_CHECK_TYPE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(TypeError, cond, "TYPE", __VA_ARGS__)
+
+// Like TORCH_CHECK, but raises NotImplementedErrors instead of Errors.
+#define TORCH_CHECK_NOT_IMPLEMENTED(cond, ...) \
+  TORCH_CHECK_WITH_MSG(NotImplementedError, cond, "TYPE", __VA_ARGS__)
+
+#define TORCH_CHECK_ALWAYS_SHOW_CPP_STACKTRACE(cond, ...) \
+  TORCH_CHECK_WITH_MSG(                                   \
+      ErrorAlwaysShowCppStacktrace, cond, "TYPE", ##__VA_ARGS__)
+
+#ifdef STRIP_ERROR_MESSAGES
+#define WARNING_MESSAGE_STRING(...) \
+  ::c10::detail::CompileTimeEmptyString {}
+#else
+#define WARNING_MESSAGE_STRING(...) ::c10::str(__VA_ARGS__)
+#endif
+
+// Report a warning to the user.  Accepts an arbitrary number of extra
+// arguments which are concatenated into the warning message using operator<<
+//
+#ifdef DISABLE_WARN
+#define _TORCH_WARN_WITH(...) ((void)0);
+#else
+#define _TORCH_WARN_WITH(warning_t, ...)                     \
+  ::c10::warn(::c10::Warning(                                \
+      warning_t(),                                           \
+      {__func__, __FILE__, static_cast<uint32_t>(__LINE__)}, \
+      WARNING_MESSAGE_STRING(__VA_ARGS__),                   \
+      false));
+#endif
+
+#define TORCH_WARN(...) _TORCH_WARN_WITH(::c10::UserWarning, __VA_ARGS__);
+
+#define TORCH_WARN_DEPRECATION(...) \
+  _TORCH_WARN_WITH(::c10::DeprecationWarning, __VA_ARGS__);
+
+// Report a warning to the user only once.  Accepts an arbitrary number of extra
+// arguments which are concatenated into the warning message using operator<<
+//
+#define _TORCH_WARN_ONCE(...)                                             \
+  C10_UNUSED static const auto C10_ANONYMOUS_VARIABLE(torch_warn_once_) = \
+      [&] {                                                               \
+        TORCH_WARN(__VA_ARGS__);                                          \
+        return true;                                                      \
+      }()
+
+#ifdef DISABLE_WARN
+#define TORCH_WARN_ONCE(...) ((void)0);
+#else
+#define TORCH_WARN_ONCE(...)                   \
+  if (::c10::WarningUtils::get_warnAlways()) { \
+    TORCH_WARN(__VA_ARGS__);                   \
+  } else {                                     \
+    _TORCH_WARN_ONCE(__VA_ARGS__);             \
+  }
+#endif
+
+// Report an error with a specific argument
+// NOTE: using the argument name in TORCH_CHECK's message is preferred
+#define TORCH_CHECK_ARG(cond, argN, ...) \
+  TORCH_CHECK(cond, "invalid argument ", argN, ": ", __VA_ARGS__)
+
+// ----------------------------------------------------------------------------
+// Deprecated macros
+// ----------------------------------------------------------------------------
+
+namespace c10 {
+namespace detail {
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ERROR(msg) is deprecated, use TORCH_CHECK(false, msg)
+instead.")
+*/
+inline void deprecated_AT_ERROR() {}
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ASSERT is deprecated, if you mean to indicate an
+internal invariant failure, use " \
+                       "TORCH_INTERNAL_ASSERT instead; if you mean to do user
+error checking, use " \ "TORCH_CHECK.  See
+https://github.com/pytorch/pytorch/issues/20287 for more details.")
+*/
+inline void deprecated_AT_ASSERT() {}
+
+/*
+// Deprecation disabled until we fix sites in our codebase
+C10_DEPRECATED_MESSAGE("AT_ASSERTM is deprecated, if you mean to indicate an
+internal invariant failure, use " \
+                       "TORCH_INTERNAL_ASSERT instead; if you mean to do user
+error checking, use " \ "TORCH_CHECK.  See
+https://github.com/pytorch/pytorch/issues/20287 for more details.")
+*/
+inline void deprecated_AT_ASSERTM() {}
+
+} // namespace detail
+} // namespace c10
+
+// Deprecated alias; this alias was deprecated because people kept mistakenly
+// using it for user error checking.  Use TORCH_INTERNAL_ASSERT or TORCH_CHECK
+// instead. See https://github.com/pytorch/pytorch/issues/20287 for more
+// details.
+#define AT_ASSERT(...)                                              \
+  do {                                                              \
+    ::c10::detail::deprecated_AT_ASSERT();                          \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(__VA_ARGS__)); \
+  } while (false)
+
+// Deprecated alias, like AT_ASSERT.  The new TORCH_INTERNAL_ASSERT macro
+// supports both 0-ary and variadic calls, so having a separate
+// message-accepting macro is not necessary.
+//
+// NB: we MUST include cond explicitly here, as MSVC will miscompile the macro
+// expansion, shunting all of __VA_ARGS__ to cond.  An alternate workaround
+// can be seen at
+// https://stackoverflow.com/questions/5134523/msvc-doesnt-expand-va-args-correctly
+#define AT_ASSERTM(cond, ...)                                             \
+  do {                                                                    \
+    ::c10::detail::deprecated_AT_ASSERTM();                               \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_INTERNAL_ASSERT(cond, __VA_ARGS__)); \
+  } while (false)
+
+// Deprecated alias; this alias was deprecated because it represents extra API
+// surface that makes it hard for people to understand what macro to use.
+// Use TORCH_CHECK(false, ...) or TORCH_INTERNAL_ASSERT(false, ...) to
+// unconditionally fail at a line of code.
+#define AT_ERROR(...)                                                        \
+  do {                                                                       \
+    ::c10::detail::deprecated_AT_ERROR();                                    \
+    C10_EXPAND_MSVC_WORKAROUND(TORCH_CHECK(false, ::c10::str(__VA_ARGS__))); \
+  } while (false)
+
+#endif // C10_UTIL_EXCEPTION_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/ExclusivelyOwned.h

@@ -0,0 +1,143 @@
+#pragma once
+
+#include <c10/util/in_place.h>
+
+namespace c10 {
+
+// See example implementation in TensorBase.h and TensorBody.h.
+// Synopsis:
+//
+// repr_type -- type to use to store an owned T in ExclusivelyOwned.
+//
+// pointer_type -- pointer-esque type to return from
+// ExclusivelyOwned's get() and operator*() methods.
+//
+// const_pointer_type -- similar to pointer_type, used for the const methods.
+//
+// static repr_type nullRepr() -- return a null instance of repr_type.
+//
+// template <class... Args>
+// static repr_type createInPlace(Args&&... args) -- used by the in-place
+// ExclusivelyOwned constructor.
+//
+// static repr_type moveToRepr(T&& x) -- move the given x into an
+// instance of repr_type. used by the ExclusivelyOwned(T&&)
+// constructor.
+//
+// static void destroyOwned(repr_type x) -- free memory for a
+// known-exclusively-owned instance of x. Replaces calling repr_type's
+// destructor. Being able to implement this more efficiently than
+// repr_type's destructor is the main reason to use ExclusivelyOwned
+// for a type.
+//
+// static T take(repr_type&) -- move out of the given repr_type into an owned T.
+//
+// static pointer_type getImpl(const repr_type&) -- return a pointer
+// to the given repr_type. May take repr_type by value if that is more
+// efficient.
+template <typename T>
+struct ExclusivelyOwnedTraits;
+
+/// ExclusivelyOwned is a smart-pointer-like wrapper around an
+/// exclusively-owned instance of some type T that normally has
+/// mandatory reference counting (currently just Tensor). If you have
+/// an isolated piece of code that knows that it has sole ownership of
+/// an object of one of these types (i.e., because you created it
+/// directly or using a factory function) and that object will not
+/// escape from that isolated piece of code, then moving the object
+/// into an ExclusivelyOwned will avoid an atomic reference count
+/// decrement at destruction time.
+///
+/// If you directly create the Tensor in the first
+/// place, you can use the in_place constructor of ExclusivelyOwned to
+/// additionally avoid doing any stores to initialize the refcount &
+/// weakcount.
+template <typename T>
+class ExclusivelyOwned {
+  using EOT = ExclusivelyOwnedTraits<T>;
+  union {
+    char dummy_;
+    typename ExclusivelyOwnedTraits<T>::repr_type repr_;
+  };
+
+ public:
+  ExclusivelyOwned() : repr_(EOT::nullRepr()) {}
+
+  explicit ExclusivelyOwned(T&& t) : repr_(EOT::moveToRepr(std::move(t))) {}
+
+  template <class... Args>
+  explicit ExclusivelyOwned(in_place_t, Args&&... args)
+      : repr_(EOT::createInPlace(std::forward<Args>(args)...)) {}
+
+  ExclusivelyOwned(const ExclusivelyOwned&) = delete;
+
+  ExclusivelyOwned(ExclusivelyOwned&& rhs) noexcept
+      : repr_(std::move(rhs.repr_)) {
+    rhs.repr_ = EOT::nullRepr();
+  }
+
+  ExclusivelyOwned& operator=(const ExclusivelyOwned&) = delete;
+
+  ExclusivelyOwned& operator=(ExclusivelyOwned&& rhs) noexcept {
+    EOT::destroyOwned(repr_);
+    repr_ = std::move(rhs.repr_);
+    rhs.repr_ = EOT::nullRepr();
+    return *this;
+  }
+
+  ExclusivelyOwned& operator=(T&& rhs) noexcept {
+    EOT::destroyOwned(repr_);
+    repr_ = EOT::moveToRepr(std::move(rhs));
+    return *this;
+  }
+
+  ~ExclusivelyOwned() {
+    EOT::destroyOwned(repr_);
+    // Don't bother to call the destructor of repr_, since we already
+    // did specialized destruction for the exclusively-owned case in
+    // destroyOwned!
+  }
+
+  // We don't provide this because it would require us to be able to
+  // differentiate an owned-but-empty T from a lack of T. This is
+  // particularly problematic for Tensor, which wants to use an
+  // undefined Tensor as its null state.
+  explicit operator bool() const noexcept = delete;
+
+  operator T() && {
+    return take();
+  }
+
+  // NOTE: the equivalent operation on MaybeOwned is a moving
+  // operator*. For ExclusivelyOwned, take() and operator*() may well
+  // have different return types, so they are different functions.
+  T take() && {
+    return EOT::take(repr_);
+  }
+
+  typename EOT::const_pointer_type operator->() const {
+    return get();
+  }
+
+  typename EOT::const_pointer_type get() const {
+    return EOT::getImpl(repr_);
+  }
+
+  typename EOT::pointer_type operator->() {
+    return get();
+  }
+
+  typename EOT::pointer_type get() {
+    return EOT::getImpl(repr_);
+  }
+
+  std::remove_pointer_t<typename EOT::const_pointer_type>& operator*() const {
+    return *get();
+  }
+
+  std::remove_pointer_t<typename EOT::pointer_type>& operator*() {
+    return *get();
+  }
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/ExclusivelyOwnedTensorTraits.h

@@ -0,0 +1,74 @@
+#pragma once
+
+#include <c10/core/TensorImpl.h>
+
+#include <utility>
+
+namespace c10 {
+// Shared ExclusivelyOwnedTraits implementation between caffe2::Tensor and
+// at::TensorBase.
+template <typename TensorType>
+struct ExclusivelyOwnedTensorTraits {
+  using repr_type = TensorType;
+  using pointer_type = TensorType*;
+  using const_pointer_type = const TensorType*;
+
+  static repr_type nullRepr() {
+    return TensorType();
+  }
+
+  template <class... Args>
+  static repr_type createInPlace(Args&&... args) {
+    return TensorType(std::forward<Args>(args)...);
+  }
+
+  static repr_type moveToRepr(TensorType&& x) {
+    return std::move(x);
+  }
+
+  static void destroyOwned(TensorType& x) {
+    TensorImpl* const toDestroy = x.unsafeReleaseTensorImpl();
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy != nullptr, "Tensor somehow got null TensorImpl?");
+    // May be 0 because UndefinedTensorImpl doesn't get its refcount
+    // incremented.
+    const bool isUndefined = toDestroy == UndefinedTensorImpl::singleton();
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy->refcount_ == 1 || (toDestroy->refcount_ == 0 && isUndefined),
+        "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
+        isUndefined,
+        " and refcount ",
+        toDestroy->refcount_,
+        ", expected 1 or, if isUndefined, 0!");
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        toDestroy->weakcount_ == 1 ||
+            (toDestroy->weakcount_ == 0 &&
+             toDestroy == UndefinedTensorImpl::singleton()),
+        "ExclusivelyOwned<Tensor> destroyed with isUndefined ",
+        isUndefined,
+        " and weakcount ",
+        toDestroy->weakcount_,
+        ", expected 1 or, if isUndefined, 0!");
+    if (!isUndefined) {
+#ifndef NDEBUG
+      // Needed to pass the debug assertions in ~intrusive_ptr_target.
+      toDestroy->refcount_ = 0;
+      toDestroy->weakcount_ = 0;
+#endif
+      delete toDestroy;
+    }
+  }
+
+  static TensorType take(TensorType& x) {
+    return std::move(x);
+  }
+
+  static pointer_type getImpl(repr_type& x) {
+    return &x;
+  }
+
+  static const_pointer_type getImpl(const repr_type& x) {
+    return &x;
+  }
+};
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/FbcodeMaps.h

@@ -0,0 +1,29 @@
+#ifndef C10_UTIL_FBCODEMAPS_H_
+#define C10_UTIL_FBCODEMAPS_H_
+
+// Map typedefs so that we can use folly's F14 maps in fbcode without
+// taking a folly dependency.
+
+#ifdef FBCODE_CAFFE2
+#include <folly/container/F14Map.h>
+#include <folly/container/F14Set.h>
+#else
+#include <unordered_map>
+#include <unordered_set>
+#endif
+
+namespace c10 {
+#ifdef FBCODE_CAFFE2
+template <typename Key, typename Value>
+using FastMap = folly::F14FastMap<Key, Value>;
+template <typename Key>
+using FastSet = folly::F14FastSet<Key>;
+#else
+template <typename Key, typename Value>
+using FastMap = std::unordered_map<Key, Value>;
+template <typename Key>
+using FastSet = std::unordered_set<Key>;
+#endif
+} // namespace c10
+
+#endif // C10_UTIL_FBCODEMAPS_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fn-inl.h

@@ -0,0 +1,274 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <cstring>
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e4m3fn::Float8_e4m3fn(float value)
+    : x(detail::fp8e4m3fn_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e4m3fn::operator float() const {
+  return detail::fp8e4m3fn_to_fp32_value(x);
+}
+
+/// Special values helper
+
+inline C10_HOST_DEVICE bool Float8_e4m3fn::isnan() const {
+  return (x & 0b01111111) == 0b01111111;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator+(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator-(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator*(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(
+    const Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(const Float8_e4m3fn& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator+=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator-=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator*=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator/=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e4m3fn a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e4m3fn b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e4m3fn b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e4m3fn b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e4m3fn b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e4m3fn& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e4m3fn& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e4m3fn& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e4m3fn& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e4m3fn a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e4m3fn b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e4m3fn b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e4m3fn b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e4m3fn b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(Float8_e4m3fn a, int b) {
+  return a + static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(Float8_e4m3fn a, int b) {
+  return a - static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(Float8_e4m3fn a, int b) {
+  return a * static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(Float8_e4m3fn a, int b) {
+  return a / static_cast<Float8_e4m3fn>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(int a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(int a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(int a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(int a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(Float8_e4m3fn a, int64_t b) {
+  return a + static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(Float8_e4m3fn a, int64_t b) {
+  return a - static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(Float8_e4m3fn a, int64_t b) {
+  return a * static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(Float8_e4m3fn a, int64_t b) {
+  return a / static_cast<Float8_e4m3fn>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(int64_t a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(int64_t a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(int64_t a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(int64_t a, Float8_e4m3fn b) {
+  return static_cast<Float8_e4m3fn>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e4m3fn to float.
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e4m3fn> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 4;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 3;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -5;
+  static constexpr int min_exponent10 = -1;
+  static constexpr int max_exponent = 8;
+  static constexpr int max_exponent10 = 2;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before = false;
+
+  static constexpr c10::Float8_e4m3fn min() {
+    return c10::Float8_e4m3fn(0x08, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn lowest() {
+    return c10::Float8_e4m3fn(0xFE, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn max() {
+    return c10::Float8_e4m3fn(0x7E, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn epsilon() {
+    return c10::Float8_e4m3fn(0x20, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn round_error() {
+    return c10::Float8_e4m3fn(0x30, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn quiet_NaN() {
+    return c10::Float8_e4m3fn(0x7F, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn denorm_min() {
+    return c10::Float8_e4m3fn(0x01, c10::Float8_e4m3fn::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fn.h

@@ -0,0 +1,247 @@
+#pragma once
+
+/// Defines the Float8_e4m3fn type (8-bit floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+/// bias = 7
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2209.05433.pdf
+/// and inspired by Half implementation from pytorch/c10/util/Half.h
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/floating_point_utils.h>
+#include <type_traits>
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+#include <cmath>
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <climits>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <limits>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <utility>
+
+#include <typeinfo> // operator typeid
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E4M3FN format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline C10_HOST_DEVICE float fp8e4m3fn_to_fp32_value(uint8_t input) {
+  /*
+   * Extend the fp8 E4M3FN number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31 27-30 24-26          0-23
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)input << 24;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the bits 0-30
+   * of the 32-bit word:
+   *
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31  27-30 24-26      0-23
+   */
+  const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF);
+  /*
+   * Renorm shift is the number of bits to shift mantissa left to make the
+   * half-precision number normalized. If the initial number is normalized, some
+   * of its high 5 bits (sign == 0 and 4-bit exponent) equals one. In this case
+   * renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note
+   * that if we shift denormalized nonsign by renorm_shift, the unit bit of
+   * mantissa will shift into exponent, turning the biased exponent into 1, and
+   * making mantissa normalized (i.e. without leading 1).
+   */
+#if defined(__CUDA_ARCH__)
+  uint32_t renorm_shift = __clz(nonsign);
+#elif defined(__SYCL_DEVICE_ONLY__)
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#elif defined(_MSC_VER)
+  unsigned long nonsign_bsr;
+  _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign);
+  uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#endif
+  renorm_shift = renorm_shift > 4 ? renorm_shift - 4 : 0;
+  /*
+   * Iff fp8e4m3fn number has all exponent and mantissa bits set to 1,
+   * the addition overflows it into bit 31, and the subsequent shift turns the
+   * high 9 bits into 1. Thus inf_nan_mask == 0x7F800000 if the fp8e4m3fn number
+   * is Nan, 0x00000000 otherwise
+   */
+  const int32_t inf_nan_mask =
+      ((int32_t)(nonsign + 0x01000000) >> 8) & INT32_C(0x7F800000);
+  /*
+   * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31
+   * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31
+   * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask ==
+   * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h)
+   * 0x00000000 otherwise
+   */
+  const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31;
+  /*
+   * 1. Shift nonsign left by renorm_shift to normalize it (if the input
+   * was denormal)
+   * 2. Shift nonsign right by 4 so the exponent (4 bits originally)
+   * becomes an 8-bit field and 3-bit mantissa shifts into the 3 high
+   * bits of the 23-bit mantissa of IEEE single-precision number.
+   * 3. Add 0x78 to the exponent (starting at bit 23) to compensate the
+   * different in exponent bias (0x7F for single-precision number less 0x07
+   * for fp8e4m3fn number).
+   * 4. Subtract renorm_shift from the exponent (starting at bit 23) to
+   * account for renormalization. As renorm_shift is less than 0x78, this
+   * can be combined with step 3.
+   * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the
+   * input was NaN or infinity.
+   * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent
+   * into zero if the input was zero.
+   * 7. Combine with the sign of the input number.
+   */
+  uint32_t result = sign |
+      ((((nonsign << renorm_shift >> 4) + ((0x78 - renorm_shift) << 23)) |
+        inf_nan_mask) &
+       ~zero_mask);
+  return fp32_from_bits(result);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FN format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e4m3fn_from_fp32_value(float f) {
+  /*
+   * Binary representation of 480.0f, which is the first value
+   * not representable in fp8e4m3fn range:
+   * 0 1111 111 - fp8e4m3fn
+   * 0 10000111 11100000000000000000000 - fp32
+   */
+  constexpr uint32_t fp8_max = UINT32_C(1087) << 20;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fn normal range
+   * into denorm representation
+   * magic number: ((127 - 7) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(141) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint8_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fp8_max) {
+    // NaN - all exponent and mantissa bits set to 1
+    result = 0x7f;
+  } else {
+    if (f_bits < (UINT32_C(121) << 23)) {
+      // Input number is smaller than 2^(-6), which is the smallest
+      // fp8e4m3fn normal number
+      f_bits =
+          fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+      result = static_cast<uint8_t>(f_bits - denorm_mask);
+    } else {
+      // resulting mantissa is odd
+      uint8_t mant_odd = (f_bits >> 20) & 1;
+
+      // update exponent, rounding bias part 1
+      f_bits += ((uint32_t)(7 - 127) << 23) + 0x7FFFF;
+
+      // rounding bias part 2
+      f_bits += mant_odd;
+
+      // take the bits!
+      result = static_cast<uint8_t>(f_bits >> 20);
+    }
+  }
+
+  result |= static_cast<uint8_t>(sign >> 24);
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e4m3fn {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fn() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fn(uint8_t bits, from_bits_t)
+      : x(bits){};
+  inline C10_HOST_DEVICE Float8_e4m3fn(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+C10_API std::ostream& operator<<(std::ostream& out, const Float8_e4m3fn& value);
+
+} // namespace c10
+
+#include <c10/util/Float8_e4m3fn-inl.h> // IWYU pragma: keep

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fnuz-inl.h

@@ -0,0 +1,90 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+/// Constructors
+
+C10_HOST_DEVICE inline Float8_e4m3fnuz::Float8_e4m3fnuz(float value)
+    : x(detail::fp8e4m3fnuz_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+C10_HOST_DEVICE inline Float8_e4m3fnuz::operator float() const {
+  return detail::fp8e4m3fnuz_to_fp32_value(x);
+}
+
+/// Special values helper
+
+C10_HOST_DEVICE inline bool Float8_e4m3fnuz::isnan() const {
+  return x == 0b10000000;
+}
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e4m3fnuz> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 4;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 3;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -6;
+  static constexpr int min_exponent10 = -1;
+  static constexpr int max_exponent = 8;
+  static constexpr int max_exponent10 = 2;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before = false;
+
+  static constexpr c10::Float8_e4m3fnuz min() {
+    return c10::Float8_e4m3fnuz(0x08, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz lowest() {
+    return c10::Float8_e4m3fnuz(0xFF, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz max() {
+    return c10::Float8_e4m3fnuz(0x7F, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz epsilon() {
+    return c10::Float8_e4m3fnuz(0x28, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz round_error() {
+    return c10::Float8_e4m3fnuz(0x38, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz infinity() {
+    // NaN (no infinities)
+    return c10::Float8_e4m3fnuz(0x80, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz quiet_NaN() {
+    return c10::Float8_e4m3fnuz(0x80, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz denorm_min() {
+    return c10::Float8_e4m3fnuz(0x01, c10::Float8_e4m3fnuz::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fnuz.h

@@ -0,0 +1,155 @@
+#pragma once
+
+/// Defines the Float8_e4m3fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+///
+/// Binary configuration remains the same as Float8_e4m3fn:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+///
+/// The key differences versus Float8_e4m3fn are:
+/// bias = 8
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/floating_point_utils.h>
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E4M3FNUZ format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+#if defined(__CUDA_ARCH__) || defined(__HIP__)
+C10_HOST_DEVICE C10_API inline float fp8e4m3fnuz_to_fp32_value(uint8_t) {
+  CUDA_KERNEL_ASSERT(false && "e4m3fnuz is not supported by CUDA or HIP");
+  return -1.0;
+}
+#else
+C10_API float fp8e4m3fnuz_to_fp32_value(uint8_t input);
+#endif
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FNUZ format, in bit representation.
+ */
+C10_HOST_DEVICE inline uint8_t fp8e4m3fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 256.0f, which is the first value not representable
+   * (i.e. the first value which would overflow in to the sign bit, resulting in
+   * a NaN) in fp8e4m3fnuz range:
+   * 1 0000 000 - fp8e4m3fnuz
+   * 0 10000111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x87) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fnuz normal range
+   * into denormalized representation.
+   * magic number: ((127 - 8) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x8C) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s.
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x78) << 23) /* 2^-7 in float32 */) {
+    // Input exponent is less than -7, the smallest e4m3fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 20) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(8 - 127) << 23) + 0x7FFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 20);
+  }
+
+  result |= sign >> 24;
+
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e4m3fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  static constexpr C10_HOST_DEVICE from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fnuz(uint8_t bits, from_bits_t)
+      : x(bits){};
+  inline C10_HOST_DEVICE Float8_e4m3fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+C10_API std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e4m3fnuz& value);
+
+} // namespace c10
+
+#include <c10/util/Float8_e4m3fnuz-inl.h> // IWYU pragma: keep

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Float8_e5m2fnuz.h

@@ -0,0 +1,154 @@
+#pragma once
+
+/// Defines the Float8_e5m2fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+///
+/// Binary configuration remains the same as e5m2:
+/// s eeeee mm
+/// 1 sign bit
+/// 5 exponent bits
+/// 2 mantissa bits
+///
+/// The key differences that e5m2fnuz brings are:
+/// bias = 16
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/floating_point_utils.h>
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E5M2FNUZ format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+#if defined(__CUDA_ARCH__) || defined(__HIP__)
+C10_HOST_DEVICE C10_API inline float fp8e5m2fnuz_to_fp32_value(uint8_t) {
+  CUDA_KERNEL_ASSERT(false && "e5m2fnuz is not supported by CUDA or HIP");
+  return -1.0;
+}
+#else
+C10_API float fp8e5m2fnuz_to_fp32_value(uint8_t input);
+#endif
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E5M2 format, in bit representation.
+ */
+C10_HOST_DEVICE inline uint8_t fp8e5m2fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 65536.0f, which is the first value not
+   * representable (i.e. the first value which would overflow in to the sign
+   * bit, resulting in a NaN) in fp8e4m3fnuz range:
+   * 1 00000 00 - fp8e5m2fnuz
+   * 0 10001111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x8F) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e5m2fnuz normal range
+   * into denormalized representation.
+   * magic number: ((127 - 16) + (23 - 2) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x85) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x70) << 23) /* 2^-15 in float32 */) {
+    // Input exponent is less than -15, the smallest e5m2fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 21) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(16 - 127) << 23) + 0xFFFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 21);
+  }
+
+  result |= sign >> 24;
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e5m2fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  static constexpr C10_HOST_DEVICE from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e5m2fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e5m2fnuz(uint8_t bits, from_bits_t)
+      : x(bits){};
+  inline C10_HOST_DEVICE Float8_e5m2fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+C10_API std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e5m2fnuz& value);
+
+} // namespace c10
+
+#include <c10/util/Float8_e5m2fnuz-inl.h> // IWYU pragma: keep

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/FunctionRef.h

@@ -0,0 +1,72 @@
+//===- llvm/ADT/STLExtras.h - Useful STL related functions ------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains some templates that are useful if you are working with the
+// STL at all.
+//
+// No library is required when using these functions.
+//
+//===----------------------------------------------------------------------===//
+
+// c10: modified from llvm::function_ref
+// c10: added more SFINAE to enable use in overloaded functions
+
+#pragma once
+
+#include <cstdint>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+/// An efficient, type-erasing, non-owning reference to a callable. This is
+/// intended for use as the type of a function parameter that is not used
+/// after the function in question returns.
+///
+/// This class does not own the callable, so it is not in general safe to store
+/// a function_ref.
+template <typename Fn>
+class function_ref;
+
+template <typename Ret, typename... Params>
+class function_ref<Ret(Params...)> {
+  Ret (*callback)(intptr_t callable, Params... params) = nullptr;
+  intptr_t callable{};
+
+  template <typename Callable>
+  static Ret callback_fn(intptr_t callable, Params... params) {
+    return (*reinterpret_cast<Callable*>(callable))(std::forward<Params>(
+        params)...);
+  }
+
+ public:
+  function_ref() = default;
+  function_ref(std::nullptr_t) {}
+
+  template <typename Callable>
+  function_ref(
+      Callable&& callable,
+      typename std::enable_if<!std::is_same<
+          typename std::remove_reference<Callable>::type,
+          function_ref>::value>::type* = nullptr,
+      typename std::enable_if<std::is_convertible<
+          typename std::invoke_result_t<Callable, Params...>,
+          Ret>::value>::type* = nullptr)
+      : callback(callback_fn<typename std::remove_reference<Callable>::type>),
+        callable(reinterpret_cast<intptr_t>(&callable)) {}
+
+  Ret operator()(Params... params) const {
+    return callback(callable, std::forward<Params>(params)...);
+  }
+
+  operator bool() const {
+    return callback;
+  }
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Half.h

@@ -0,0 +1,506 @@
+#pragma once
+
+/// Defines the Half type (half-precision floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32, instead of using CUDA half intrinsics.
+/// Most uses of this type within ATen are memory bound, including the
+/// element-wise kernels, and the half intrinsics aren't efficient on all GPUs.
+/// If you are writing a compute bound kernel, you can use the CUDA half
+/// intrinsics directly on the Half type from device code.
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+#include <c10/util/TypeSafeSignMath.h>
+#include <c10/util/complex.h>
+#include <c10/util/floating_point_utils.h>
+#include <type_traits>
+
+#if defined(__cplusplus) && (__cplusplus >= 201103L)
+#include <cmath>
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <complex>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <limits>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <utility>
+
+#ifdef __CUDACC__
+#include <cuda_fp16.h>
+#endif
+
+#ifdef __HIPCC__
+#include <hip/hip_fp16.h>
+#endif
+
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#elif defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+
+#include <typeinfo> // operator typeid
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline uint32_t fp16_ieee_to_fp32_bits(uint16_t h) {
+  /*
+   * Extend the half-precision floating-point number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+-----+------------+-------------------+
+   *      | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  31  26-30    16-25            0-15
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)h << 16;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the bits 0-30
+   * of the 32-bit word:
+   *
+   *      +---+-----+------------+-------------------+
+   *      | 0 |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  30  27-31     17-26            0-16
+   */
+  const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF);
+  /*
+   * Renorm shift is the number of bits to shift mantissa left to make the
+   * half-precision number normalized. If the initial number is normalized, some
+   * of its high 6 bits (sign == 0 and 5-bit exponent) equals one. In this case
+   * renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note
+   * that if we shift denormalized nonsign by renorm_shift, the unit bit of
+   * mantissa will shift into exponent, turning the biased exponent into 1, and
+   * making mantissa normalized (i.e. without leading 1).
+   */
+#ifdef _MSC_VER
+  unsigned long nonsign_bsr;
+  _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign);
+  uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+  uint32_t renorm_shift = __builtin_clz(nonsign);
+#endif
+  renorm_shift = renorm_shift > 5 ? renorm_shift - 5 : 0;
+  /*
+   * Iff half-precision number has exponent of 15, the addition overflows
+   * it into bit 31, and the subsequent shift turns the high 9 bits
+   * into 1. Thus inf_nan_mask == 0x7F800000 if the half-precision number
+   * had exponent of 15 (i.e. was NaN or infinity) 0x00000000 otherwise
+   */
+  const int32_t inf_nan_mask =
+      ((int32_t)(nonsign + 0x04000000) >> 8) & INT32_C(0x7F800000);
+  /*
+   * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31
+   * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31
+   * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask ==
+   * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h)
+   * 0x00000000 otherwise
+   */
+  const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31;
+  /*
+   * 1. Shift nonsign left by renorm_shift to normalize it (if the input
+   * was denormal)
+   * 2. Shift nonsign right by 3 so the exponent (5 bits originally)
+   * becomes an 8-bit field and 10-bit mantissa shifts into the 10 high
+   * bits of the 23-bit mantissa of IEEE single-precision number.
+   * 3. Add 0x70 to the exponent (starting at bit 23) to compensate the
+   * different in exponent bias (0x7F for single-precision number less 0xF
+   * for half-precision number).
+   * 4. Subtract renorm_shift from the exponent (starting at bit 23) to
+   * account for renormalization. As renorm_shift is less than 0x70, this
+   * can be combined with step 3.
+   * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the
+   * input was NaN or infinity.
+   * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent
+   * into zero if the input was zero.
+   * 7. Combine with the sign of the input number.
+   */
+  return sign |
+      ((((nonsign << renorm_shift >> 3) + ((0x70 - renorm_shift) << 23)) |
+        inf_nan_mask) &
+       ~zero_mask);
+}
+
+/*
+ * Convert a 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format.
+ *
+ * @note The implementation relies on IEEE-like (no assumption about rounding
+ * mode and no operations on denormals) floating-point operations and bitcasts
+ * between integer and floating-point variables.
+ */
+C10_HOST_DEVICE inline float fp16_ieee_to_fp32_value(uint16_t h) {
+  /*
+   * Extend the half-precision floating-point number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+-----+------------+-------------------+
+   *      | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  31  26-30    16-25            0-15
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)h << 16;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the high bits
+   * of the 32-bit word:
+   *
+   *      +-----+------------+---------------------+
+   *      |EEEEE|MM MMMM MMMM|0 0000 0000 0000 0000|
+   *      +-----+------------+---------------------+
+   * Bits  27-31    17-26            0-16
+   */
+  const uint32_t two_w = w + w;
+
+  /*
+   * Shift mantissa and exponent into bits 23-28 and bits 13-22 so they become
+   * mantissa and exponent of a single-precision floating-point number:
+   *
+   *       S|Exponent |          Mantissa
+   *      +-+---+-----+------------+----------------+
+   *      |0|000|EEEEE|MM MMMM MMMM|0 0000 0000 0000|
+   *      +-+---+-----+------------+----------------+
+   * Bits   | 23-31   |           0-22
+   *
+   * Next, there are some adjustments to the exponent:
+   * - The exponent needs to be corrected by the difference in exponent bias
+   * between single-precision and half-precision formats (0x7F - 0xF = 0x70)
+   * - Inf and NaN values in the inputs should become Inf and NaN values after
+   * conversion to the single-precision number. Therefore, if the biased
+   * exponent of the half-precision input was 0x1F (max possible value), the
+   * biased exponent of the single-precision output must be 0xFF (max possible
+   * value). We do this correction in two steps:
+   *   - First, we adjust the exponent by (0xFF - 0x1F) = 0xE0 (see exp_offset
+   * below) rather than by 0x70 suggested by the difference in the exponent bias
+   * (see above).
+   *   - Then we multiply the single-precision result of exponent adjustment by
+   * 2**(-112) to reverse the effect of exponent adjustment by 0xE0 less the
+   * necessary exponent adjustment by 0x70 due to difference in exponent bias.
+   *     The floating-point multiplication hardware would ensure than Inf and
+   * NaN would retain their value on at least partially IEEE754-compliant
+   * implementations.
+   *
+   * Note that the above operations do not handle denormal inputs (where biased
+   * exponent == 0). However, they also do not operate on denormal inputs, and
+   * do not produce denormal results.
+   */
+  constexpr uint32_t exp_offset = UINT32_C(0xE0) << 23;
+  // const float exp_scale = 0x1.0p-112f;
+  constexpr uint32_t scale_bits = (uint32_t)15 << 23;
+  float exp_scale_val = 0;
+  std::memcpy(&exp_scale_val, &scale_bits, sizeof(exp_scale_val));
+  const float exp_scale = exp_scale_val;
+  const float normalized_value =
+      fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+
+  /*
+   * Convert denormalized half-precision inputs into single-precision results
+   * (always normalized). Zero inputs are also handled here.
+   *
+   * In a denormalized number the biased exponent is zero, and mantissa has
+   * on-zero bits. First, we shift mantissa into bits 0-9 of the 32-bit word.
+   *
+   *                  zeros           |  mantissa
+   *      +---------------------------+------------+
+   *      |0000 0000 0000 0000 0000 00|MM MMMM MMMM|
+   *      +---------------------------+------------+
+   * Bits             10-31                0-9
+   *
+   * Now, remember that denormalized half-precision numbers are represented as:
+   *    FP16 = mantissa * 2**(-24).
+   * The trick is to construct a normalized single-precision number with the
+   * same mantissa and thehalf-precision input and with an exponent which would
+   * scale the corresponding mantissa bits to 2**(-24). A normalized
+   * single-precision floating-point number is represented as: FP32 = (1 +
+   * mantissa * 2**(-23)) * 2**(exponent - 127) Therefore, when the biased
+   * exponent is 126, a unit change in the mantissa of the input denormalized
+   * half-precision number causes a change of the constructed single-precision
+   * number by 2**(-24), i.e. the same amount.
+   *
+   * The last step is to adjust the bias of the constructed single-precision
+   * number. When the input half-precision number is zero, the constructed
+   * single-precision number has the value of FP32 = 1 * 2**(126 - 127) =
+   * 2**(-1) = 0.5 Therefore, we need to subtract 0.5 from the constructed
+   * single-precision number to get the numerical equivalent of the input
+   * half-precision number.
+   */
+  constexpr uint32_t magic_mask = UINT32_C(126) << 23;
+  constexpr float magic_bias = 0.5f;
+  const float denormalized_value =
+      fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+
+  /*
+   * - Choose either results of conversion of input as a normalized number, or
+   * as a denormalized number, depending on the input exponent. The variable
+   * two_w contains input exponent in bits 27-31, therefore if its smaller than
+   * 2**27, the input is either a denormal number, or zero.
+   * - Combine the result of conversion of exponent and mantissa with the sign
+   * of the input number.
+   */
+  constexpr uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+  const uint32_t result = sign |
+      (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value)
+                                   : fp32_to_bits(normalized_value));
+  return fp32_from_bits(result);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation.
+ *
+ * @note The implementation relies on IEEE-like (no assumption about rounding
+ * mode and no operations on denormals) floating-point operations and bitcasts
+ * between integer and floating-point variables.
+ */
+inline uint16_t fp16_ieee_from_fp32_value(float f) {
+  // const float scale_to_inf = 0x1.0p+112f;
+  // const float scale_to_zero = 0x1.0p-110f;
+  constexpr uint32_t scale_to_inf_bits = (uint32_t)239 << 23;
+  constexpr uint32_t scale_to_zero_bits = (uint32_t)17 << 23;
+  float scale_to_inf_val = 0, scale_to_zero_val = 0;
+  std::memcpy(&scale_to_inf_val, &scale_to_inf_bits, sizeof(scale_to_inf_val));
+  std::memcpy(
+      &scale_to_zero_val, &scale_to_zero_bits, sizeof(scale_to_zero_val));
+  const float scale_to_inf = scale_to_inf_val;
+  const float scale_to_zero = scale_to_zero_val;
+
+#if defined(_MSC_VER) && _MSC_VER == 1916
+  float base = ((signbit(f) != 0 ? -f : f) * scale_to_inf) * scale_to_zero;
+#else
+  float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+#endif
+
+  const uint32_t w = fp32_to_bits(f);
+  const uint32_t shl1_w = w + w;
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+  if (bias < UINT32_C(0x71000000)) {
+    bias = UINT32_C(0x71000000);
+  }
+
+  base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+  const uint32_t bits = fp32_to_bits(base);
+  const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
+  const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
+  const uint32_t nonsign = exp_bits + mantissa_bits;
+  return static_cast<uint16_t>(
+      (sign >> 16) |
+      (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign));
+}
+
+} // namespace detail
+
+struct alignas(2) Half {
+  unsigned short x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  // HIP wants __host__ __device__ tag, CUDA does not
+#if defined(USE_ROCM)
+  C10_HOST_DEVICE Half() = default;
+#else
+  Half() = default;
+#endif
+
+  constexpr C10_HOST_DEVICE Half(unsigned short bits, from_bits_t) : x(bits){};
+  inline C10_HOST_DEVICE Half(float value);
+  inline C10_HOST_DEVICE operator float() const;
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  inline C10_HOST_DEVICE Half(const __half& value);
+  inline C10_HOST_DEVICE operator __half() const;
+#endif
+#ifdef SYCL_LANGUAGE_VERSION
+  inline C10_HOST_DEVICE Half(const sycl::half& value);
+  inline C10_HOST_DEVICE operator sycl::half() const;
+#endif
+};
+
+// TODO : move to complex.h
+template <>
+struct alignas(4) complex<Half> {
+  Half real_;
+  Half imag_;
+
+  // Constructors
+  complex() = default;
+  // Half constructor is not constexpr so the following constructor can't
+  // be constexpr
+  C10_HOST_DEVICE explicit inline complex(const Half& real, const Half& imag)
+      : real_(real), imag_(imag) {}
+  C10_HOST_DEVICE inline complex(const c10::complex<float>& value)
+      : real_(value.real()), imag_(value.imag()) {}
+
+  // Conversion operator
+  inline C10_HOST_DEVICE operator c10::complex<float>() const {
+    return {real_, imag_};
+  }
+
+  constexpr C10_HOST_DEVICE Half real() const {
+    return real_;
+  }
+  constexpr C10_HOST_DEVICE Half imag() const {
+    return imag_;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator+=(const complex<Half>& other) {
+    real_ = static_cast<float>(real_) + static_cast<float>(other.real_);
+    imag_ = static_cast<float>(imag_) + static_cast<float>(other.imag_);
+    return *this;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator-=(const complex<Half>& other) {
+    real_ = static_cast<float>(real_) - static_cast<float>(other.real_);
+    imag_ = static_cast<float>(imag_) - static_cast<float>(other.imag_);
+    return *this;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator*=(const complex<Half>& other) {
+    auto a = static_cast<float>(real_);
+    auto b = static_cast<float>(imag_);
+    auto c = static_cast<float>(other.real());
+    auto d = static_cast<float>(other.imag());
+    real_ = a * c - b * d;
+    imag_ = a * d + b * c;
+    return *this;
+  }
+};
+
+// In some versions of MSVC, there will be a compiler error when building.
+// C4146: unary minus operator applied to unsigned type, result still unsigned
+// C4804: unsafe use of type 'bool' in operation
+// It can be addressed by disabling the following warning.
+#ifdef _MSC_VER
+#pragma warning(push)
+#pragma warning(disable : 4146)
+#pragma warning(disable : 4804)
+#pragma warning(disable : 4018)
+#endif
+
+// The overflow checks may involve float to int conversion which may
+// trigger precision loss warning. Re-enable the warning once the code
+// is fixed. See T58053069.
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+
+// bool can be converted to any type.
+// Without specializing on bool, in pytorch_linux_trusty_py2_7_9_build:
+// `error: comparison of constant '255' with boolean expression is always false`
+// for `f > limit::max()` below
+template <typename To, typename From>
+typename std::enable_if<std::is_same<From, bool>::value, bool>::type overflows(
+    From /*f*/) {
+  return false;
+}
+
+// skip isnan and isinf check for integral types
+template <typename To, typename From>
+typename std::enable_if<
+    std::is_integral<From>::value && !std::is_same<From, bool>::value,
+    bool>::type
+overflows(From f) {
+  using limit = std::numeric_limits<typename scalar_value_type<To>::type>;
+  if (!limit::is_signed && std::numeric_limits<From>::is_signed) {
+    // allow for negative numbers to wrap using two's complement arithmetic.
+    // For example, with uint8, this allows for `a - b` to be treated as
+    // `a + 255 * b`.
+    return greater_than_max<To>(f) ||
+        (c10::is_negative(f) && -static_cast<uint64_t>(f) > limit::max());
+  } else {
+    return c10::less_than_lowest<To>(f) || greater_than_max<To>(f);
+  }
+}
+
+template <typename To, typename From>
+typename std::enable_if<std::is_floating_point<From>::value, bool>::type
+overflows(From f) {
+  using limit = std::numeric_limits<typename scalar_value_type<To>::type>;
+  if (limit::has_infinity && std::isinf(static_cast<double>(f))) {
+    return false;
+  }
+  if (!limit::has_quiet_NaN && (f != f)) {
+    return true;
+  }
+  return f < limit::lowest() || f > limit::max();
+}
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+#ifdef _MSC_VER
+#pragma warning(pop)
+#endif
+
+template <typename To, typename From>
+typename std::enable_if<is_complex<From>::value, bool>::type overflows(From f) {
+  // casts from complex to real are considered to overflow if the
+  // imaginary component is non-zero
+  if (!is_complex<To>::value && f.imag() != 0) {
+    return true;
+  }
+  // Check for overflow componentwise
+  // (Technically, the imag overflow check is guaranteed to be false
+  // when !is_complex<To>, but any optimizer worth its salt will be
+  // able to figure it out.)
+  return overflows<
+             typename scalar_value_type<To>::type,
+             typename From::value_type>(f.real()) ||
+      overflows<
+             typename scalar_value_type<To>::type,
+             typename From::value_type>(f.imag());
+}
+
+C10_API std::ostream& operator<<(std::ostream& out, const Half& value);
+
+} // namespace c10
+
+#include <c10/util/Half-inl.h> // IWYU pragma: keep

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/IdWrapper.h

@@ -0,0 +1,78 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <cstddef>
+#include <functional>
+#include <utility>
+
+namespace c10 {
+
+/**
+ * This template simplifies generation of simple classes that wrap an id
+ * in a typesafe way. Namely, you can use it to create a very lightweight
+ * type that only offers equality comparators and hashing. Example:
+ *
+ *   struct MyIdType final : IdWrapper<MyIdType, uint32_t> {
+ *     constexpr explicit MyIdType(uint32_t id): IdWrapper(id) {}
+ *   };
+ *
+ * Then in the global top level namespace:
+ *
+ *   C10_DEFINE_HASH_FOR_IDWRAPPER(MyIdType);
+ *
+ * That's it - equality operators and hash functions are automatically defined
+ * for you, given the underlying type supports it.
+ */
+template <class ConcreteType, class UnderlyingType>
+class IdWrapper {
+ public:
+  using underlying_type = UnderlyingType;
+  using concrete_type = ConcreteType;
+
+ protected:
+  constexpr explicit IdWrapper(underlying_type id) noexcept(
+      noexcept(underlying_type(std::declval<underlying_type>())))
+      : id_(id) {}
+
+  constexpr underlying_type underlyingId() const
+      noexcept(noexcept(underlying_type(std::declval<underlying_type>()))) {
+    return id_;
+  }
+
+ private:
+  friend size_t hash_value(const concrete_type& v) {
+    return std::hash<underlying_type>()(v.id_);
+  }
+
+  // TODO Making operator== noexcept if underlying type is noexcept equality
+  // comparable doesn't work with GCC 4.8.
+  //      Fix this once we don't need GCC 4.8 anymore.
+  friend constexpr bool operator==(
+      const concrete_type& lhs,
+      const concrete_type& rhs) noexcept {
+    return lhs.id_ == rhs.id_;
+  }
+
+  // TODO Making operator!= noexcept if operator== is noexcept doesn't work with
+  // GCC 4.8.
+  //      Fix this once we don't need GCC 4.8 anymore.
+  friend constexpr bool operator!=(
+      const concrete_type& lhs,
+      const concrete_type& rhs) noexcept {
+    return !(lhs == rhs);
+  }
+
+  underlying_type id_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_HASH_FOR_IDWRAPPER(ClassName) \
+  namespace std {                                \
+  template <>                                    \
+  struct hash<ClassName> {                       \
+    size_t operator()(ClassName x) const {       \
+      return hash_value(x);                      \
+    }                                            \
+  };                                             \
+  }

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Load.h

@@ -0,0 +1,38 @@
+#pragma once
+#include <c10/macros/Macros.h>
+#include <cstring>
+
+namespace c10 {
+namespace detail {
+
+template <typename T>
+struct LoadImpl {
+  C10_HOST_DEVICE static T apply(const void* src) {
+    return *reinterpret_cast<const T*>(src);
+  }
+};
+
+template <>
+struct LoadImpl<bool> {
+  C10_HOST_DEVICE static bool apply(const void* src) {
+    static_assert(sizeof(bool) == sizeof(char));
+    // NOTE: [Loading boolean values]
+    // Protect against invalid boolean values by loading as a byte
+    // first, then converting to bool (see gh-54789).
+    return *reinterpret_cast<const unsigned char*>(src);
+  }
+};
+
+} // namespace detail
+
+template <typename T>
+C10_HOST_DEVICE T load(const void* src) {
+  return c10::detail::LoadImpl<T>::apply(src);
+}
+
+template <typename scalar_t>
+C10_HOST_DEVICE scalar_t load(const scalar_t* src) {
+  return c10::detail::LoadImpl<scalar_t>::apply(src);
+}
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Logging.h

@@ -0,0 +1,375 @@
+#ifndef C10_UTIL_LOGGING_H_
+#define C10_UTIL_LOGGING_H_
+
+#include <climits>
+#include <exception>
+#include <functional>
+#include <limits>
+#include <sstream>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Flags.h>
+#include <c10/util/StringUtil.h>
+
+// CAFFE2_LOG_THRESHOLD is a compile time flag that would allow us to turn off
+// logging at compile time so no logging message below that level is produced
+// at all. The value should be between INT_MIN and CAFFE_FATAL.
+#ifndef CAFFE2_LOG_THRESHOLD
+// If we have not defined the compile time log threshold, we keep all the
+// log cases.
+#define CAFFE2_LOG_THRESHOLD INT_MIN
+#endif // CAFFE2_LOG_THRESHOLD
+
+// Below are different implementations for glog and non-glog cases.
+#ifdef C10_USE_GLOG
+#include <c10/util/logging_is_google_glog.h>
+#else // !C10_USE_GLOG
+#include <c10/util/logging_is_not_google_glog.h>
+#endif // C10_USE_GLOG
+
+C10_DECLARE_int(caffe2_log_level);
+C10_DECLARE_bool(caffe2_use_fatal_for_enforce);
+
+// Some versions of GLOG support less-spammy version of LOG_EVERY_MS. If it's
+// not available - just short-circuit to the always working one one.
+// We define the C10_ name to avoid confusing other files
+#ifdef LOG_EVERY_MS
+#define C10_LOG_EVERY_MS(severity, ms) LOG_EVERY_MS(severity, ms)
+#else
+#define C10_LOG_EVERY_MS(severity, ms) LOG(severity)
+#endif
+
+// Same for LOG_FIRST_N
+#ifdef LOG_FIRST_N
+#define C10_LOG_FIRST_N(severity, n) LOG_FIRST_N(severity, n)
+#else
+#define C10_LOG_FIRST_N(severity, n) LOG(severity)
+#endif
+
+// Same for LOG_EVERY_N
+#ifdef LOG_EVERY_N
+#define C10_LOG_EVERY_N(severity, n) LOG_EVERY_N(severity, n)
+#else
+#define C10_LOG_EVERY_N(severity, n) LOG(severity)
+#endif
+
+namespace c10 {
+
+using std::string;
+
+// Functions that we use for initialization.
+C10_API bool InitCaffeLogging(int* argc, char** argv);
+C10_API void UpdateLoggingLevelsFromFlags();
+
+[[noreturn]] C10_API void ThrowEnforceNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    const std::string& msg,
+    const void* caller = nullptr);
+
+[[noreturn]] C10_API void ThrowEnforceNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    const char* msg,
+    const void* caller = nullptr);
+
+[[noreturn]] C10_API inline void ThrowEnforceNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    detail::CompileTimeEmptyString /*msg*/,
+    const void* caller = nullptr) {
+  ThrowEnforceNotMet(file, line, condition, "", caller);
+}
+
+[[noreturn]] C10_API void ThrowEnforceFiniteNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    const std::string& msg,
+    const void* caller = nullptr);
+
+[[noreturn]] C10_API void ThrowEnforceFiniteNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    const char* msg,
+    const void* caller = nullptr);
+
+[[noreturn]] C10_API inline void ThrowEnforceFiniteNotMet(
+    const char* file,
+    const int line,
+    const char* condition,
+    detail::CompileTimeEmptyString /*msg*/,
+    const void* caller = nullptr) {
+  ThrowEnforceFiniteNotMet(file, line, condition, "", caller);
+}
+
+constexpr bool IsUsingGoogleLogging() {
+#ifdef C10_USE_GLOG
+  return true;
+#else
+  return false;
+#endif
+}
+
+/**
+ * A utility to allow one to show log info to stderr after the program starts.
+ *
+ * This is similar to calling GLOG's --logtostderr, or setting caffe2_log_level
+ * to smaller than INFO. You are recommended to only use this in a few sparse
+ * cases, such as when you want to write a tutorial or something. Normally, use
+ * the commandline flags to set the log level.
+ */
+C10_API void ShowLogInfoToStderr();
+
+C10_API void SetStackTraceFetcher(std::function<string(void)> fetcher);
+
+using EnforceNotMet = ::c10::Error;
+
+#define CAFFE_ENFORCE(condition, ...)                               \
+  do {                                                              \
+    if (C10_UNLIKELY(!(condition))) {                               \
+      ::c10::ThrowEnforceNotMet(                                    \
+          __FILE__, __LINE__, #condition, ::c10::str(__VA_ARGS__)); \
+    }                                                               \
+  } while (false)
+
+#define CAFFE_ENFORCE_FINITE(condition, ...)                        \
+  do {                                                              \
+    if (C10_UNLIKELY(!(condition))) {                               \
+      ::c10::ThrowEnforceFiniteNotMet(                              \
+          __FILE__, __LINE__, #condition, ::c10::str(__VA_ARGS__)); \
+    }                                                               \
+  } while (false)
+
+#define CAFFE_ENFORCE_WITH_CALLER(condition, ...)                         \
+  do {                                                                    \
+    if (C10_UNLIKELY(!(condition))) {                                     \
+      ::c10::ThrowEnforceNotMet(                                          \
+          __FILE__, __LINE__, #condition, ::c10::str(__VA_ARGS__), this); \
+    }                                                                     \
+  } while (false)
+
+#define CAFFE_THROW(...) \
+  ::c10::ThrowEnforceNotMet(__FILE__, __LINE__, "", ::c10::str(__VA_ARGS__))
+
+/**
+ * Rich logging messages
+ *
+ * CAFFE_ENFORCE_THAT can be used with one of the "checker functions" that
+ * capture input argument values and add it to the exception message. E.g.
+ * `CAFFE_ENFORCE_THAT(Equals(foo(x), bar(y)), "Optional additional message")`
+ * would evaluate both foo and bar only once and if the results are not equal -
+ * include them in the exception message.
+ *
+ * Some of the basic checker functions like Equals or Greater are already
+ * defined below. Other header might define customized checkers by adding
+ * functions to caffe2::enforce_detail namespace. For example:
+ *
+ *   namespace caffe2 { namespace enforce_detail {
+ *   inline EnforceFailMessage IsVector(const vector<int64_t>& shape) {
+ *     if (shape.size() == 1) { return EnforceOK(); }
+ *     return c10::str("Shape ", shape, " is not a vector");
+ *   }
+ *   }}
+ *
+ * With further usages like `CAFFE_ENFORCE_THAT(IsVector(Input(0).dims()))`
+ *
+ * Convenient wrappers for binary operations like CAFFE_ENFORCE_EQ are provided
+ * too. Please use them instead of TORCH_CHECK_EQ and friends for failures in
+ * user-provided input.
+ */
+
+namespace enforce_detail {
+
+template <typename T1, typename T2>
+std::string enforceFailMsgImpl(const T1& x, const T2& y) {
+  return c10::str(x, " vs ", y);
+}
+
+template <typename T1, typename T2, typename... Args>
+std::string enforceFailMsgImpl(const T1& x, const T2& y, const Args&... args) {
+  return c10::str(x, " vs ", y, ". ", args...);
+}
+
+// GCC7 is getting an internal compiler error on the new
+// implementation, so keep the old one (which evaluates the error
+// message eagerly and therefore is undesirable for general use
+// compared to the new one) around for it.
+#if defined(__GNUG__) && __GNUC__ <= 7 && !defined(__clang__)
+template <typename Pred, typename T1, typename T2, typename... Args>
+void enforceThatImpl(
+    Pred p,
+    const T1& lhs,
+    const T2& rhs,
+    const char* file,
+    int line,
+    const char* expr,
+    const void* caller,
+    const Args&... args) {
+  if (C10_UNLIKELY(!(p(lhs, rhs)))) {
+    ::c10::ThrowEnforceNotMet(
+        file,
+        line,
+        expr,
+        ::c10::enforce_detail::enforceFailMsgImpl(lhs, rhs, args...),
+        caller);
+  }
+}
+
+#define CAFFE_ENFORCE_THAT_IMPL(op, lhs, rhs, expr, ...) \
+  ::c10::enforce_detail::enforceThatImpl(                \
+      op, lhs, rhs, __FILE__, __LINE__, expr, nullptr, ##__VA_ARGS__)
+
+#define CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(op, lhs, rhs, expr, ...) \
+  ::c10::enforce_detail::enforceThatImpl(                            \
+      op, (lhs), (rhs), __FILE__, __LINE__, expr, this, ##__VA_ARGS__)
+
+#else
+template <typename Pred, typename T1, typename T2, typename GetFailMsgFunc>
+void enforceThatImpl(
+    Pred p,
+    const T1& lhs,
+    const T2& rhs,
+    const char* file,
+    int line,
+    const char* expr,
+    const void* caller,
+    GetFailMsgFunc getFailMsg) {
+  if (C10_UNLIKELY(!(p(lhs, rhs)))) {
+    ::c10::ThrowEnforceNotMet(file, line, expr, getFailMsg(lhs, rhs), caller);
+  }
+}
+
+#define CAFFE_ENFORCE_THAT_IMPL(op, lhs, rhs, expr, ...)  \
+  ::c10::enforce_detail::enforceThatImpl(                 \
+      op,                                                 \
+      (lhs),                                              \
+      (rhs),                                              \
+      __FILE__,                                           \
+      __LINE__,                                           \
+      expr,                                               \
+      nullptr,                                            \
+      [&](const auto& arg1, const auto& arg2) {           \
+        return ::c10::enforce_detail::enforceFailMsgImpl( \
+            arg1, arg2, ##__VA_ARGS__);                   \
+      })
+
+#define CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(op, lhs, rhs, expr, ...) \
+  ::c10::enforce_detail::enforceThatImpl(                            \
+      op,                                                            \
+      (lhs),                                                         \
+      (rhs),                                                         \
+      __FILE__,                                                      \
+      __LINE__,                                                      \
+      expr,                                                          \
+      this,                                                          \
+      [&](const auto& arg1, const auto& arg2) {                      \
+        return ::c10::enforce_detail::enforceFailMsgImpl(            \
+            arg1, arg2, ##__VA_ARGS__);                              \
+      })
+#endif
+
+} // namespace enforce_detail
+
+#define CAFFE_ENFORCE_THAT(cmp, op, lhs, rhs, ...) \
+  CAFFE_ENFORCE_THAT_IMPL(cmp, lhs, rhs, #lhs " " #op " " #rhs, ##__VA_ARGS__)
+
+#define CAFFE_ENFORCE_BINARY_OP(cmp, op, x, y, ...) \
+  CAFFE_ENFORCE_THAT_IMPL(cmp, x, y, #x " " #op " " #y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_EQ(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::equal_to<void>(), ==, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_NE(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::not_equal_to<void>(), !=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_LE(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::less_equal<void>(), <=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_LT(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::less<void>(), <, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_GE(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::greater_equal<void>(), >=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_GT(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP(std::greater<void>(), >, x, y, ##__VA_ARGS__)
+
+#define CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(cmp, op, x, y, ...) \
+  CAFFE_ENFORCE_THAT_IMPL_WITH_CALLER(                          \
+      cmp, x, y, #x " " #op " " #y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_EQ_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(          \
+      std::equal_to<void>(), ==, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_NE_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(          \
+      std::not_equal_to<void>(), !=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_LE_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(          \
+      std::less_equal<void>(), <=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_LT_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(std::less<void>(), <, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_GE_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(          \
+      std::greater_equal<void>(), >=, x, y, ##__VA_ARGS__)
+#define CAFFE_ENFORCE_GT_WITH_CALLER(x, y, ...) \
+  CAFFE_ENFORCE_BINARY_OP_WITH_CALLER(          \
+      std::greater<void>(), >, x, y, ##__VA_ARGS__)
+
+/**
+ * Very lightweight logging for the first time API usage. It's beneficial for
+ * tracking of individual functionality usage in larger applications.
+ *
+ * In order to ensure light-weightedness of logging, we utilize static variable
+ * trick - LogAPIUsage will be invoked only once and further invocations will
+ * just do an atomic check.
+ *
+ * Example:
+ *   // Logs caller info with an arbitrary text event, if there is a usage.
+ *   C10_LOG_API_USAGE_ONCE("my_api");
+ */
+#define C10_LOG_API_USAGE_ONCE(...)                        \
+  C10_UNUSED static bool C10_ANONYMOUS_VARIABLE(logFlag) = \
+      ::c10::detail::LogAPIUsageFakeReturn(__VA_ARGS__);
+
+// API usage logging capabilities
+C10_API void SetAPIUsageLogger(std::function<void(const std::string&)> logger);
+C10_API void LogAPIUsage(const std::string& context);
+
+C10_API void SetAPIUsageMetadataLogger(
+    std::function<void(
+        const std::string&,
+        const std::map<std::string, std::string>& metadata_map)> logger);
+C10_API void LogAPIUsageMetadata(
+    const std::string& context,
+    const std::map<std::string, std::string>& metadata_map);
+
+// PyTorch ddp usage logging capabilities
+// DDPLoggingData holds data that can be logged in applications
+// for analysis and debugging. Data structure is defined in
+// c10 directory so that it can be easily imported by both c10
+// and torch files.
+struct DDPLoggingData {
+  // logging fields that are string types.
+  std::map<std::string, std::string> strs_map;
+  // logging fields that are int64_t types.
+  std::map<std::string, int64_t> ints_map;
+};
+
+C10_API void SetPyTorchDDPUsageLogger(
+    std::function<void(const DDPLoggingData&)> logger);
+C10_API void LogPyTorchDDPUsage(const DDPLoggingData& ddpData);
+
+namespace detail {
+// Return value is needed to do the static variable initialization trick
+C10_API bool LogAPIUsageFakeReturn(const std::string& context);
+} // namespace detail
+
+// Initializes the c10 logger.
+C10_API void initLogging();
+
+// Sets the rank, which will be included in log messages
+C10_API void SetGlobalRank(int64_t rank);
+
+} // namespace c10
+
+#endif // C10_UTIL_LOGGING_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/MaybeOwned.h

@@ -0,0 +1,233 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/in_place.h>
+
+#include <memory>
+#include <type_traits>
+
+namespace c10 {
+
+/// MaybeOwnedTraits<T> describes how to borrow from T.  Here is how we
+/// can implement borrowing from an arbitrary type T using a raw
+/// pointer to const:
+template <typename T>
+struct MaybeOwnedTraitsGenericImpl {
+  using owned_type = T;
+  using borrow_type = const T*;
+
+  static borrow_type createBorrow(const owned_type& from) {
+    return &from;
+  }
+
+  static void assignBorrow(borrow_type& lhs, borrow_type rhs) {
+    lhs = rhs;
+  }
+
+  static void destroyBorrow(borrow_type& /*toDestroy*/) {}
+
+  static const owned_type& referenceFromBorrow(const borrow_type& borrow) {
+    return *borrow;
+  }
+
+  static const owned_type* pointerFromBorrow(const borrow_type& borrow) {
+    return borrow;
+  }
+
+  static bool debugBorrowIsValid(const borrow_type& borrow) {
+    return borrow != nullptr;
+  }
+};
+
+/// It is possible to eliminate the extra layer of indirection for
+/// borrows for some types that we control. For examples, see
+/// intrusive_ptr.h and TensorBody.h.
+
+template <typename T>
+struct MaybeOwnedTraits;
+
+// Explicitly enable MaybeOwned<shared_ptr<T>>, rather than allowing
+// MaybeOwned to be used for any type right away.
+template <typename T>
+struct MaybeOwnedTraits<std::shared_ptr<T>>
+    : public MaybeOwnedTraitsGenericImpl<std::shared_ptr<T>> {};
+
+/// A smart pointer around either a borrowed or owned T. When
+/// constructed with borrowed(), the caller MUST ensure that the
+/// borrowed-from argument outlives this MaybeOwned<T>. Compare to
+/// Rust's std::borrow::Cow
+/// (https://doc.rust-lang.org/std/borrow/enum.Cow.html), but note
+/// that it is probably not suitable for general use because C++ has
+/// no borrow checking. Included here to support
+/// Tensor::expect_contiguous.
+template <typename T>
+class MaybeOwned final {
+  using borrow_type = typename MaybeOwnedTraits<T>::borrow_type;
+  using owned_type = typename MaybeOwnedTraits<T>::owned_type;
+
+  bool isBorrowed_;
+  union {
+    borrow_type borrow_;
+    owned_type own_;
+  };
+
+  /// Don't use this; use borrowed() instead.
+  explicit MaybeOwned(const owned_type& t)
+      : isBorrowed_(true), borrow_(MaybeOwnedTraits<T>::createBorrow(t)) {}
+
+  /// Don't use this; use owned() instead.
+  explicit MaybeOwned(T&& t) noexcept(
+      std::is_nothrow_move_constructible<T>::value)
+      : isBorrowed_(false), own_(std::move(t)) {}
+
+  /// Don't use this; use owned() instead.
+  template <class... Args>
+  explicit MaybeOwned(in_place_t, Args&&... args)
+      : isBorrowed_(false), own_(std::forward<Args>(args)...) {}
+
+ public:
+  explicit MaybeOwned() : isBorrowed_(true), borrow_() {}
+
+  // Copying a borrow yields another borrow of the original, as with a
+  // T*. Copying an owned T yields another owned T for safety: no
+  // chains of borrowing by default! (Note you could get that behavior
+  // with MaybeOwned<T>::borrowed(*rhs) if you wanted it.)
+  MaybeOwned(const MaybeOwned& rhs) : isBorrowed_(rhs.isBorrowed_) {
+    if (C10_LIKELY(rhs.isBorrowed_)) {
+      MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+    } else {
+      new (&own_) T(rhs.own_);
+    }
+  }
+
+  MaybeOwned& operator=(const MaybeOwned& rhs) {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      if (rhs.isBorrowed_) {
+        own_.~T();
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+        isBorrowed_ = true;
+      } else {
+        own_ = rhs.own_;
+      }
+    } else {
+      if (C10_LIKELY(rhs.isBorrowed_)) {
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+      } else {
+        MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+        new (&own_) T(rhs.own_);
+        isBorrowed_ = false;
+      }
+    }
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(isBorrowed_ == rhs.isBorrowed_);
+    return *this;
+  }
+
+  MaybeOwned(MaybeOwned&& rhs) noexcept(
+      std::is_nothrow_move_constructible_v<T>&&
+          std::is_nothrow_move_assignable_v<borrow_type>)
+      : isBorrowed_(rhs.isBorrowed_) {
+    if (C10_LIKELY(rhs.isBorrowed_)) {
+      MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+    } else {
+      new (&own_) T(std::move(rhs.own_));
+    }
+  }
+
+  MaybeOwned& operator=(MaybeOwned&& rhs) noexcept(
+      std::is_nothrow_move_assignable_v<T>&& std::is_nothrow_move_assignable_v<
+          borrow_type>&& std::is_nothrow_move_constructible_v<T>&&
+          std::is_nothrow_destructible_v<T>&&
+              std::is_nothrow_destructible_v<borrow_type>) {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      if (rhs.isBorrowed_) {
+        own_.~T();
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+        isBorrowed_ = true;
+      } else {
+        own_ = std::move(rhs.own_);
+      }
+    } else {
+      if (C10_LIKELY(rhs.isBorrowed_)) {
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+      } else {
+        MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+        new (&own_) T(std::move(rhs.own_));
+        isBorrowed_ = false;
+      }
+    }
+    return *this;
+  }
+
+  static MaybeOwned borrowed(const T& t) {
+    return MaybeOwned(t);
+  }
+
+  static MaybeOwned owned(T&& t) noexcept(
+      std::is_nothrow_move_constructible<T>::value) {
+    return MaybeOwned(std::move(t));
+  }
+
+  template <class... Args>
+  static MaybeOwned owned(in_place_t, Args&&... args) {
+    return MaybeOwned(in_place, std::forward<Args>(args)...);
+  }
+
+  ~MaybeOwned() noexcept(std::is_nothrow_destructible_v<T>&&
+                             std::is_nothrow_destructible_v<borrow_type>) {
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      own_.~T();
+    } else {
+      MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+    }
+  }
+
+  // This is an implementation detail!  You should know what you're doing
+  // if you are testing this.  If you just want to guarantee ownership move
+  // this into a T
+  bool unsafeIsBorrowed() const {
+    return isBorrowed_;
+  }
+
+  const T& operator*() const& {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+    }
+    return C10_LIKELY(isBorrowed_)
+        ? MaybeOwnedTraits<T>::referenceFromBorrow(borrow_)
+        : own_;
+  }
+
+  const T* operator->() const {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+    }
+    return C10_LIKELY(isBorrowed_)
+        ? MaybeOwnedTraits<T>::pointerFromBorrow(borrow_)
+        : &own_;
+  }
+
+  // If borrowed, copy the underlying T. If owned, move from
+  // it. borrowed/owned state remains the same, and either we
+  // reference the same borrow as before or we are an owned moved-from
+  // T.
+  T operator*() && {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+      return MaybeOwnedTraits<T>::referenceFromBorrow(borrow_);
+    } else {
+      return std::move(own_);
+    }
+  }
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Optional.h

@@ -0,0 +1,47 @@
+#ifndef C10_UTIL_OPTIONAL_H_
+#define C10_UTIL_OPTIONAL_H_
+
+#include <optional>
+#include <type_traits>
+
+// Macros.h is not needed, but it does namespace shenanigans that lots
+// of downstream code seems to rely on. Feel free to remove it and fix
+// up builds.
+#include <c10/macros/Macros.h>
+#include <c10/util/Metaprogramming.h>
+
+namespace c10 {
+using std::bad_optional_access;
+using std::in_place;
+using std::in_place_t;
+using std::make_optional;
+using std::nullopt;
+using std::nullopt_t;
+using std::optional;
+
+namespace detail_ {
+// the call to convert<A>(b) has return type A and converts b to type A iff b
+// decltype(b) is implicitly convertible to A
+template <class U>
+constexpr U convert(U v) {
+  return v;
+}
+} // namespace detail_
+template <class T, class F>
+constexpr T value_or_else(const optional<T>& v, F&& func) {
+  static_assert(
+      std::is_convertible<typename std::invoke_result_t<F>, T>::value,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? *v : detail_::convert<T>(std::forward<F>(func)());
+}
+
+template <class T, class F>
+constexpr T value_or_else(optional<T>&& v, F&& func) {
+  static_assert(
+      std::is_convertible<typename std::invoke_result_t<F>, T>::value,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? constexpr_move(std::move(v).contained_val())
+                       : detail_::convert<T>(std::forward<F>(func)());
+}
+} // namespace c10
+#endif // C10_UTIL_OPTIONAL_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Registry.h

@@ -0,0 +1,327 @@
+#ifndef C10_UTIL_REGISTRY_H_
+#define C10_UTIL_REGISTRY_H_
+
+/**
+ * Simple registry implementation that uses static variables to
+ * register object creators during program initialization time.
+ */
+
+// NB: This Registry works poorly when you have other namespaces.
+// Make all macro invocations from inside the at namespace.
+
+#include <algorithm>
+#include <cstdio>
+#include <cstdlib>
+#include <functional>
+#include <memory>
+#include <mutex>
+#include <stdexcept>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Type.h>
+
+namespace c10 {
+
+template <typename KeyType>
+inline std::string KeyStrRepr(const KeyType& /*key*/) {
+  return "[key type printing not supported]";
+}
+
+template <>
+inline std::string KeyStrRepr(const std::string& key) {
+  return key;
+}
+
+enum RegistryPriority {
+  REGISTRY_FALLBACK = 1,
+  REGISTRY_DEFAULT = 2,
+  REGISTRY_PREFERRED = 3,
+};
+
+/**
+ * @brief A template class that allows one to register classes by keys.
+ *
+ * The keys are usually a std::string specifying the name, but can be anything
+ * that can be used in a std::map.
+ *
+ * You should most likely not use the Registry class explicitly, but use the
+ * helper macros below to declare specific registries as well as registering
+ * objects.
+ */
+template <class SrcType, class ObjectPtrType, class... Args>
+class Registry {
+ public:
+  typedef std::function<ObjectPtrType(Args...)> Creator;
+
+  Registry(bool warning = true)
+      : registry_(), priority_(), terminate_(true), warning_(warning) {}
+
+  void Register(
+      const SrcType& key,
+      Creator creator,
+      const RegistryPriority priority = REGISTRY_DEFAULT) {
+    std::lock_guard<std::mutex> lock(register_mutex_);
+    // The if statement below is essentially the same as the following line:
+    // TORCH_CHECK_EQ(registry_.count(key), 0) << "Key " << key
+    //                                   << " registered twice.";
+    // However, TORCH_CHECK_EQ depends on google logging, and since registration
+    // is carried out at static initialization time, we do not want to have an
+    // explicit dependency on glog's initialization function.
+    if (registry_.count(key) != 0) {
+      auto cur_priority = priority_[key];
+      if (priority > cur_priority) {
+#ifdef DEBUG
+        std::string warn_msg =
+            "Overwriting already registered item for key " + KeyStrRepr(key);
+        fprintf(stderr, "%s\n", warn_msg.c_str());
+#endif
+        registry_[key] = creator;
+        priority_[key] = priority;
+      } else if (priority == cur_priority) {
+        std::string err_msg =
+            "Key already registered with the same priority: " + KeyStrRepr(key);
+        fprintf(stderr, "%s\n", err_msg.c_str());
+        if (terminate_) {
+          std::exit(1);
+        } else {
+          throw std::runtime_error(err_msg);
+        }
+      } else if (warning_) {
+        std::string warn_msg =
+            "Higher priority item already registered, skipping registration of " +
+            KeyStrRepr(key);
+        fprintf(stderr, "%s\n", warn_msg.c_str());
+      }
+    } else {
+      registry_[key] = creator;
+      priority_[key] = priority;
+    }
+  }
+
+  void Register(
+      const SrcType& key,
+      Creator creator,
+      const std::string& help_msg,
+      const RegistryPriority priority = REGISTRY_DEFAULT) {
+    Register(key, creator, priority);
+    help_message_[key] = help_msg;
+  }
+
+  inline bool Has(const SrcType& key) {
+    return (registry_.count(key) != 0);
+  }
+
+  ObjectPtrType Create(const SrcType& key, Args... args) {
+    auto it = registry_.find(key);
+    if (it == registry_.end()) {
+      // Returns nullptr if the key is not registered.
+      return nullptr;
+    }
+    return it->second(args...);
+  }
+
+  /**
+   * Returns the keys currently registered as a std::vector.
+   */
+  std::vector<SrcType> Keys() const {
+    std::vector<SrcType> keys;
+    keys.reserve(registry_.size());
+    for (const auto& it : registry_) {
+      keys.push_back(it.first);
+    }
+    return keys;
+  }
+
+  inline const std::unordered_map<SrcType, std::string>& HelpMessage() const {
+    return help_message_;
+  }
+
+  const char* HelpMessage(const SrcType& key) const {
+    auto it = help_message_.find(key);
+    if (it == help_message_.end()) {
+      return nullptr;
+    }
+    return it->second.c_str();
+  }
+
+  // Used for testing, if terminate is unset, Registry throws instead of
+  // calling std::exit
+  void SetTerminate(bool terminate) {
+    terminate_ = terminate;
+  }
+
+ private:
+  std::unordered_map<SrcType, Creator> registry_;
+  std::unordered_map<SrcType, RegistryPriority> priority_;
+  bool terminate_;
+  const bool warning_;
+  std::unordered_map<SrcType, std::string> help_message_;
+  std::mutex register_mutex_;
+
+  C10_DISABLE_COPY_AND_ASSIGN(Registry);
+};
+
+template <class SrcType, class ObjectPtrType, class... Args>
+class Registerer {
+ public:
+  explicit Registerer(
+      const SrcType& key,
+      Registry<SrcType, ObjectPtrType, Args...>* registry,
+      typename Registry<SrcType, ObjectPtrType, Args...>::Creator creator,
+      const std::string& help_msg = "") {
+    registry->Register(key, creator, help_msg);
+  }
+
+  explicit Registerer(
+      const SrcType& key,
+      const RegistryPriority priority,
+      Registry<SrcType, ObjectPtrType, Args...>* registry,
+      typename Registry<SrcType, ObjectPtrType, Args...>::Creator creator,
+      const std::string& help_msg = "") {
+    registry->Register(key, creator, help_msg, priority);
+  }
+
+  template <class DerivedType>
+  static ObjectPtrType DefaultCreator(Args... args) {
+    return ObjectPtrType(new DerivedType(args...));
+  }
+};
+
+/**
+ * C10_DECLARE_TYPED_REGISTRY is a macro that expands to a function
+ * declaration, as well as creating a convenient typename for its corresponding
+ * registerer.
+ */
+// Note on C10_IMPORT and C10_EXPORT below: we need to explicitly mark DECLARE
+// as import and DEFINE as export, because these registry macros will be used
+// in downstream shared libraries as well, and one cannot use *_API - the API
+// macro will be defined on a per-shared-library basis. Semantically, when one
+// declares a typed registry it is always going to be IMPORT, and when one
+// defines a registry (which should happen ONLY ONCE and ONLY IN SOURCE FILE),
+// the instantiation unit is always going to be exported.
+//
+// The only unique condition is when in the same file one does DECLARE and
+// DEFINE - in Windows compilers, this generates a warning that dllimport and
+// dllexport are mixed, but the warning is fine and linker will be properly
+// exporting the symbol. Same thing happens in the gflags flag declaration and
+// definition caes.
+#define C10_DECLARE_TYPED_REGISTRY(                                      \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                     \
+  C10_API ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*  \
+  RegistryName();                                                        \
+  typedef ::c10::Registerer<SrcType, PtrType<ObjectType>, ##__VA_ARGS__> \
+      Registerer##RegistryName
+
+#define TORCH_DECLARE_TYPED_REGISTRY(                                     \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                      \
+  TORCH_API ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>* \
+  RegistryName();                                                         \
+  typedef ::c10::Registerer<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>  \
+      Registerer##RegistryName
+
+#define C10_DEFINE_TYPED_REGISTRY(                                         \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                       \
+  C10_EXPORT ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>* \
+  RegistryName() {                                                         \
+    static ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*   \
+        registry = new ::c10::                                             \
+            Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>();       \
+    return registry;                                                       \
+  }
+
+#define C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(                            \
+    RegistryName, SrcType, ObjectType, PtrType, ...)                          \
+  C10_EXPORT ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*    \
+  RegistryName() {                                                            \
+    static ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>*      \
+        registry =                                                            \
+            new ::c10::Registry<SrcType, PtrType<ObjectType>, ##__VA_ARGS__>( \
+                false);                                                       \
+    return registry;                                                          \
+  }
+
+// Note(Yangqing): The __VA_ARGS__ below allows one to specify a templated
+// creator with comma in its templated arguments.
+#define C10_REGISTER_TYPED_CREATOR(RegistryName, key, ...)                  \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key, RegistryName(), ##__VA_ARGS__);
+
+#define C10_REGISTER_TYPED_CREATOR_WITH_PRIORITY(                           \
+    RegistryName, key, priority, ...)                                       \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key, priority, RegistryName(), ##__VA_ARGS__);
+
+#define C10_REGISTER_TYPED_CLASS(RegistryName, key, ...)                    \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key,                                                                  \
+      RegistryName(),                                                       \
+      Registerer##RegistryName::DefaultCreator<__VA_ARGS__>,                \
+      ::c10::demangle_type<__VA_ARGS__>());
+
+#define C10_REGISTER_TYPED_CLASS_WITH_PRIORITY(                             \
+    RegistryName, key, priority, ...)                                       \
+  static Registerer##RegistryName C10_ANONYMOUS_VARIABLE(g_##RegistryName)( \
+      key,                                                                  \
+      priority,                                                             \
+      RegistryName(),                                                       \
+      Registerer##RegistryName::DefaultCreator<__VA_ARGS__>,                \
+      ::c10::demangle_type<__VA_ARGS__>());
+
+// C10_DECLARE_REGISTRY and C10_DEFINE_REGISTRY are hard-wired to use
+// std::string as the key type, because that is the most commonly used cases.
+#define C10_DECLARE_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DECLARE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define TORCH_DECLARE_REGISTRY(RegistryName, ObjectType, ...) \
+  TORCH_DECLARE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_REGISTRY_WITHOUT_WARNING(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(                               \
+      RegistryName, std::string, ObjectType, std::unique_ptr, ##__VA_ARGS__)
+
+#define C10_DECLARE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DECLARE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define TORCH_DECLARE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  TORCH_DECLARE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_SHARED_REGISTRY(RegistryName, ObjectType, ...) \
+  C10_DEFINE_TYPED_REGISTRY(                                      \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+#define C10_DEFINE_SHARED_REGISTRY_WITHOUT_WARNING( \
+    RegistryName, ObjectType, ...)                  \
+  C10_DEFINE_TYPED_REGISTRY_WITHOUT_WARNING(        \
+      RegistryName, std::string, ObjectType, std::shared_ptr, ##__VA_ARGS__)
+
+// C10_REGISTER_CREATOR and C10_REGISTER_CLASS are hard-wired to use std::string
+// as the key
+// type, because that is the most commonly used cases.
+#define C10_REGISTER_CREATOR(RegistryName, key, ...) \
+  C10_REGISTER_TYPED_CREATOR(RegistryName, #key, __VA_ARGS__)
+
+#define C10_REGISTER_CREATOR_WITH_PRIORITY(RegistryName, key, priority, ...) \
+  C10_REGISTER_TYPED_CREATOR_WITH_PRIORITY(                                  \
+      RegistryName, #key, priority, __VA_ARGS__)
+
+#define C10_REGISTER_CLASS(RegistryName, key, ...) \
+  C10_REGISTER_TYPED_CLASS(RegistryName, #key, __VA_ARGS__)
+
+#define C10_REGISTER_CLASS_WITH_PRIORITY(RegistryName, key, priority, ...) \
+  C10_REGISTER_TYPED_CLASS_WITH_PRIORITY(                                  \
+      RegistryName, #key, priority, __VA_ARGS__)
+
+} // namespace c10
+
+#endif // C10_UTIL_REGISTRY_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/ScopeExit.h

@@ -0,0 +1,53 @@
+#pragma once
+
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+/**
+ * Mostly copied from https://llvm.org/doxygen/ScopeExit_8h_source.html
+ */
+template <typename Callable>
+class scope_exit {
+  Callable ExitFunction;
+  bool Engaged = true; // False once moved-from or release()d.
+
+ public:
+  template <typename Fp>
+  // constructor accepting a forwarding reference can hide the
+  // move constructor
+  // @lint-ignore CLANGTIDY
+  explicit scope_exit(Fp&& F) : ExitFunction(std::forward<Fp>(F)) {}
+
+  scope_exit(scope_exit&& Rhs) noexcept
+      : ExitFunction(std::move(Rhs.ExitFunction)), Engaged(Rhs.Engaged) {
+    Rhs.release();
+  }
+  scope_exit(const scope_exit&) = delete;
+  scope_exit& operator=(scope_exit&&) = delete;
+  scope_exit& operator=(const scope_exit&) = delete;
+
+  void release() {
+    Engaged = false;
+  }
+
+  ~scope_exit() {
+    if (Engaged) {
+      ExitFunction();
+    }
+  }
+};
+
+// Keeps the callable object that is passed in, and execute it at the
+// destruction of the returned object (usually at the scope exit where the
+// returned object is kept).
+//
+// Interface is specified by p0052r2.
+template <typename Callable>
+scope_exit<typename std::decay<Callable>::type> make_scope_exit(Callable&& F) {
+  return scope_exit<typename std::decay<Callable>::type>(
+      std::forward<Callable>(F));
+}
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/SmallBuffer.h

@@ -0,0 +1,88 @@
+#pragma once
+#include <array>
+#include <type_traits>
+
+/** Helper class for allocating temporary fixed size arrays with SBO.
+ *
+ * This is intentionally much simpler than SmallVector, to improve performance
+ * at the expense of many features:
+ * - No zero-initialization for numeric types
+ * - No resizing after construction
+ * - No copy/move
+ * - No non-trivial types
+ */
+
+namespace c10 {
+
+template <typename T, size_t N>
+class SmallBuffer {
+  static_assert(
+      std::is_trivial<T>::value,
+      "SmallBuffer is intended for POD types");
+
+  std::array<T, N> storage_;
+  size_t size_{};
+  T* data_{};
+
+ public:
+  SmallBuffer(size_t size) : size_(size) {
+    if (size > N) {
+      data_ = new T[size];
+    } else {
+      data_ = &storage_[0];
+    }
+  }
+
+  SmallBuffer(const SmallBuffer&) = delete;
+  SmallBuffer& operator=(const SmallBuffer&) = delete;
+
+  // move constructor is needed in function return
+  SmallBuffer(SmallBuffer&& rhs) noexcept : size_{rhs.size_} {
+    rhs.size_ = 0;
+    if (size_ > N) {
+      data_ = rhs.data_;
+      rhs.data_ = nullptr;
+    } else {
+      storage_ = std::move(rhs.storage_);
+      data_ = &storage_[0];
+    }
+  }
+
+  SmallBuffer& operator=(SmallBuffer&&) = delete;
+
+  ~SmallBuffer() {
+    if (size_ > N) {
+      delete[] data_;
+    }
+  }
+
+  T& operator[](int64_t idx) {
+    return data()[idx];
+  }
+  const T& operator[](int64_t idx) const {
+    return data()[idx];
+  }
+  T* data() {
+    return data_;
+  }
+  const T* data() const {
+    return data_;
+  }
+  size_t size() const {
+    return size_;
+  }
+  T* begin() {
+    return data_;
+  }
+  const T* begin() const {
+    return data_;
+  }
+  T* end() {
+    return data_ + size_;
+  }
+  const T* end() const {
+    return data_ + size_;
+  }
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/StringUtil.h

@@ -0,0 +1,202 @@
+#ifndef C10_UTIL_STRINGUTIL_H_
+#define C10_UTIL_STRINGUTIL_H_
+
+#include <c10/macros/Macros.h>
+#include <c10/util/string_utils.h>
+#include <c10/util/string_view.h>
+
+#include <cstddef>
+#include <ostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wshorten-64-to-32")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wshorten-64-to-32")
+#endif
+
+namespace c10 {
+
+namespace detail {
+
+// Obtains the base name from a full path.
+C10_API std::string StripBasename(const std::string& full_path);
+
+C10_API std::string ExcludeFileExtension(const std::string& full_path);
+
+struct CompileTimeEmptyString {
+  operator const std::string&() const {
+    static const std::string empty_string_literal;
+    return empty_string_literal;
+  }
+  operator const char*() const {
+    return "";
+  }
+};
+
+template <typename T>
+struct CanonicalizeStrTypes {
+  using type = const T&;
+};
+
+template <size_t N>
+struct CanonicalizeStrTypes<char[N]> {
+  using type = const char*;
+};
+
+inline std::ostream& _str(std::ostream& ss) {
+  return ss;
+}
+
+template <typename T>
+inline std::ostream& _str(std::ostream& ss, const T& t) {
+  // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+  ss << t;
+  return ss;
+}
+
+template <>
+inline std::ostream& _str<CompileTimeEmptyString>(
+    std::ostream& ss,
+    const CompileTimeEmptyString&) {
+  return ss;
+}
+
+template <typename T, typename... Args>
+inline std::ostream& _str(std::ostream& ss, const T& t, const Args&... args) {
+  return _str(_str(ss, t), args...);
+}
+
+template <typename... Args>
+struct _str_wrapper final {
+  static std::string call(const Args&... args) {
+    std::ostringstream ss;
+    _str(ss, args...);
+    return ss.str();
+  }
+};
+
+// Specializations for already-a-string types.
+template <>
+struct _str_wrapper<std::string> final {
+  // return by reference to avoid the binary size of a string copy
+  static const std::string& call(const std::string& str) {
+    return str;
+  }
+};
+
+template <>
+struct _str_wrapper<const char*> final {
+  static const char* call(const char* str) {
+    return str;
+  }
+};
+
+// For c10::str() with an empty argument list (which is common in our assert
+// macros), we don't want to pay the binary size for constructing and
+// destructing a stringstream or even constructing a string.
+template <>
+struct _str_wrapper<> final {
+  static CompileTimeEmptyString call() {
+    return CompileTimeEmptyString();
+  }
+};
+
+} // namespace detail
+
+// Convert a list of string-like arguments into a single string.
+template <typename... Args>
+inline decltype(auto) str(const Args&... args) {
+  return detail::_str_wrapper<
+      typename detail::CanonicalizeStrTypes<Args>::type...>::call(args...);
+}
+
+template <class Container>
+inline std::string Join(const std::string& delimiter, const Container& v) {
+  std::stringstream s;
+  int cnt = static_cast<int64_t>(v.size()) - 1;
+  for (auto i = v.begin(); i != v.end(); ++i, --cnt) {
+    s << (*i) << (cnt ? delimiter : "");
+  }
+  return s.str();
+}
+
+// Replace all occurrences of "from" substring to "to" string.
+// Returns number of replacements
+size_t C10_API
+ReplaceAll(std::string& s, c10::string_view from, c10::string_view to);
+
+/// Represents a location in source code (for debugging).
+struct C10_API SourceLocation {
+  const char* function;
+  const char* file;
+  uint32_t line;
+};
+
+std::ostream& operator<<(std::ostream& out, const SourceLocation& loc);
+
+// unix isprint but insensitive to locale
+inline static bool isPrint(char s) {
+  return s > 0x1f && s < 0x7f;
+}
+
+inline void printQuotedString(std::ostream& stmt, const string_view str) {
+  stmt << "\"";
+  for (auto s : str) {
+    switch (s) {
+      case '\\':
+        stmt << "\\\\";
+        break;
+      case '\'':
+        stmt << "\\'";
+        break;
+      case '\"':
+        stmt << "\\\"";
+        break;
+      case '\a':
+        stmt << "\\a";
+        break;
+      case '\b':
+        stmt << "\\b";
+        break;
+      case '\f':
+        stmt << "\\f";
+        break;
+      case '\n':
+        stmt << "\\n";
+        break;
+      case '\r':
+        stmt << "\\r";
+        break;
+      case '\t':
+        stmt << "\\t";
+        break;
+      case '\v':
+        stmt << "\\v";
+        break;
+      default:
+        if (isPrint(s)) {
+          stmt << s;
+        } else {
+          // C++ io has stateful formatting settings. Messing with
+          // them is probably worse than doing this manually.
+          char buf[4] = "000";
+          buf[2] += s % 8;
+          s /= 8;
+          buf[1] += s % 8;
+          s /= 8;
+          buf[0] += s;
+          stmt << "\\" << buf;
+        }
+        break;
+    }
+  }
+  stmt << "\"";
+}
+
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+#endif // C10_UTIL_STRINGUTIL_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/ThreadLocal.h

@@ -0,0 +1,153 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+
+/**
+ * Android versions with libgnustl incorrectly handle thread_local C++
+ * qualifier with composite types. NDK up to r17 version is affected.
+ *
+ * (A fix landed on Jun 4 2018:
+ * https://android-review.googlesource.com/c/toolchain/gcc/+/683601)
+ *
+ * In such cases, use c10::ThreadLocal<T> wrapper
+ * which is `pthread_*` based with smart pointer semantics.
+ *
+ * In addition, convenient macro C10_DEFINE_TLS_static is available.
+ * To define static TLS variable of type std::string, do the following
+ * ```
+ *  C10_DEFINE_TLS_static(std::string, str_tls_);
+ *  ///////
+ *  {
+ *    *str_tls_ = "abc";
+ *    assert(str_tls_->length(), 3);
+ *  }
+ * ```
+ *
+ * (see c10/test/util/ThreadLocal_test.cpp for more examples)
+ */
+#if !defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+#if defined(C10_ANDROID) && defined(__GLIBCXX__) && __GLIBCXX__ < 20180604
+#define C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE
+#endif // defined(C10_ANDROID) && defined(__GLIBCXX__) && __GLIBCXX__ < 20180604
+
+#endif // !defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+#if defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+#include <c10/util/Exception.h>
+#include <errno.h>
+#include <pthread.h>
+#include <memory>
+namespace c10 {
+
+/**
+ * @brief Temporary thread_local C++ qualifier replacement for Android
+ * based on `pthread_*`.
+ * To be used with composite types that provide default ctor.
+ */
+template <typename Type>
+class ThreadLocal {
+ public:
+  ThreadLocal() {
+    pthread_key_create(
+        &key_, [](void* buf) { delete static_cast<Type*>(buf); });
+  }
+
+  ~ThreadLocal() {
+    if (void* current = pthread_getspecific(key_)) {
+      delete static_cast<Type*>(current);
+    }
+
+    pthread_key_delete(key_);
+  }
+
+  ThreadLocal(const ThreadLocal&) = delete;
+  ThreadLocal& operator=(const ThreadLocal&) = delete;
+
+  Type& get() {
+    if (void* current = pthread_getspecific(key_)) {
+      return *static_cast<Type*>(current);
+    }
+
+    std::unique_ptr<Type> ptr = std::make_unique<Type>();
+    if (0 == pthread_setspecific(key_, ptr.get())) {
+      return *ptr.release();
+    }
+
+    int err = errno;
+    TORCH_INTERNAL_ASSERT(false, "pthread_setspecific() failed, errno = ", err);
+  }
+
+  Type& operator*() {
+    return get();
+  }
+
+  Type* operator->() {
+    return &get();
+  }
+
+ private:
+  pthread_key_t key_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_TLS_static(Type, Name) static ::c10::ThreadLocal<Type> Name
+
+#define C10_DECLARE_TLS_class_static(Class, Type, Name) \
+  static ::c10::ThreadLocal<Type> Name
+
+#define C10_DEFINE_TLS_class_static(Class, Type, Name) \
+  ::c10::ThreadLocal<Type> Class::Name
+
+#else // defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+namespace c10 {
+
+/**
+ * @brief Default thread_local implementation for non-Android cases.
+ * To be used with composite types that provide default ctor.
+ */
+template <typename Type>
+class ThreadLocal {
+ public:
+  using Accessor = Type* (*)();
+  explicit ThreadLocal(Accessor accessor) : accessor_(accessor) {}
+
+  ThreadLocal(const ThreadLocal&) = delete;
+  ThreadLocal& operator=(const ThreadLocal&) = delete;
+
+  Type& get() {
+    return *accessor_();
+  }
+
+  Type& operator*() {
+    return get();
+  }
+
+  Type* operator->() {
+    return &get();
+  }
+
+ private:
+  Accessor accessor_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_TLS_static(Type, Name)     \
+  static ::c10::ThreadLocal<Type> Name([]() { \
+    static thread_local Type var;             \
+    return &var;                              \
+  })
+
+#define C10_DECLARE_TLS_class_static(Class, Type, Name) \
+  static ::c10::ThreadLocal<Type> Name
+
+#define C10_DEFINE_TLS_class_static(Class, Type, Name) \
+  ::c10::ThreadLocal<Type> Class::Name([]() {          \
+    static thread_local Type var;                      \
+    return &var;                                       \
+  })
+
+#endif // defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Type.h

@@ -0,0 +1,30 @@
+#ifndef C10_UTIL_TYPE_H_
+#define C10_UTIL_TYPE_H_
+
+#include <cstddef>
+#include <string>
+#ifdef __GXX_RTTI
+#include <typeinfo>
+#endif // __GXX_RTTI
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/// Utility to demangle a C++ symbol name.
+C10_API std::string demangle(const char* name);
+
+/// Returns the printable name of the type.
+template <typename T>
+inline const char* demangle_type() {
+#ifdef __GXX_RTTI
+  static const auto& name = *(new std::string(demangle(typeid(T).name())));
+  return name.c_str();
+#else // __GXX_RTTI
+  return "(RTTI disabled, cannot show name)";
+#endif // __GXX_RTTI
+}
+
+} // namespace c10
+
+#endif // C10_UTIL_TYPE_H_

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/TypeCast.h

@@ -0,0 +1,168 @@
+#pragma once
+#include <c10/macros/Macros.h>
+#include <c10/util/BFloat16.h>
+#include <c10/util/Float8_e4m3fn.h>
+#include <c10/util/Float8_e4m3fnuz.h>
+#include <c10/util/Float8_e5m2.h>
+#include <c10/util/Float8_e5m2fnuz.h>
+#include <c10/util/Half.h>
+
+#include <type_traits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+template <typename dest_t, typename src_t>
+struct needs_real {
+  constexpr static bool value =
+      (is_complex<src_t>::value && !is_complex<dest_t>::value);
+};
+
+template <bool, typename src_t>
+struct maybe_real {
+  C10_HOST_DEVICE static inline src_t apply(src_t src) {
+    return src;
+  }
+};
+
+template <typename src_t>
+struct maybe_real<true, src_t> {
+  C10_HOST_DEVICE static inline decltype(auto) apply(src_t src) {
+    return src.real();
+  }
+};
+
+// Note: deliberately ignores undefined behavior, consistent with NumPy.
+// PyTorch's type conversions can cause a variety of undefined behavior,
+// including float to integral overflow and signed to unsigned integer overflow.
+// Some of this undefined behavior is addressed below.
+template <typename dest_t, typename src_t>
+struct static_cast_with_inter_type {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline dest_t apply(
+      src_t src) {
+    constexpr bool real = needs_real<dest_t, src_t>::value;
+    auto r = maybe_real<real, src_t>::apply(src);
+    return static_cast<dest_t>(r);
+  }
+};
+
+// Partial template instantiation for casting to uint8.
+// Note: Converting from negative float values to unsigned integer types is
+// undefined behavior in C++, and current CPU and GPU compilers exhibit
+// divergent behavior. Casting from negative float values to signed
+// integer types and then to unsigned integer types is not undefined,
+// however, so this cast improves the consistency of type conversions
+// to uint8 across compilers.
+// Further note: Type conversions across compilers still have other undefined
+// and divergent behavior.
+template <typename src_t>
+struct static_cast_with_inter_type<uint8_t, src_t> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline uint8_t apply(
+      src_t src) {
+    constexpr bool real = needs_real<uint8_t, src_t>::value;
+    return static_cast<uint8_t>(
+        static_cast<int64_t>(maybe_real<real, src_t>::apply(src)));
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::BFloat16> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::BFloat16 src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::Float8_e5m2> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e5m2 src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e5m2fnuz> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e5m2fnuz src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e4m3fn> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e4m3fn src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e4m3fnuz> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e4m3fnuz src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::Half> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Half src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::complex<double>> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::complex<double> src) {
+    return static_cast<c10::complex<c10::Half>>(
+        static_cast<c10::complex<float>>(src));
+  }
+};
+
+template <typename To, typename From>
+C10_HOST_DEVICE To convert(From f) {
+  return static_cast_with_inter_type<To, From>::apply(f);
+}
+
+// Define separately to avoid being inlined and prevent code-size bloat
+C10_API void report_overflow(const char* name);
+
+template <typename To, typename From>
+To checked_convert(From f, const char* name) {
+  // Converting to bool can't overflow so we exclude this case from checking.
+  if (!std::is_same<To, bool>::value && overflows<To, From>(f)) {
+    report_overflow(name);
+  }
+  return convert<To, From>(f);
+}
+
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+// Trigger tests for D25440771. TODO: Remove this line any time you want.

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/TypeIndex.h

@@ -0,0 +1,196 @@
+#pragma once
+
+#include <c10/util/C++17.h>
+#include <c10/util/ConstexprCrc.h>
+#include <c10/util/IdWrapper.h>
+#include <c10/util/string_view.h>
+#include <cinttypes>
+#include <functional>
+
+namespace c10 {
+namespace util {
+
+// TODO Make it work for more compilers
+
+// Intel compiler works
+#if defined(__INTEL_COMPILER)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+
+// Clang works
+#elif defined(__clang__)
+
+// except for NVCC
+#if defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// Windows works
+#elif defined(_MSC_VER)
+
+// except for NVCC
+#if defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// GCC works
+#elif defined(__GNUC__)
+
+// except when gcc < 9
+#if (__GNUC__ < 9) || defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// some other compiler we don't know about
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+struct type_index final : IdWrapper<type_index, uint64_t> {
+  constexpr explicit type_index(uint64_t checksum) : IdWrapper(checksum) {}
+
+  // Allow usage in std::map / std::set
+  // TODO Disallow this and rather use std::unordered_map/set everywhere
+  friend constexpr bool operator<(type_index lhs, type_index rhs) noexcept {
+    return lhs.underlyingId() < rhs.underlyingId();
+  }
+
+  friend std::ostream& operator<<(std::ostream& stream, type_index typeId) {
+    return stream << typeId.underlyingId();
+  }
+};
+
+namespace detail {
+
+#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \
+    __GNUC__ < 5
+// Getting __PRETTY_FUNCTION__ at compile time only works with GCC >= 5
+#error "You're running a too old version of GCC. We need GCC 5 or later."
+#endif
+
+#if defined(__clang__) && __clang_major__ < 4
+// Getting __PRETTY_FUNCTION__ at compile time only works with Clang >= 4
+#error "You're running a too old version of Clang. We need Clang 4 or later."
+#endif
+
+inline constexpr string_view extract(
+    string_view prefix,
+    string_view suffix,
+    string_view str) {
+#if !defined(__CUDA_ARCH__) // CUDA doesn't like std::logic_error in device code
+  return (!str.starts_with(prefix) || !str.ends_with(suffix))
+      ? (throw std::logic_error("Invalid pattern"), string_view())
+      : str.substr(prefix.size(), str.size() - prefix.size() - suffix.size());
+#else
+  return str.substr(prefix.size(), str.size() - prefix.size() - suffix.size());
+#endif
+}
+
+template <typename T>
+inline C10_TYPENAME_CONSTEXPR c10::string_view fully_qualified_type_name_impl() {
+#if defined(_MSC_VER) && !defined(__clang__)
+#if defined(__NVCC__)
+  return extract(
+      "c10::basic_string_view<char> c10::util::detail::fully_qualified_type_name_impl<",
+      ">()",
+      __FUNCSIG__);
+#else
+  return extract(
+      "class c10::basic_string_view<char> __cdecl c10::util::detail::fully_qualified_type_name_impl<",
+      ">(void)",
+      __FUNCSIG__);
+#endif
+#elif defined(__clang__)
+  return extract(
+      "c10::string_view c10::util::detail::fully_qualified_type_name_impl() [T = ",
+      "]",
+      __PRETTY_FUNCTION__);
+#elif defined(__GNUC__)
+  return extract(
+#if C10_TYPENAME_SUPPORTS_CONSTEXPR
+      "constexpr c10::string_view c10::util::detail::fully_qualified_type_name_impl() [with T = ",
+#else
+      "c10::string_view c10::util::detail::fully_qualified_type_name_impl() [with T = ",
+#endif
+      "; c10::string_view = c10::basic_string_view<char>]",
+      __PRETTY_FUNCTION__);
+#endif
+}
+
+#if !defined(__CUDA_ARCH__)
+template <typename T>
+inline constexpr uint64_t type_index_impl() {
+// Idea: __PRETTY_FUNCTION__ (or __FUNCSIG__ on msvc) contains a qualified name
+// of this function, including its template parameter, i.e. including the
+// type we want an id for. We use this name and run crc64 on it to get a type
+// id.
+#if defined(_MSC_VER) && !defined(__clang__)
+  return crc64(__FUNCSIG__, sizeof(__FUNCSIG__)).checksum();
+#elif defined(__clang__)
+  return crc64(__PRETTY_FUNCTION__, sizeof(__PRETTY_FUNCTION__)).checksum();
+#elif defined(__GNUC__)
+  return crc64(__PRETTY_FUNCTION__, sizeof(__PRETTY_FUNCTION__)).checksum();
+#endif
+}
+#endif
+
+} // namespace detail
+
+template <typename T>
+inline constexpr type_index get_type_index() {
+#if !defined(__CUDA_ARCH__)
+  // To enforce that this is really computed at compile time, we pass the
+  // type index through std::integral_constant.
+  return type_index{std::integral_constant<
+      uint64_t,
+      detail::type_index_impl<std::decay_t<T>>()>::value};
+#else
+  // There's nothing in theory preventing us from running this on device code
+  // except for nvcc throwing a compiler error if we enable it.
+  return (abort(), type_index(0));
+#endif
+}
+
+#if !defined(TORCH_PEDANTIC)
+// Use precomputed hashsum for std::string
+// Needed to workaround ambiguity in class name resolution
+// into __PRETTY_FUNCTION__ when abovementioned class is defined in inlined
+// namespace. In multi-ABI C++ library, `std::string` is an alias to
+// `std::__cxx11::basic_string<char>` which depending on compiler flags can be
+// resolved to `basic_string<char>` either in `std` namespace or in
+// `std::__cxx11` one (`__cxx11` is an inline namespace)
+template <>
+inline constexpr type_index get_type_index<std::string>() {
+  // hashsum for std::basic_string<char>
+  return type_index{4193213214807308375ULL};
+}
+#endif
+
+template <typename T>
+inline C10_TYPENAME_CONSTEXPR string_view
+get_fully_qualified_type_name() noexcept {
+#if C10_TYPENAME_SUPPORTS_CONSTEXPR
+  constexpr
+#else
+  static
+#endif
+      string_view name = detail::fully_qualified_type_name_impl<T>();
+  return name;
+}
+} // namespace util
+} // namespace c10
+
+C10_DEFINE_HASH_FOR_IDWRAPPER(c10::util::type_index);

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/TypeList.h

@@ -0,0 +1,516 @@
+#pragma once
+
+#include <c10/util/C++17.h>
+#include <c10/util/TypeTraits.h>
+#include <algorithm>
+
+namespace c10 {
+namespace guts {
+
+template <class... T>
+struct false_t : std::false_type {};
+template <template <class> class... T>
+struct false_higher_t : std::false_type {};
+
+namespace typelist {
+
+/**
+ * Type holding a list of types for compile time type computations
+ */
+template <class... Items>
+struct typelist final {
+ public:
+  typelist() = delete; // not for instantiation
+};
+
+/**
+ * Returns the number of types in a typelist
+ * Example:
+ *   3  ==  size<typelist<int, int, double>>::value
+ */
+template <class TypeList>
+struct size final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::size<T>, T must be typelist<...>.");
+};
+template <class... Types>
+struct size<typelist<Types...>> final {
+  static constexpr size_t value = sizeof...(Types);
+};
+
+/**
+ * Transforms a list of types into a tuple holding these types.
+ * Example:
+ *   std::tuple<int, string>  ==  to_tuple_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct to_tuple final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::to_tuple<T>, T must be typelist<...>.");
+};
+template <class... Types>
+struct to_tuple<typelist<Types...>> final {
+  using type = std::tuple<Types...>;
+};
+template <class TypeList>
+using to_tuple_t = typename to_tuple<TypeList>::type;
+
+/**
+ * Creates a typelist containing the types of a given tuple.
+ * Example:
+ *   typelist<int, string>  ==  from_tuple_t<std::tuple<int, string>>
+ */
+template <class Tuple>
+struct from_tuple final {
+  static_assert(
+      false_t<Tuple>::value,
+      "In typelist::from_tuple<T>, T must be std::tuple<...>.");
+};
+template <class... Types>
+struct from_tuple<std::tuple<Types...>> final {
+  using type = typelist<Types...>;
+};
+template <class Tuple>
+using from_tuple_t = typename from_tuple<Tuple>::type;
+
+/**
+ * Concatenates multiple type lists.
+ * Example:
+ *   typelist<int, string, int>  ==  concat_t<typelist<int, string>,
+ * typelist<int>>
+ */
+template <class... TypeLists>
+struct concat final {
+  static_assert(
+      false_t<TypeLists...>::value,
+      "In typelist::concat<T1, ...>, the T arguments each must be typelist<...>.");
+};
+template <class... Head1Types, class... Head2Types, class... TailLists>
+struct concat<typelist<Head1Types...>, typelist<Head2Types...>, TailLists...>
+    final {
+  using type =
+      typename concat<typelist<Head1Types..., Head2Types...>, TailLists...>::
+          type;
+};
+template <class... HeadTypes>
+struct concat<typelist<HeadTypes...>> final {
+  using type = typelist<HeadTypes...>;
+};
+template <>
+struct concat<> final {
+  using type = typelist<>;
+};
+template <class... TypeLists>
+using concat_t = typename concat<TypeLists...>::type;
+
+/**
+ * Filters the types in a type list by a type trait.
+ * Examples:
+ *   typelist<int&, const string&&>  ==  filter_t<std::is_reference,
+ * typelist<void, string, int&, bool, const string&&, int>>
+ */
+template <template <class> class Condition, class TypeList>
+struct filter final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::filter<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class Head, class... Tail>
+struct filter<Condition, typelist<Head, Tail...>> final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::filter<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  using type = std::conditional_t<
+      Condition<Head>::value,
+      concat_t<
+          typelist<Head>,
+          typename filter<Condition, typelist<Tail...>>::type>,
+      typename filter<Condition, typelist<Tail...>>::type>;
+};
+template <template <class> class Condition>
+struct filter<Condition, typelist<>> final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::filter<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  using type = typelist<>;
+};
+template <template <class> class Condition, class TypeList>
+using filter_t = typename filter<Condition, TypeList>::type;
+
+/**
+ * Counts how many types in the list fulfill a type trait
+ * Examples:
+ *   2  ==  count_if<std::is_reference, typelist<void, string, int&, bool, const
+ * string&&, int>>
+ */
+template <template <class> class Condition, class TypeList>
+struct count_if final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::count_if<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::count_if<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+  // TODO Direct implementation might be faster
+  static constexpr size_t value = size<filter_t<Condition, TypeList>>::value;
+};
+
+/**
+ * Checks if a typelist contains a certain type.
+ * Examples:
+ *  contains<typelist<int, string>, string> == true_type
+ *  contains<typelist<int, string>, double> == false_type
+ */
+namespace detail {
+template <class TypeList, class Type, class Enable = void>
+struct contains {};
+template <class Type>
+struct contains<typelist<>, Type, void> : std::false_type {};
+template <class Type, class Head, class... Tail>
+struct contains<
+    typelist<Head, Tail...>,
+    Type,
+    std::enable_if_t<std::is_same<Head, Type>::value>> : std::true_type {};
+template <class Type, class Head, class... Tail>
+struct contains<
+    typelist<Head, Tail...>,
+    Type,
+    std::enable_if_t<!std::is_same<Head, Type>::value>>
+    : contains<typelist<Tail...>, Type> {};
+} // namespace detail
+template <class TypeList, class Type>
+using contains = typename detail::contains<TypeList, Type>::type;
+
+/**
+ * Returns true iff the type trait is true for all types in the type list
+ * Examples:
+ *   true   ==  all<std::is_reference, typelist<int&, const float&&, const
+ * MyClass&>>::value false  ==  all<std::is_reference, typelist<int&, const
+ * float&&, MyClass>>::value
+ */
+template <template <class> class Condition, class TypeList>
+struct all {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::all<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class... Types>
+struct all<Condition, typelist<Types...>>
+    : guts::conjunction<Condition<Types>...> {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::all<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+};
+
+/**
+ * Returns true iff the type trait is true for any type in the type list
+ * Examples:
+ *   true   ==  true_for_any_type<std::is_reference, typelist<int, const
+ * float&&, const MyClass>>::value false  ==
+ * true_for_any_type<std::is_reference, typelist<int, const float,
+ * MyClass>>::value
+ */
+template <template <class> class Condition, class TypeList>
+struct true_for_any_type final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::true_for_any_type<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class... Types>
+struct true_for_any_type<Condition, typelist<Types...>> final
+    : guts::disjunction<Condition<Types>...> {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::true_for_any_type<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+};
+
+/**
+ * Maps types of a type list using a type trait
+ * Example:
+ *  typelist<int&, double&, string&>  ==  map_t<std::add_lvalue_reference_t,
+ * typelist<int, double, string>>
+ */
+template <template <class> class Mapper, class TypeList>
+struct map final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::map<Mapper, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Mapper, class... Types>
+struct map<Mapper, typelist<Types...>> final {
+  using type = typelist<Mapper<Types>...>;
+};
+template <template <class> class Mapper, class TypeList>
+using map_t = typename map<Mapper, TypeList>::type;
+
+/**
+ * Returns the first element of a type list.
+ * Example:
+ *   int  ==  head_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct head final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::head<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct head<typelist<Head, Tail...>> final {
+  using type = Head;
+};
+template <class TypeList>
+using head_t = typename head<TypeList>::type;
+
+/**
+ * Returns the first element of a type list, or the specified default if the
+ * type list is empty. Example: int  ==  head_t<bool, typelist<int, string>>
+ *   bool  ==  head_t<bool, typelist<>>
+ */
+template <class Default, class TypeList>
+struct head_with_default final {
+  using type = Default;
+};
+template <class Default, class Head, class... Tail>
+struct head_with_default<Default, typelist<Head, Tail...>> final {
+  using type = Head;
+};
+template <class Default, class TypeList>
+using head_with_default_t = typename head_with_default<Default, TypeList>::type;
+
+/**
+ * Returns the N-th element of a type list.
+ * Example:
+ * int == element_t<1, typelist<float, int, char>>
+ */
+
+/// Base template.
+template <size_t Index, class TypeList>
+struct element final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::element<T>, the T argument must be typelist<...>.");
+};
+
+/// Successful case, we have reached the zero index and can "return" the head
+/// type.
+template <class Head, class... Tail>
+struct element<0, typelist<Head, Tail...>> {
+  using type = Head;
+};
+
+/// Error case, we have an index but ran out of types! It will only be selected
+/// if `Ts...` is actually empty!
+template <size_t Index, class... Ts>
+struct element<Index, typelist<Ts...>> {
+  static_assert(
+      Index < sizeof...(Ts),
+      "Index is out of bounds in typelist::element");
+};
+
+/// Shave off types until we hit the <0, Head, Tail...> or <Index> case.
+template <size_t Index, class Head, class... Tail>
+struct element<Index, typelist<Head, Tail...>>
+    : element<Index - 1, typelist<Tail...>> {};
+
+/// Convenience alias.
+template <size_t Index, class TypeList>
+using element_t = typename element<Index, TypeList>::type;
+
+/**
+ * Returns the last element of a type list.
+ * Example:
+ *   int  ==  last_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct last final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::last<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct last<typelist<Head, Tail...>> final {
+  using type = typename last<typelist<Tail...>>::type;
+};
+template <class Head>
+struct last<typelist<Head>> final {
+  using type = Head;
+};
+template <class TypeList>
+using last_t = typename last<TypeList>::type;
+static_assert(std::is_same<int, last_t<typelist<double, float, int>>>::value);
+
+/**
+ * Take/drop a number of arguments from a typelist.
+ * Example:
+ *   typelist<int, string> == take_t<typelist<int, string, bool>, 2>
+ *   typelist<bool> == drop_t<typelist<int, string, bool>, 2>
+ */
+namespace detail {
+template <class TypeList, size_t offset, class IndexSequence>
+struct take_elements final {};
+
+template <class TypeList, size_t offset, size_t... Indices>
+struct take_elements<TypeList, offset, std::index_sequence<Indices...>> final {
+  using type = typelist<typename element<offset + Indices, TypeList>::type...>;
+};
+} // namespace detail
+
+template <class TypeList, size_t num>
+struct take final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::take<T, num>, the T argument must be typelist<...>.");
+  static_assert(
+      num <= size<TypeList>::value,
+      "Tried to typelist::take more elements than there are in the list");
+  using type = typename detail::
+      take_elements<TypeList, 0, std::make_index_sequence<num>>::type;
+};
+template <class TypeList, size_t num>
+using take_t = typename take<TypeList, num>::type;
+
+template <class TypeList, size_t num>
+struct drop final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::drop<T, num>, the T argument must be typelist<...>.");
+  static_assert(
+      num <= size<TypeList>::value,
+      "Tried to typelist::drop more elements than there are in the list");
+  using type = typename detail::take_elements<
+      TypeList,
+      num,
+      std::make_index_sequence<size<TypeList>::value - num>>::type;
+};
+template <class TypeList, size_t num>
+using drop_t = typename drop<TypeList, num>::type;
+
+/**
+ * Like drop, but returns an empty list rather than an assertion error if `num`
+ * is larger than the size of the TypeList.
+ * Example:
+ *   typelist<> == drop_if_nonempty_t<typelist<string, bool>, 2>
+ *   typelist<> == drop_if_nonempty_t<typelist<int, string, bool>, 3>
+ */
+template <class TypeList, size_t num>
+struct drop_if_nonempty final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::drop<T, num>, the T argument must be typelist<...>.");
+  using type = typename detail::take_elements<
+      TypeList,
+      std::min(num, size<TypeList>::value),
+      std::make_index_sequence<
+          size<TypeList>::value - std::min(num, size<TypeList>::value)>>::type;
+};
+template <class TypeList, size_t num>
+using drop_if_nonempty_t = typename drop_if_nonempty<TypeList, num>::type;
+
+/**
+ * Reverses a typelist.
+ * Example:
+ *   typelist<int, string>  == reverse_t<typelist<string, int>>
+ */
+template <class TypeList>
+struct reverse final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::reverse<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct reverse<typelist<Head, Tail...>> final {
+  using type =
+      concat_t<typename reverse<typelist<Tail...>>::type, typelist<Head>>;
+};
+template <>
+struct reverse<typelist<>> final {
+  using type = typelist<>;
+};
+template <class TypeList>
+using reverse_t = typename reverse<TypeList>::type;
+
+/**
+ * Find the index of the first type in a typelist fulfilling a type trait
+ * condition. Example:
+ *
+ * 2 == find_if<typelist<char, int, char&, int&>, std::is_reference>::value
+ */
+template <class TypeList, template <class> class Condition, class Enable = void>
+struct find_if final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::find_if<TypeList, Condition>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition>
+struct find_if<typelist<>, Condition, void> final {
+  static_assert(
+      false_higher_t<Condition>::value,
+      "In typelist::find_if<Type/List, Condition>, didn't find any type fulfilling the Condition.");
+};
+template <class Head, class... Tail, template <class> class Condition>
+struct find_if<
+    typelist<Head, Tail...>,
+    Condition,
+    std::enable_if_t<Condition<Head>::value>>
+    final {
+  static constexpr size_t value = 0;
+};
+template <class Head, class... Tail, template <class> class Condition>
+struct find_if<
+    typelist<Head, Tail...>,
+    Condition,
+    std::enable_if_t<!Condition<Head>::value>>
+    final {
+  static constexpr size_t value =
+      1 + find_if<typelist<Tail...>, Condition>::value;
+};
+
+/**
+ * Maps a list of types into a list of values.
+ * Examples:
+ *   // Example 1
+ *   auto sizes =
+ *     map_types_to_values<typelist<int64_t, bool, uint32_t>>(
+ *       [] (auto t) { return sizeof(decltype(t)::type); }
+ *     );
+ *   //  sizes  ==  std::tuple<size_t, size_t, size_t>{8, 1, 4}
+ *
+ *   // Example 2
+ *   auto shared_ptrs =
+ *     map_types_to_values<typelist<int, double>>(
+ *       [] (auto t) { return make_shared<typename decltype(t)::type>(); }
+ *     );
+ *   // shared_ptrs == std::tuple<shared_ptr<int>, shared_ptr<double>>()
+ */
+namespace detail {
+template <class T>
+struct type_ final {
+  using type = T;
+};
+template <class TypeList>
+struct map_types_to_values final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::map_types_to_values<T>, the T argument must be typelist<...>.");
+};
+template <class... Types>
+struct map_types_to_values<typelist<Types...>> final {
+  template <class Func>
+  static std::tuple<c10::invoke_result_t<Func, type_<Types>>...> call(
+      Func&& func) {
+    return std::tuple<c10::invoke_result_t<Func, type_<Types>>...>{
+        std::forward<Func>(func)(type_<Types>())...};
+  }
+};
+} // namespace detail
+
+template <class TypeList, class Func>
+decltype(auto) map_types_to_values(Func&& func) {
+  return detail::map_types_to_values<TypeList>::call(std::forward<Func>(func));
+}
+
+} // namespace typelist
+} // namespace guts
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/TypeSafeSignMath.h

@@ -0,0 +1,144 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <limits>
+#include <type_traits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wstring-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wstring-conversion")
+#endif
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+/// Returns false since we cannot have x < 0 if x is unsigned.
+template <typename T>
+static inline constexpr bool is_negative(
+    const T& /*x*/,
+    std::true_type /*is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if a signed variable x < 0
+template <typename T>
+static inline constexpr bool is_negative(
+    const T& x,
+    std::false_type /*is_unsigned*/) {
+  return x < T(0);
+}
+
+/// Returns true if x < 0
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :-(
+template <typename T>
+inline constexpr bool is_negative(const T& x) {
+  return is_negative(x, std::is_unsigned<T>());
+}
+
+/// Returns the sign of an unsigned variable x as 0, 1
+template <typename T>
+static inline constexpr int signum(const T& x, std::true_type /*is_unsigned*/) {
+  return T(0) < x;
+}
+
+/// Returns the sign of a signed variable x as -1, 0, 1
+template <typename T>
+static inline constexpr int signum(
+    const T& x,
+    std::false_type /*is_unsigned*/) {
+  return (T(0) < x) - (x < T(0));
+}
+
+/// Returns the sign of x as -1, 0, 1
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :-(
+template <typename T>
+inline constexpr int signum(const T& x) {
+  return signum(x, std::is_unsigned<T>());
+}
+
+/// Returns true if a and b are not both negative
+template <typename T, typename U>
+inline constexpr bool signs_differ(const T& a, const U& b) {
+  return is_negative(a) != is_negative(b);
+}
+
+// Suppress sign compare warning when compiling with GCC
+// as later does not account for short-circuit rule before
+// raising the warning, see https://godbolt.org/z/Tr3Msnz99
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wsign-compare"
+#endif
+
+/// Returns true if x is greater than the greatest value of the type Limit
+template <typename Limit, typename T>
+inline constexpr bool greater_than_max(const T& x) {
+  constexpr bool can_overflow =
+      std::numeric_limits<T>::digits > std::numeric_limits<Limit>::digits;
+  return can_overflow && x > std::numeric_limits<Limit>::max();
+}
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
+/// Returns true if x < lowest(Limit). Standard comparison
+template <typename Limit, typename T>
+static inline constexpr bool less_than_lowest(
+    const T& x,
+    std::false_type /*limit_is_unsigned*/,
+    std::false_type /*x_is_unsigned*/) {
+  return x < std::numeric_limits<Limit>::lowest();
+}
+
+/// Returns false since all the limit is signed and therefore includes
+/// negative values but x cannot be negative because it is unsigned
+template <typename Limit, typename T>
+static inline constexpr bool less_than_lowest(
+    const T& /*x*/,
+    std::false_type /*limit_is_unsigned*/,
+    std::true_type /*x_is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if x < 0, where 0 is constructed from T.
+/// Limit is not signed, so its lower value is zero
+template <typename Limit, typename T>
+static inline constexpr bool less_than_lowest(
+    const T& x,
+    std::true_type /*limit_is_unsigned*/,
+    std::false_type /*x_is_unsigned*/) {
+  return x < T(0);
+}
+
+/// Returns false sign both types are unsigned
+template <typename Limit, typename T>
+static inline constexpr bool less_than_lowest(
+    const T& /*x*/,
+    std::true_type /*limit_is_unsigned*/,
+    std::true_type /*x_is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if x is less than the lowest value of type T
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(const T& x) {
+  return less_than_lowest<Limit>(
+      x, std::is_unsigned<Limit>(), std::is_unsigned<T>());
+}
+
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/TypeTraits.h

@@ -0,0 +1,152 @@
+#pragma once
+
+#include <c10/util/C++17.h>
+
+namespace c10 {
+namespace guts {
+
+/**
+ * is_equality_comparable<T> is true_type iff the equality operator is defined
+ * for T.
+ */
+template <class T, class Enable = void>
+struct is_equality_comparable : std::false_type {};
+template <class T>
+struct is_equality_comparable<
+    T,
+    void_t<decltype(std::declval<T&>() == std::declval<T&>())>>
+    : std::true_type {};
+template <class T>
+using is_equality_comparable_t = typename is_equality_comparable<T>::type;
+
+/**
+ * is_hashable<T> is true_type iff std::hash is defined for T
+ */
+template <class T, class Enable = void>
+struct is_hashable : std::false_type {};
+template <class T>
+struct is_hashable<T, void_t<decltype(std::hash<T>()(std::declval<T&>()))>>
+    : std::true_type {};
+template <class T>
+using is_hashable_t = typename is_hashable<T>::type;
+
+/**
+ * is_function_type<T> is true_type iff T is a plain function type (i.e.
+ * "Result(Args...)")
+ */
+template <class T>
+struct is_function_type : std::false_type {};
+template <class Result, class... Args>
+struct is_function_type<Result(Args...)> : std::true_type {};
+template <class T>
+using is_function_type_t = typename is_function_type<T>::type;
+
+/**
+ * is_instantiation_of<T, I> is true_type iff I is a template instantiation of T
+ * (e.g. vector<int> is an instantiation of vector) Example:
+ *    is_instantiation_of_t<vector, vector<int>> // true
+ *    is_instantiation_of_t<pair, pair<int, string>> // true
+ *    is_instantiation_of_t<vector, pair<int, string>> // false
+ */
+template <template <class...> class Template, class T>
+struct is_instantiation_of : std::false_type {};
+template <template <class...> class Template, class... Args>
+struct is_instantiation_of<Template, Template<Args...>> : std::true_type {};
+template <template <class...> class Template, class T>
+using is_instantiation_of_t = typename is_instantiation_of<Template, T>::type;
+
+namespace detail {
+/**
+ * strip_class: helper to remove the class type from pointers to `operator()`.
+ */
+
+template <typename T>
+struct strip_class {};
+template <typename Class, typename Result, typename... Args>
+struct strip_class<Result (Class::*)(Args...)> {
+  using type = Result(Args...);
+};
+template <typename Class, typename Result, typename... Args>
+struct strip_class<Result (Class::*)(Args...) const> {
+  using type = Result(Args...);
+};
+template <typename T>
+using strip_class_t = typename strip_class<T>::type;
+} // namespace detail
+
+/**
+ * Evaluates to true_type, iff the given class is a Functor
+ * (i.e. has a call operator with some set of arguments)
+ */
+
+template <class Functor, class Enable = void>
+struct is_functor : std::false_type {};
+template <class Functor>
+struct is_functor<
+    Functor,
+    std::enable_if_t<is_function_type<
+        detail::strip_class_t<decltype(&Functor::operator())>>::value>>
+    : std::true_type {};
+
+/**
+ * lambda_is_stateless<T> is true iff the lambda type T is stateless
+ * (i.e. does not have a closure).
+ * Example:
+ *  auto stateless_lambda = [] (int a) {return a;};
+ *  lambda_is_stateless<decltype(stateless_lambda)> // true
+ *  auto stateful_lambda = [&] (int a) {return a;};
+ *  lambda_is_stateless<decltype(stateful_lambda)> // false
+ */
+namespace detail {
+template <class LambdaType, class FuncType>
+struct is_stateless_lambda__ final {
+  static_assert(
+      !std::is_same<LambdaType, LambdaType>::value,
+      "Base case shouldn't be hit");
+};
+// implementation idea: According to the C++ standard, stateless lambdas are
+// convertible to function pointers
+template <class LambdaType, class C, class Result, class... Args>
+struct is_stateless_lambda__<LambdaType, Result (C::*)(Args...) const>
+    : std::is_convertible<LambdaType, Result (*)(Args...)> {};
+template <class LambdaType, class C, class Result, class... Args>
+struct is_stateless_lambda__<LambdaType, Result (C::*)(Args...)>
+    : std::is_convertible<LambdaType, Result (*)(Args...)> {};
+
+// case where LambdaType is not even a functor
+template <class LambdaType, class Enable = void>
+struct is_stateless_lambda_ final : std::false_type {};
+// case where LambdaType is a functor
+template <class LambdaType>
+struct is_stateless_lambda_<
+    LambdaType,
+    std::enable_if_t<is_functor<LambdaType>::value>>
+    : is_stateless_lambda__<LambdaType, decltype(&LambdaType::operator())> {};
+} // namespace detail
+template <class T>
+using is_stateless_lambda = detail::is_stateless_lambda_<std::decay_t<T>>;
+
+/**
+ * is_type_condition<C> is true_type iff C<...> is a type trait representing a
+ * condition (i.e. has a constexpr static bool ::value member) Example:
+ *   is_type_condition<std::is_reference>  // true
+ */
+template <template <class> class C, class Enable = void>
+struct is_type_condition : std::false_type {};
+template <template <class> class C>
+struct is_type_condition<
+    C,
+    std::enable_if_t<
+        std::is_same<bool, std::remove_cv_t<decltype(C<int>::value)>>::value>>
+    : std::true_type {};
+
+/**
+ * is_fundamental<T> is true_type iff the lambda type T is a fundamental type
+ * (that is, arithmetic type, void, or nullptr_t). Example: is_fundamental<int>
+ * // true We define it here to resolve a MSVC bug. See
+ * https://github.com/pytorch/pytorch/issues/30932 for details.
+ */
+template <class T>
+struct is_fundamental : std::is_fundamental<T> {};
+} // namespace guts
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Unicode.h

@@ -0,0 +1,14 @@
+#pragma once
+
+#if defined(_WIN32)
+#include <c10/util/Exception.h>
+#include <c10/util/win32-headers.h>
+#include <string>
+#endif
+
+namespace c10 {
+#if defined(_WIN32)
+C10_API std::wstring u8u16(const std::string& str);
+C10_API std::string u16u8(const std::wstring& wstr);
+#endif
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/UniqueVoidPtr.h

@@ -0,0 +1,124 @@
+#pragma once
+#include <memory>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+using DeleterFnPtr = void (*)(void*);
+
+namespace detail {
+
+// Does not delete anything
+C10_API void deleteNothing(void*);
+
+// A detail::UniqueVoidPtr is an owning smart pointer like unique_ptr, but
+// with three major differences:
+//
+//    1) It is specialized to void
+//
+//    2) It is specialized for a function pointer deleter
+//       void(void* ctx); i.e., the deleter doesn't take a
+//       reference to the data, just to a context pointer
+//       (erased as void*).  In fact, internally, this pointer
+//       is implemented as having an owning reference to
+//       context, and a non-owning reference to data; this is why
+//       you release_context(), not release() (the conventional
+//       API for release() wouldn't give you enough information
+//       to properly dispose of the object later.)
+//
+//    3) The deleter is guaranteed to be called when the unique
+//       pointer is destructed and the context is non-null; this is different
+//       from std::unique_ptr where the deleter is not called if the
+//       data pointer is null.
+//
+// Some of the methods have slightly different types than std::unique_ptr
+// to reflect this.
+//
+class UniqueVoidPtr {
+ private:
+  // Lifetime tied to ctx_
+  void* data_;
+  std::unique_ptr<void, DeleterFnPtr> ctx_;
+
+ public:
+  UniqueVoidPtr() : data_(nullptr), ctx_(nullptr, &deleteNothing) {}
+  explicit UniqueVoidPtr(void* data)
+      : data_(data), ctx_(nullptr, &deleteNothing) {}
+  UniqueVoidPtr(void* data, void* ctx, DeleterFnPtr ctx_deleter)
+      : data_(data), ctx_(ctx, ctx_deleter ? ctx_deleter : &deleteNothing) {}
+  void* operator->() const {
+    return data_;
+  }
+  void clear() {
+    ctx_ = nullptr;
+    data_ = nullptr;
+  }
+  void* get() const {
+    return data_;
+  }
+  void* get_context() const {
+    return ctx_.get();
+  }
+  void* release_context() {
+    return ctx_.release();
+  }
+  std::unique_ptr<void, DeleterFnPtr>&& move_context() {
+    return std::move(ctx_);
+  }
+  C10_NODISCARD bool compare_exchange_deleter(
+      DeleterFnPtr expected_deleter,
+      DeleterFnPtr new_deleter) {
+    if (get_deleter() != expected_deleter)
+      return false;
+    ctx_ = std::unique_ptr<void, DeleterFnPtr>(ctx_.release(), new_deleter);
+    return true;
+  }
+
+  template <typename T>
+  T* cast_context(DeleterFnPtr expected_deleter) const {
+    if (get_deleter() != expected_deleter)
+      return nullptr;
+    return static_cast<T*>(get_context());
+  }
+  operator bool() const {
+    return data_ || ctx_;
+  }
+  DeleterFnPtr get_deleter() const {
+    return ctx_.get_deleter();
+  }
+};
+
+// Note [How UniqueVoidPtr is implemented]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// UniqueVoidPtr solves a common problem for allocators of tensor data, which
+// is that the data pointer (e.g., float*) which you are interested in, is not
+// the same as the context pointer (e.g., DLManagedTensor) which you need
+// to actually deallocate the data.  Under a conventional deleter design, you
+// have to store extra context in the deleter itself so that you can actually
+// delete the right thing.  Implementing this with standard C++ is somewhat
+// error-prone: if you use a std::unique_ptr to manage tensors, the deleter will
+// not be called if the data pointer is nullptr, which can cause a leak if the
+// context pointer is non-null (and the deleter is responsible for freeing both
+// the data pointer and the context pointer).
+//
+// So, in our reimplementation of unique_ptr, which just store the context
+// directly in the unique pointer, and attach the deleter to the context
+// pointer itself.  In simple cases, the context pointer is just the pointer
+// itself.
+
+inline bool operator==(const UniqueVoidPtr& sp, std::nullptr_t) noexcept {
+  return !sp;
+}
+inline bool operator==(std::nullptr_t, const UniqueVoidPtr& sp) noexcept {
+  return !sp;
+}
+inline bool operator!=(const UniqueVoidPtr& sp, std::nullptr_t) noexcept {
+  return sp;
+}
+inline bool operator!=(std::nullptr_t, const UniqueVoidPtr& sp) noexcept {
+  return sp;
+}
+
+} // namespace detail
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/Unroll.h

@@ -0,0 +1,29 @@
+#pragma once
+#include <c10/macros/Macros.h>
+
+// Utility to guarantee complete unrolling of a loop where the bounds are known
+// at compile time. Various pragmas achieve similar effects, but are not as
+// portable across compilers.
+
+// Example: c10::ForcedUnroll<4>{}(f); is equivalent to f(0); f(1); f(2); f(3);
+
+namespace c10 {
+
+template <int n>
+struct ForcedUnroll {
+  template <typename Func>
+  C10_ALWAYS_INLINE void operator()(const Func& f) const {
+    ForcedUnroll<n - 1>{}(f);
+    f(n - 1);
+  }
+};
+
+template <>
+struct ForcedUnroll<1> {
+  template <typename Func>
+  C10_ALWAYS_INLINE void operator()(const Func& f) const {
+    f(0);
+  }
+};
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/accumulate.h

@@ -0,0 +1,134 @@
+// Copyright 2004-present Facebook. All Rights Reserved.
+
+#pragma once
+
+#include <c10/util/ArrayRef.h>
+
+#include <iterator>
+#include <numeric>
+#include <type_traits>
+
+namespace c10 {
+
+/// Sum of a list of integers; accumulates into the int64_t datatype
+template <
+    typename C,
+    typename std::enable_if<
+        std::is_integral<typename C::value_type>::value,
+        int>::type = 0>
+inline int64_t sum_integers(const C& container) {
+  // std::accumulate infers return type from `init` type, so if the `init` type
+  // is not large enough to hold the result, computation can overflow. We use
+  // `int64_t` here to avoid this.
+  return std::accumulate(
+      container.begin(), container.end(), static_cast<int64_t>(0));
+}
+
+/// Sum of integer elements referred to by iterators; accumulates into the
+/// int64_t datatype
+template <
+    typename Iter,
+    typename std::enable_if<
+        std::is_integral<
+            typename std::iterator_traits<Iter>::value_type>::value,
+        int>::type = 0>
+inline int64_t sum_integers(Iter begin, Iter end) {
+  // std::accumulate infers return type from `init` type, so if the `init` type
+  // is not large enough to hold the result, computation can overflow. We use
+  // `int64_t` here to avoid this.
+  return std::accumulate(begin, end, static_cast<int64_t>(0));
+}
+
+/// Product of a list of integers; accumulates into the int64_t datatype
+template <
+    typename C,
+    typename std::enable_if<
+        std::is_integral<typename C::value_type>::value,
+        int>::type = 0>
+inline int64_t multiply_integers(const C& container) {
+  // std::accumulate infers return type from `init` type, so if the `init` type
+  // is not large enough to hold the result, computation can overflow. We use
+  // `int64_t` here to avoid this.
+  return std::accumulate(
+      container.begin(),
+      container.end(),
+      static_cast<int64_t>(1),
+      std::multiplies<>());
+}
+
+/// Product of integer elements referred to by iterators; accumulates into the
+/// int64_t datatype
+template <
+    typename Iter,
+    typename std::enable_if<
+        std::is_integral<
+            typename std::iterator_traits<Iter>::value_type>::value,
+        int>::type = 0>
+inline int64_t multiply_integers(Iter begin, Iter end) {
+  // std::accumulate infers return type from `init` type, so if the `init` type
+  // is not large enough to hold the result, computation can overflow. We use
+  // `int64_t` here to avoid this.
+  return std::accumulate(
+      begin, end, static_cast<int64_t>(1), std::multiplies<>());
+}
+
+/// Return product of all dimensions starting from k
+/// Returns 1 if k>=dims.size()
+template <
+    typename C,
+    typename std::enable_if<
+        std::is_integral<typename C::value_type>::value,
+        int>::type = 0>
+inline int64_t numelements_from_dim(const int k, const C& dims) {
+  TORCH_INTERNAL_ASSERT_DEBUG_ONLY(k >= 0);
+
+  if (k > static_cast<int>(dims.size())) {
+    return 1;
+  } else {
+    auto cbegin = dims.cbegin();
+    std::advance(cbegin, k);
+    return multiply_integers(cbegin, dims.cend());
+  }
+}
+
+/// Product of all dims up to k (not including dims[k])
+/// Throws an error if k>dims.size()
+template <
+    typename C,
+    typename std::enable_if<
+        std::is_integral<typename C::value_type>::value,
+        int>::type = 0>
+inline int64_t numelements_to_dim(const int k, const C& dims) {
+  TORCH_INTERNAL_ASSERT(0 <= k);
+  TORCH_INTERNAL_ASSERT((unsigned)k <= dims.size());
+
+  auto cend = dims.cbegin();
+  std::advance(cend, k);
+  return multiply_integers(dims.cbegin(), cend);
+}
+
+/// Product of all dims between k and l (including dims[k] and excluding
+/// dims[l]) k and l may be supplied in either order
+template <
+    typename C,
+    typename std::enable_if<
+        std::is_integral<typename C::value_type>::value,
+        int>::type = 0>
+inline int64_t numelements_between_dim(int k, int l, const C& dims) {
+  TORCH_INTERNAL_ASSERT(0 <= k);
+  TORCH_INTERNAL_ASSERT(0 <= l);
+
+  if (k > l) {
+    std::swap(k, l);
+  }
+
+  TORCH_INTERNAL_ASSERT((unsigned)l < dims.size());
+
+  auto cbegin = dims.cbegin();
+  auto cend = dims.cbegin();
+  std::advance(cbegin, k);
+  std::advance(cend, l);
+  return multiply_integers(cbegin, cend);
+}
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/c10/util/bits.h

@@ -0,0 +1,61 @@
+#pragma once
+#include <cstdint>
+
+#include <c10/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * bits1x8 is an uninterpreted dtype of a tensor with 1 bit (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits1x8 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits1x8() = default;
+  C10_HOST_DEVICE explicit bits1x8(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits2x4 is an uninterpreted dtype of a tensor with 2 bits (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits2x4 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits2x4() = default;
+  C10_HOST_DEVICE explicit bits2x4(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits4x2 is an uninterpreted dtype of a tensor with 4 bits (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits4x2 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits4x2() = default;
+  C10_HOST_DEVICE explicit bits4x2(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits8 is an uninterpreted dtype of a tensor with 8 bits, without any
+ * semantics defined.
+ */
+struct alignas(1) bits8 {
+  uint8_t val_;
+  bits8() = default;
+  C10_HOST_DEVICE explicit bits8(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits16 is an uninterpreted dtype of a tensor with 16 bits, without any
+ * semantics defined.
+ */
+struct alignas(2) bits16 {
+  uint16_t val_;
+  bits16() = default;
+  C10_HOST_DEVICE explicit bits16(uint16_t val) : val_(val) {}
+};
+
+} // namespace c10