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ckpts/universal/global_step20/zero/14.mlp.dense_4h_to_h.weight/exp_avg.pt

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+size 33555612

ckpts/universal/global_step20/zero/18.mlp.dense_4h_to_h.weight/exp_avg_sq.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:300476c759a9584056de01692721be4fb0c87c24610b227124cb5a950e8e36a9
+size 33555627

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/init.h

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+#pragma once
+
+#include <torch/csrc/utils/pybind.h>
+
+namespace torch::jit {
+
+void initJITBindings(PyObject* module);
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/module_python.h

@@ -0,0 +1,35 @@
+#pragma once
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace py = pybind11;
+
+namespace torch::jit {
+
+inline c10::optional<Module> as_module(py::handle obj) {
+  static py::handle ScriptModule =
+      py::module::import("torch.jit").attr("ScriptModule");
+  if (py::isinstance(obj, ScriptModule)) {
+    return py::cast<Module>(obj.attr("_c"));
+  }
+  return c10::nullopt;
+}
+
+inline c10::optional<Object> as_object(py::handle obj) {
+  static py::handle ScriptObject =
+      py::module::import("torch").attr("ScriptObject");
+  if (py::isinstance(obj, ScriptObject)) {
+    return py::cast<Object>(obj);
+  }
+
+  static py::handle RecursiveScriptClass =
+      py::module::import("torch.jit").attr("RecursiveScriptClass");
+  if (py::isinstance(obj, RecursiveScriptClass)) {
+    return py::cast<Object>(obj.attr("_c"));
+  }
+  return c10::nullopt;
+}
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/pybind.h

@@ -0,0 +1,213 @@
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+#include <ATen/core/ivalue.h>
+#include <ATen/core/symbol.h>
+#include <c10/util/irange.h>
+#include <torch/csrc/DynamicTypes.h>
+#include <torch/csrc/THP.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/jit/frontend/tracer.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/utils/pybind.h>
+
+#include <pybind11/functional.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+namespace py = pybind11;
+
+namespace torch::jit {
+
+// This is a variant of shared_ptr that "sees through" a wrapper.
+// We use it to convert Value, Node, Block and node to "wrapped" Python
+// values. When we destruct the C++ object, the wrapper's pointer will
+// be set to 0 and any future dereferencing will throw. We need this
+// because the Python objects may hang around after the C++ object
+// has already been destroyed.
+// This also needs the magic type_caster below, which is from the
+// workaround offered in https://github.com/pybind/pybind11/issues/2751
+template <typename T>
+class unwrapping_shared_ptr {
+  static_assert(
+      std::is_same<T, torch::jit::Value>::value ||
+          std::is_same<T, torch::jit::Node>::value ||
+          std::is_same<T, torch::jit::Block>::value,
+      "unwrapping type only defined for Graph object types");
+
+ private:
+  std::shared_ptr<torch::jit::Wrap<T>> impl;
+
+ public:
+  unwrapping_shared_ptr() : impl({}) {}
+  explicit unwrapping_shared_ptr(T* p) : impl(p->wrap()) {
+    impl->clear_cb = &clear_registered_instances;
+  }
+  T* get() const {
+    if (!impl->elem) {
+      throw std::logic_error("has been invalidated");
+    }
+    return impl->elem;
+  }
+  // we need to disable the overloaded & for PyBind11 < 2.3 due.
+  // see https://github.com/pybind/pybind11/pull/1435
+#if (PYBIND11_VERSION_MAJOR > 2) || \
+    ((PYBIND11_VERSION_MAJOR == 2) && (PYBIND11_VERSION_MINOR >= 3))
+  T** operator&() {
+    if (!impl->elem) {
+      throw std::logic_error("has been invalidated");
+    }
+    return &(impl->elem);
+  }
+#endif
+};
+
+} // namespace torch::jit
+
+PYBIND11_DECLARE_HOLDER_TYPE(T, torch::jit::unwrapping_shared_ptr<T>, true);
+
+namespace pybind11::detail {
+
+#define CREATE_UNWRAPPING_CASTER(Class)                                                   \
+  template <>                                                                             \
+  struct type_caster<Class> : public type_caster_base<Class> {                            \
+   public:                                                                                \
+    using type = Class;                                                                   \
+    using holder_type = torch::jit::unwrapping_shared_ptr<Class>;                         \
+                                                                                          \
+    bool load(handle src, bool convert) {                                                 \
+      return load_impl<type_caster<Class>>(src, convert);                                 \
+    }                                                                                     \
+                                                                                          \
+    explicit operator type*() {                                                           \
+      return static_cast<type*>(value);                                                   \
+    }                                                                                     \
+    explicit operator type&() {                                                           \
+      return *static_cast<type*>(value);                                                  \
+    }                                                                                     \
+                                                                                          \
+   protected:                                                                             \
+    friend class type_caster_generic;                                                     \
+                                                                                          \
+    bool load_value(value_and_holder&& v_h) {                                             \
+      if (v_h.holder_constructed()) {                                                     \
+        value = v_h.template holder<holder_type>().get();                                 \
+        return true;                                                                      \
+      } else {                                                                            \
+        throw cast_error(                                                                 \
+            "Unable to cast from non-held to held instance (#Class& to Holder<#Class>)"); \
+      }                                                                                   \
+    }                                                                                     \
+  }
+
+CREATE_UNWRAPPING_CASTER(torch::jit::Node);
+CREATE_UNWRAPPING_CASTER(torch::jit::Value);
+CREATE_UNWRAPPING_CASTER(torch::jit::Block);
+
+#undef CREATE_UNWRAPPING_CASTER
+
+template <>
+struct type_caster<torch::jit::IValue> {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  PYBIND11_TYPE_CASTER(torch::jit::IValue, _("IValue"));
+
+  bool load(handle src, bool) {
+    try {
+      value = torch::jit::toTypeInferredIValue(src);
+      return true;
+    } catch (std::exception& e) {
+      return false;
+    }
+  }
+
+  static handle cast(
+      torch::jit::IValue src,
+      return_value_policy /* policy */,
+      handle /* parent */) {
+    return torch::jit::toPyObject(std::move(src)).release();
+  }
+};
+
+template <>
+struct type_caster<torch::jit::Symbol> {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  PYBIND11_TYPE_CASTER(torch::jit::Symbol, _("Symbol"));
+
+  bool load(handle src, bool) {
+    // TODO: Is there a way to py::cast that doesn't raise an exception on
+    // failure?  Can we catch pybind11::cast_error here instead?
+    std::string src_str;
+    try {
+      src_str = py::cast<std::string>(src);
+    } catch (std::exception& e) {
+      return false;
+    }
+    value = torch::jit::Symbol::fromQualString(src_str);
+    return true;
+  }
+
+  static handle cast(
+      torch::jit::Symbol src,
+      return_value_policy /* policy */,
+      handle /* parent */) {
+    return py::cast(std::string(src.toQualString()), return_value_policy::copy)
+        .release();
+  }
+};
+
+template <>
+struct type_caster<torch::jit::AttributeKind> {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  PYBIND11_TYPE_CASTER(torch::jit::AttributeKind, _("AttributeKind"));
+
+  bool load(handle src, bool) {
+    return false;
+  }
+
+  static handle cast(
+      torch::jit::AttributeKind src,
+      return_value_policy /* policy */,
+      handle /* parent */) {
+    return py::cast(
+               std::string(torch::jit::toString(src)),
+               return_value_policy::copy)
+        .release();
+  }
+};
+
+// See https://github.com/pybind/pybind11/issues/637
+using ListCasterBase = pybind11::detail::
+    list_caster<std::vector<torch::jit::Node*>, torch::jit::Node*>;
+template <>
+struct type_caster<std::vector<torch::jit::Node*>> : ListCasterBase {
+  static handle cast(
+      const std::vector<torch::jit::Node*>& src,
+      return_value_policy,
+      handle parent) {
+    return ListCasterBase::cast(src, return_value_policy::reference, parent);
+  }
+  static handle cast(
+      const std::vector<torch::jit::Node*>* src,
+      return_value_policy pol,
+      handle parent) {
+    return cast(*src, pol, parent);
+  }
+};
+
+} // namespace pybind11::detail
+
+namespace torch::jit {
+
+static inline py::tuple tuple_tail(const py::tuple& tup) {
+  py::tuple r(tup.size() - 1);
+  for (const auto i : c10::irange(1, tup.size())) {
+    r[i - 1] = tup[i];
+  }
+  return r;
+}
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/pybind_utils.h

@@ -0,0 +1,1158 @@
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <ATen/core/qualified_name.h>
+#include <ATen/core/stack.h>
+#include <pybind11/complex.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/pytypes.h>
+#include <torch/csrc/Device.h>
+#include <torch/csrc/Dtype.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/Layout.h>
+#include <torch/csrc/QScheme.h>
+#include <torch/csrc/Stream.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/frontend/schema_matching.h>
+#include <torch/csrc/jit/frontend/tracer.h>
+#include <torch/csrc/jit/python/module_python.h>
+#include <torch/csrc/jit/python/python_custom_class.h>
+#include <torch/csrc/jit/python/python_tracer.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/runtime/operator.h>
+#include <torch/csrc/utils/pybind.h>
+#include <torch/csrc/utils/python_arg_parser.h>
+#include <torch/csrc/utils/six.h>
+#ifdef USE_DISTRIBUTED
+#include <torch/csrc/distributed/rpc/py_rref.h>
+#include <torch/csrc/distributed/rpc/rref_impl.h>
+#endif
+
+#include <ATen/core/function_schema.h>
+#include <c10/core/Stream.h>
+#ifdef USE_C10D_NCCL
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAStream.h>
+#endif
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <c10/util/irange.h>
+
+#include <algorithm>
+#include <cstddef>
+#include <string>
+#include <utility>
+#include <vector>
+
+// The visibility attribute is to avoid a warning about storing a field in the
+// struct that has a different visibility (from pybind) than the struct.
+#ifdef _WIN32
+#define VISIBILITY_HIDDEN
+#else
+#define VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
+#endif
+
+namespace torch::jit {
+
+using ResolutionCallback = std::function<py::object(std::string)>;
+
+void clear_registered_instances(void* ptr);
+
+TORCH_PYTHON_API IValue toIValue(
+    py::handle obj,
+    const TypePtr& type,
+    c10::optional<int32_t> N = c10::nullopt);
+
+TORCH_PYTHON_API py::object toPyObject(IValue ivalue);
+
+// Hack to overload the behavior of toIValue to accept Python
+// numbers in places where a Tensor is expected
+// See also torch::should_allow_numbers_as_tensors
+class ToIValueAllowNumbersAsTensors {
+  bool old_;
+
+ public:
+  ToIValueAllowNumbersAsTensors(bool enable);
+  ~ToIValueAllowNumbersAsTensors();
+};
+
+// Wrap Python function to guard deref
+// NB: Need VISIBILITY_HIDDEN for silencing compiler error,
+// 'torch::jit::PythonFunctionGuard' declared with greater visibility than the
+// type of its field 'torch::jit::PythonFunctionGuard::func_'
+struct VISIBILITY_HIDDEN PythonFunctionGuard {
+  explicit PythonFunctionGuard(py::function func) : func_(std::move(func)) {}
+
+  ~PythonFunctionGuard() {
+    pybind11::gil_scoped_acquire ag;
+    func_.dec_ref();
+    // explicitly setting PyObject* to nullptr to prevent py::object's dtor to
+    // decref on the PyObject again.
+    // See Note [Destructing py::object] in python_ivalue.h
+    func_.ptr() = nullptr;
+  }
+
+  py::function func_;
+};
+
+// The PythonFutureWrapper for ivalue::Future
+//
+// NB: VISIBILITY_HIDDEN is for silencing compiling error,
+// "error: 'torch::jit::PythonFutureWrapper' declared with greater visibility
+// than the type of its field 'torch::jit::PythonFutureWrapper::unwrap_func'
+// [-Werror=attributes]"
+//
+// NB: inherit from enable_shared_from_this because then(py::function) needs to
+//     get a shared_ptr from this pointer.
+struct VISIBILITY_HIDDEN PythonFutureWrapper
+    : std::enable_shared_from_this<PythonFutureWrapper> {
+  using UnwrapFunc = std::function<void(py::object)>;
+
+  explicit PythonFutureWrapper(
+      c10::intrusive_ptr<c10::ivalue::Future> fut,
+      c10::optional<UnwrapFunc> unwrap_func = c10::nullopt)
+      : fut(std::move(fut)), unwrap_func(std::move(unwrap_func)) {}
+
+  explicit PythonFutureWrapper(const PythonFutureWrapper&) = delete;
+  PythonFutureWrapper& operator=(const PythonFutureWrapper&) = delete;
+
+  bool done() {
+    return fut->completed();
+  }
+
+  py::object value() {
+    // acquiring GIL as toPyObject creates new py::object
+    // without grabbing the GIL.
+    py::gil_scoped_acquire acquire;
+    py::object py_obj = toPyObject(fut->value());
+    // unwrap_func is a general compositional function that takes in a
+    // py::object and executes some python function. It is currently mostly used
+    // to throw python exceptions.
+    if (unwrap_func) {
+      (*unwrap_func)(py_obj);
+    }
+    return py_obj;
+  }
+
+  py::object wait() {
+    fut->wait();
+    if (jit::tracer::isTracing()) {
+      auto graph = jit::tracer::getTracingState()->graph;
+
+      Value* fut_val = jit::tracer::getValueTrace(fut);
+      auto output = graph->insert(aten::wait, {fut_val});
+      jit::tracer::setValueTrace(fut->value(), output);
+    }
+    return value();
+  }
+
+  // The py::function cb arg must take a std::shared_ptr<PythonFutureWrapper>
+  // (i.e., torch._C.Future) as the only argument. If the type mismatches, an
+  // error will be thrown when waiting for the value of this returned Future.
+  std::shared_ptr<PythonFutureWrapper> then(py::function cb) {
+    // We need this an additional layer of wrapper here to guard the
+    // destruction of the py::function object. Because, the
+    // Future owns a reference to the py::function in its callback
+    // vector, but Future does not acquire GIL on destruction.
+    auto pf = std::make_shared<PythonFunctionGuard>(std::move(cb));
+
+    return std::make_shared<jit::PythonFutureWrapper>(fut->then(
+        // Capture a copy of the ivalue::Future instead of the `this` pointer
+        // because the PythonFutureWrapper object could have been deleted
+        // when the callbacks are fired. For example, RPC only captures the
+        // ivalue::Future instead of PythonFutureWrapper in JitFuture's
+        // callback functions. Hence, if user code does not hold a reference to
+        // this PythonFutureWrapper object, there is no guarantee that the
+        // PythonFutureWrapper is still valid when running the callback.
+        [pyFut(this->getPtr()),
+         pf(std::move(pf))](c10::ivalue::Future& /* unused */) -> IValue {
+          try {
+            pybind11::gil_scoped_acquire ag;
+            return toIValue(pf->func_(pyFut), PyObjectType::get());
+          } catch (py::error_already_set& e) {
+            auto err = std::runtime_error(c10::str(
+                "Got the following error when running the callback: ",
+                e.what()));
+            {
+              pybind11::gil_scoped_acquire ag;
+              // Release ownership on py::objects and also restore Python
+              // Error Indicator.
+              e.restore();
+              // Clear the Python Error Indicator as we has recorded the
+              // exception in the response message.
+              PyErr_Clear();
+            }
+
+            throw err;
+          }
+        },
+        PyObjectType::get()));
+  }
+
+  void add_done_callback(py::function cb) {
+    auto pf = std::make_shared<PythonFunctionGuard>(std::move(cb));
+    // NOLINTNEXTLINE(modernize-avoid-bind)
+    fut->addCallback(std::bind(
+        [pyFut(this->getPtr())](std::shared_ptr<PythonFunctionGuard> pf) {
+          try {
+            pybind11::gil_scoped_acquire ag;
+            pf->func_(pyFut);
+          } catch (py::error_already_set& e) {
+            {
+              pybind11::gil_scoped_acquire ag;
+              // Release ownership on py::objects and also restore Python
+              // Error Indicator.
+              e.restore();
+              // Clear the Python Error Indicator as we has recorded the
+              // exception in the response message.
+              PyErr_Clear();
+            }
+            // Log and ignore exceptions raised through the callback
+            LOG(ERROR) << "Got the following error when running the callback: "
+                       << e.what();
+
+          } catch (const std::exception& e) {
+            // Log and ignore exceptions raised through the callback
+            LOG(ERROR) << "Got the following error when running the callback: "
+                       << e.what();
+          }
+        },
+        std::move(pf)));
+  }
+
+  void markCompleted(const py::object& pyValue) {
+    DCHECK(PyGILState_Check());
+    IValue value = toIValue(pyValue, PyObjectType::get());
+
+    py::gil_scoped_release release;
+    fut->markCompleted(std::move(value));
+  }
+
+  c10::intrusive_ptr<c10::ivalue::Future> fut;
+  // unwrap_func works like a callback for the value returned by
+  // PythonFutureWrapper::wait().
+  c10::optional<UnwrapFunc> unwrap_func;
+
+ private:
+  std::shared_ptr<PythonFutureWrapper> getPtr() {
+    return shared_from_this();
+  }
+};
+
+// The PythonAwaitWrapper for ivalue::Await
+//
+// Expresses delayed function execution with Lazy semantic.
+// i.e. Await[W] in eager mode can be used as W.
+// When the attribute of W type is requested, Await[W] will return the
+// attribute of W, transparently calling wait() beforehand.
+// No Lazy semantic for script, explicit wait(Await[W]) -> W must be called to
+// convert to type W.
+//
+// The Await object takes shared ownership of specified function and the
+// arguments. After first call for wait() it owns the result. Deliberately no
+// type inference for eager mode.
+struct VISIBILITY_HIDDEN PythonAwaitWrapper
+    : std::enable_shared_from_this<PythonAwaitWrapper> {
+  explicit PythonAwaitWrapper(c10::intrusive_ptr<c10::ivalue::Await> aw)
+      : aw_(std::move(aw)) {}
+  explicit PythonAwaitWrapper(py::handle input) {
+    args_ = py::tuple(1u);
+    args_[0] = input;
+    auto type = PyObjectType::get();
+    aw_ = c10::make_intrusive<c10::ivalue::Await>(type);
+    aw_->markCompleted(toIValue(input, type));
+  }
+
+  explicit PythonAwaitWrapper(py::function pf, py::tuple args) {
+    pyfg_ = std::make_shared<torch::jit::PythonFunctionGuard>(std::move(pf));
+    args_ = std::move(args);
+    std::function<IValue()> f = [fg(pyfg_), &args(args_)]() {
+      pybind11::gil_scoped_acquire ag;
+      return toIValue(fg->func_(*args), PyObjectType::get());
+    };
+    aw_ = c10::make_intrusive<c10::ivalue::Await>(
+        PyObjectType::get(), std::move(f));
+  }
+
+  explicit PythonAwaitWrapper(const PythonAwaitWrapper&) = delete;
+  PythonAwaitWrapper& operator=(const PythonAwaitWrapper&) = delete;
+
+  py::object wait() {
+    py::gil_scoped_acquire acquire;
+    return toPyObject(aw_->wait());
+  }
+
+  // Nowait semantic means trivial case when Await is constructed from the
+  // result
+  bool is_nowait() {
+    return pyfg_ == nullptr;
+  }
+
+  const py::function fn() {
+    TORCH_CHECK(
+        pyfg_, "Await constructed as awaitable_nowait does not have fn");
+    return pyfg_->func_;
+  }
+
+  const py::tuple args() {
+    return args_;
+  }
+
+  TypePtr type() {
+    return aw_->type();
+  }
+
+  c10::intrusive_ptr<c10::ivalue::Await> aw_;
+  std::shared_ptr<torch::jit::PythonFunctionGuard> pyfg_;
+  py::tuple args_;
+
+ private:
+  std::shared_ptr<PythonAwaitWrapper> getPtr() {
+    return shared_from_this();
+  }
+};
+
+// error reporting: when reporting user-caused errors, these functions should
+// not use AT_ERROR macros, since these macros add stack trace information
+// that is confusing to display to the end user since it always reports
+// locations in libtorch code rather than user code.
+
+inline std::shared_ptr<CompilationUnit> get_python_cu() {
+  return py::module::import("torch.jit._state")
+      .attr("_python_cu")
+      .cast<std::shared_ptr<CompilationUnit>>();
+}
+
+struct TypedIValue : public std::pair<IValue, TypePtr> {
+  using pair::pair;
+
+  IValue& ivalue() {
+    return this->first;
+  }
+  TypePtr& type() {
+    return this->second;
+  }
+};
+
+inline TypedIValue toDictKeyIValue(py::handle key) {
+  if (py::isinstance<py::str>(key)) {
+    return TypedIValue(
+        ConstantString::create(py::cast<std::string>(key)), StringType::get());
+  } else if (py::isinstance<py::int_>(key)) {
+    return TypedIValue(py::cast<int64_t>(key), IntType::get());
+  } else if (py::isinstance<py::float_>(key)) {
+    return TypedIValue(py::cast<double>(key), FloatType::get());
+  } else {
+    AT_ERROR("Dictionary inputs may only have string, int, or float keys");
+  }
+}
+
+inline c10::optional<TypePtr> unifyOrInitializeType(
+    const TypePtr& accum,
+    const TypePtr& unify) {
+  if (!accum) {
+    return unify;
+  }
+  return unifyTypes(accum, unify);
+}
+
+using InferredType = c10::InferredType;
+
+InferredType tryToInferContainerType(py::handle input, bool primitiveTypeOnly);
+
+// Try to infer the type of a Python object
+// The type cannot be inferred if:
+//   input is an empty container (list, dict)
+//   input is an list with element types that cannot be unified
+//   input is an dict with key or value types that cannot be unified
+inline InferredType tryToInferType(py::handle input) {
+  // Try tensor types
+  if (THPVariable_Check(input.ptr())) {
+    return InferredType(TensorType::get());
+  }
+
+  if (input.is_none()) {
+    return InferredType(NoneType::get());
+  }
+
+  if (py::isinstance<StrongFunctionPtr>(input)) {
+    auto fn = py::cast<StrongFunctionPtr>(input).function_;
+    return InferredType(FunctionType::create(fn));
+  }
+
+  // Try basic types first
+  if (py::isinstance<py::bool_>(input)) {
+    return InferredType(BoolType::get());
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+  } else if (py::isinstance<py::int_>(input)) {
+    return InferredType(IntType::get());
+  } else if (py::isinstance<py::float_>(input)) {
+    return InferredType(FloatType::get());
+  } else if (PyComplex_CheckExact(input.ptr())) {
+    return InferredType(ComplexType::get());
+  } else if (py::isinstance<py::str>(input)) {
+    return InferredType(StringType::get());
+  } else if (THPLayout_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPDevice_Check(input.ptr())) {
+    return InferredType(DeviceObjType::get());
+  } else if (THPGenerator_Check(input.ptr())) {
+    return InferredType(GeneratorType::get());
+  } else if (THPStream_Check(input.ptr())) {
+    return InferredType(StreamObjType::get());
+  } else if (THPDtype_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPQScheme_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPLayout_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  }
+
+  auto enum_type = py::module::import("enum").attr("Enum");
+  py::bool_ isEnumValue = py::isinstance(input, enum_type);
+  if (py::cast<bool>(isEnumValue)) {
+    auto enum_class = input.attr("__class__");
+    auto enum_type = py::cast<TypePtr>(
+        py::module::import("torch.jit.annotations")
+            .attr("try_ann_to_type")(enum_class, SourceRange()));
+    return InferredType(std::move(enum_type));
+  }
+
+  py::bool_ isClass =
+      py::module::import("inspect").attr("isclass")(input.get_type());
+  if (py::cast<bool>(isClass)) {
+    // Assume that the class is compiled already or will compile. Invalidate
+    // this later if needed.
+    bool class_compiled = true;
+
+    // Check if the type is already compiled.
+    py::object existing_ty = py::module::import("torch.jit._state")
+                                 .attr("_get_script_class")(input.get_type());
+
+    if (existing_ty.is_none()) {
+      // If not, try to compile it.
+      py::bool_ can_compile = py::module::import("torch._jit_internal")
+                                  .attr("can_compile_class")(input.get_type());
+
+      if (py::cast<bool>(can_compile)) {
+        // Try to compile the class. This is wrapped in a try-catch because
+        // compilation of class types can raise an Exception and in that case,
+        // we want to defer to other attempts at type inference below rather
+        // than fail compilation altogether.
+        try {
+          py::module::import("torch.jit._script")
+              .attr("_recursive_compile_class")(
+                  input.get_type(), SourceRange());
+        } catch (...) {
+          // Invalidate the assumption that the class compiled so that we don't
+          // look up and return its JIT type as the type for the input.
+          class_compiled = false;
+        }
+      }
+    }
+
+    // If the class compiled successfully, look up the existing JIT type by
+    // qualified name and return it.
+    if (class_compiled) {
+      auto script_class = py::module::import("torch.jit._state")
+                              .attr("_get_script_class")(input.get_type());
+
+      if (!script_class.is_none()) {
+        auto class_type = py::cast<ClassTypePtr>(script_class);
+
+        if (class_type && !class_type->is_module()) {
+          return InferredType(std::move(class_type));
+        }
+      }
+    }
+  }
+
+  if (py::isinstance<Object>(input)) {
+    auto object = py::cast<Object>(input);
+    return InferredType(object.type());
+#ifdef USE_RPC
+  } else if (py::isinstance<torch::distributed::rpc::PyRRef>(input)) {
+    auto rref_ivalue = input.cast<torch::distributed::rpc::PyRRef>().toIValue();
+    return InferredType(rref_ivalue.type());
+#endif
+  }
+
+  auto await_type = py::module::import("torch._awaits").attr("_Await");
+  py::bool_ is_await = py::isinstance(input, await_type);
+  if (py::cast<bool>(is_await)) {
+    auto awptr = input.cast<std::shared_ptr<PythonAwaitWrapper>>();
+    return InferredType(AwaitType::create(awptr->aw_->elementType()));
+  }
+
+  if (as_module(py::cast<py::object>(input))) {
+    return InferredType("Cannot infer type of ScriptModule");
+  }
+
+  auto module_type = py::module::import("torch.nn").attr("Module");
+  py::bool_ is_module = py::isinstance(input, module_type);
+  if (py::cast<bool>(is_module)) {
+    return InferredType("Cannot infer concrete type of torch.nn.Module");
+  }
+
+  // Try container types
+  return tryToInferContainerType(input, false);
+}
+
+// This function is similar to tryToInferType, but it only tries to infer
+// primitive types (int, float, bool, complex) or nested container of primitive
+// types.
+inline InferredType tryToInferPrimitiveType(py::handle input) {
+  if (input.is_none()) {
+    return InferredType(NoneType::get());
+  }
+
+  // Only primitive data type
+  if (py::isinstance<py::bool_>(input)) {
+    return InferredType(BoolType::get());
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+  } else if (py::isinstance<py::int_>(input)) {
+    return InferredType(IntType::get());
+  } else if (py::isinstance<py::float_>(input)) {
+    return InferredType(FloatType::get());
+  } else if (PyComplex_CheckExact(input.ptr())) {
+    return InferredType(ComplexType::get());
+  }
+
+  // Try container types
+  return tryToInferContainerType(input, true);
+}
+
+inline InferredType tryToInferContainerType(
+    py::handle input,
+    bool primitiveTypeOnly = false) {
+  if (six::isTuple(input)) {
+    py::tuple tuple = py::cast<py::tuple>(input);
+    std::vector<TypePtr> element_types;
+    element_types.reserve(tuple.size());
+
+    for (py::handle elem : tuple) {
+      auto type_match = primitiveTypeOnly ? tryToInferPrimitiveType(elem)
+                                          : tryToInferType(elem);
+      if (type_match.success()) {
+        element_types.push_back(type_match.type());
+      } else {
+        // Forward error message along
+        return type_match.reason();
+      }
+    }
+    return InferredType(TupleType::create(std::move(element_types)));
+  } else if (PyDict_Check(input.ptr())) {
+    // Check to make sure we can generate useful input/output types
+    auto dict = py::cast<py::dict>(input);
+    size_t len = py::len(dict);
+    if (!len) {
+      return InferredType("Dictionary inputs must have entries");
+    }
+
+    TypePtr key_type = nullptr;
+    TypePtr value_type = nullptr;
+
+    for (auto entry : dict) {
+      // Try to infer the key type and unify it with the existing one
+      auto entry_key_type_match = primitiveTypeOnly
+          ? tryToInferPrimitiveType(entry.first)
+          : tryToInferType(entry.first);
+      if (!entry_key_type_match.success()) {
+        return entry_key_type_match.reason();
+      }
+      auto unified_key =
+          unifyOrInitializeType(key_type, entry_key_type_match.type());
+      if (!unified_key) {
+        return InferredType(c10::str(
+            "Dictionary inputs to traced functions must have consistent type. Found ",
+            key_type->repr_str(),
+            " and ",
+            (entry_key_type_match.type())->repr_str()));
+      }
+
+      // Try to infer the value type and unify it with the existing one
+      auto entry_value_type_match = primitiveTypeOnly
+          ? tryToInferPrimitiveType(entry.second)
+          : tryToInferType(entry.second);
+      if (!entry_value_type_match.success()) {
+        return entry_value_type_match.reason();
+      }
+      auto unified_value =
+          unifyOrInitializeType(value_type, entry_value_type_match.type());
+      if (!unified_value) {
+        return InferredType(c10::str(
+            "Dictionary inputs to traced functions must have consistent type. Found ",
+            value_type->repr_str(),
+            " and ",
+            (entry_value_type_match.type())->repr_str()));
+      }
+
+      key_type = *unified_key;
+      value_type = *unified_value;
+    }
+    return InferredType(
+        DictType::create(std::move(key_type), std::move(value_type)));
+  } else if (PyList_Check(input.ptr())) {
+    auto list = py::cast<py::list>(input);
+    size_t len = py::len(list);
+    if (!len) {
+      return InferredType("List trace inputs must have elements");
+    }
+
+    TypePtr element_type = nullptr;
+    for (auto elem : list) {
+      auto element_type_match = primitiveTypeOnly
+          ? tryToInferPrimitiveType(elem)
+          : tryToInferType(elem);
+      if (!element_type_match.success()) {
+        return InferredType(c10::str(
+            "Could not infer type of list element: ",
+            element_type_match.reason()));
+      }
+      auto unified_type =
+          unifyOrInitializeType(element_type, element_type_match.type());
+      if (!unified_type) {
+        return InferredType(c10::str(
+            "List inputs to traced functions must have consistent element type. Found ",
+            element_type->repr_str(),
+            " and ",
+            (element_type_match.type())->repr_str()));
+      }
+      element_type = *unified_type;
+    }
+    return InferredType(ListType::create(element_type));
+  } else {
+    if (primitiveTypeOnly) {
+      return InferredType(c10::str(
+          "Only tuple, list, or dict (possibly nested) of primitive types (bool, float, int, complex)",
+          "are supported ",
+          "as inputs or outputs of traced functions",
+          ", but instead got value of type ",
+          py::str(input.get_type().attr("__name__")),
+          "."));
+    } else {
+      // TODO: this message is not correct anymore, since this InferredType is
+      // used from a bunch of circumstances unrelated to tracing. We can re-use
+      // this instead of the attribute_failure stuff in concreteType
+      return InferredType(c10::str(
+          "Only tensors and (possibly nested) tuples of tensors, lists, or dicts",
+          "are supported ",
+          "as inputs or outputs of traced functions",
+          ", but instead got value of type ",
+          py::str(input.get_type().attr("__name__")),
+          "."));
+    }
+  }
+}
+
+inline bool isTraceableType(const TypePtr& type) {
+  if (type->isSubtypeOf(*TensorType::get())) {
+    return true;
+  }
+
+  if (auto list_type = type->cast<ListType>()) {
+    return isTraceableType(list_type->getElementType());
+  }
+
+  if (auto tuple_type = type->cast<TupleType>()) {
+    return std::all_of(
+        tuple_type->elements().begin(),
+        tuple_type->elements().end(),
+        [](const TypePtr& element_type) {
+          return isTraceableType(element_type);
+        });
+  }
+
+  if (auto dict_type = type->cast<DictType>()) {
+    return isTraceableType(dict_type->getValueType());
+  }
+
+  return false;
+}
+
+inline IValue toTypeInferredIValue(py::handle input) {
+  auto match = tryToInferType(input);
+  if (!match.success()) {
+    auto object = py::cast<py::object>(input);
+    if (auto mod = as_module(object)) {
+      // if obj is already a ScriptModule, just return its ivalue
+      auto ptr = mod.value()._ivalue();
+      // explict copy semantics for strong ownership of the resource.
+      return c10::intrusive_ptr<c10::ivalue::Object>::reclaim_copy(
+          ptr.release());
+    }
+
+    // Check if the obj is a ScriptObject.
+    if (auto script_obj = as_object(object)) {
+      auto ptr = script_obj.value()._ivalue();
+      return c10::intrusive_ptr<c10::ivalue::Object>::reclaim_copy(
+          ptr.release());
+    }
+    AT_ERROR(
+        "Tracer cannot infer type of ", py::str(input), "\n:", match.reason());
+  }
+  return toIValue(input, match.type());
+}
+
+inline Stack toTraceableStack(const py::tuple& inputs) {
+  auto info = toTypeInferredIValue(inputs);
+  TORCH_CHECK(
+      isTraceableType(info.type()),
+      "Type '",
+      info.type()->repr_str(),
+      "' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and"
+      " Tuples of Tensors can be traced");
+  return info.toTupleRef().elements().vec();
+}
+
+// Serialize the python dictionary into a traceable stack.
+inline Stack toTraceableStack(const py::dict& inputs) {
+  Stack res;
+  for (auto it = inputs.begin(); it != inputs.end(); it++) {
+    if (THPVariable_Check(it->second.ptr())) {
+      res.push_back(toIValue(it->second, tryToInferType(it->second).type()));
+    }
+  }
+  return res;
+}
+
+inline IValue createGenericList(py::handle obj, const TypePtr& elem_type) {
+  auto elems = c10::impl::GenericList(elem_type);
+  for (auto elem : obj) {
+    elems.push_back(toIValue(elem, elem_type));
+  }
+  return IValue(elems);
+}
+
+inline IValue createGenericDict(
+    const py::dict& obj,
+    const TypePtr& key_type,
+    const TypePtr& value_type) {
+  c10::impl::GenericDict elems(key_type, value_type);
+  elems.reserve(py::len(obj));
+  for (auto& entry : obj) {
+    elems.insert(
+        toIValue(entry.first, key_type), toIValue(entry.second, value_type));
+  }
+  return IValue(elems);
+}
+
+template <class T>
+inline void guardAgainstNamedTensor(const T& var) {
+  TORCH_CHECK(
+      !var.has_names(),
+      "NYI: Named tensors are currently unsupported in TorchScript. As a  "
+      "workaround please drop names via `tensor = tensor.rename(None)`.");
+}
+
+// Extract custom class registered with torchbind
+template <typename T>
+c10::intrusive_ptr<T> toCustomClass(py::handle obj) {
+  static_assert(
+      std::is_base_of<CustomClassHolder, T>::value, "T is not a CustomClass");
+  const auto& type = c10::getCustomClassType<c10::intrusive_ptr<T>>();
+  c10::IValue ivalue = toIValue(obj, type);
+  return std::move(ivalue).toCustomClass<T>();
+}
+
+// Small wrapper around getting the type name string from Python to make
+// types easier to interpret, e.g. give the structural type for a NamedTuple
+inline std::string friendlyTypeName(py::handle obj) {
+  if (py::isinstance<py::tuple>(obj) && py::hasattr(obj, "_fields")) {
+    auto field_names =
+        py::cast<std::vector<std::string>>(py::getattr(obj, "_fields"));
+    std::stringstream ss;
+    ss << py::str(obj.get_type().attr("__name__"));
+    ss << " (aka NamedTuple(";
+    bool first = true;
+    for (auto& field_name : field_names) {
+      if (!first) {
+        ss << ", ";
+      }
+      ss << field_name;
+      first = false;
+    }
+    ss << "))";
+    return ss.str();
+  } else {
+    return py::str(obj.get_type().attr("__name__"));
+  }
+}
+
+// Thrown when trying to create a schema for a list of python
+// arguments that cannot be converted.
+// Can be caught by the caller to attempt to use other schema
+// when there is an overloaded operator.
+struct schema_match_error : public std::runtime_error {
+  using std::runtime_error::runtime_error;
+};
+
+inline IValue argumentToIValue(
+    const FunctionSchema& schema,
+    size_t argumentPosition,
+    py::handle object) {
+  const auto& argument = schema.arguments().at(argumentPosition);
+  try {
+    return toIValue(object, argument.real_type(), argument.N());
+  } catch (const py::cast_error& error) {
+    throw schema_match_error(c10::str(
+        schema.formatTypeMismatchMsg(
+            argument,
+            friendlyTypeName(object),
+            argumentPosition,
+            py::repr(object)),
+        "\nCast error details: ",
+        error.what()));
+  } catch (const py::error_already_set& error) {
+    throw schema_match_error(c10::str(
+        schema.formatTypeMismatchMsg(
+            argument,
+            friendlyTypeName(object),
+            argumentPosition,
+            py::repr(object)),
+        "\n Python error details: ",
+        error.what()));
+  }
+}
+
+inline IValue returnToIValue(const TypePtr& type, py::handle object) {
+  try {
+    return toIValue(object, type);
+  } catch (const py::cast_error& error) {
+    throw std::runtime_error(c10::str(
+        " expected value of type ",
+        type->str(),
+        " for return value but instead got value of type ",
+        py::str(object.get_type().attr("__name__")),
+        ".",
+        "\nValue: ",
+        py::repr(object),
+        "\nCast error details: ",
+        error.what()));
+  }
+}
+
+inline py::object getScriptedClassOrError(const c10::NamedTypePtr& classType) {
+  auto py_class =
+      py::module::import("torch.jit._state")
+          .attr("_get_python_class")(classType->name()->qualifiedName());
+  if (py_class.is_none()) {
+    std::stringstream err;
+    err << "Unknown reference to ScriptClass ";
+    err << classType->name()->qualifiedName();
+    err << ". (Did you forget to import it?)";
+    throw std::runtime_error(err.str());
+  }
+  return py_class;
+}
+
+struct VISIBILITY_HIDDEN tuple_slice {
+  /*implicit*/ tuple_slice(py::tuple tup_)
+      : tup(std::move(tup_)), b(0), e(tup.size()) {}
+  tuple_slice(py::tuple tup_, int64_t b_)
+      : tup(std::move(tup_)), b(b_), e(tup.size()) {}
+  tuple_slice(py::tuple tup_, int64_t b_, int64_t e_)
+      : tup(std::move(tup_)), b(b_), e(e_) {}
+  py::detail::tuple_iterator begin() const {
+    return {tup, static_cast<pybind11::ssize_t>(b)};
+  }
+  py::detail::tuple_iterator end() const {
+    return {tup, static_cast<pybind11::ssize_t>(e)};
+  }
+  size_t size() const {
+    return e - b;
+  }
+  py::detail::tuple_accessor operator[](size_t index) const {
+    return {tup, static_cast<size_t>(b + index)};
+  }
+
+ private:
+  py::tuple tup;
+  int64_t b;
+  int64_t e;
+};
+
+inline Stack createStackForSchema(
+    const FunctionSchema& schema,
+    const tuple_slice& args,
+    const py::kwargs& kwargs,
+    c10::optional<IValue> self) {
+  size_t all_arguments = (self ? 1 : 0) + args.size() + kwargs.size();
+  if (all_arguments > schema.arguments().size()) {
+    throw schema_match_error(c10::str(
+        schema.name(),
+        "() expected at most ",
+        schema.arguments().size(),
+        " argument(s) but received ",
+        all_arguments,
+        " argument(s). Declaration: ",
+        schema));
+  }
+  Stack stack;
+  stack.reserve(schema.arguments().size());
+
+  int64_t arg_idx = 0;
+  if (self) {
+    push(stack, std::move(*self));
+    arg_idx++;
+  }
+  // First push all positional args.
+  for (const auto& arg : args) {
+    // ...but refuse to do it if the schema says that this was supposed
+    // to be keyword only
+    if (schema.arguments()[arg_idx].kwarg_only()) {
+      throw schema_match_error(c10::str(
+          schema.name(),
+          "() takes ",
+          arg_idx,
+          " positional argument(s) but ",
+          self ? 1 + args.size() : args.size(),
+          " was/were given.  Declaration: ",
+          schema));
+    }
+    // Use the type information from the schema to convert the PyObject.
+    push(stack, argumentToIValue(schema, stack.size(), arg));
+    arg_idx++;
+  }
+
+  // Now for every remaining non-positional argument in the schema, look for it
+  // in the kwargs dict and push it if found, or use its default value if it
+  // has one.
+  size_t consumed_kwargs = 0;
+  for (size_t i = stack.size(); i < schema.arguments().size(); ++i) {
+    const auto& arg = schema.arguments()[i];
+    if (kwargs.contains(arg.name().c_str())) {
+      push(stack, argumentToIValue(schema, i, kwargs[arg.name().c_str()]));
+      consumed_kwargs += 1;
+    } else if (arg.default_value()) {
+      push(stack, *arg.default_value());
+    } else {
+      throw schema_match_error(c10::str(
+          schema.name(),
+          "() is missing value for argument '",
+          arg.name(),
+          "'. Declaration: ",
+          schema));
+    }
+  }
+
+  if (consumed_kwargs != kwargs.size()) {
+    std::vector<std::string> names;
+    for (const auto& kwarg : kwargs) {
+      names.emplace_back(py::cast<std::string>(kwarg.first));
+    }
+    throw schema_match_error(schema.findErrorInKwargs(names));
+  }
+
+  return stack;
+}
+
+inline py::object createPyObjectForStack(Stack&& stack) {
+  if (stack.empty()) {
+    return py::none();
+  }
+
+  // Return a simple value and not a single-element tuple if there is only one
+  // return value.
+  if (stack.size() == 1) {
+    return toPyObject(std::move(stack[0]));
+  }
+
+  // If there is more than one return value, pop them into a py::tuple.
+  py::tuple return_values(stack.size());
+  for (const auto ret : c10::irange(return_values.size())) {
+    return_values[ret] = toPyObject(std::move(stack[ret]));
+  }
+
+  return std::move(return_values);
+}
+
+// TODO: Remove once we clean up the GraphExecutor usage.
+inline Stack evilDeprecatedBadCreateStackDoNotUse(
+    const py::tuple& tuple,
+    at::ArrayRef<Value*> inputs,
+    size_t reserve_extra_space = 0) {
+  if (tuple.size() != inputs.size()) {
+    AT_ERROR(
+        "expected " + std::to_string(inputs.size()) + " inputs, but got " +
+        std::to_string(tuple.size()));
+  }
+  Stack result;
+  result.reserve(tuple.size() + reserve_extra_space);
+  for (const auto i : c10::irange(inputs.size())) {
+    result.push_back(toIValue(std::move(tuple[i]), inputs[i]->type()));
+  }
+  return result;
+}
+
+// Run `callee`, potentially inserting a CallFunction/CallMethod node into the
+// tracing graph.
+inline py::object runAndInsertCall(
+    Function& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs,
+    c10::optional<IValue> self,
+    // Lambda that tells this function how to insert `callee` into the graph if
+    // we're tracing.
+    const std::function<Value*(Graph&, const MatchedSchema& match)>&
+        callInserter) {
+  auto stack =
+      createStackForSchema(callee.getSchema(), args, kwargs, std::move(self));
+  const auto& tracing_state = tracer::getTracingState();
+  if (!tracing_state) {
+    pybind11::gil_scoped_release no_gil_guard;
+    // If we're not tracing, just run the callee as normal.
+    callee.run(stack);
+  } else {
+    // If we are tracing, insert the appropriate CallFunction or CallMethod node
+    // and then run the callee with tracing disabled.
+
+    // Get the graph `Value`s that represent the input IValues
+    auto inputs = last(stack, callee.num_inputs());
+    auto input_values =
+        fmap(inputs, [](const IValue& v) { return tracer::getValueTrace(v); });
+    TORCH_INTERNAL_ASSERT(callee.getSchema().returns().size() == 1)
+    auto return_type = callee.getSchema().returns().at(0).type();
+    auto graph = tracing_state->graph;
+    std::vector<NamedValue> named_values;
+    named_values.reserve(input_values.size());
+    for (Value* v : input_values) {
+      named_values.emplace_back(v);
+    }
+
+    // Add a call node.
+    MatchedSchema match = matchSchema(
+        callee.getSchema(),
+        tracer::getPythonInterpreterSourceRange(),
+        *graph,
+        named_values,
+        {});
+    auto output_value = callInserter(*graph, match);
+
+    // Actually run the callee. Pause the tracer so that we don't double-add the
+    // callee nodes.
+    {
+      pybind11::gil_scoped_release no_gil_guard;
+      ResourceGuard guard(tracer::pauseTracing());
+      callee.run(stack);
+    }
+
+    // Associate the output IValues with the output `Value`s in the graph
+    tracer::setValueTrace(stack.back(), output_value);
+  }
+
+  TORCH_CHECK(
+      !stack.empty(),
+      "Expected values in the stack after execution but found none");
+  return toPyObject(std::move(stack.back()));
+}
+
+inline c10::optional<py::object> maybeTorchFunctionDispatch(
+    const py::object& callee,
+    const tuple_slice& args_no_self,
+    const py::kwargs& kwargs,
+    const c10::QualifiedName qualname) {
+  std::vector<py::handle> args_vec;
+  for (const auto& arg : args_no_self) {
+    args_vec.push_back(arg);
+  }
+  py::tuple args = py::cast(args_vec);
+
+  // Handle __torch_function__ dispatch
+  std::vector<PyObject*> overloaded_args;
+  size_t total_arg_num = args.size() + kwargs.size();
+  for (const auto& arg : args) {
+    is_tensor_and_append_overloaded(arg.ptr(), &overloaded_args);
+    is_tensor_list_and_append_overloaded(
+        arg.ptr(),
+        &overloaded_args,
+        static_cast<int>(total_arg_num),
+        false /* throw_error */);
+  }
+  // NB: for kwargs, we cannot guarantee the order of appending
+  // is the same as the argument order in operator's schema.
+  // This is suboptimal, but should be fine. Later when we have
+  // better schema matching and argument parsing, we could
+  // match the operator in `operations` first, then the order will
+  // be guaranteed.
+  for (auto item : kwargs) {
+    is_tensor_and_append_overloaded(item.second.ptr(), &overloaded_args);
+    is_tensor_list_and_append_overloaded(
+        item.second.ptr(),
+        &overloaded_args,
+        total_arg_num,
+        false /* throw_error */);
+  }
+  if (!overloaded_args.empty()) {
+    return pybind11::reinterpret_steal<py::object>(
+        handle_torch_function_no_python_arg_parser(
+            /*overloaded_args=*/overloaded_args,
+            /*args=*/args.ptr(),
+            /*kwargs=*/kwargs.ptr(),
+            /*func_name=*/qualname.name().c_str(),
+            /*torch_api_function=*/callee.ptr(),
+            /*module_name=*/qualname.prefix().c_str()));
+  }
+
+  return c10::nullopt;
+}
+
+inline py::object invokeScriptFunctionFromPython(
+    Function& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs) {
+  // TODO: we could add __torch_function__ dispatch here but I don't know
+  // the implications of doing so
+
+  return runAndInsertCall(
+      callee,
+      args,
+      kwargs,
+      /*self=*/c10::nullopt,
+      [&](Graph& graph, const MatchedSchema& match) {
+        return graph.insertFunctionCall(&callee, match);
+      });
+}
+
+inline py::object invokeScriptMethodFromPython(
+    Method& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs) {
+  auto self = callee.owner()._ivalue();
+
+  if (auto torch_fn_result = maybeTorchFunctionDispatch(
+          py::cast(callee), args, kwargs, callee.name())) {
+    return *torch_fn_result;
+  }
+
+  return runAndInsertCall(
+      callee.function(),
+      args,
+      kwargs,
+      self,
+      [&](Graph& graph, const MatchedSchema& match) {
+        return graph.insertMethodCall(callee.name(), match);
+      });
+}
+
+TORCH_PYTHON_API std::pair<std::shared_ptr<Operator>, Stack> getOpWithStack(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    py::args args,
+    const py::kwargs& kwargs);
+
+TORCH_PYTHON_API py::object invokeOperatorFromPython(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    py::args args,
+    const py::kwargs& kwargs,
+    c10::optional<c10::DispatchKey> dk = c10::nullopt);
+
+TORCH_PYTHON_API py::object _get_operation_for_overload_or_packet(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    Symbol symbol,
+    py::args args,
+    const py::kwargs& kwargs,
+    bool is_overload,
+    c10::optional<c10::DispatchKey> dk = c10::nullopt);
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_arg_flatten.h

@@ -0,0 +1,119 @@
+#pragma once
+
+#include <c10/util/hash.h>
+#include <c10/util/irange.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/jit/python/pybind.h>
+
+#include <ATen/ATen.h>
+#include <functional>
+#include <tuple>
+#include <vector>
+
+namespace torch::jit::python {
+
+struct IODescriptor {
+  struct VariableMetadata {
+    VariableMetadata(const autograd::Variable& var)
+        : sizes(var.sizes().vec()),
+          type(var.scalar_type()),
+          device(var.device()),
+          requires_grad(var.requires_grad()) {}
+
+    bool operator==(const VariableMetadata& o) const {
+      return std::tie(device, requires_grad, type, sizes) ==
+          std::tie(o.device, o.requires_grad, o.type, o.sizes);
+    }
+
+    static size_t hash(const VariableMetadata& m) {
+      return c10::get_hash(m.sizes, m.device, m.requires_grad, m.type);
+    }
+
+    std::vector<int64_t> sizes;
+    at::ScalarType type;
+    at::Device device;
+    bool requires_grad;
+  };
+
+  bool operator==(const IODescriptor& o) const {
+    return std::tie(structure, metadata, grad_enabled) ==
+        std::tie(o.structure, o.metadata, o.grad_enabled);
+  }
+
+  static size_t hash(const IODescriptor& o) {
+    return c10::get_hash(o.structure, o.metadata, o.grad_enabled);
+  }
+
+  void extend(const autograd::variable_list& list) {
+    metadata.reserve(metadata.size() + list.size());
+    for (auto& var : list)
+      metadata.emplace_back(var);
+  }
+
+  // Description of argument structure. Variables are replaced with
+  // different characters, depending on their flags, beginnings and
+  // ends of tuples and lists are denoted by a pair of parenthesis
+  // of their corresponding kind. They should always be paired.
+  // Example desc: (vv[v(v)v])
+  // NOTE: if extend() was ever called then metadata.size() can be
+  // different than the number of 'v's in structure.
+  std::string structure;
+  std::vector<std::string> strings;
+  std::vector<VariableMetadata> metadata;
+  bool grad_enabled = false;
+};
+
+static inline std::ostream& operator<<(
+    std::ostream& out,
+    const IODescriptor::VariableMetadata& meta) {
+  at::Device meta_device = meta.device;
+  auto& t = at::getDeprecatedTypeProperties(
+      meta_device.is_cpu() ? at::Backend::CPU : at::Backend::CUDA, meta.type);
+  out << t << "(requires_grad=" << meta.requires_grad;
+  if (meta_device.is_cuda()) {
+    out << ", device=" << meta_device.index();
+  }
+  out << ") {";
+  for (const auto i : c10::irange(meta.sizes.size())) {
+    if (i > 0)
+      out << ", ";
+    out << meta.sizes[i];
+  }
+  out << "}";
+  return out;
+}
+
+static inline std::ostream& operator<<(
+    std::ostream& out,
+    const IODescriptor& desc) {
+  out << desc.structure << "\n";
+  out << "  with grad_enabled=" << desc.grad_enabled << "\n";
+  for (const auto i : c10::irange(desc.metadata.size())) {
+    out << "  with v" << i << " having type " << desc.metadata[i] << "\n";
+  }
+  return out;
+}
+
+struct ParsedArgs {
+  // Flat vector of Variables found in arguments
+  autograd::variable_list vars;
+  // Metadata describing nesting of objects received from Python and
+  // metadata of vars and whether grad is enabled.
+  IODescriptor desc;
+
+  void extend(const autograd::variable_list& list) {
+    if (list.empty())
+      return;
+    vars.reserve(vars.size() + list.size());
+    for (auto& var : list)
+      vars.emplace_back(var);
+    desc.extend(list);
+  }
+};
+
+ParsedArgs flatten(py::handle obj);
+PyObject* unflatten(
+    at::ArrayRef<autograd::Variable> vars,
+    const IODescriptor& structure);
+
+} // namespace torch::jit::python

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_custom_class.h

@@ -0,0 +1,20 @@
+#pragma once
+
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/utils/pybind.h>
+#include <torch/custom_class.h>
+
+namespace torch::jit {
+
+void initPythonCustomClassBindings(PyObject* module);
+
+struct ScriptClass {
+  ScriptClass(c10::StrongTypePtr class_type)
+      : class_type_(std::move(class_type)) {}
+
+  py::object __call__(py::args args, py::kwargs kwargs);
+
+  c10::StrongTypePtr class_type_;
+};
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_dict.h

@@ -0,0 +1,126 @@
+#pragma once
+
+#include <ATen/core/Dict.h>
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace torch::jit {
+
+void initScriptDictBindings(PyObject* module);
+
+/// An iterator over the keys of ScriptDict. This is used to support
+/// .keys() and iteration.
+class ScriptDictKeyIterator final {
+ public:
+  ScriptDictKeyIterator(
+      c10::impl::GenericDict::iterator iter,
+      c10::impl::GenericDict::iterator end)
+      : iter_(std::move(iter)), end_(std::move(end)) {}
+  IValue next();
+
+ private:
+  c10::impl::GenericDict::iterator iter_;
+  c10::impl::GenericDict::iterator end_;
+};
+
+/// An iterator over the key-value pairs of ScriptDict. This is used to support
+/// .items().
+class ScriptDictIterator final {
+ public:
+  ScriptDictIterator(
+      c10::impl::GenericDict::iterator iter,
+      c10::impl::GenericDict::iterator end)
+      : iter_(std::move(iter)), end_(std::move(end)) {}
+  IValue next();
+
+ private:
+  c10::impl::GenericDict::iterator iter_;
+  c10::impl::GenericDict::iterator end_;
+};
+
+/// A wrapper around c10::Dict that can be exposed in Python via pybind
+/// with an API identical to the Python dictionary class. This allows
+/// dictionaries to have reference semantics across the Python/TorchScript
+/// boundary.
+class ScriptDict final {
+ public:
+  // Constructor.
+  ScriptDict(IValue data) : dict_(AnyType::get(), AnyType::get()) {
+    TORCH_INTERNAL_ASSERT(data.isGenericDict());
+    dict_ = data.toGenericDict();
+  }
+
+  // Get the type of the dictionary.
+  DictTypePtr type() const {
+    return DictType::create(dict_.keyType(), dict_.valueType());
+  }
+
+  // Return a string representation that can be used
+  // to reconstruct the instance.
+  std::string repr() const {
+    std::ostringstream s;
+    s << '{';
+    bool f = false;
+    for (auto const& kv : dict_) {
+      if (f) {
+        s << ", ";
+      }
+      s << kv.key() << ": " << kv.value();
+      f = true;
+    }
+    s << '}';
+    return s.str();
+  }
+
+  // Return an iterator over the keys of the dictionary.
+  ScriptDictKeyIterator iter() const {
+    auto begin = dict_.begin();
+    auto end = dict_.end();
+    return ScriptDictKeyIterator(begin, end);
+  }
+
+  // Return an iterator over the key-value pairs of the dictionary.
+  ScriptDictIterator items() const {
+    auto begin = dict_.begin();
+    auto end = dict_.end();
+    return ScriptDictIterator(begin, end);
+  }
+
+  // Interpret the dictionary as a boolean; empty means false, non-empty means
+  // true.
+  bool toBool() const {
+    return !(dict_.empty());
+  }
+
+  // Get the value for the given key. Throws std::out_of_range if the key does
+  // not exist.
+  IValue getItem(const IValue& key) {
+    return dict_.at(key);
+  };
+
+  // Set the value for the given key.
+  void setItem(const IValue& key, const IValue& value) {
+    dict_.insert_or_assign(key, value);
+  };
+
+  // Check whether the dictionary contains the given key.
+  bool contains(const IValue& key) {
+    return dict_.contains(key);
+  }
+
+  // Delete the given key from the dictionary.
+  bool delItem(const IValue& key) {
+    return dict_.erase(key);
+  }
+
+  // Get the size of the dictionary.
+  int64_t len() const {
+    return dict_.size();
+  }
+
+  // A c10::Dict instance that holds the actual data.
+  c10::impl::GenericDict dict_;
+};
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_ir.h

@@ -0,0 +1,50 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/utils/object_ptr.h>
+
+namespace torch::jit {
+
+void initPythonIRBindings(PyObject* module);
+
+// execute a Python function, used for Ops we can't optimize but that we want to
+// optimize around
+struct ConcretePythonOp : public PythonOp {
+  static Symbol Kind;
+
+  ConcretePythonOp(Graph* graph) : PythonOp(graph, ::c10::prim::PythonOp) {}
+  ConcretePythonOp* init(
+      THPObjectPtr&& pyobj,
+      const std::string& cconv,
+      pyobj_list&& scalar_args) {
+    this->pyobj = std::move(pyobj);
+    this->scalar_args = std::move(scalar_args);
+    this->cconv = cconv;
+    return this;
+  }
+  // The Python object which contains the implementation of this function.
+  // This is either a class (non-legacy) or an object (legacy).  See
+  // TraceInterpreterState for execution semantics.
+  THPObjectPtr pyobj;
+  // The calling convention for the Python function.
+  // 'c' -- constant argument
+  // 'd' -- dynamic argument
+  std::string cconv;
+  // Scalar arguments to the Python function.  Not necessarily passed to
+  // the function in this order; see cconv for the correct order.
+  std::vector<THPObjectPtr> scalar_args;
+
+  std::string name() const override;
+  void cloneFrom(Node* other_) override;
+  Node* allocNewInstance(Graph* g) override {
+    return new ConcretePythonOp(g);
+  }
+  // recover the autograd.Function instance, if this PythonOp's function
+  // was originally SomeFunction.apply
+  // used in ONNX for discovering symbolics
+  c10::optional<THPObjectPtr> autogradFunction() const override;
+  void writeScalars(std::ostream& out) const override;
+  void lint_python() const override;
+};
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_ivalue.h

@@ -0,0 +1,97 @@
+#pragma once
+#include <ATen/core/ivalue.h>
+#include <pybind11/pybind11.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/python_headers.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace py = pybind11;
+
+namespace c10::ivalue {
+
+// concrete ivalue Holder that hold a py::object
+struct C10_EXPORT ConcretePyObjectHolder final : PyObjectHolder {
+ public:
+  static c10::intrusive_ptr<PyObjectHolder> create(py::object py_obj) {
+    return c10::make_intrusive<ConcretePyObjectHolder>(std::move(py_obj));
+  }
+
+  static c10::intrusive_ptr<PyObjectHolder> create(const py::handle& handle) {
+    py::gil_scoped_acquire ag;
+    return c10::make_intrusive<ConcretePyObjectHolder>(
+        handle.cast<py::object>());
+  }
+
+  PyObject* getPyObject() override {
+    return py_obj_.ptr();
+  }
+
+  InferredType tryToInferType() override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::tryToInferType(py_obj_);
+  }
+
+  IValue toIValue(const TypePtr& type, c10::optional<int32_t> N = c10::nullopt)
+      override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::toIValue(py_obj_, type, N);
+  }
+
+  std::string toStr() override {
+    pybind11::gil_scoped_acquire ag;
+    return py::str(py_obj_);
+  }
+
+  std::vector<at::Tensor> extractTensors() override {
+    // We could implement this entirely in C++ via pybind11 but it turns out to
+    // be substantially slower. Namely, the total time taken by markCompleted on
+    // a CUDAFuture is 21.5us with this implementation, but goes up to 58.7us
+    // when using C++. The reason is unclear.
+    try {
+      pybind11::gil_scoped_acquire ag;
+      static py::object& extractorFn = *new py::object(
+          py::module::import("torch._jit_internal").attr("_extract_tensors"));
+      return extractorFn(py_obj_).cast<std::vector<at::Tensor>>();
+    } catch (py::error_already_set& e) {
+      auto err = std::runtime_error(
+          c10::str("Cannot extract tensors from value: ", e.what()));
+      {
+        pybind11::gil_scoped_acquire ag;
+        e.restore();
+        PyErr_Clear();
+      }
+      throw err;
+    }
+  }
+
+  // Note [Destructing py::object]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~
+  //
+  // (1) Why py_obj_ = py::none(); does not work. Because we also need to
+  // acquire GIL when destructing py::object of None that de-references None.
+  // https://docs.python.org/3/c-api/none.html#c.Py_RETURN_NONE
+  //
+  // https://stackoverflow.com/questions/15287590/why-should-py-increfpy-none-be-required-before-returning-py-none-in-c
+  //
+  // (2) Why we need to call dec_ref() explicitly. Because py::object of
+  // nullptr, on destruction, effectively does nothing because of it calls
+  // Py_XDECREF(NULL) underlying.
+  // https://docs.python.org/3/c-api/refcounting.html#c.Py_XDECREF
+  ~ConcretePyObjectHolder() override {
+    pybind11::gil_scoped_acquire ag;
+    py_obj_.dec_ref();
+    // explicitly setting PyObject* to nullptr to prevent py::object's dtor to
+    // decref on the PyObject again.
+    py_obj_.ptr() = nullptr;
+  }
+
+  // explicit construction to avoid errornous implicit conversion and
+  // copy-initialization
+  explicit ConcretePyObjectHolder(py::object py_obj)
+      : py_obj_(std::move(py_obj)) {}
+
+ private:
+  py::object py_obj_;
+};
+
+} // namespace c10::ivalue

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_list.h

@@ -0,0 +1,228 @@
+#pragma once
+
+#include <ATen/core/Dict.h>
+#include <ATen/core/List.h>
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <c10/util/Optional.h>
+#include <pybind11/detail/common.h>
+#include <torch/csrc/utils/pybind.h>
+#include <cstddef>
+#include <stdexcept>
+
+namespace torch::jit {
+
+void initScriptListBindings(PyObject* module);
+
+/// An iterator over the elements of ScriptList. This is used to support
+/// __iter__(), .
+class ScriptListIterator final {
+ public:
+  ScriptListIterator(
+      c10::impl::GenericList::iterator iter,
+      c10::impl::GenericList::iterator end)
+      : iter_(iter), end_(end) {}
+  IValue next();
+  bool done() const;
+
+ private:
+  c10::impl::GenericList::iterator iter_;
+  c10::impl::GenericList::iterator end_;
+};
+
+/// A wrapper around c10::List that can be exposed in Python via pybind
+/// with an API identical to the Python list class. This allows
+/// lists to have reference semantics across the Python/TorchScript
+/// boundary.
+class ScriptList final {
+ public:
+  // TODO: Do these make sense?
+  using size_type = size_t;
+  using diff_type = ptrdiff_t;
+  using ssize_t = Py_ssize_t;
+
+  // Constructor for empty lists created during slicing, extending, etc.
+  ScriptList(const TypePtr& type) : list_(AnyType::get()) {
+    auto list_type = type->expect<ListType>();
+    list_ = c10::impl::GenericList(list_type);
+  }
+
+  // Constructor for instances based on existing lists (e.g. a
+  // Python instance or a list nested inside another).
+  ScriptList(IValue data) : list_(AnyType::get()) {
+    TORCH_INTERNAL_ASSERT(data.isList());
+    list_ = data.toList();
+  }
+
+  ListTypePtr type() const {
+    return ListType::create(list_.elementType());
+  }
+
+  // Return a string representation that can be used
+  // to reconstruct the instance.
+  std::string repr() const {
+    std::ostringstream s;
+    s << '[';
+    bool f = false;
+    for (auto const& elem : list_) {
+      if (f) {
+        s << ", ";
+      }
+      s << IValue(elem);
+      f = true;
+    }
+    s << ']';
+    return s.str();
+  }
+
+  // Return an iterator over the elements of the list.
+  ScriptListIterator iter() const {
+    auto begin = list_.begin();
+    auto end = list_.end();
+    return ScriptListIterator(begin, end);
+  }
+
+  // Interpret the list as a boolean; empty means false, non-empty means
+  // true.
+  bool toBool() const {
+    return !(list_.empty());
+  }
+
+  // Get the value for the given index.
+  IValue getItem(diff_type idx) {
+    idx = wrap_index(idx);
+    return list_.get(idx);
+  };
+
+  // Set the value corresponding to the given index.
+  void setItem(diff_type idx, const IValue& value) {
+    idx = wrap_index(idx);
+    return list_.set(idx, value);
+  }
+
+  // Check whether the list contains the given value.
+  bool contains(const IValue& value) {
+    for (const auto& elem : list_) {
+      if (elem == value) {
+        return true;
+      }
+    }
+
+    return false;
+  }
+
+  // Delete the item at the given index from the list.
+  void delItem(diff_type idx) {
+    idx = wrap_index(idx);
+    auto iter = list_.begin() + idx;
+    list_.erase(iter);
+  }
+
+  // Get the size of the list.
+  ssize_t len() const {
+    return list_.size();
+  }
+
+  // Count the number of times a value appears in the list.
+  ssize_t count(const IValue& value) const {
+    ssize_t total = 0;
+
+    for (const auto& elem : list_) {
+      if (elem == value) {
+        ++total;
+      }
+    }
+
+    return total;
+  }
+
+  // Remove the first occurrence of a value from the list.
+  void remove(const IValue& value) {
+    auto list = list_;
+
+    int64_t idx = -1, i = 0;
+
+    for (const auto& elem : list) {
+      if (elem == value) {
+        idx = i;
+        break;
+      }
+
+      ++i;
+    }
+
+    if (idx == -1) {
+      throw py::value_error();
+    }
+
+    list.erase(list.begin() + idx);
+  }
+
+  // Append a value to the end of the list.
+  void append(const IValue& value) {
+    list_.emplace_back(value);
+  }
+
+  // Clear the contents of the list.
+  void clear() {
+    list_.clear();
+  }
+
+  // Append the contents of an iterable to the list.
+  void extend(const IValue& iterable) {
+    list_.append(iterable.toList());
+  }
+
+  // Remove and return the element at the specified index from the list. If no
+  // index is passed, the last element is removed and returned.
+  IValue pop(c10::optional<size_type> idx = c10::nullopt) {
+    IValue ret;
+
+    if (idx) {
+      idx = wrap_index(*idx);
+      ret = list_.get(*idx);
+      list_.erase(list_.begin() + *idx);
+    } else {
+      ret = list_.get(list_.size() - 1);
+      list_.pop_back();
+    }
+
+    return ret;
+  }
+
+  // Insert a value before the given index.
+  void insert(const IValue& value, diff_type idx) {
+    // wrap_index cannot be used; idx == len() is allowed
+    if (idx < 0) {
+      idx += len();
+    }
+
+    if (idx < 0 || idx > len()) {
+      throw std::out_of_range("list index out of range");
+    }
+
+    list_.insert(list_.begin() + idx, value);
+  }
+
+  // A c10::List instance that holds the actual data.
+  c10::impl::GenericList list_;
+
+ private:
+  // Wrap an index so that it can safely be used to access
+  // the list. For list of size sz, this function can successfully
+  // wrap indices in the range [-sz, sz-1]
+  diff_type wrap_index(diff_type idx) {
+    auto sz = len();
+    if (idx < 0) {
+      idx += sz;
+    }
+
+    if (idx < 0 || idx >= sz) {
+      throw std::out_of_range("list index out of range");
+    }
+
+    return idx;
+  }
+};
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_tracer.h

@@ -0,0 +1,45 @@
+#pragma once
+
+#include <torch/csrc/jit/frontend/source_range.h>
+#include <torch/csrc/jit/frontend/tracer.h>
+#include <torch/csrc/python_headers.h>
+#include <torch/csrc/utils/pybind.h>
+
+#include <memory>
+#include <string>
+
+namespace torch::jit {
+
+struct Module;
+
+namespace tracer {
+void initPythonTracerBindings(PyObject* module);
+
+SourceRange getPythonInterpreterSourceRange();
+
+Node* preRecordPythonTrace(
+    THPObjectPtr pyobj,
+    const std::string& arg_types,
+    at::ArrayRef<autograd::Variable> inputs,
+    std::vector<THPObjectPtr> scalar_args);
+
+std::pair<std::shared_ptr<Graph>, Stack> createGraphByTracingWithDict(
+    const py::function& func,
+    const py::dict& inputs_dict,
+    Stack inputs,
+    const py::function& var_name_lookup_fn,
+    bool strict,
+    bool force_outplace,
+    Module* self = nullptr,
+    const std::vector<std::string>& argument_names = {});
+
+std::pair<std::shared_ptr<Graph>, Stack> createGraphByTracing(
+    const py::function& func,
+    Stack inputs,
+    const py::function& var_name_lookup_fn,
+    bool strict,
+    bool force_outplace,
+    Module* self = nullptr,
+    const std::vector<std::string>& argument_names = {});
+} // namespace tracer
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_tree_views.h

@@ -0,0 +1,9 @@
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+namespace torch::jit {
+
+void initTreeViewBindings(PyObject* module);
+
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/script_init.h

@@ -0,0 +1,7 @@
+#pragma once
+
+#include <torch/csrc/jit/python/pybind.h>
+
+namespace torch::jit {
+void initJitScriptBindings(PyObject* module);
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/update_graph_executor_opt.h

@@ -0,0 +1,6 @@
+#pragma once
+#include <torch/csrc/Export.h>
+namespace torch::jit {
+TORCH_API void setGraphExecutorOptimize(bool o);
+TORCH_API bool getGraphExecutorOptimize();
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/utf8_decoding_ignore.h

@@ -0,0 +1,6 @@
+#pragma once
+#include <torch/csrc/Export.h>
+namespace torch::jit {
+TORCH_API void setUTF8DecodingIgnore(bool o);
+TORCH_API bool getUTF8DecodingIgnore();
+} // namespace torch::jit

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/analysis.h

@@ -0,0 +1,406 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+class HasRand : public IRVisitor {
+ public:
+  HasRand(StmtPtr stmt) : stmt_(std::move(stmt)) {
+    stmt_->accept(this);
+  }
+
+  bool has_rand() const {
+    return has_rand_;
+  }
+
+ private:
+  void visit(IntrinsicsPtr v) override {
+    if (v->op_type() == IntrinsicsOp::kRand) {
+      has_rand_ = true;
+    } else {
+      IRVisitor::visit(std::move(v));
+    }
+  }
+  StmtPtr stmt_;
+  bool has_rand_ = false;
+};
+
+template <typename Op>
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class NodeFinder : public IRVisitor {
+ public:
+  void visit(NodePtr<Op> v) override {
+    nodes.push_back((NodePtr<Op>)v);
+    IRVisitor::visit(v);
+  }
+
+  static std::vector<NodePtr<Op>> find(StmtPtr s) {
+    NodeFinder<Op> nf;
+    s->accept(&nf);
+    return nf.nodes;
+  }
+
+  static std::vector<NodePtr<Op>> find(ExprPtr e) {
+    NodeFinder<Op> nf;
+    e->accept(&nf);
+    return nf.nodes;
+  }
+
+  std::vector<NodePtr<Op>> nodes;
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class VarFinder : public IRVisitor {
+ public:
+  void visit(VarPtr v) override {
+    vars_.insert(v);
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<VarPtr> find(StmtPtr s) {
+    VarFinder nf;
+    s->accept(&nf);
+    return nf.vars();
+  }
+
+  static std::unordered_set<VarPtr> find(ExprPtr e) {
+    VarFinder nf;
+    e->accept(&nf);
+    return nf.vars();
+  }
+
+  const std::unordered_set<VarPtr>& vars() {
+    return vars_;
+  }
+
+ private:
+  std::unordered_set<VarPtr> vars_;
+};
+
+class BufFinder : public IRVisitor {
+ public:
+  void visit(BufPtr v) override {
+    bufs_.insert(v);
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<BufPtr> find(StmtPtr s) {
+    BufFinder nf;
+    s->accept(&nf);
+    return nf.bufs();
+  }
+
+  static std::unordered_set<BufPtr> find(ExprPtr e) {
+    BufFinder nf;
+    e->accept(&nf);
+    return nf.bufs();
+  }
+
+  const std::unordered_set<BufPtr>& bufs() {
+    return bufs_;
+  }
+
+ private:
+  std::unordered_set<BufPtr> bufs_;
+};
+
+// Finds all kinds of write operations to the provided Buf.
+class WritesToBuf : public IRVisitor {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  WritesToBuf(BufPtr target) : target_(std::move(target)) {}
+
+  std::vector<StmtPtr> writes() {
+    return writes_;
+  }
+
+  static std::vector<StmtPtr> find(StmtPtr s, BufPtr b) {
+    WritesToBuf finder(std::move(b));
+    s->accept(&finder);
+    return finder.writes();
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    if (v->buf() == target_) {
+      writes_.push_back(v);
+    }
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (v->buf() == target_) {
+      writes_.push_back(v);
+    }
+  }
+
+  BufPtr target_;
+  std::vector<StmtPtr> writes_;
+};
+
+class StmtsReadingBuf : public IRVisitor {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  StmtsReadingBuf(BufPtr target) : target_(std::move(target)) {}
+
+  std::vector<StmtPtr> reads() {
+    return reads_;
+  }
+
+  static std::vector<StmtPtr> find(StmtPtr s, BufPtr b) {
+    StmtsReadingBuf finder(std::move(b));
+    s->accept(&finder);
+    return finder.reads();
+  }
+
+ private:
+  bool readsBuffer(StmtPtr s) {
+    auto loads = NodeFinder<Load>::find(std::move(s));
+    for (const auto& l : loads) {
+      if (l->buf() == target_) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  void visit(StorePtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(LetPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(CondPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (readsBuffer(v)) {
+      reads_.push_back(v);
+    }
+  }
+
+  BufPtr target_;
+  std::vector<StmtPtr> reads_;
+};
+
+class ExternalAllocBufFinder : public IRVisitor {
+ public:
+  void visit(ExternalCallWithAllocPtr v) override {
+    const auto& bufs_out = v->buf_out_args();
+    bufs_.insert(bufs_out.begin(), bufs_out.end());
+    IRVisitor::visit(std::move(v));
+  }
+
+  static std::unordered_set<BufPtr> find(StmtPtr s) {
+    ExternalAllocBufFinder f;
+    s->accept(&f);
+    return f.bufs();
+  }
+
+  static std::unordered_set<BufPtr> find(ExprPtr e) {
+    ExternalAllocBufFinder f;
+    e->accept(&f);
+    return f.bufs();
+  }
+
+  const std::unordered_set<BufPtr>& bufs() {
+    return bufs_;
+  }
+
+ private:
+  std::unordered_set<BufPtr> bufs_;
+};
+
+// Traverses the IR to determine if a particular Var is modified within it.
+class ModifiesVarChecker : public IRVisitor {
+ public:
+  ModifiesVarChecker(VarPtr v) : var_(std::move(v)) {}
+
+  static bool check(StmtPtr s, VarPtr v) {
+    ModifiesVarChecker checker(std::move(v));
+    s->accept(&checker);
+    return checker.found();
+  }
+
+  bool found() {
+    return found_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    if (v->buf()->base_handle() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(AtomicAddPtr v) override {
+    if (v->buf()->base_handle() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(LetPtr v) override {
+    if (v->var() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  void visit(ForPtr v) override {
+    if (v->var() == var_) {
+      found_ = true;
+      return;
+    }
+    IRVisitor::visit(std::move(v));
+  }
+
+  VarPtr var_;
+  bool found_{false};
+};
+
+// Traverse the Block stmt to identify the live range of the specified buf. The
+// live range, indicated by a pair of integers, specifies the first and last
+// stmt in block stmts that access to the buf.
+class BufLiveRange : public IRVisitor {
+ public:
+  BufLiveRange(BufPtr b) : buf_(std::move(b)) {}
+
+  static std::tuple<int32_t, int32_t> liveRange(StmtPtr s, BufPtr b) {
+    BlockPtr block = to<Block>(std::move(s));
+    // We Only analyze buffer live ranges for block stmts.
+    if (!block) {
+      return std::make_tuple(0, 0);
+    }
+
+    BufLiveRange analyzer(std::move(b));
+    block->accept(&analyzer);
+    return analyzer.getLiveRange();
+  }
+
+ private:
+  std::tuple<int32_t, int32_t> getLiveRange() {
+    return std::make_tuple(begin_, end_);
+  }
+
+  bool hasBufReads(StmtPtr s) {
+    auto loads1 = NodeFinder<Load>::find(s);
+    for (const auto& l : loads1) {
+      if (l->buf() == buf_) {
+        return true;
+      }
+    }
+    auto loads2 = NodeFinder<ExternalCall>::find(s);
+    for (const auto& l : loads2) {
+      for (const auto& lb : l->buf_args()) {
+        if (lb == buf_) {
+          return true;
+        }
+      }
+    }
+    auto loads3 = NodeFinder<ExternalCallWithAlloc>::find(std::move(s));
+    for (const auto& l : loads3) {
+      for (const auto& lb : l->buf_args()) {
+        if (lb == buf_) {
+          return true;
+        }
+      }
+    }
+    return false;
+  }
+
+  bool hasBufWrites(StmtPtr s) {
+    auto writes1 = NodeFinder<Store>::find(s);
+    for (const auto& w : writes1) {
+      if (w->buf() == buf_) {
+        return true;
+      }
+    }
+    auto writes2 = NodeFinder<ExternalCall>::find(s);
+    for (const auto& w : writes2) {
+      if (w->buf() == buf_) {
+        return true;
+      }
+    }
+    auto writes3 = NodeFinder<ExternalCallWithAlloc>::find(std::move(s));
+    for (const auto& w : writes3) {
+      for (const auto& wb : w->buf_out_args()) {
+        if (wb == buf_) {
+          return true;
+        }
+      }
+    }
+    return false;
+  }
+
+  void findAccAndUpdateLiveRange(StmtPtr s) {
+    bool has_reads = hasBufReads(s), has_writes = hasBufWrites(std::move(s));
+    if (has_reads || has_writes) {
+      if (begin_ == -1) {
+        begin_ = curr_index_;
+      };
+      end_ = curr_index_;
+    }
+  }
+
+  void visit(BlockPtr v) override {
+    for (const StmtPtr& s : *v) {
+      curr_index_ += 1;
+      findAccAndUpdateLiveRange(s);
+    }
+  }
+
+  BufPtr buf_;
+  int32_t begin_ = -1;
+  int32_t end_ = -1;
+  int32_t curr_index_ = -1;
+};
+
+// A class that analyzes the given program relevant for Block backend
+// It creates a map of multi dim buffers and their flat versions
+class CreateBufferMap : public IRVisitor {
+ public:
+  const std::unordered_map<std::string, BufPtr>& getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    auto load_node = to<Load>(v->value());
+    if (load_node) {
+      auto t_buf = load_node->buf();
+      map_input_to_tensor_bufs_.emplace(t_buf->name_hint(), v->buf());
+    } else {
+      auto add_node = to<Add>(v->value());
+      auto mul_node = to<Mul>(v->value());
+      // This means for now, v->value() can be Add or Mul
+      TORCH_INTERNAL_ASSERT(add_node || mul_node, buildErrorMessage());
+      map_input_to_tensor_bufs_.emplace(v->buf()->name_hint(), v->buf());
+    }
+    v->value()->accept(this);
+  }
+  std::unordered_map<std::string, BufPtr> map_input_to_tensor_bufs_;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/block_codegen.h

@@ -0,0 +1,150 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Block backend.
+class BlockAnalysis : public IRVisitor {
+ public:
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set<BufPtr>& loads() const {
+    return loads_;
+  }
+
+  const std::unordered_set<BufPtr>& stores() const {
+    return store_targets_;
+  }
+
+  int block_size() const {
+    return block_size_;
+  }
+
+  bool areBufsInMap(const std::unordered_set<BufPtr>& bufs) const;
+
+  BufPtr getMultiDimBuf(BufPtr buf) const;
+
+  std::string getInputName(BufPtr buf) const;
+
+  std::string getFlatInputName(BufPtr buf) const {
+    return getInputName(std::move(buf)) + "_flat";
+  }
+
+  std::unordered_map<std::string, BufPtr> getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_map<std::string, BufPtr> map_input_to_tensor_bufs_;
+  std::unordered_set<BufPtr> store_targets_;
+  std::unordered_set<BufPtr> loads_;
+  int block_size_ = 32;
+};
+
+// A class that overrides the underlying IRPrinter to produce Block.
+class BlockPrinter : public IRPrinter {
+ public:
+  BlockPrinter(std::ostream* os, BlockAnalysis* block_analysis)
+      : IRPrinter(*os), block_analysis_(block_analysis) {}
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  BlockAnalysis* block_analysis_;
+  std::unordered_map<std::string, int> dim_values_map;
+  std::vector<std::string> dim_names = {"N", "H", "W", "C"};
+  std::vector<std::string> flat_dim_names = {"N", "NH", "NHW", "NHWC"};
+  void PrintTensorInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintArguments(const std::unordered_set<BufPtr>& bufs);
+  void PrintBufferInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintDistribution(const std::unordered_set<BufPtr>& bufs);
+  void PrintLoop(const std::unordered_set<BufPtr>& bufs, bool block_idx = true);
+  void PrintReshapeInfo(
+      const std::unordered_set<BufPtr>& bufs,
+      bool reverse = false);
+  void PrintDMAs(const std::unordered_set<BufPtr>& bufs);
+  void PrintAdjustBuffers(const std::unordered_set<BufPtr>& bufs);
+
+  void visit(ForPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AddPtr v) override;
+  void visit(MulPtr v) override;
+};
+
+class TORCH_API BlockCodeGen : public CodeGen {
+ public:
+  template <typename... Ts>
+  /* implicit */
+  BlockCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector<BufferArg>({BufferArg(ts)...}),
+            at::Device(at::kCPU)) {
+    Initialize();
+  }
+
+  BlockCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::Device(at::kCPU),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~BlockCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  void Initialize();
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<BlockPrinter> printer_;
+  std::unique_ptr<BlockAnalysis> block_analysis_;
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_inference.h

@@ -0,0 +1,80 @@
+#pragma once
+
+#include <map>
+#include <unordered_map>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/mem_dependency_checker.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class Buf;
+class Stmt;
+
+enum C10_API_ENUM TensorAccessKind { kLoad, kStore, kMutate };
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct TORCH_API TensorAccessBoundsInfo {
+  TensorAccessKind kind;
+  std::vector<ExprPtr> start;
+  std::vector<ExprPtr> stop;
+};
+
+using BoundsInfo =
+    std::unordered_map<BufPtr, std::vector<TensorAccessBoundsInfo>>;
+
+TORCH_API BoundsInfo inferBounds(StmtPtr s, bool distinctAccessKinds = true);
+
+// Bounds inference caching the analysis. The MemDependencyChecker must already
+// have been run.
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr s,
+    bool distinctAccessKinds = true);
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    ExprPtr e,
+    bool distinctAccessKinds = true);
+
+TORCH_API void printBoundsInfo(const BoundsInfo& v);
+
+TORCH_API std::vector<ExprPtr> getBoundExtents(
+    const std::vector<TensorAccessBoundsInfo>& infos);
+
+// The kind of dependency found, in increasing order of exclusivity.
+enum class HazardKind {
+  ReadAfterWrite,
+  WriteAfterRead,
+  WriteAfterWrite,
+  NoDependency,
+};
+TORCH_API HazardKind getPotentialHazards(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+
+// Returns true if there is a conflicting overlap between accesses in
+// statements A and B. A conflicting overlap is an overlap in buffer accesses
+// where at least one of the accesses is a Store.
+TORCH_API bool hasConflictingOverlap(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+// Same as above, between accesses in stores S1 and S2.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S1,
+    StorePtr S2);
+// Same as above, between accesses in store S and load L.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S,
+    LoadPtr L);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_overlap.h

@@ -0,0 +1,128 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+
+#include <deque>
+#include <utility>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+// A simple class containing the start and end of a range in a single dimension.
+struct TORCH_API Bound {
+  ExprPtr start{nullptr};
+  ExprPtr end{nullptr};
+
+  // This stores whether or not the start and end of this Bound have previously
+  // been swapped. This occurs when the bound is in a loop with a negative
+  // stride.
+  bool swapped{false};
+
+  Bound() = default;
+  Bound(ExprPtr s, ExprPtr e) : start(std::move(s)), end(std::move(e)) {}
+
+  void print() const;
+  bool equals(const Bound& other) const;
+
+  // The comparison operators are conservative. If the compare operator returns
+  // true, it means that all the elements satisfy the logical expression. But
+  // the false does not mean the opposite comparison is satisfied. It could be
+  // but not always.
+  bool operator==(const Bound& other) const;
+  bool operator!=(const Bound& other) const;
+  bool operator<(const Bound& other) const;
+  bool operator<=(const Bound& other) const;
+  bool operator>(const Bound& other) const;
+  bool operator>=(const Bound& other) const;
+
+  void swap() {
+    std::swap(start, end);
+    swapped = !swapped;
+  }
+};
+
+struct BoundHash {
+  size_t operator()(const Bound& b) const {
+    return std::hash<ExprPtr>()(b.start) ^ std::hash<ExprPtr>()(b.end);
+  }
+};
+
+// The type of overlap found. Each condition is true only if none of the
+// previous conditions hold.
+//     ContainedOrEqual: All elements in the Bound A are in the Bound B (this
+//                       includes the case where the bounds are equal).
+//     Contains: All elements in the Bound B are in the Bound B.
+//     PartialOverlap: Any elements in the Bound B are in the Bound A.
+//     NoOverlap: No elements in the Bound A are in the bound B.
+enum class OverlapKind {
+  ContainedOrEqual,
+  Contains,
+  PartialOverlap,
+  NoOverlap
+};
+
+// The Bound comparison result.
+//     True: Every Bound element always satisfies the given comparison operator
+//     False: Every Bound element always does NOT satisfy the given comparison
+//     operator
+//     NotDetermined: Some elements satisfy the given comparison operator and
+//     some elements not
+enum class CmpEvalResult { True, False, NotDetermined };
+
+// Returns the kind of overlap between Bound A and Bound A in a single
+// dimension.
+OverlapKind TORCH_API boundOverlap(Bound A, Bound B);
+
+// The comparison is conservative and the compare result is deterministic.
+// It means that every element of the Bound to be compared needs to satisfy
+// the given comparison operator.
+CmpEvalResult TORCH_API compareBound(
+    const Bound& a,
+    const Bound& b,
+    const CompareSelectOperation& cmp_op);
+
+// A multi dimensional bound representing the bound of a set of indices.
+using IndexBounds = std::vector<Bound>;
+
+// Returns true if two IndexBounds are equivalent.
+bool TORCH_API indexBoundsEquals(const IndexBounds& A, const IndexBounds& B);
+
+// Flattens a multi dimensional bound to a single dimension. The IndexBounds "a"
+// *must* encapsulate the entire range of the buffer.
+Bound TORCH_API flattenBounds(const IndexBounds& a);
+
+// Determines the kind of overlap in X dimensions.
+OverlapKind TORCH_API overlaps(const IndexBounds& a, const IndexBounds& b);
+
+// Returns the Bound slices created by subtracing bound B from bound A.
+// Multiple Bounds can be returned in the case where B slices A into two
+// distinct regions with no overlap.
+//
+// For example:
+//    subtractBound((0, 10), (2, 4)) => [(0, 1), (5, 10)]
+//       bound A: (0, 10)
+//       bound B: (2, 4)
+//       If we remove slice (2, 4) from the slice (0, 10), we will be left
+//       with 2 slices, one at the start (0, 1), and one at the end (5, 10).
+//       So, the result of this subtraction is [(0, 1), (5, 10)].
+//
+// Note: this doesn't use IndexBounds because the Bounds returned do not
+// represent multiple different dimensions.
+std::vector<Bound> TORCH_API subtractBound(Bound a, Bound b);
+
+// Returns the bound slices created by subtracting the IndexBounds B from A.
+std::vector<IndexBounds> TORCH_API subtractIndicesBounds(
+    const IndexBounds& A,
+    const IndexBounds& B,
+    OverlapKind overlap);
+std::vector<IndexBounds> TORCH_API
+subtractIndicesBounds(const IndexBounds& A, const IndexBounds& B);
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/codegen.h

@@ -0,0 +1,283 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename T>
+class PaddedBuffer;
+
+class TORCH_API CodeGen {
+ public:
+  class BufferArg;
+  class CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(StmtPtr stmt, Ts... ts)
+      : stmt_(std::move(stmt)), buffer_args_({BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(
+      StmtPtr stmt,
+      std::vector<BufferArg> buffer_args,
+      at::Device device = at::kCPU,
+      std::string kernel_func_name = "func");
+
+  virtual ~CodeGen() = default;
+
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  void set_stmt(StmtPtr s) {
+    stmt_ = s;
+  }
+
+  void apply_mutator(IRMutator* mutator) {
+    stmt_ = stmt_->accept_mutator(mutator);
+  }
+
+  void apply_visitor(IRVisitor* visitor) {
+    stmt_->accept(visitor);
+  }
+
+  std::vector<BufferArg>& buffer_args() {
+    return buffer_args_;
+  }
+
+  const std::vector<BufferArg>& buffer_args() const {
+    return buffer_args_;
+  }
+
+  at::Device device() {
+    return device_;
+  }
+
+  // This function returns the generated code as
+  // a string.
+  virtual std::string getCodeText(const std::string& attr = "") {
+    return ("");
+  }
+
+  // TODO: Figure out how to unify these call interfaces.
+
+  /// Call a function with a vector of CallArgs, which are tagged
+  /// unions that properly type the arguments.
+  virtual void call(const std::vector<CallArg>& args) = 0;
+
+  /// Call a function faster than a regular `call` by assuming that
+  /// the generated kernel already knows the type of the arguments, so
+  /// they can be type-punned with `void*`s.
+  virtual void call_raw(const std::vector<void*>& args) = 0;
+
+  /// Call a function even faster than a regular call, by assuming
+  /// that the number of thread blocks can be derived from `numel` via
+  /// a simple division, rather than evaluating an expression.
+  virtual void call_with_numel(void** args, int64_t numel);
+
+  virtual at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) {
+    return at::empty_strided(
+        size, stride, dtype_opt, layout_opt, device_opt, pin_memory_opt);
+  }
+
+  const std::string& kernel_func_name() const {
+    return kernel_func_name_;
+  }
+
+  void allocIntermediateBufs();
+
+ protected:
+  static void* argToPtr(const BufferArg& bufferArg, const CallArg& callArg);
+
+ private:
+  StmtPtr stmt_;
+  std::vector<BufferArg> buffer_args_;
+  at::Device device_ = at::kCPU;
+  std::string kernel_func_name_ = "func";
+};
+
+class TORCH_API ExtCallMemoryReuse : public IRMutator {
+  static std::unordered_map<std::string, std::string> makeExtCallFuncNameMap();
+  static const std::unordered_map<std::string, std::string> extCallFuncNameMap_;
+
+ public:
+  explicit ExtCallMemoryReuse(
+      const std::vector<CodeGen::BufferArg>& bufferArgs);
+  ~ExtCallMemoryReuse() override = default;
+  StmtPtr mutate(ExternalCallPtr v) override;
+
+ private:
+  std::unordered_set<BufPtr> bufferArgs_;
+};
+
+class CodeGen::BufferArg {
+ public:
+  BufferArg(const Tensor& tensor) : buf_(tensor.buf()) {}
+  BufferArg(const VarHandle& var) : var_(var.node()), isVar_(true) {}
+  BufferArg(const BufHandle& buf) : buf_(buf.node()) {}
+  BufferArg(BufPtr buf) : buf_(std::move(buf)) {}
+
+  VarPtr var() const {
+    return isVar_ ? var_ : buf_->base_handle();
+  }
+
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  bool isVar() const {
+    return isVar_;
+  }
+
+  Dtype dtype() const {
+    return isVar_ ? var_->dtype() : buf_->dtype();
+  }
+
+ private:
+  VarPtr var_ = nullptr;
+  BufPtr buf_ = nullptr;
+  bool isVar_ = false;
+};
+
+class CodeGen::CallArg {
+ public:
+  template <typename T>
+  CallArg(const PaddedBuffer<T>& buffer);
+
+  template <typename T>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init,cppcoreguidelines-pro-type-const-cast)
+  CallArg(const std::vector<T>& buffer)
+      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+      : data_(const_cast<T*>(buffer.data())) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CallArg(void* ptr) : data_(ptr) {}
+
+#define ARG_TYPE_CTOR(Type, Name)      \
+  CallArg(Type v) {                    \
+    memcpy(buffer_, &v, sizeof(Type)); \
+    data_ = (void*)buffer_;            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_TYPE_CTOR);
+#undef ARG_TYPE_CTOR
+
+  void* data() const {
+    return data_;
+  }
+
+  CallArg(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+  }
+
+  CallArg& operator=(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+    return *this;
+  }
+
+#define ARG_PTR_DEFINE(Type, Name)                  \
+  Type* Name##Ptr() const {                         \
+    TORCH_INTERNAL_ASSERT(data_ == (void*)buffer_); \
+    return (Type*)data_;                            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_PTR_DEFINE);
+#undef ARG_PTR_DEFINE
+
+ private:
+  void* data_;
+  // Regarding a scalar value, CallArg uses void**=&data_ to store it. But the
+  // bit width of a pointer is 32bit on a 32bit platform. It cannot store the
+  // scalar if the bit width of the scalar is larger than 32bit, such as double
+  // and long. Hence, we add 8 bytes buffer dedicated to storing the scalar
+  // value regardless its bit width is less or greater than 32bits.
+  char buffer_[8] = {0}; // 64bits
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class RegisterCodeGenList {
+ public:
+  TORCH_API static RegisterCodeGenList& GetInstance() {
+    static RegisterCodeGenList codegen_list;
+    return codegen_list;
+  }
+
+  using StmtFactoryMethod = std::function<std::unique_ptr<CodeGen>(
+      StmtPtr stmt,
+      const std::vector<CodeGen::BufferArg>&,
+      at::Device device,
+      const std::string& kernel_func_name)>;
+
+  TORCH_API StmtFactoryMethod FindStmtFactoryMethod(const std::string& name);
+  RegisterCodeGenList(const RegisterCodeGenList&) = delete;
+  RegisterCodeGenList& operator=(const RegisterCodeGenList&) = delete;
+
+ private:
+  template <class CodeGenType>
+  friend class RegisterCodeGen;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  RegisterCodeGenList() = default;
+  TORCH_API void AddStmtFactoryMethod(
+      const std::string& name,
+      const StmtFactoryMethod& stmt_factory_method);
+
+  std::unordered_map<std::string, StmtFactoryMethod> stmt_factory_methods_;
+};
+
+template <class CodeGenType>
+class RegisterCodeGen {
+ public:
+  explicit RegisterCodeGen(const std::string& name) {
+    RegisterCodeGenList& codegen_list = RegisterCodeGenList::GetInstance();
+    codegen_list.AddStmtFactoryMethod(
+        name,
+        [](StmtPtr stmt,
+           const std::vector<CodeGen::BufferArg>& params,
+           at::Device device,
+           const std::string& kernel_func_name) {
+          // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+          std::unique_ptr<CodeGen> method(
+              new CodeGenType(stmt, params, device, kernel_func_name));
+          return method;
+        });
+  }
+};
+
+TORCH_API std::unique_ptr<CodeGen> CreateCodeGen(
+    const std::string& name,
+    StmtPtr stmt,
+    const std::vector<CodeGen::BufferArg>& params,
+    at::Device device = at::kCPU,
+    const std::string& kernel_func_name = "func");
+
+class TORCH_API GenericIntrinsicsExpander : public IRMutator {
+ protected:
+  ExprPtr mutate(IntrinsicsPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_codegen.h

@@ -0,0 +1,102 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class CppVarNameRewriter;
+
+// Generates C++ code from the IR.
+//
+// Vector operations are unrolled.
+// For example:
+// C[Ramp(0, 1, 3)] = A[Ramp(0, 2, 3)] + B[Ramp(0, 3, 3)];
+// is unrolled into:
+// C[0] = A[0] + B[0];
+// C[1] = A[2] + B[3];
+// C[2] = A[4] + B[6];
+class TORCH_API CppPrinter : public IRPrinter {
+ public:
+  explicit CppPrinter(std::ostream* os);
+  ~CppPrinter() override;
+
+  void printPrologue();
+
+  using IRPrinter::visit;
+
+  // Binary expressions.
+  void visit(ModPtr) override;
+  void visit(MaxPtr) override;
+  void visit(MinPtr) override;
+
+  // Conditional expressions.
+  void visit(CompareSelectPtr) override;
+  void visit(IfThenElsePtr) override;
+
+  // Tensor operations.
+  void visit(AllocatePtr) override;
+  void visit(FreePtr) override;
+  void visit(LoadPtr) override;
+  void visit(StorePtr) override;
+
+  // Casts.
+  void visit(CastPtr) override;
+  void visit(BitCastPtr) override;
+
+  // Calls.
+  void visit(IntrinsicsPtr) override;
+  void visit(ExternalCallPtr) override;
+
+  // Vars.
+  void visit(LetPtr) override;
+  void visit(VarPtr) override;
+
+  // Vector data types.
+  void visit(RampPtr) override;
+  void visit(BroadcastPtr) override;
+
+ private:
+  int lane_;
+  std::unordered_map<VarPtr, ExprPtr> vector_vars_;
+};
+
+class TORCH_API CppCodeGen : public CodeGen {
+ public:
+  CppCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~CppCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    call(std::vector<CallArg>({CallArg(ts)...}));
+  }
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  void init();
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<CppPrinter> printer_;
+  std::unique_ptr<CppVarNameRewriter> var_name_rewriter_;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_intrinsics.h

@@ -0,0 +1,36 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto cpp_intrinsics_definition = R"(
+namespace std {
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T rsqrt(T v) {
+  return 1.0f / std::sqrt(v);
+}
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T frac(T v) {
+  T intpart;
+  return std::modf(v, &intpart);
+}
+
+template <typename From, typename To>
+To bitcast(const From& v) {
+  assert(sizeof(To) == sizeof(From));
+  To res;
+  std::memcpy(&res, &v, sizeof(From));
+  return res;
+}
+
+} // namespace std
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cuda_codegen.h

@@ -0,0 +1,295 @@
+#pragma once
+
+#include <unordered_map>
+#include <unordered_set>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/eval.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Cuda backends.
+class CudaAnalysis : public IRVisitor {
+ public:
+  CudaAnalysis() {
+    gpu_block_extents_ = {alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+    gpu_thread_extents_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+  }
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set<VarPtr>& thread_local_bufs() const {
+    return thread_local_bufs_;
+  }
+
+  const std::unordered_set<VarPtr>& cross_block_bufs() const {
+    return cross_block_bufs_;
+  }
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return gpu_block_extents_;
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return gpu_thread_extents_;
+  }
+
+ private:
+  void visit(StorePtr v) override {
+    store_targets_.insert(v->buf());
+  }
+
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(PlacementAllocatePtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_set<BufPtr> store_targets_;
+  std::unordered_set<VarPtr> thread_local_bufs_;
+  std::unordered_set<VarPtr> cross_block_bufs_;
+
+  std::vector<ExprPtr> gpu_block_extents_;
+  std::vector<ExprPtr> gpu_thread_extents_;
+};
+
+// An IRMutator that replaces binding loop options with Cuda metavars, and masks
+// statements blocks which should execute with less reach than the launch
+// parameter extent.
+//
+// We do this by segmenting each block into chunks which should have the same
+// execution parameters, then if those params differ from the max mask each dim.
+class GPUMetaVarRewriter : public IRMutator {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit GPUMetaVarRewriter(const CudaAnalysis* cuda_analysis)
+      : cuda_analysis_(cuda_analysis) {
+    gpu_block_vars_ = {
+        alloc<Var>("blockIdx.x", kInt),
+        alloc<Var>("blockIdx.y", kInt),
+        alloc<Var>("blockIdx.z", kInt)};
+    gpu_thread_vars_ = {
+        alloc<Var>("threadIdx.x", kInt),
+        alloc<Var>("threadIdx.y", kInt),
+        alloc<Var>("threadIdx.z", kInt)};
+
+    current_block_reach_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+    current_thread_reach_ = {
+        alloc<IntImm>(1), alloc<IntImm>(1), alloc<IntImm>(1)};
+  }
+
+  StmtPtr mutate(ForPtr v) override;
+  StmtPtr mutate(BlockPtr v) override;
+
+  const std::vector<VarPtr>& gpu_block_vars() const {
+    return gpu_block_vars_;
+  }
+
+  const std::vector<VarPtr>& gpu_thread_vars() const {
+    return gpu_thread_vars_;
+  }
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return cuda_analysis_->gpu_block_extents();
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return cuda_analysis_->gpu_thread_extents();
+  }
+
+ private:
+  // When processing a block, stores the contents of each sub-segment.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  class Segment {
+   public:
+    void reset(bool mask) {
+      stmts_.clear();
+      mask_ = mask;
+    }
+
+    bool empty() const {
+      return stmts_.empty();
+    }
+
+    std::vector<StmtPtr>& stmts() {
+      return stmts_;
+    }
+    bool mask() {
+      return mask_;
+    }
+
+   private:
+    std::vector<StmtPtr> stmts_;
+    bool mask_{true};
+  };
+
+  // Returns true if the current execution scope is equivalent to the launch
+  // parameters.
+  bool isFullExtent();
+
+  std::vector<VarPtr> gpu_block_vars_;
+  std::vector<VarPtr> gpu_thread_vars_;
+
+  std::vector<ExprPtr> current_block_reach_;
+  std::vector<ExprPtr> current_thread_reach_;
+
+  const CudaAnalysis* cuda_analysis_;
+};
+
+// A class that overrides the underlying IRPrinter to produce Cuda C.
+class CudaPrinter : public IRPrinter {
+ public:
+  explicit CudaPrinter(
+      std::ostream* os,
+      const CudaAnalysis* cuda_analysis,
+      bool has_random)
+      : IRPrinter(*os), cuda_analysis_(cuda_analysis) {
+    if (has_random) {
+      rand_func_ = alloc<Var>("rand", kHandle);
+    }
+  }
+
+  void visit(CastPtr v) override;
+  void visit(IntrinsicsPtr v) override;
+  void visit(ForPtr v) override;
+
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(AtomicAddPtr v) override;
+  void visit(MaxPtr v) override;
+  void visit(MinPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(LetPtr v) override;
+
+  void visit(ExternalCallPtr v) override;
+
+  VarPtr rand_func() const {
+    return rand_func_;
+  }
+
+  std::string dtypeToCppString(const Dtype& dtype) override;
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  VarPtr rand_func_;
+  const CudaAnalysis* cuda_analysis_;
+
+  void print_flat_alloc(AllocatePtr alloc);
+};
+
+// Construct Cuda C from the buffer and tensor input, and invoke the kernel
+// when real arguments are provided.
+class TORCH_CUDA_CU_API CudaCodeGen : public CodeGen {
+ public:
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CudaCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector<BufferArg>({BufferArg(ts)...}),
+            at::Device(at::kCUDA, at::cuda::current_device())) {
+    Initialize();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CudaCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::Device(at::kCUDA, at::cuda::current_device()),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~CudaCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+  void call_with_numel(void** args, int64_t numel) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    call(std::vector<CallArg>({CallArg(ts)...}));
+  }
+
+  at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) override;
+
+  const std::vector<ExprPtr>& gpu_block_extents() const {
+    return cuda_analysis_->gpu_block_extents();
+  }
+
+  const std::vector<ExprPtr>& gpu_thread_extents() const {
+    return cuda_analysis_->gpu_thread_extents();
+  }
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  void Initialize();
+
+  void CompileToNVRTC(const std::string& code, const std::string& func_name);
+
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<CudaPrinter> printer_;
+  std::unique_ptr<CudaAnalysis> cuda_analysis_;
+  std::unique_ptr<GPUMetaVarRewriter> metavar_rewriter_;
+  std::unordered_set<std::string> taken_func_names;
+  std::mutex eval_lock_;
+  CUfunction function_;
+  bool has_random_ = false;
+  int thread_block_size_ = -1;
+
+  std::vector<bool> arg_pos_in_extents_;
+#ifdef TORCH_ENABLE_LLVM
+  std::vector<ExprEval<LLVMCodeGen>> block_extents_eval_;
+  std::vector<ExprEval<LLVMCodeGen>> thread_extents_eval_;
+#else
+  std::vector<ExprEval<SimpleIREvaluator>> block_extents_eval_;
+  std::vector<ExprEval<SimpleIREvaluator>> thread_extents_eval_;
+#endif
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cuda_random.h

@@ -0,0 +1,104 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto philox_random_string = R"(
+
+class Philox {
+public:
+  __device__ inline Philox(unsigned long long seed,
+                           unsigned long long subsequence,
+                           unsigned long long offset) {
+    key.x = (unsigned int)seed;
+    key.y = (unsigned int)(seed >> 32);
+    counter = make_uint4(0, 0, 0, 0);
+    counter.z = (unsigned int)(subsequence);
+    counter.w = (unsigned int)(subsequence >> 32);
+    STATE = 0;
+    incr_n(offset / 4);
+  }
+
+  __device__ inline unsigned long operator()() {
+    if(STATE == 0) {
+      uint4 counter_ = counter;
+      uint2 key_ = key;
+      for(int i = 0; i < 9; i++) {
+        counter_ = single_round(counter_, key_);
+        key_.x += (kPhilox10A); key_.y += (kPhilox10B);
+      }
+      output = single_round(counter_, key_);
+      incr();
+    }
+    unsigned long ret;
+    switch(STATE) {
+      case 0: ret = output.x; break;
+      case 1: ret = output.y; break;
+      case 2: ret = output.z; break;
+      case 3: ret = output.w; break;
+    }
+    STATE = (STATE + 1) % 4;
+    return ret;
+  }
+
+private:
+  uint4 counter;
+  uint4 output;
+  uint2 key;
+  unsigned int STATE;
+  __device__ inline void incr_n(unsigned long long n) {
+    unsigned int nlo = (unsigned int)(n);
+    unsigned int nhi = (unsigned int)(n >> 32);
+    counter.x += nlo;
+    if (counter.x < nlo)
+      nhi++;
+    counter.y += nhi;
+    if (nhi <= counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ inline void incr() {
+    if (++counter.x)
+      return;
+    if (++counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ unsigned int mulhilo32(unsigned int a, unsigned int b,
+                                    unsigned int *result_high) {
+    *result_high = __umulhi(a, b);
+    return a*b;
+  }
+
+  __device__ inline uint4 single_round(uint4 ctr, uint2 key) {
+    unsigned int hi0;
+    unsigned int hi1;
+    unsigned int lo0 = mulhilo32(kPhiloxSA, ctr.x, &hi0);
+    unsigned int lo1 = mulhilo32(kPhiloxSB, ctr.z, &hi1);
+
+    uint4 ret = {hi1 ^ ctr.y ^ key.x, lo1, hi0 ^ ctr.w ^ key.y, lo0};
+    return ret;
+  }
+
+  static const unsigned long kPhilox10A = 0x9E3779B9;
+  static const unsigned long kPhilox10B = 0xBB67AE85;
+  static const unsigned long kPhiloxSA = 0xD2511F53;
+  static const unsigned long kPhiloxSB = 0xCD9E8D57;
+};
+
+// Inverse of 2^32.
+#define M_RAN_INVM32 2.3283064e-10f
+__device__  __inline__ float Uint32ToFloat(unsigned int x) {
+  return x * M_RAN_INVM32;
+}
+
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/eval.h

@@ -0,0 +1,346 @@
+#pragma once
+
+#include <cmath>
+#include <cstring>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Logging.h>
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+#include <torch/csrc/jit/tensorexpr/var_substitutor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class InterpValue {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  InterpValue() : dtype_(kInt) {
+    Intvalues.push_back(0);
+  }
+
+  template <typename T>
+  InterpValue(Dtype dtype, T v) : dtype_(dtype) {
+#define TYPE_CASE(Type, Name)  \
+  if (dtype == k##Name) {      \
+    Name##values.push_back(v); \
+    return;                    \
+  }
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    throw unsupported_dtype();
+  }
+
+#define VALUE_CTOR(Type, Name)            \
+  InterpValue(Type v) : dtype_(k##Name) { \
+    Name##values.push_back(v);            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_CTOR);
+#undef VALUE_CTOR
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::quint8 v) : dtype_(kQUInt8) {
+    QUInt8values.emplace_back(v.val_);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::qint8 v) : dtype_(kQInt8) {
+    QInt8values.emplace_back(v.val_);
+  }
+
+#define VALUE_VEC_CTOR(Type, Name)        \
+  InterpValue(const std::vector<Type>& v) \
+      : dtype_(Dtype(k##Name, v.size())), Name##values(v) {}
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_VEC_CTOR);
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::quint8, QUInt8)
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::qint8, QInt8)
+#undef VALUE_VEC_CTOR
+
+  template <typename T>
+  T as() const;
+
+  template <typename T>
+  const std::vector<T>& as_vec() const;
+
+  int64_t intValue() const;
+
+  Dtype dtype() const {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+
+#define VALUE_STORAGE(Type, Name) std::vector<Type> Name##values;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_STORAGE);
+  VALUE_STORAGE(c10::qint8, QInt8);
+  VALUE_STORAGE(c10::quint8, QUInt8);
+#undef VALUE_STORAGE
+  void* ptr;
+};
+
+#define VALUE_AS_DISPATCH(Type, Name)         \
+  template <>                                 \
+  inline Type InterpValue::as<Type>() const { \
+    if (dtype_ != k##Name) {                  \
+      throw unsupported_dtype();              \
+    }                                         \
+    return Name##values[0];                   \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_DISPATCH);
+VALUE_AS_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_DISPATCH
+
+#define VALUE_AS_VEC_DISPATCH(Type, Name)                             \
+  template <>                                                         \
+  inline const std::vector<Type>& InterpValue::as_vec<Type>() const { \
+    if (dtype_.scalar_type() != ScalarType::Name) {                   \
+      throw unsupported_dtype();                                      \
+    }                                                                 \
+    return Name##values;                                              \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_VEC_DISPATCH);
+VALUE_AS_VEC_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_VEC_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_VEC_DISPATCH
+
+template <typename Type>
+auto underlyingValue(Type x) {
+  return x;
+}
+
+template <>
+inline auto underlyingValue<c10::quint8>(c10::quint8 x) {
+  return x.val_;
+}
+
+template <>
+inline auto underlyingValue<c10::qint8>(c10::qint8 x) {
+  return x.val_;
+}
+
+template <typename To, typename From>
+To raw_bitcast(const From& src) {
+  TORCH_CHECK(sizeof(To) == sizeof(From), "Invalid bitcast invocation");
+  To storage;
+  std::memcpy(&storage, &src, sizeof(To));
+  return reinterpret_cast<To&>(storage);
+}
+
+class SimpleIREvaluatorImpl;
+class TORCH_API SimpleIREvaluator : public CodeGen {
+ public:
+  SimpleIREvaluator(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~SimpleIREvaluator() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> args({CallArg(ts)...});
+    call(args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e);
+  InterpValue value() const;
+
+ private:
+  void bindArg(const BufferArg& buf, void* data);
+  void expand_intrinsics() {
+    GenericIntrinsicsExpander intrinsics_expander;
+    apply_mutator(&intrinsics_expander);
+  }
+
+  std::unique_ptr<SimpleIREvaluatorImpl> impl_;
+};
+
+template <class CodeGenType>
+class ExprEval {
+ public:
+  using BufferArg = CodeGen::BufferArg;
+  using CallArg = CodeGen::CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, Ts... ts)
+      : ExprEval(expr, {BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, const std::vector<BufferArg>& buffer_args)
+      : dtype_(expr.dtype()) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<BufferArg> buffer_args_extended = buffer_args;
+    BufHandle ret_buf("ret_val", {1}, dtype_);
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprHandle> indices;
+    ExprHandle zero = IntImm::make(0);
+    for (size_t i = 0; i < ret_buf.ndim(); i++) {
+      indices.push_back(zero);
+    }
+    StmtPtr store_stmt = Store::make(ret_buf, indices, expr);
+    buffer_args_extended.emplace_back(ret_buf);
+    codegen_.reset(new CodeGenType(store_stmt, buffer_args_extended));
+  }
+
+  template <typename... Ts>
+  void operator()(Ts... ts) {
+    call(ts...);
+  }
+
+  void operator()(const std::vector<CallArg>& call_args) {
+    call(call_args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e) {
+    codegen_->bindVar(v, e);
+  }
+
+  void bindVar(const VarHandle& v, const ExprHandle& e) {
+    codegen_->bindVar(v.node(), e.node());
+  }
+
+  template <typename... Ts>
+  void call(Ts... ts) {
+    call({CallArg(ts)...});
+  }
+
+  void call(const std::vector<CallArg>& call_args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> call_args_extended = call_args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                           \
+  case ScalarType::Name: {                              \
+    std::vector<Type> ret_val_arg(1);                   \
+    call_args_extended.push_back(CallArg(ret_val_arg)); \
+    codegen_->call(call_args_extended);                 \
+    ret_value_ = InterpValue(ret_val_arg[0]);           \
+  } break;
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::quint8, QUInt8);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        call_args_extended.emplace_back(ret_val_arg.data());
+        codegen_->call(call_args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  void call_raw(const std::vector<void*>& args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<void*> args_extended = args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                    \
+  case ScalarType::Name: {                       \
+    std::vector<Type> ret_val_arg(1);            \
+    args_extended.push_back(ret_val_arg.data()); \
+    codegen_->call_raw(args_extended);           \
+    ret_value_ = InterpValue(ret_val_arg[0]);    \
+  } break;
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      TYPE_CASE(c10::quint8, QUInt8);
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        args_extended.push_back(ret_val_arg.data());
+        codegen_->call_raw(args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  template <typename T>
+  T value(const std::vector<void*>& args) {
+    call_raw(args);
+    return ret_value_.as<T>();
+  }
+
+  template <typename T, typename... Ts>
+  T value(Ts... ts) {
+    call(std::forward<Ts>(ts)...);
+    return ret_value_.as<T>();
+  }
+
+  Dtype dtype() {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+  std::unique_ptr<CodeGenType> codegen_;
+  InterpValue ret_value_;
+};
+
+// Evaluates the given expression and returns an int64_t value if the result of
+// the given expression is int64_t.
+c10::optional<int64_t> evalInt(ExprPtr e);
+
+// Substitutes the given vars with their corresponding expressions in the input
+// expression.
+inline ExprPtr Substitute(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return expr->accept_mutator(&var_sub);
+}
+
+// Substitutes the given vars with their corresponding expressions in the input
+// statement.
+inline StmtPtr Substitute(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return stmt->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input expression and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline ExprPtr SubstituteInClone(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Expr::clone(std::move(expr))->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input statement and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline StmtPtr SubstituteInClone(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Stmt::clone(std::move(stmt))->accept_mutator(&var_sub);
+}
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/exceptions.h

@@ -0,0 +1,91 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+#include <sstream>
+#include <stdexcept>
+
+// Forward declarations of types
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+class Expr;
+class Stmt;
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+// Forward declarations of functions
+namespace std {
+TORCH_API std::string to_string(const torch::jit::tensorexpr::ExprPtr);
+TORCH_API std::string to_string(const torch::jit::tensorexpr::StmtPtr);
+} // namespace std
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class unsupported_dtype : public std::runtime_error {
+ public:
+  explicit unsupported_dtype() : std::runtime_error("UNSUPPORTED DTYPE") {}
+  explicit unsupported_dtype(const std::string& err)
+      : std::runtime_error("UNSUPPORTED DTYPE: " + err) {}
+};
+
+class out_of_range_index : public std::runtime_error {
+ public:
+  explicit out_of_range_index() : std::runtime_error("OUT OF RANGE INDEX") {}
+  explicit out_of_range_index(const std::string& err)
+      : std::runtime_error("OUT OF RANGE INDEX: " + err) {}
+};
+
+class unimplemented_lowering : public std::runtime_error {
+ public:
+  explicit unimplemented_lowering()
+      : std::runtime_error("UNIMPLEMENTED LOWERING") {}
+  explicit unimplemented_lowering(ExprPtr expr)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(expr)) {}
+  explicit unimplemented_lowering(StmtPtr stmt)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(stmt)) {}
+};
+
+class malformed_input : public std::runtime_error {
+ public:
+  explicit malformed_input() : std::runtime_error("MALFORMED INPUT") {}
+  explicit malformed_input(const std::string& err)
+      : std::runtime_error("MALFORMED INPUT: " + err) {}
+  explicit malformed_input(ExprPtr expr)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(expr)) {}
+  explicit malformed_input(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_input(StmtPtr stmt)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(stmt)) {}
+  explicit malformed_input(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(stmt)) {}
+};
+
+class malformed_ir : public std::runtime_error {
+ public:
+  explicit malformed_ir() : std::runtime_error("MALFORMED IR") {}
+  explicit malformed_ir(const std::string& err)
+      : std::runtime_error("MALFORMED IR: " + err) {}
+  explicit malformed_ir(ExprPtr expr)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(expr)) {}
+  explicit malformed_ir(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_ir(StmtPtr stmt)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(stmt)) {}
+  explicit malformed_ir(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(stmt)) {}
+};
+
+TORCH_API std::string buildErrorMessage(const std::string& s = "");
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/expr.h

@@ -0,0 +1,499 @@
+/**
+ * This file implements the core classes for Tensor Expressions.
+ *
+ * The structure of the expressions is inspired by Halide/TVM IR.
+ */
+#pragma once
+
+#include <c10/core/MemoryFormat.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+enum IRNodeType {
+  kPrimitive,
+  kAdd,
+  kSub,
+  kMul,
+  kDiv,
+  kMod,
+  kMax,
+  kMin,
+  kAnd,
+  kOr,
+  kLshift,
+  kRshift,
+  kXor,
+  kCompareSelect,
+  kCast,
+  kBitCast,
+  kOther,
+};
+
+// The common base between all expression node.
+class TORCH_API Expr : public std::enable_shared_from_this<Expr> {
+ public:
+  explicit Expr(Dtype dtype, IRNodeType expr_type = kOther)
+      : dtype_(dtype), expr_type_(expr_type) {}
+  virtual ~Expr() = default;
+  Dtype dtype() const {
+    return dtype_;
+  }
+  virtual void accept(IRVisitor* visitor) = 0;
+  virtual ExprPtr accept_mutator(IRMutator* mutator) = 0;
+
+  IRNodeType expr_type() const {
+    return expr_type_;
+  }
+  // Is this a fixed (constant) immediate value.
+  virtual bool isConstant() const {
+    return false;
+  }
+
+  void set_dtype(Dtype dtype) {
+    dtype_ = dtype;
+  }
+
+  /*
+   * Make a deep copy of the given expression.
+   *
+   * All sub-expressions inside the given expressions are also cloned. Note
+   * that the variables are not deep-copied since they are immutable.
+   */
+  static ExprPtr clone(ExprPtr s);
+
+ protected:
+  std::shared_ptr<Expr> getptr() {
+    return shared_from_this();
+  }
+
+ private:
+  Dtype dtype_;
+  IRNodeType expr_type_;
+};
+
+// A CRTP pattern to accept visitors for children class,
+// and dispatch back to the children.
+template <class Op, class Base = Expr>
+class ExprNode : public Base {
+ public:
+  using ExprNodeBase = ExprNode<Op>;
+  void accept(IRVisitor* visitor) override {
+    visitor->visit(static_to<Op>(Base::getptr()));
+  }
+  ExprPtr accept_mutator(IRMutator* mutator) override;
+  // pass the constructor to the base class
+  using Base::Base;
+};
+
+// A wrapper object to the underlying ExprNode.
+// Also serves the primary way to build and operate on other expressions.
+class TORCH_API ExprHandle {
+ public:
+  ExprHandle() = default;
+  explicit ExprHandle(ExprPtr node) : base_expr_node_(std::move(node)) {}
+
+  ExprPtr node() {
+    return base_expr_node_;
+  }
+
+  ExprPtr node() const {
+    return base_expr_node_;
+  }
+
+  bool empty() const {
+    return base_expr_node_ == nullptr;
+  }
+
+#define IMM_EXPR_DECLARE(Type, Name) ExprHandle(Type v);
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_EXPR_DECLARE);
+#undef IMM_EXPR_DECLARE
+
+  template <class Op>
+  NodePtr<Op> AsNode() {
+    return to<Op>(this->node());
+  }
+
+  template <class Op>
+  NodePtr<Op> AsNode() const {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+    return const_cast<ExprHandle*>(this)->AsNode<Op>();
+  }
+
+  Dtype dtype() const {
+    return node()->dtype();
+  }
+
+  // Handling the math operators.
+  ExprHandle operator+(const ExprHandle& other) const;
+  ExprHandle operator-(const ExprHandle& other) const;
+  ExprHandle operator*(const ExprHandle& other) const;
+  ExprHandle operator/(const ExprHandle& other) const;
+  ExprHandle operator%(const ExprHandle& other) const;
+  ExprHandle operator==(const ExprHandle& other) const;
+  ExprHandle operator!=(const ExprHandle& other) const;
+  ExprHandle operator>(const ExprHandle& other) const;
+  ExprHandle operator>=(const ExprHandle& other) const;
+  ExprHandle operator<(const ExprHandle& other) const;
+  ExprHandle operator<=(const ExprHandle& other) const;
+  ExprHandle operator&(const ExprHandle& other) const;
+  ExprHandle operator|(const ExprHandle& other) const;
+  ExprHandle operator&&(const ExprHandle& other) const;
+  ExprHandle operator||(const ExprHandle& other) const;
+  ExprHandle operator^(const ExprHandle& other) const;
+  ExprHandle operator<<(const ExprHandle& other) const;
+  ExprHandle operator>>(const ExprHandle& other) const;
+
+ private:
+  ExprPtr base_expr_node_ = nullptr;
+};
+
+// The underlying representation node to a Var.
+// Currently, each Var object represents a unique variable, even though the
+// names might be the same. We should consider add a unique_name as well.
+class TORCH_API Var : public ExprNode<Var> {
+ public:
+  static ExprHandle make(const std::string& name_hint, Dtype dtype) {
+    return ExprHandle(alloc<Var>(name_hint, dtype));
+  }
+  static ExprHandle make(Dtype dtype) {
+    return ExprHandle(alloc<Var>("", dtype));
+  }
+
+  // TODO: unique_name
+  const std::string& name_hint() const {
+    return name_hint_;
+  }
+
+  void set_name_hint(const std::string& name) {
+    name_hint_ = name;
+  }
+
+  void set_name_hint(std::string&& name) {
+    name_hint_ = std::move(name);
+  }
+
+  Var(std::string name_hint, Dtype dtype)
+      : ExprNodeBase(dtype, kPrimitive), name_hint_(std::move(name_hint)) {}
+
+ private:
+  std::string name_hint_;
+};
+
+TORCH_API std::vector<ExprPtr> make_contiguous_strides(
+    const std::vector<ExprHandle>& dims);
+TORCH_API std::vector<ExprPtr> make_channels_last_strides(
+    const std::vector<ExprHandle>& dims);
+
+class TORCH_API Buf : public ExprNode<Buf> {
+ public:
+  static BufHandle make(const std::vector<ExprHandle>& dims, Dtype dtype);
+
+  static BufHandle make(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      const std::vector<ExprHandle>& strides,
+      Dtype dtype);
+
+  static BufHandle make(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      Dtype dtype,
+      c10::optional<ExprHandle> initializer = c10::nullopt,
+      c10::optional<std::vector<ExprHandle>> strides = c10::nullopt,
+      c10::optional<ExprHandle> qscale = c10::nullopt,
+      c10::optional<ExprHandle> qzero = c10::nullopt);
+
+  // TODO: unique_name
+  VarPtr base_handle() const {
+    return base_handle_;
+  }
+  void set_base_handle(VarPtr base_handle) {
+    base_handle_ = std::move(base_handle);
+  }
+
+  const std::string& name_hint() const {
+    return base_handle_->name_hint();
+  }
+  void set_name_hint(const std::string& name_hint) {
+    base_handle_->set_name_hint(name_hint);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Buf(const std::string& name_hint,
+      const std::vector<ExprPtr>& dims,
+      Dtype dtype,
+      ExprPtr initializer = nullptr,
+      c10::optional<std::vector<ExprPtr>> strides = c10::nullopt,
+      ExprPtr qscale = nullptr,
+      ExprPtr qzero = nullptr)
+      : Buf(alloc<Var>(name_hint, kHandle),
+            dims,
+            dtype,
+            std::move(initializer),
+            std::move(strides),
+            std::move(qscale),
+            std::move(qzero)) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Buf(VarPtr var,
+      std::vector<ExprPtr> dims,
+      Dtype dtype,
+      ExprPtr initializer = nullptr,
+      c10::optional<std::vector<ExprPtr>> strides = c10::nullopt,
+      ExprPtr qscale = nullptr,
+      ExprPtr qzero = nullptr);
+
+  size_t ndim() const {
+    return dims_.size();
+  }
+  ExprPtr dim(size_t index) const {
+    if (index >= ndim()) {
+      throw out_of_range_index();
+    }
+    return dims_[index];
+  }
+  std::vector<ExprPtr> dims() const {
+    return dims_;
+  }
+  void set_dims(std::vector<ExprPtr> dims) {
+    dims_ = std::move(dims);
+  }
+
+  std::vector<ExprPtr> strides() const {
+    return strides_;
+  }
+
+  void set_strides(std::vector<ExprPtr> strides) {
+    strides_ = std::move(strides);
+  }
+
+  ExprPtr initializer() const {
+    return initializer_;
+  };
+
+  ExprPtr qzero() const {
+    return qzero_;
+  }
+
+  ExprPtr qscale() const {
+    return qscale_;
+  }
+
+  void set_qzero(ExprPtr qzero) {
+    qzero_ = std::move(qzero);
+  }
+
+  void set_qscale(ExprPtr qscale) {
+    qscale_ = std::move(qscale);
+  }
+
+  bool hasConstantDims() const {
+    for (const auto& d : dims_) {
+      if (!d->isConstant()) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  bool is_contiguous(
+      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const;
+
+  // The channels-last 1d can benefit the performance of some operators like
+  // conv1d. But the MemoryFormat enum has not covered this layout yet. Hence,
+  // we abstract a dedicated function to check channels-last 1d contiguous.
+  //
+  // Channels-last 1d:
+  //   dims:              n   c    l
+  //   strides(nlc):    c*l   1    c
+  bool is_channels_last_1d_contiguous() const {
+    if (dims_.size() != 3) {
+      return false;
+    }
+    return is_stride_one(1) && is_cont_with(2, 1) && is_cont_with(0, 2);
+  }
+
+ private:
+  bool is_cont_with(int cur_dim, int adjacent_dim) const;
+  bool is_stride_one(int cur_dim) const;
+
+  VarPtr base_handle_;
+  std::vector<ExprPtr> dims_;
+  std::vector<ExprPtr> strides_;
+  ExprPtr initializer_;
+  // qscale_ and qzero_ are used only for quantized dtypes Bufs: kQUInt8, kQInt8
+  ExprPtr qscale_;
+  ExprPtr qzero_;
+};
+
+class TORCH_API BufHandle : public ExprHandle {
+ public:
+  BufHandle(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      Dtype dtype)
+      : ExprHandle(Buf::make(name_hint, dims, dtype)) {}
+
+  BufHandle(
+      const std::string& name_hint,
+      const std::vector<ExprHandle>& dims,
+      const std::vector<ExprHandle>& strides,
+      Dtype dtype)
+      : ExprHandle(Buf::make(name_hint, dims, strides, dtype)) {}
+
+  BufHandle(const std::vector<ExprHandle>& dims, Dtype dtype)
+      : ExprHandle(Buf::make("_", dims, dtype)) {}
+
+  explicit BufHandle(Dtype dtype) : ExprHandle(Buf::make("_", {}, dtype)) {}
+
+  explicit BufHandle(BufPtr node) : ExprHandle(std::move(node)) {}
+  BufPtr node() const {
+    return static_to<Buf>(ExprHandle::node());
+  }
+  BufPtr node() {
+    return static_to<Buf>(ExprHandle::node());
+  }
+
+  template <typename... Ts>
+  inline ExprHandle load(const Ts&... ts) const;
+
+  template <typename T>
+  inline ExprHandle load(const std::vector<T>& args) const;
+
+  inline ExprHandle load(const std::vector<ExprHandle>& args) const;
+
+  StorePtr store(const std::vector<ExprHandle>& args, const ExprHandle& val)
+      const;
+
+  bool operator==(const BufHandle& other) const {
+    return this->node() == other.node();
+  }
+  bool operator!=(const BufHandle& other) const {
+    return !(*this == other);
+  }
+
+  const std::string& name_hint() const {
+    return this->node()->name_hint();
+  }
+
+  bool empty() const {
+    return (this->node() == nullptr);
+  }
+
+  size_t ndim() const {
+    return node()->ndim();
+  }
+
+  std::vector<ExprHandle> dims() const;
+
+  ExprHandle dim(size_t index) const {
+    return ExprHandle(node()->dim(index));
+  }
+
+  bool is_contiguous(
+      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
+    return node()->is_contiguous(memory_format);
+  }
+
+  bool is_channels_last_1d_contiguous() const {
+    return node()->is_channels_last_1d_contiguous();
+  }
+};
+
+// An expression to construct the underlying variable node.
+// Note: do not store any info here, since it is often possible to slice this
+// object. For example: VarHandle x('x'); ExprHandle x2 = x;
+class TORCH_API VarHandle : public ExprHandle {
+ public:
+  // Creates an empty VarHandle whose base Var is set to nullptr.
+  VarHandle() : ExprHandle() {}
+
+  explicit VarHandle(Dtype dtype) : ExprHandle(Var::make(dtype)) {}
+
+  VarHandle(const std::string& name_hint, Dtype dtype)
+      : ExprHandle(Var::make(name_hint, dtype)) {}
+
+  explicit VarHandle(VarPtr node) : ExprHandle(std::move(node)) {}
+
+  VarPtr node() const {
+    return static_to<Var>(ExprHandle::node());
+  }
+  bool operator==(const VarHandle& other) const {
+    return this->node() == other.node();
+  }
+  bool operator!=(const VarHandle& other) const {
+    return !(*this == other);
+  }
+
+  const std::string& name_hint() const {
+    return this->node()->name_hint();
+  }
+  bool empty() const {
+    return (this->node() == nullptr);
+  }
+};
+
+template <class Op, class Base>
+ExprPtr ExprNode<Op, Base>::accept_mutator(IRMutator* mutator) {
+  return mutator->mutate(static_to<Op>(Base::getptr()));
+}
+
+inline bool same_node(const ExprHandle& expr1, const ExprHandle& expr2) {
+  return expr1.AsNode<Expr>() == expr2.AsNode<Expr>();
+}
+
+TORCH_API ExprHandle sin(const ExprHandle& v);
+TORCH_API ExprHandle cos(const ExprHandle& v);
+TORCH_API ExprHandle tan(const ExprHandle& v);
+TORCH_API ExprHandle asin(const ExprHandle& v);
+TORCH_API ExprHandle acos(const ExprHandle& v);
+TORCH_API ExprHandle atan(const ExprHandle& v);
+TORCH_API ExprHandle sinh(const ExprHandle& v);
+TORCH_API ExprHandle cosh(const ExprHandle& v);
+TORCH_API ExprHandle tanh(const ExprHandle& v);
+TORCH_API ExprHandle sigmoid(const ExprHandle& v);
+TORCH_API ExprHandle exp(const ExprHandle& v);
+TORCH_API ExprHandle expm1(const ExprHandle& v);
+TORCH_API ExprHandle abs(const ExprHandle& v);
+TORCH_API ExprHandle log(const ExprHandle& v);
+TORCH_API ExprHandle fast_tanh(const ExprHandle& v);
+TORCH_API ExprHandle fast_sigmoid(const ExprHandle& v);
+TORCH_API ExprHandle fast_log(const ExprHandle& v);
+TORCH_API ExprHandle log_vml(const ExprHandle& v);
+TORCH_API ExprHandle log2(const ExprHandle& v);
+TORCH_API ExprHandle log10(const ExprHandle& v);
+TORCH_API ExprHandle log1p(const ExprHandle& v);
+TORCH_API ExprHandle erf(const ExprHandle& v);
+TORCH_API ExprHandle erfc(const ExprHandle& v);
+TORCH_API ExprHandle sqrt(const ExprHandle& v);
+TORCH_API ExprHandle rsqrt(const ExprHandle& v);
+TORCH_API ExprHandle ceil(const ExprHandle& v);
+TORCH_API ExprHandle floor(const ExprHandle& v);
+TORCH_API ExprHandle round(const ExprHandle& v);
+TORCH_API ExprHandle trunc(const ExprHandle& v);
+TORCH_API ExprHandle frac(const ExprHandle& v);
+TORCH_API ExprHandle lgamma(const ExprHandle& v);
+TORCH_API ExprHandle atan2(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle pow(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle fmod(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle remainder(const ExprHandle& v1, const ExprHandle& v2);
+TORCH_API ExprHandle isnan(const ExprHandle& v1);
+TORCH_API ExprHandle Relu(const ExprHandle& v1);
+
+TORCH_API ExprHandle
+ifThenElse(const ExprHandle& c, const ExprHandle& t, const ExprHandle& f);
+
+TORCH_API ExprHandle expr_to_vec(ExprHandle v, int lanes);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions.h

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <ATen/Config.h>
+#include <ATen/Functions.h>
+#include <c10/macros/Macros.h>
+#include <torch/csrc/Export.h>
+#include <cstdint>
+#include <vector>
+
+#define FOR_ALL_EXTERNAL_FUNCTIONS(_)   \
+  _(nnc_aten_adaptive_avg_pool2d)       \
+  _(nnc_aten_addmm)                     \
+  _(nnc_aten_conv2d)                    \
+  _(nnc_aten_conv1d)                    \
+  _(nnc_aten_conv1d_out)                \
+  _(nnc_aten_dequantize)                \
+  _(nnc_aten_dequantize_out)            \
+  _(nnc_aten_embedding)                 \
+  _(nnc_aten_matmul)                    \
+  _(nnc_aten_mv)                        \
+  _(nnc_aten_mm)                        \
+  _(nnc_aten_mean)                      \
+  _(nnc_aten_max_red)                   \
+  _(nnc_aten_max_red_out)               \
+  _(nnc_aten_quantized_conv1d)          \
+  _(nnc_aten_quantized_conv1d_out)      \
+  _(nnc_aten_quantized_conv2d)          \
+  _(nnc_aten_quantized_conv2d_out)      \
+  _(nnc_aten_quantized_conv2d_relu)     \
+  _(nnc_aten_quantized_conv2d_relu_out) \
+  _(nnc_aten_quantized_linear)          \
+  _(nnc_aten_quantized_linear_out)      \
+  _(nnc_aten_quantized_linear_relu)     \
+  _(nnc_aten_quantized_add)             \
+  _(nnc_aten_quantized_cat)             \
+  _(nnc_aten_quantized_mul)             \
+  _(nnc_aten_quantized_mul_out)         \
+  _(nnc_aten_quantized_mul_scalar)      \
+  _(nnc_aten_quantized_mul_scalar_out)  \
+  _(nnc_aten_quantized_relu)            \
+  _(nnc_aten_quantized_sigmoid)         \
+  _(nnc_aten_quantized_sigmoid_out)     \
+  _(nnc_aten_quantize_per_tensor)       \
+  _(nnc_aten_quantize_per_tensor_out)   \
+  _(nnc_aten_triangular_solve)          \
+  _(nnc_aten_upsample_nearest2d)        \
+  _(nnc_aten_upsample_nearest2d_out)    \
+  _(nnc_prepacked_conv2d_clamp_run)     \
+  _(nnc_prepacked_linear_clamp_run)
+
+#define DECLARE_EXTERNAL_FUNCTION(NAME) \
+  TORCH_API void NAME(                  \
+      int64_t bufs_num,                 \
+      void** buf_data,                  \
+      int64_t* buf_ranks,               \
+      int64_t* buf_dims,                \
+      int64_t* buf_strides,             \
+      int8_t* buf_dtypes,               \
+      int64_t args_num,                 \
+      int64_t* extra_args);
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+struct QIData final {
+  double scale;
+  int64_t zero;
+  c10::ScalarType scalarType;
+};
+std::vector<at::Tensor> constructTensors(
+    int64_t bufs_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    c10::optional<std::vector<std::pair<size_t, QIData>>> qdataArg =
+        c10::nullopt);
+
+std::vector<at::Tensor> constructTensors2(
+    int64_t bufs_in_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    c10::optional<std::vector<std::pair<size_t, QIData>>> qdataArg =
+        c10::nullopt,
+    size_t bufs_out_num = 0);
+
+#ifdef C10_MOBILE
+extern "C" {
+#endif
+void DispatchParallel(
+    int8_t* func,
+    int64_t start,
+    int64_t stop,
+    int8_t* packed_data) noexcept;
+
+FOR_ALL_EXTERNAL_FUNCTIONS(DECLARE_EXTERNAL_FUNCTION)
+#if AT_MKLDNN_ENABLED()
+DECLARE_EXTERNAL_FUNCTION(nnc_mkldnn_prepacked_conv_run);
+#endif
+
+TORCH_API void nnc_aten_free(int64_t bufs_num, void** ptrs) noexcept;
+
+#ifdef C10_MOBILE
+} // extern "C"
+#endif
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+#undef DECLARE_EXTERNAL_FUNCTION

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_core.h

@@ -0,0 +1,29 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/csrc/Export.h>
+#include <cstdint>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+#ifdef C10_MOBILE
+extern "C" {
+#endif
+void DispatchParallel(
+    int8_t* func,
+    int64_t start,
+    int64_t stop,
+    int8_t* packed_data) noexcept;
+
+TORCH_API void nnc_aten_free(int64_t bufs_num, void** ptrs) noexcept;
+
+#ifdef C10_MOBILE
+} // extern "C"
+#endif
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_registry.h

@@ -0,0 +1,61 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <cstdint>
+#include <string>
+#include <unordered_map>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// The external functions that could be called from NNC must have the same
+// signature defined by `NNCExternalFunction`.
+//
+// Why this signature?
+// It was picked for two reasons: 1) it should be generic enough to represent
+// most of the ops we might want to call, 2) it should be possible to generate a
+// code for this call in LLVM codegen.
+// The first 5 parameters allow to pass any number of contiguous CPU tensors in
+// case we need to run aten ops (TODO: support different devices). The first
+// buffer in the array is assumed to be the output buffer. We couldn't use
+// `at::Tensor` (or `c10::IValue`) type there directly as it would mean that
+// we'd need to declare it in LLVM codegen in LLVM IR form, which would be very
+// cumbersome and hard to maintain. Note that the dimensions of all tensors are
+// concatenated into a single array buf_dims. We do not need to pass its length,
+// since it can be deduced from total number of buffers and their ranks.
+//
+// The last 2 arguments allow to pass any non-tensor arguments encoded as an
+// array of int64_t values. The way they are encoded is not specified and could
+// be arbitrary - whatever the most convenient for the specific bridge function
+// is.
+//
+// The bridge functions must not throw exceptions - properly propagating them
+// from the generated code is too cumbersome, and thus all calls to functions
+// that could throw must be wrapped with try-catch blocks.
+using NNCExternalFunction = void (*)(
+    int64_t bufs_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    int64_t args_num,
+    int64_t* extra_args);
+
+// Return a global map "function-name" -> "function-pointer" for all registered
+// in NNC external functions
+TORCH_API std::unordered_map<std::string, NNCExternalFunction>&
+getNNCFunctionRegistry();
+
+// To register a new external function in NNC one needs to create an instance of
+// this struct
+struct RegisterNNCExternalFunction {
+  RegisterNNCExternalFunction(const std::string& name, NNCExternalFunction fn) {
+    getNNCFunctionRegistry()[name] = fn;
+  }
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/fwd_decls.h

@@ -0,0 +1,129 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <memory>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename Node>
+using NodePtr = std::shared_ptr<Node>;
+
+template <typename To, typename From>
+NodePtr<To> to(NodePtr<From> x) {
+  return std::dynamic_pointer_cast<To>(x);
+}
+
+template <typename To, typename From>
+NodePtr<To> static_to(NodePtr<From> x) {
+  return std::static_pointer_cast<To>(x);
+}
+
+template <typename Node, typename... Args>
+NodePtr<Node> alloc(Args&&... args) {
+  return std::make_shared<Node>(std::forward<Args>(args)...);
+}
+
+class Buf;
+class Expr;
+class Stmt;
+class Var;
+
+using BufPtr = NodePtr<Buf>;
+using ExprPtr = NodePtr<Expr>;
+using StmtPtr = NodePtr<Stmt>;
+using VarPtr = NodePtr<Var>;
+
+class ExprHandle;
+class VarHandle;
+class BufHandle;
+
+class Add;
+class And;
+class BitCast;
+class Broadcast;
+class Cast;
+class CompareSelect;
+class Div;
+class IfThenElse;
+class Intrinsics;
+class Let;
+class Load;
+class Lshift;
+class Max;
+class MaxTerm;
+class Min;
+class MinTerm;
+class Mod;
+class Mul;
+class Or;
+class Polynomial;
+class Ramp;
+class ReduceOp;
+class RoundOff;
+class Rshift;
+class Store;
+class Sub;
+class Term;
+class Xor;
+using AddPtr = NodePtr<Add>;
+using AndPtr = NodePtr<And>;
+using BitCastPtr = NodePtr<BitCast>;
+using BroadcastPtr = NodePtr<Broadcast>;
+using CastPtr = NodePtr<Cast>;
+using CompareSelectPtr = NodePtr<CompareSelect>;
+using DivPtr = NodePtr<Div>;
+using IfThenElsePtr = NodePtr<IfThenElse>;
+using IntrinsicsPtr = NodePtr<Intrinsics>;
+using LetPtr = NodePtr<Let>;
+using LoadPtr = NodePtr<Load>;
+using LshiftPtr = NodePtr<Lshift>;
+using MaxPtr = NodePtr<Max>;
+using MaxTermPtr = NodePtr<MaxTerm>;
+using MinPtr = NodePtr<Min>;
+using MinTermPtr = NodePtr<MinTerm>;
+using ModPtr = NodePtr<Mod>;
+using MulPtr = NodePtr<Mul>;
+using OrPtr = NodePtr<Or>;
+using PolynomialPtr = NodePtr<Polynomial>;
+using RampPtr = NodePtr<Ramp>;
+using ReduceOpPtr = NodePtr<ReduceOp>;
+using RoundOffPtr = NodePtr<RoundOff>;
+using RshiftPtr = NodePtr<Rshift>;
+using StorePtr = NodePtr<Store>;
+using SubPtr = NodePtr<Sub>;
+using TermPtr = NodePtr<Term>;
+using XorPtr = NodePtr<Xor>;
+
+class Allocate;
+class AtomicAdd;
+class Block;
+class Cond;
+class ExternalCall;
+class ExternalCallWithAlloc;
+class For;
+class Free;
+class FreeExt;
+class PlacementAllocate;
+class SyncThreads;
+using AllocatePtr = NodePtr<Allocate>;
+using AtomicAddPtr = NodePtr<AtomicAdd>;
+using BlockPtr = NodePtr<Block>;
+using CondPtr = NodePtr<Cond>;
+using ExternalCallPtr = NodePtr<ExternalCall>;
+using ExternalCallWithAllocPtr = NodePtr<ExternalCallWithAlloc>;
+using ForPtr = NodePtr<For>;
+using FreePtr = NodePtr<Free>;
+using FreeExtPtr = NodePtr<FreeExt>;
+using PlacementAllocatePtr = NodePtr<PlacementAllocate>;
+using SyncThreadsPtr = NodePtr<SyncThreads>;
+
+#define IMM_DECLARE(Type, Name) \
+  class Name##Imm;              \
+  using Name##ImmPtr = NodePtr<Name##Imm>;
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/graph_opt.h

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Optimize aten::cat ops in the given subgraph.
+//
+// Moving users of cat to its inputs.
+//    Cat ops get lowered into multiple loops, one per input. When the result
+//    of cat is used by some other op, it results in a situation where inlining
+//    of cat does not happen. This in turn results in intermediate buffers
+//    being created for the result of cat, since it is not inlined.
+//
+//    For example, consider the following graph:
+//       graph(%x : Float(10, strides=[1], device=cpu),
+//             %y : Float(20, strides=[1], device=cpu)):
+//         %dim : int = prim::Constant[value=0]()
+//         %xy_list : Tensor[] = prim::ListConstruct(%x, %y)
+//         %cat : Float(60, strides=[1], device=cpu) = aten::cat(%xy_list, %dim)
+//         %5 : Float(60, strides=[1], device=cpu) = aten::log(%cat)
+//         return (%5))IR";
+//
+//     This will get lowered into:
+//         Allocate(aten_cat);
+//         for (...)
+//           aten_cat[...] = x[...]
+//         for (...)
+//           aten_cat[...] = y[...]
+//         for (...)
+//           aten_log[...] = log(aten_cat[...])
+//         Free(aten_cat);
+//     Note that aten_cat is not inlined into aten_log and it results in
+//     an intermediate buffer allocation as well.
+//
+//     Optimization:
+//        We move the ops that use the result of `cat` into its inputs whenever
+//     possible.
+//
+//     The graph above will be transformed to:
+//        graph(%x : Float(10, strides=[1], device=cpu),
+//              %y : Float(20, strides=[1], device=cpu)):
+//          %3 : int = prim::Constant[value=0]()
+//          %7 : Float(10, strides=[1], device=cpu) = aten::log(%x)
+//          %8 : Float(20, strides=[1], device=cpu) = aten::log(%y)
+//          %9 : Tensor[] = prim::ListConstruct(%7, %8)
+//          %10 : Float(60, strides=[1], device=cpu) = aten::cat(%9, %3)
+//          return (%10)
+//
+//     This will get lowered into:
+//         for (...)
+//           aten_cat[...] = log(x[...])
+//         for (...)
+//           aten_cat[...] = log(y[...])
+//     aten_cat is the output buffer here.
+
+bool OptimizeCat(const std::shared_ptr<Graph>& graph);
+
+TORCH_API void annotateInputShapes(
+    const std::shared_ptr<Graph>& graph,
+    const std::vector<c10::optional<at::Tensor>>& example_inputs);
+TORCH_API std::shared_ptr<Graph> removeUnusedSelfArgument(
+    const std::shared_ptr<Graph>& graph);
+TORCH_API std::shared_ptr<Graph> removeGraphOutput(
+    const std::shared_ptr<Graph>& graph,
+    size_t idx);
+TORCH_API std::shared_ptr<Graph> replaceListOutputWithTuple(
+    const std::shared_ptr<Graph>& graph);
+
+// Perform \p ITERS rounds of "trimming" for the given \p GRAPH.
+//
+// Trimming means that we try to remove a small portion of the graph while
+// keeping it valid. This is useful for debugging when we try to find a minimal
+// example reproducing the issue at hand. When ITERS is 0, the graph remains
+// unchanged, when ITERS is a big number, the graph usually becomes empty.
+TORCH_API std::shared_ptr<Graph> trimGraph(
+    const std::shared_ptr<Graph>& graph,
+    int64_t iters);
+
+// Scan all values in the given graph and replace each dimension with a size Xi
+// present in \p SIZES with a symbolic shape Yi. Return a vector of symbol
+// values [Y0, Y1, .., Yn].
+//
+// For example:
+// Input:
+// graph(%x : Float(10, 20, 30, 40)):
+//   %y : Float(10, 20, 30, 40) = aten::relu(%x)
+//   return %y
+//
+// If we run makeShapesSymbolic(graph, {20, 40}), then we'll get:
+//
+// graph(%x : Float(10, SS(-3), 30, SS(-5))):
+//   %y : Float(10, SS(-3), 30, SS(-5)) = aten::relu(%x)
+//   return %y
+//
+// and get {-3, -5} as the return value.
+TORCH_API std::vector<int64_t> makeShapesSymbolic(
+    std::shared_ptr<Graph>& graph,
+    const std::vector<int64_t>& sizes);
+
+// Inspect the graph and report whether it can be converted to TE IR.
+// TODO: add error reporting for graphs that can't be converted.
+TORCH_API bool isGraphCompilable(const std::shared_ptr<Graph>& graph);
+
+// Examine the graph and (hackily) fill in missing tensor type info, such as
+// scalar type, device, and strides. Ideally, this should be done by a proper
+// dtype/device/shape propagation passes, but until they are ready we can use
+// this, not always correct, workaround pass.
+TORCH_API void fixupMissingShapeInfo(const std::shared_ptr<Graph>& graph);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/half_support.h

@@ -0,0 +1,217 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Walk the Statement looking for Half size loads/stores.
+class HalfChecker : public IRVisitor {
+ public:
+  HalfChecker(const std::vector<CodeGen::BufferArg>& args) {
+    for (const auto& BA : args) {
+      hasHalf_ |= BA.dtype().scalar_type() == ScalarType::Half;
+    }
+  }
+
+  bool hasHalf() const {
+    return hasHalf_;
+  }
+
+  bool hasBFloat16() const {
+    return hasBFloat16_;
+  }
+
+  void visit(LoadPtr v) override {
+    hasHalf_ |= v->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+  void visit(StorePtr v) override {
+    hasHalf_ |= v->buf()->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->buf()->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+  void visit(HalfImmPtr v) override {
+    hasHalf_ = true;
+  }
+
+  void visit(BFloat16ImmPtr v) override {
+    hasBFloat16_ = true;
+  }
+
+  void visit(CastPtr v) override {
+    hasHalf_ |= v->dtype().scalar_type() == ScalarType::Half;
+    hasBFloat16_ |= v->dtype().scalar_type() == ScalarType::BFloat16;
+    IRVisitor::visit(v);
+  }
+
+ private:
+  bool hasHalf_{false};
+  bool hasBFloat16_{false};
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class HalfRewriter : public IRMutator {
+  ExprPtr mutate(LoadPtr v) override {
+    ExprPtr child = IRMutator::mutate(v);
+    if (!isHalf(child)) {
+      return child;
+    }
+
+    ExprPtr ret = alloc<Cast>(
+        child->dtype().cloneWithScalarType(ScalarType::Float), child);
+
+    inserted_half_casts_.insert(ret);
+    return ret;
+  }
+
+  StmtPtr mutate(StorePtr v) override {
+    // Since mutation changes the `value()` expression in-place, we need to
+    // get the dtype of the `value()` before that is mutated.
+    auto newType = v->value()->dtype();
+    ExprPtr new_val = v->value()->accept_mutator(this);
+    auto bufType = v->buf()->dtype();
+
+    if (isHalf(newType.scalar_type())) {
+      new_val = alloc<Cast>(newType, new_val);
+      inserted_half_casts_.insert(new_val);
+    }
+
+    // The scalar_type of value is not Half while the buf is Half
+    if (!isHalf(newType.scalar_type()) && isHalf(bufType.scalar_type())) {
+      new_val = alloc<Cast>(
+          newType.cloneWithScalarType(bufType.scalar_type()), new_val);
+      inserted_half_casts_.insert(new_val);
+    }
+
+    v->set_value(new_val);
+    return v;
+  }
+
+  ExprPtr mutate(HalfImmPtr v) override {
+    return alloc<Cast>(kFloat, v);
+  }
+
+  ExprPtr mutate(BFloat16ImmPtr v) override {
+    return alloc<Cast>(kFloat, v);
+  }
+
+  ExprPtr mutate(CastPtr v) override {
+    ExprPtr child = v->src_value()->accept_mutator(this);
+
+    // just don't allow half casts we didn't insert.
+    if (isHalf(v)) {
+      if (inserted_half_casts_.count(v) < 1) {
+        v->set_src_value(child);
+        v->set_dtype(v->dtype().cloneWithScalarType(c10::kFloat));
+        return v;
+      }
+    }
+
+    // Remove Half(Float()) and friends.
+    CastPtr cast_child = to<Cast>(child);
+    if (cast_child) {
+      auto cast_to_double = v->dtype().scalar_type() == ScalarType::Double;
+      auto from_half = isHalf(cast_child->src_value());
+      // Cannot simplify the double(float(half)) to double(half) as NNC does
+      // not support cast BF16 to double directly.
+      auto not_cast_half_to_doulbe = !(cast_to_double && from_half);
+      if (v->dtype().is_floating_point() &&
+          cast_child->dtype().is_floating_point() && not_cast_half_to_doulbe) {
+        return alloc<Cast>(v->dtype(), cast_child->src_value());
+      }
+    }
+
+    if (child == v->src_value()) {
+      return v;
+    }
+
+    return alloc<Cast>(v->dtype(), child);
+  }
+
+  StmtPtr mutate(LetPtr v) override {
+    if (isHalf(v->var()->dtype().scalar_type())) {
+      VarPtr load_new_var = alloc<Var>(v->var()->name_hint(), kFloat);
+      ExprPtr new_value = alloc<Cast>(
+          v->var()->dtype().cloneWithScalarType(ScalarType::Float),
+          v->value()->accept_mutator(this));
+      var_map[v->var()] = load_new_var;
+
+      return alloc<Let>(load_new_var, new_value);
+    }
+
+    return IRMutator::mutate(v);
+  }
+
+  ExprPtr mutate(VarPtr v) override {
+    auto it = var_map.find(v);
+    if (it != var_map.end()) {
+      return it->second;
+    }
+
+    return v;
+  }
+
+  template <typename T>
+  ExprPtr mutateArithmetic(T v) {
+    IRMutator::mutate(v);
+    if (isHalf(v)) {
+      v->set_dtype(v->dtype().cloneWithScalarType(c10::kFloat));
+    }
+    return v;
+  }
+
+  ExprPtr mutate(AddPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(SubPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MulPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(DivPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MaxPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(MinPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(CompareSelectPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(BroadcastPtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(IfThenElsePtr v) override {
+    return mutateArithmetic(v);
+  }
+  ExprPtr mutate(IntrinsicsPtr v) override {
+    return mutateArithmetic(v);
+  }
+
+ private:
+  static bool isHalf(ScalarType st) {
+    return st == ScalarType::Half || st == ScalarType::BFloat16;
+  }
+
+  static bool isHalf(ExprPtr v) {
+    return isHalf(v->dtype().scalar_type());
+  }
+
+  std::unordered_set<ExprPtr> inserted_half_casts_;
+  std::unordered_map<VarPtr, VarPtr> var_map;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/hash_provider.h

@@ -0,0 +1,304 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct TORCH_API SimplifierHashType {
+  SimplifierHashType() = default;
+  explicit SimplifierHashType(size_t s) : _h(s) {}
+
+  bool operator==(const SimplifierHashType& other) const;
+  bool operator!=(const SimplifierHashType& other) const;
+  bool operator<(const SimplifierHashType& other) const;
+  bool operator==(const size_t other) const;
+  bool operator!=(const size_t other) const;
+
+  size_t _h{0};
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+namespace std {
+template <>
+struct hash<torch::jit::tensorexpr::SimplifierHashType> {
+  size_t operator()(const torch::jit::tensorexpr::SimplifierHashType& k) const {
+    return k._h;
+  }
+};
+
+} // namespace std
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+#define CACHE_GUARD()  \
+  if (cachedHash(v)) { \
+    return;            \
+  }
+
+class Term;
+class Polynomial;
+
+/* Expression hasher providing comparable values representing sub-exprs.
+ * Uses memoization to avoid excessive recursion. */
+class TORCH_API HashProvider : public IRVisitor {
+ public:
+  template <class T>
+  SimplifierHashType hash(T e) {
+    // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+    e->accept(this);
+    return hashOf(e);
+  }
+
+  bool cachedHash(ExprPtr e) {
+    return exprToHash_.find(e) != exprToHash_.end();
+  }
+  bool cachedHash(StmtPtr s) {
+    return stmtToHash_.find(s) != stmtToHash_.end();
+  }
+
+  void clearCache() {
+    exprToHash_.clear();
+    stmtToHash_.clear();
+  }
+
+  void visit(AddPtr v) override;
+  void visit(SubPtr v) override;
+  void visit(MulPtr v) override;
+  void visit(DivPtr v) override;
+  void visit(ModPtr v) override;
+  void visit(RoundOffPtr v) override;
+  void visit(MaxPtr v) override;
+  void visit(MinPtr v) override;
+  void visit(AndPtr v) override;
+  void visit(OrPtr v) override;
+  void visit(XorPtr v) override;
+  void visit(LshiftPtr v) override;
+  void visit(RshiftPtr v) override;
+  void visit(CompareSelectPtr v) override;
+
+// NOLINTNEXTLINE
+#define IMM_VISIT(Type, Name)                    \
+  void visit(Name##ImmPtr v) override {          \
+    CACHE_GUARD();                               \
+    putHash(v, hash_combine(#Name, v->value())); \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_VISIT);
+#undef IMM_VISIT
+
+  void visit(CastPtr v) override;
+  void visit(VarPtr v) override;
+  void visit(RampPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(ForPtr v) override;
+  void visit(BroadcastPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(IntrinsicsPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(CondPtr v) override;
+  void visit(TermPtr v) override;
+  void visit(PolynomialPtr v) override;
+  void visit(MaxTermPtr v) override;
+  void visit(MinTermPtr v) override;
+
+  template <typename... Types>
+  SimplifierHashType hash_combine(const Types&... args) {
+    SimplifierHashType seed;
+    _hash_combine(seed, args...);
+    return seed;
+  }
+
+ private:
+  SimplifierHashType hashOf(ExprPtr e) {
+    auto it = exprToHash_.find(e);
+    if (it != exprToHash_.end()) {
+      return it->second;
+    }
+
+    // As a failsafe fall back to IRPrinter.
+    std::stringstream ss;
+    IRPrinter printer(ss);
+    e->accept(&printer);
+    SimplifierHashType hash = SimplifierHashType(te_hash(ss.str()));
+    putHash(std::move(e), hash);
+
+    return hash;
+  }
+
+  SimplifierHashType hashOf(StmtPtr s) {
+    auto it = stmtToHash_.find(s);
+    if (it != stmtToHash_.end()) {
+      return it->second;
+    }
+
+    // As a failsafe fall back to IRPrinter.
+    std::stringstream ss;
+    IRPrinter printer(ss);
+    s->accept(&printer);
+    SimplifierHashType hash = SimplifierHashType(te_hash(ss.str()));
+    putHash(std::move(s), hash);
+
+    return hash;
+  }
+
+  // Hash funcs for various types, numbers are random.
+  template <typename T>
+  void _hash_combine(SimplifierHashType& seed, const T& val) {
+    seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, const char* val) {
+    seed._h ^= te_hash(val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  // at:::Half doesn't have a prime_number_hash, so cast to short.
+  void _hash_combine(SimplifierHashType& seed, const at::Half& val) {
+    seed._h ^=
+        te_hash((uint16_t)val) + 0x1f752c19 + (seed._h << 7) + (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, const Dtype& val) {
+    seed._h ^= te_hash(val.ToCppString()) + 0x1f752c19 + (seed._h << 7) +
+        (seed._h >> 4);
+  }
+
+  void _hash_combine(SimplifierHashType& seed, ExprPtr e) {
+    _hash_combine(seed, hash(std::move(e)));
+  }
+
+  template <typename T, typename... Types>
+  void _hash_combine(
+      SimplifierHashType& seed,
+      const T& val,
+      const Types&... args) {
+    _hash_combine(seed, val);
+    _hash_combine(seed, args...);
+  }
+
+  void putHash(ExprPtr e, SimplifierHashType h) {
+    auto res = exprToHash_.emplace(e, h);
+    if (res.second == false) {
+      // This is always a logic bug since we should check the cache first.
+      throw std::runtime_error("hash collision");
+    }
+  }
+  void putHash(StmtPtr s, SimplifierHashType h) {
+    auto res = stmtToHash_.emplace(s, h);
+    if (res.second == false) {
+      // This is always a logic bug since we should check the cache first.
+      throw std::runtime_error("hash collision");
+    }
+  }
+
+  std::unordered_map<ExprPtr, SimplifierHashType> exprToHash_;
+  std::unordered_map<StmtPtr, SimplifierHashType> stmtToHash_;
+  UniqueNameManager name_manager_;
+
+  size_t te_hash(SimplifierHashType val) {
+    return val._h;
+  }
+
+  size_t te_hash(int64_t val) {
+    // put the thing down.
+    size_t h = val ^ 0x647AA4D20C0B;
+    // bit flip it.
+    size_t h2 = ~h;
+    // and reverse byte order.
+    size_t h3 = 0;
+    for (unsigned int i = 0; i < 64; i += 8) {
+      h3 |= ((h2 >> i) & 0xFF) << (64 - i - 8);
+    }
+    return h3;
+  }
+
+  size_t te_hash(int32_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(uint32_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(uint64_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(int16_t val) {
+    int64_t v2 = val;
+    return te_hash(v2);
+  }
+
+  size_t te_hash(std::string val) {
+    size_t hash{0};
+    int64_t intval{0};
+    int64_t s = val.size() - 1;
+    while (s >= 0) {
+      for (unsigned int i = 0; i < 8; ++i) {
+        if (s < 0)
+          break;
+        // NOLINTNEXTLINE(bugprone-signed-char-misuse)
+        int64_t c = val.data()[s];
+        intval |= (c << (i * 8));
+
+        s--;
+      }
+      hash ^= te_hash(intval);
+      intval = 0;
+    }
+
+    return hash;
+  }
+
+  size_t te_hash(double d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int64_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(float d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int32_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(at::Half d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int16_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+
+  size_t te_hash(at::BFloat16 d) {
+    // memcpy as type punning. Should be optimized out.
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int16_t n;
+    std::memcpy(&n, &d, sizeof d);
+    return te_hash(n);
+  }
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/intrinsic_symbols.h

@@ -0,0 +1,22 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <c10/util/ArrayRef.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct SymbolAddress {
+  const char* symbol;
+  void* address;
+
+  SymbolAddress(const char* sym, void* addr) : symbol(sym), address(addr) {}
+};
+
+c10::ArrayRef<SymbolAddress> getIntrinsicSymbols();
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+#endif // TORCH_ENABLE_LLVM

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir.h

@@ -0,0 +1,934 @@
+#pragma once
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+
+#include <ATen/core/ivalue.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+enum CompareSelectOperation {
+  kEQ = 0,
+  kGT,
+  kGE,
+  kLT,
+  kLE,
+  kNE,
+};
+
+enum CompareSelectBias {
+  kUnbiased,
+  kLikely,
+  kUnlikely,
+};
+
+inline int getPrecedence(IRNodeType ty) {
+  // Match C++ operator precedence rules, since some pretty-print expressions to
+  // C++. SEE: https://en.cppreference.com/w/cpp/language/operator_precedence
+  switch (ty) {
+    case kPrimitive:
+      return 0;
+    case kCast:
+    case kBitCast:
+      return 2;
+    case kAdd:
+    case kSub:
+      return 6;
+    case kMul:
+    case kDiv:
+    case kMod:
+      return 5;
+    case kMax:
+    case kMin:
+      return 99;
+    case kAnd:
+      return 11;
+    case kOr:
+      return 13;
+    case kLshift:
+    case kRshift:
+      return 7;
+    case kXor:
+      return 12;
+    case kCompareSelect:
+      return 16;
+    default:
+      return 99;
+  }
+}
+
+class TORCH_API Cast : public ExprNode<Cast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<Cast>(dtype, src_value.node()));
+  }
+  Cast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kCast), src_value_(std::move(src_value)) {}
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle cast(const ExprHandle& src_value) {
+  return Cast::make(Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// This is a bitwise cast, akin to bitcast in LLVM
+class TORCH_API BitCast : public ExprNode<BitCast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<BitCast>(dtype, src_value.node()));
+  }
+  BitCast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kBitCast), src_value_(std::move(src_value)) {
+    TORCH_CHECK(src_value_->dtype().byte_size() == dtype.byte_size());
+  }
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle bitcast(const ExprHandle& src_value) {
+  return BitCast::make(
+      Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// Represent the expression node for binary operators.
+// A CRTP pattern to share common code among the operators.
+template <typename Op>
+class BinaryOpNode : public ExprNode<Op> {
+ public:
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    return ExprHandle(alloc<Op>(lhs.node(), rhs.node()));
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  BinaryOpNode(
+      ExprPtr lhs_v,
+      ExprPtr rhs_v,
+      IRNodeType expr_type,
+      ScalarType ret_type = ScalarType::Undefined)
+      : ExprNode<Op>(
+            // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+            BinaryOpDtype(lhs_v->dtype(), rhs_v->dtype(), ret_type),
+            expr_type),
+        lhs_(CastIfNeeded(std::move(lhs_v), ExprNode<Op>::dtype())),
+        rhs_(CastIfNeeded(std::move(rhs_v), ExprNode<Op>::dtype())) {}
+
+ private:
+  static ExprPtr CastIfNeeded(ExprPtr expr, Dtype dst_dtype) {
+    if (expr->dtype() == dst_dtype) {
+      return expr;
+    }
+    return Cast::make(dst_dtype, ExprHandle(std::move(expr))).node();
+  }
+
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+};
+
+namespace detail {
+template <typename T>
+void bin_op_deducer(BinaryOpNode<T>);
+bool bin_op_deducer(...);
+} // namespace detail
+
+class TORCH_API Add : public BinaryOpNode<Add> {
+ public:
+  Add(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAdd) {}
+};
+
+class TORCH_API Sub : public BinaryOpNode<Sub> {
+ public:
+  Sub(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kSub) {}
+};
+
+class TORCH_API Mul : public BinaryOpNode<Mul> {
+ public:
+  Mul(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMul) {}
+};
+
+class TORCH_API Div : public BinaryOpNode<Div> {
+ public:
+  Div(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kDiv) {}
+};
+
+class TORCH_API Mod : public BinaryOpNode<Mod> {
+ public:
+  Mod(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMod) {}
+};
+
+template <typename Op>
+class BitwiseOpNode : public BinaryOpNode<Op> {
+ public:
+  BitwiseOpNode(ExprPtr lhs, ExprPtr rhs, IRNodeType type)
+      : BinaryOpNode<Op>(std::move(lhs), std::move(rhs), type) {}
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    if (!lhs.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("lhs/rhs dtype mismatch");
+    }
+    return BinaryOpNode<Op>::make(lhs, rhs);
+  }
+};
+
+class TORCH_API And : public BitwiseOpNode<And> {
+ public:
+  And(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAnd) {}
+};
+
+class TORCH_API Or : public BitwiseOpNode<Or> {
+ public:
+  Or(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kOr) {}
+};
+
+class TORCH_API Xor : public BitwiseOpNode<Xor> {
+ public:
+  Xor(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kXor) {}
+};
+
+class TORCH_API Lshift : public BitwiseOpNode<Lshift> {
+ public:
+  Lshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kLshift) {}
+};
+
+class TORCH_API Rshift : public BitwiseOpNode<Rshift> {
+ public:
+  Rshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kRshift) {}
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Max : public BinaryOpNode<Max> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Max(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMax),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Max>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Min : public BinaryOpNode<Min> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Min(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMin),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Min>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// Encode typed immediate values e.g. IntImm, FloatImm.
+#define IMM_DECLARE(Type, Name)                               \
+  class TORCH_API Name##Imm : public ExprNode<Name##Imm> {    \
+   public:                                                    \
+    Name##Imm(Type value)                                     \
+        : ExprNodeBase(k##Name, kPrimitive), value_(value) {} \
+    bool isConstant() const override {                        \
+      return true;                                            \
+    }                                                         \
+    Type value() const {                                      \
+      return value_;                                          \
+    }                                                         \
+    static ExprHandle make(Type value) {                      \
+      return ExprHandle(alloc<Name##Imm>(value));             \
+    }                                                         \
+                                                              \
+   private:                                                   \
+    Type value_;                                              \
+  };
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+// Get immediate by ScalarType.
+template <typename T>
+ExprPtr getImmediateByType(ScalarType immType, T initialVal) {
+  switch (immType) {
+#define TYPE_CASE(Type, Name) \
+  case ScalarType::Name:      \
+    return alloc<Name##Imm>(Type(initialVal));
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    default:
+      throw unsupported_dtype();
+  }
+  return nullptr;
+}
+
+template <typename T>
+ExprPtr getImmediateByType(Dtype dtype, T initialVal) {
+  return getImmediateByType<T>(dtype.scalar_type(), initialVal);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprPtr& e, T v) {
+  return getImmediateByType<T>(e->dtype(), v);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprHandle& e, T v) {
+  return immLike(e.node(), v);
+}
+
+inline c10::optional<int64_t> intValue(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)      \
+  if (auto v = to<Name##Imm>(e)) { \
+    return v->value();             \
+  }
+  AT_FORALL_INT_TYPES(TYPE_CASE);
+#undef TYPE_CASE
+  return c10::nullopt;
+}
+
+inline c10::optional<int64_t> intValue(const ExprHandle& e) {
+  return intValue(e.node());
+}
+
+template <typename T>
+T immediateAs(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value();                     \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return 0;
+}
+
+template <typename T>
+T immediateAs(const ExprHandle& e) {
+  return immediateAs<T>(e.node());
+}
+
+template <typename T>
+bool immediateEquals(const ExprPtr& e, T val) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value() == val;              \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return false;
+}
+
+TORCH_API bool immediateIsNegative(const ExprPtr& e);
+
+TORCH_API bool immediateIsPositive(const ExprPtr& e);
+
+TORCH_API bool immediateIsZero(const ExprPtr& e);
+
+// Represents a ramp vector node:
+//     [base, base + 1 * stride, ... , base + (lanes - 1) * stride]
+class TORCH_API Ramp : public ExprNode<Ramp> {
+ public:
+  ExprPtr base() const {
+    return base_;
+  }
+  ExprPtr stride() const {
+    return stride_;
+  }
+
+  void set_base(ExprPtr base) {
+    base_ = std::move(base);
+  }
+
+  void set_stride(ExprPtr stride) {
+    stride_ = std::move(stride);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& base,
+      const ExprHandle& stride,
+      int lanes) {
+    if (stride.dtype() != base.dtype()) {
+      throw malformed_input("Bad stride in Ramp");
+    }
+    return ExprHandle(alloc<Ramp>(base.node(), stride.node(), lanes));
+  }
+  int lanes() const {
+    return lanes_;
+  }
+
+  Ramp(ExprPtr base, ExprPtr stride, int lanes)
+      : ExprNodeBase(Dtype(base->dtype(), lanes)),
+        base_(std::move(base)),
+        stride_(std::move(stride)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr base_;
+  ExprPtr stride_;
+  int lanes_;
+};
+
+class TORCH_API Load : public ExprNode<Load> {
+ public:
+  VarPtr base_handle() const {
+    return buf_->base_handle();
+  }
+  std::vector<ExprPtr> indices() const {
+    return indices_;
+  }
+  ExprPtr flat_index() const {
+    TORCH_CHECK(indices_.size() == 1, "Indices haven't been flattened.");
+    return indices_[0];
+  }
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  void set_buf(BufPtr buf) {
+    buf_ = std::move(buf);
+  }
+
+  void set_indices(std::vector<ExprPtr> indices) {
+    indices_ = std::move(indices);
+  }
+
+  static ExprHandle make(
+      Dtype dtype,
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+  static ExprHandle make(
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+
+  Load(Dtype dtype, BufPtr base_handle, std::vector<ExprPtr> indices);
+  Load(BufPtr base_handle, const std::vector<ExprPtr>& indices);
+
+ private:
+  BufPtr buf_;
+  std::vector<ExprPtr> indices_;
+};
+
+class TORCH_API Broadcast : public ExprNode<Broadcast> {
+ public:
+  ExprPtr value() const {
+    return value_;
+  }
+
+  void set_value(ExprPtr value) {
+    value_ = std::move(value);
+  }
+
+  int lanes() const {
+    return lanes_;
+  }
+  static ExprHandle make(const ExprHandle& value, int lanes) {
+    return ExprHandle(alloc<Broadcast>(value.node(), lanes));
+  }
+  Broadcast(ExprPtr value, int lanes)
+      : ExprNodeBase(Dtype(value->dtype(), lanes)),
+        value_(std::move(value)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr value_;
+  int lanes_;
+};
+
+class TORCH_API IfThenElse : public ExprNode<IfThenElse> {
+ public:
+  ExprPtr condition() const {
+    return condition_;
+  }
+
+  // Lazily evaluated only if condition is true
+  ExprPtr true_value() const {
+    return true_;
+  }
+
+  // Lazily evaluated only if condition is false
+  ExprPtr false_value() const {
+    return false_;
+  }
+
+  void set_condition(ExprPtr condition) {
+    condition_ = std::move(condition);
+  }
+
+  void set_true_value(ExprPtr true_value) {
+    true_ = std::move(true_value);
+  }
+
+  void set_false_value(ExprPtr false_value) {
+    false_ = std::move(false_value);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& c,
+      const ExprHandle& t,
+      const ExprHandle& f) {
+    if (!c.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (c.dtype().lanes() != 1) {
+      throw unsupported_dtype();
+    }
+    if (t.dtype() != f.dtype()) {
+      throw malformed_input("Bad dtype in IfThenElse");
+    }
+    return ExprHandle(alloc<IfThenElse>(c.node(), t.node(), f.node()));
+  }
+
+  IfThenElse(ExprPtr c, ExprPtr t, ExprPtr f)
+      : ExprNodeBase(t->dtype()),
+        condition_(std::move(c)),
+        true_(std::move(t)),
+        false_(std::move(f)) {}
+
+ private:
+  ExprPtr condition_;
+  ExprPtr true_;
+  ExprPtr false_;
+};
+
+class TORCH_API CompareSelect : public ExprNode<CompareSelect> {
+ public:
+  CompareSelectOperation compare_select_op() const {
+    return compare_op_;
+  }
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+  ExprPtr ret_val1() const {
+    return this->ret_val1_;
+  }
+  ExprPtr ret_val2() const {
+    return this->ret_val2_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  void set_ret_val1(ExprPtr ret_val1) {
+    ret_val1_ = std::move(ret_val1);
+  }
+
+  void set_ret_val2(ExprPtr ret_val2) {
+    ret_val2_ = std::move(ret_val2);
+  }
+
+  CompareSelectBias bias() const {
+    return bias_;
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        IntImm::make(1).node(),
+        IntImm::make(0).node(),
+        cmp_op,
+        bias));
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      const ExprHandle& ret_val1,
+      const ExprHandle& ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype() || ret_val1.dtype() != ret_val2.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        ret_val1.node(),
+        ret_val2.node(),
+        cmp_op,
+        bias));
+  }
+
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      ExprPtr ret_val1,
+      ExprPtr ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(ret_val1->dtype()),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(std::move(ret_val1)),
+        ret_val2_(std::move(ret_val2)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(kInt),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(alloc<IntImm>(1)),
+        ret_val2_(alloc<IntImm>(0)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+ private:
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+  ExprPtr ret_val1_;
+  ExprPtr ret_val2_;
+  CompareSelectOperation compare_op_;
+  CompareSelectBias bias_;
+};
+
+enum IntrinsicsOp {
+  kSin,
+  kCos,
+  kTan,
+  kAsin,
+  kAcos,
+  kAtan,
+  kAtan2,
+  kSinh,
+  kCosh,
+  kTanh,
+  kSigmoid,
+  kExp,
+  kExpm1,
+  kAbs,
+  kLog,
+  kLog2,
+  kLog10,
+  kLog1p,
+  kErf,
+  kErfc,
+  kSqrt,
+  kRsqrt,
+  kPow,
+  kCeil,
+  kFloor,
+  kRound,
+  kTrunc,
+  kFmod,
+  kRemainder,
+  kLgamma,
+  kFrac,
+  kIsNan,
+  kRand, // We need more discussions on this. Should we consider stateful?
+  kMaxIntrinsicsOp,
+};
+
+class TORCH_API Intrinsics : public ExprNode<Intrinsics> {
+ public:
+  static ExprHandle make(IntrinsicsOp op_type, const ExprHandle& v1) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const ExprHandle& v1,
+      const ExprHandle& v2) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node(), v2.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const std::vector<ExprHandle>& params) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprPtr> params_nodes(params.size());
+    for (size_t i = 0; i < params.size(); i++) {
+      params_nodes[i] = params[i].node();
+    }
+    return ExprHandle(alloc<Intrinsics>(op_type, params_nodes));
+  }
+
+  static ExprHandle make(IntrinsicsOp op_type, Dtype dtype) {
+    return ExprHandle(alloc<Intrinsics>(op_type, dtype));
+  }
+
+  IntrinsicsOp op_type() const {
+    return op_type_;
+  }
+
+  std::string func_name() const {
+    switch (op_type()) {
+      case kSin:
+        return "sin";
+      case kCos:
+        return "cos";
+      case kTan:
+        return "tan";
+      case kAsin:
+        return "asin";
+      case kAcos:
+        return "acos";
+      case kAtan:
+        return "atan";
+      case kAtan2:
+        return "atan2";
+      case kSinh:
+        return "sinh";
+      case kCosh:
+        return "cosh";
+      case kTanh:
+        return "tanh";
+      case kSigmoid:
+        return "sigmoid";
+      case kExp:
+        return "exp";
+      case kAbs:
+        return "abs";
+      case kLog:
+        return "log";
+      case kLog2:
+        return "log2";
+      case kLog10:
+        return "log10";
+      case kLog1p:
+        return "log1p";
+      case kErf:
+        return "erf";
+      case kSqrt:
+        return "sqrt";
+      case kRsqrt:
+        return "rsqrt";
+      case kPow:
+        return "pow";
+      case kCeil:
+        return "ceil";
+      case kFloor:
+        return "floor";
+      case kRound:
+        return "round";
+      case kTrunc:
+        return "trunc";
+      case kRand:
+        return "rand";
+      case kFmod:
+        return "fmod";
+      case kRemainder:
+        return "remainder";
+      case kLgamma:
+        return "lgamma";
+      case kExpm1:
+        return "expm1";
+      case kErfc:
+        return "erfc";
+      case kFrac:
+        return "frac";
+      case kIsNan:
+        return "isnan";
+      default:
+        throw std::runtime_error(
+            "invalid op_type: " + c10::to_string(op_type()));
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, Dtype dtype)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_({}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 0) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype())),
+        params_({std::move(v1)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 1) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1, ExprPtr v2)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype(), v2->dtype())),
+        params_({std::move(v1), std::move(v2)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 2) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, params)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(
+      IntrinsicsOp op_type,
+      Dtype dtype,
+      const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  bool isPure() const {
+    return op_type_ != kRand;
+  }
+
+  int nparams() const {
+    return params_.size();
+  }
+
+  ExprPtr param(int index) const {
+    return params_[index];
+  }
+  const std::vector<ExprPtr>& params() const {
+    return params_;
+  }
+
+  void set_params(std::vector<ExprPtr> params) {
+    params_ = std::move(params);
+  }
+
+  static int OpArgCount(IntrinsicsOp op_type);
+
+ private:
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1);
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1, Dtype dt2);
+  static Dtype IntrinsicsDtype(
+      IntrinsicsOp op_type,
+      const std::vector<ExprPtr>& params);
+
+  std::vector<ExprPtr> params_;
+  IntrinsicsOp op_type_;
+};
+
+TORCH_API std::vector<ExprPtr> ExprHandleVectorToExprVector(
+    const std::vector<ExprHandle>&);
+TORCH_API std::vector<ExprHandle> ExprVectorToExprHandleVector(
+    const std::vector<ExprPtr>&);
+TORCH_API std::vector<VarPtr> VarHandleVectorToVarVector(
+    const std::vector<VarHandle>&);
+TORCH_API std::vector<VarHandle> VarVectorToVarHandleVector(
+    const std::vector<VarPtr>&);
+TORCH_API ExprPtr flatten_index(
+    const std::vector<ExprPtr>& dims,
+    const std::vector<ExprPtr>& indices,
+    const std::vector<ExprPtr>& strides);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_cloner.h

@@ -0,0 +1,65 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <vector>
+
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRCloner : public IRMutator {
+ public:
+  ~IRCloner() override = default;
+  ExprPtr mutate(AddPtr v) override;
+  ExprPtr mutate(SubPtr v) override;
+  ExprPtr mutate(MulPtr v) override;
+  ExprPtr mutate(DivPtr v) override;
+  ExprPtr mutate(ModPtr v) override;
+  ExprPtr mutate(MaxPtr v) override;
+  ExprPtr mutate(MinPtr v) override;
+  ExprPtr mutate(AndPtr v) override;
+  ExprPtr mutate(OrPtr v) override;
+  ExprPtr mutate(XorPtr v) override;
+  ExprPtr mutate(LshiftPtr v) override;
+  ExprPtr mutate(RshiftPtr v) override;
+  ExprPtr mutate(CompareSelectPtr v) override;
+#define IMM_MUTATE_DECLARE(Type, Name) ExprPtr mutate(Name##ImmPtr v) override;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_MUTATE_DECLARE);
+#undef IMM_MUTATE_DECLARE
+  ExprPtr mutate(CastPtr v) override;
+  ExprPtr mutate(BitCastPtr v) override;
+  ExprPtr mutate(VarPtr v) override;
+  ExprPtr mutate(BufPtr v) override;
+  ExprPtr mutate(RampPtr v) override;
+  ExprPtr mutate(LoadPtr v) override;
+  ExprPtr mutate(BroadcastPtr v) override;
+  ExprPtr mutate(IfThenElsePtr v) override;
+  ExprPtr mutate(IntrinsicsPtr v) override;
+
+  ExprPtr mutate(TermPtr v) override;
+  ExprPtr mutate(PolynomialPtr v) override;
+  ExprPtr mutate(RoundOffPtr v) override;
+  ExprPtr mutate(MaxTermPtr v) override;
+  ExprPtr mutate(MinTermPtr v) override;
+
+  ExprPtr mutate(ReduceOpPtr v) override;
+
+  StmtPtr mutate(ForPtr v) override;
+  StmtPtr mutate(BlockPtr v) override;
+  StmtPtr mutate(StorePtr v) override;
+  StmtPtr mutate(AtomicAddPtr v) override;
+  StmtPtr mutate(SyncThreadsPtr v) override;
+  StmtPtr mutate(ExternalCallPtr v) override;
+  StmtPtr mutate(ExternalCallWithAllocPtr v) override;
+
+  StmtPtr mutate(AllocatePtr v) override;
+  StmtPtr mutate(FreePtr v) override;
+  StmtPtr mutate(LetPtr v) override;
+  StmtPtr mutate(CondPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_mutator.h

@@ -0,0 +1,66 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRMutator {
+ public:
+  virtual ~IRMutator() = default;
+  virtual ExprPtr mutate(AddPtr v);
+  virtual ExprPtr mutate(SubPtr v);
+  virtual ExprPtr mutate(MulPtr v);
+  virtual ExprPtr mutate(DivPtr v);
+  virtual ExprPtr mutate(ModPtr v);
+  virtual ExprPtr mutate(MaxPtr v);
+  virtual ExprPtr mutate(MinPtr v);
+  virtual ExprPtr mutate(AndPtr v);
+  virtual ExprPtr mutate(OrPtr v);
+  virtual ExprPtr mutate(XorPtr v);
+  virtual ExprPtr mutate(LshiftPtr v);
+  virtual ExprPtr mutate(RshiftPtr v);
+  virtual ExprPtr mutate(CompareSelectPtr v);
+#define IMM_MUTATE_DECLARE(Type, Name) virtual ExprPtr mutate(Name##ImmPtr v);
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_MUTATE_DECLARE);
+#undef IMM_MUTATE_DECLARE
+  virtual ExprPtr mutate(CastPtr v);
+  virtual ExprPtr mutate(BitCastPtr v);
+  virtual ExprPtr mutate(VarPtr v);
+  virtual ExprPtr mutate(BufPtr v);
+  virtual ExprPtr mutate(RampPtr v);
+  virtual ExprPtr mutate(LoadPtr v);
+  virtual ExprPtr mutate(BroadcastPtr v);
+  virtual ExprPtr mutate(IfThenElsePtr v);
+  virtual ExprPtr mutate(IntrinsicsPtr v);
+
+  virtual ExprPtr mutate(TermPtr v);
+  virtual ExprPtr mutate(PolynomialPtr v);
+  virtual ExprPtr mutate(RoundOffPtr v);
+  virtual ExprPtr mutate(MaxTermPtr v);
+  virtual ExprPtr mutate(MinTermPtr v);
+
+  virtual ExprPtr mutate(ReduceOpPtr v);
+
+  virtual StmtPtr mutate(ForPtr v);
+  virtual StmtPtr mutate(BlockPtr v);
+  virtual StmtPtr mutate(StorePtr v);
+  virtual StmtPtr mutate(AtomicAddPtr v);
+  virtual StmtPtr mutate(SyncThreadsPtr v);
+  virtual StmtPtr mutate(ExternalCallPtr v);
+  virtual StmtPtr mutate(ExternalCallWithAllocPtr v);
+
+  virtual StmtPtr mutate(AllocatePtr v);
+  virtual StmtPtr mutate(FreePtr v);
+  virtual StmtPtr mutate(FreeExtPtr v);
+  virtual StmtPtr mutate(PlacementAllocatePtr v);
+  virtual StmtPtr mutate(LetPtr v);
+  virtual StmtPtr mutate(CondPtr v);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_printer.h

@@ -0,0 +1,130 @@
+#pragma once
+
+#include <ostream>
+
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Tensor;
+
+class TORCH_API IRPrinter : public IRVisitor {
+ public:
+  explicit IRPrinter(std::ostream& os) : printer_os_(this, os) {}
+
+  void print(ExprHandle);
+  void print(Expr&);
+  void print(Stmt&);
+  void visit(AddPtr v) override;
+  void visit(SubPtr v) override;
+  void visit(MulPtr v) override;
+  void visit(DivPtr v) override;
+  void visit(ModPtr v) override;
+  void visit(MaxPtr v) override;
+  void visit(MinPtr v) override;
+  void visit(AndPtr v) override;
+  void visit(OrPtr v) override;
+  void visit(XorPtr v) override;
+  void visit(LshiftPtr v) override;
+  void visit(RshiftPtr v) override;
+  void visit(CompareSelectPtr v) override;
+#define IMM_PRINT_VISIT(Type, Name) void visit(Name##ImmPtr v) override;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_PRINT_VISIT);
+#undef IMM_PRINT_VISIT
+  void visit(CastPtr v) override;
+  void visit(BitCastPtr v) override;
+  void visit(VarPtr v) override;
+  void visit(BufPtr v) override;
+  void visit(RampPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(BroadcastPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(IntrinsicsPtr v) override;
+  void visit(TermPtr v) override;
+  void visit(PolynomialPtr v) override;
+  void visit(RoundOffPtr v) override;
+  void visit(MaxTermPtr v) override;
+  void visit(MinTermPtr v) override;
+  void visit(ReduceOpPtr v) override;
+
+  void visit(AtomicAddPtr v) override;
+  void visit(SyncThreadsPtr v) override;
+  void visit(ExternalCallPtr v) override;
+  void visit(ExternalCallWithAllocPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(ForPtr v) override;
+  void visit(CondPtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AllocatePtr v) override;
+  void visit(FreePtr v) override;
+  void visit(FreeExtPtr v) override;
+  void visit(PlacementAllocatePtr v) override;
+  void visit(LetPtr v) override;
+
+  // A child class may have a difference rule for generating dtype
+  // string, e.g. CUDA needs int64_t to be generated as long long.
+  virtual std::string dtypeToCppString(const Dtype& dtype);
+
+  std::ostream& os() {
+    return printer_os_;
+  }
+
+  class PrinterStream : public std::ostream {
+   public:
+    PrinterStream(IRPrinter* printer, std::ostream& os)
+        : std::ostream(os.rdbuf()), printer_(printer) {}
+
+    IRPrinter* printer() {
+      return printer_;
+    }
+
+   private:
+    IRPrinter* printer_ = nullptr;
+  };
+
+ protected:
+  std::string to_string(CompareSelectOperation op);
+
+  UniqueNameManager* name_manager() {
+    return &name_manager_;
+  }
+  void emitIndent();
+
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  int indent_ = 0;
+
+ private:
+  PrinterStream printer_os_;
+  UniqueNameManager name_manager_;
+};
+
+TORCH_API std::ostream& operator<<(std::ostream& stream, const Expr&);
+TORCH_API std::ostream& operator<<(std::ostream& stream, const ExprHandle&);
+TORCH_API std::ostream& operator<<(std::ostream& stream, const Stmt&);
+TORCH_API std::ostream& operator<<(std::ostream& stream, const Tensor&);
+
+TORCH_API void print(ExprPtr expr);
+TORCH_API void print(StmtPtr stmt);
+TORCH_API void print(const Tensor& t);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+namespace std {
+
+using torch::jit::tensorexpr::Expr;
+using torch::jit::tensorexpr::ExprPtr;
+using torch::jit::tensorexpr::Stmt;
+using torch::jit::tensorexpr::StmtPtr;
+using torch::jit::tensorexpr::Tensor;
+
+TORCH_API std::string to_string(ExprPtr expr);
+TORCH_API std::string to_string(StmtPtr stmt);
+TORCH_API std::string to_string(const Tensor& t);
+} // namespace std

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_simplifier.h

@@ -0,0 +1,554 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/bounds_overlap.h>
+#include <torch/csrc/jit/tensorexpr/eval.h>
+#include <torch/csrc/jit/tensorexpr/hash_provider.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_mutator.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+
+#include <utility>
+
+/* IR Simplification
+ *
+ * Simplifies expressions in two stages:
+ *  1. Recursively traverse the map combining similar operations into Terms
+ * (interacted via Multiplication) and Polynomials (interacted via Addition). We
+ * reorder the components of each Term or Polynomial into a consistent order to
+ * allow combination or cancelling of like terms.
+ *  2. Once the format of the tree is minimal, expand each Term into a sequence
+ * of Muls, and each Polynomial into a sequence of Ads.
+ */
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A bunch of helpers for determine the Dtype of the output of a multi argument
+// Term or Polynomial.
+template <class ExprType>
+Dtype promoteTypesVec(ExprPtr s, std::vector<ExprType>& v) {
+  Dtype t = s->dtype();
+  bool first = true;
+
+  for (const auto& e : v) {
+    if (first) {
+      t = Dtype(t.scalar_type(), e->dtype().lanes());
+      first = false;
+    }
+    t = promoteTypes(t, e->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesVec(std::vector<ExprType>& v) {
+  if (v.empty()) {
+    throw malformed_input("empty list of types");
+  }
+
+  Dtype t = v[0]->dtype();
+  for (const auto& e : v) {
+    t = promoteTypes(t, e->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesMap(
+    ExprPtr s,
+    std::unordered_map<SimplifierHashType, ExprType>& m) {
+  Dtype t = s->dtype();
+  bool first = true;
+  for (auto& e : m) {
+    if (first) {
+      t = Dtype(t.scalar_type(), e.second->dtype().lanes());
+      first = false;
+    }
+    t = promoteTypes(t, e.second->dtype());
+  }
+  return t;
+}
+
+template <class ExprType>
+Dtype promoteTypesVar(ExprType e) {
+  return e->dtype();
+}
+
+template <class ExprType, class... Args>
+Dtype promoteTypesVar(ExprType e, Args... es) {
+  Dtype lhs = e->dtype();
+  Dtype rhs = promoteTypesVar(es...);
+  if (e->isConstant()) {
+    lhs = Dtype(lhs.scalar_type(), rhs.lanes());
+  }
+
+  return promoteTypes(lhs, rhs);
+}
+
+// Uses the evaluator to fold an Expression with constant terms.
+// E.g. evaluateOp(Add(3, 4)) => 7.
+// Expr v must not have any unbound Vars.
+inline ExprPtr evaluateOp(ExprPtr v) {
+  ExprHandle handle(v);
+  ExprEval<SimpleIREvaluator> eval(handle);
+
+  switch (v->dtype().scalar_type()) {
+#define TYPE_CASE(Type, Name)                                 \
+  case ScalarType::Name: {                                    \
+    Type val = eval.value<Type>();                            \
+    return getImmediateByType(v->dtype().scalar_type(), val); \
+  }
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    default:
+      LOG(FATAL) << "Unsupported datatype: " << v->dtype();
+      return nullptr;
+  }
+  return nullptr;
+}
+
+// A Term represents a grouping of Exprs through multiplication.
+// E.g. product(scalar, *variables).
+class Term : public ExprNode<Term> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(HashProvider& hasher, ExprPtr s, Args... ts)
+      : ExprNodeBase(promoteTypesVar(s, ts...)), scalar_(s), hasher_(hasher) {
+    CHECK(s->isConstant());
+    addComponent(ts...);
+    sort();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(HashProvider& hasher, ExprPtr s, std::vector<ExprPtr> v)
+      : ExprNodeBase(promoteTypesVec(s, v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Convenience constructor from a map of hash -> var, used when merging Terms.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Term(
+      HashProvider& hasher,
+      ExprPtr s,
+      std::unordered_map<SimplifierHashType, ExprPtr> varmap)
+      : ExprNodeBase(promoteTypesMap(s, varmap)), scalar_(s), hasher_(hasher) {
+    for (auto& p : varmap) {
+      addComponent(p.second);
+    }
+    sort();
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+  // Produce a hash of just the variable components of this term, to determine
+  // if it can be combined with another term.
+  SimplifierHashType hashVars() const;
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Sort by hash to normalize order of components.
+  void sort();
+};
+
+// Polynomial represents a grouping of Exprs by addition.
+// E.g. sum(*variables, scalar).
+// This would better be called Expression, but, naming conflict...
+class Polynomial : public ExprNode<Polynomial> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, ExprPtr s, Args... ts)
+      : ExprNodeBase(promoteTypesVar(s, ts...)), scalar_(s), hasher_(hasher) {
+    CHECK(s->isConstant());
+    addTerm(ts...);
+    sort();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, ExprPtr s, std::vector<TermPtr> v)
+      : ExprNodeBase(promoteTypesVec(s, v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Helper constructor for list of terms with no scalar component.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(HashProvider& hasher, std::vector<TermPtr> terms)
+      : ExprNodeBase(promoteTypesVec(terms)),
+        variables_(std::move(terms)),
+        scalar_(getImmediateByType(dtype(), 0)),
+        hasher_(hasher) {
+    sort();
+  }
+
+  // Convenience constructor for map of hash -> var, used when merging
+  // Polynomials.
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Polynomial(
+      HashProvider& hasher,
+      ExprPtr s,
+      std::unordered_map<SimplifierHashType, TermPtr> varmap)
+      : ExprNodeBase(promoteTypesMap(s, varmap)), scalar_(s), hasher_(hasher) {
+    for (auto& p : varmap) {
+      addTerm(p.second);
+    }
+    sort();
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<TermPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+  SimplifierHashType hashVars() const;
+
+ private:
+  std::vector<TermPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+
+  void addTerm(TermPtr t) {
+    variables_.push_back(std::move(t));
+  }
+  template <class... Ts>
+  void addTerm(TermPtr t, Ts&&... ts) {
+    addTerm(std::move(t));
+    addTerm(std::forward<Ts>(ts)...);
+  }
+
+  // Sort by hash to normalize order of terms.
+  void sort();
+};
+
+class RoundOff : public BinaryOpNode<RoundOff> {
+ public:
+  RoundOff(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(lhs, rhs, IRNodeType::kOther) {}
+};
+
+class MaxTerm : public ExprNode<MaxTerm> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MaxTerm(HashProvider& hasher, ExprPtr s, bool p, Args... ts)
+      : ExprNodeBase(s ? promoteTypesVar(s, ts...) : promoteTypesVar(ts...)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    addComponent(ts...);
+    uniquefy();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MaxTerm(HashProvider& hasher, ExprPtr s, bool p, std::vector<ExprPtr> v)
+      : ExprNodeBase(s ? promoteTypesVec(s, v) : promoteTypesVec(v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    uniquefy();
+  }
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+  bool propagate_nans_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Uniquefy the terms using their hash.
+  void uniquefy();
+};
+
+class MinTerm : public ExprNode<MinTerm> {
+ public:
+  template <class... Args>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MinTerm(HashProvider& hasher, ExprPtr s, bool p, Args... ts)
+      : ExprNodeBase(s ? promoteTypesVar(s, ts...) : promoteTypesVar(ts...)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    addComponent(ts...);
+    uniquefy();
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  MinTerm(HashProvider& hasher, ExprPtr s, bool p, std::vector<ExprPtr> v)
+      : ExprNodeBase(s ? promoteTypesVec(s, v) : promoteTypesVec(v)),
+        variables_(std::move(v)),
+        scalar_(s),
+        hasher_(hasher),
+        propagate_nans_(p) {
+    uniquefy();
+  }
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  ExprPtr scalar() const {
+    return scalar_;
+  }
+  const std::vector<ExprPtr>& variables() const {
+    return variables_;
+  }
+  HashProvider& hasher() const {
+    return hasher_;
+  }
+
+ private:
+  std::vector<ExprPtr> variables_;
+  ExprPtr scalar_;
+  HashProvider& hasher_;
+  bool propagate_nans_;
+
+  void addComponent() {}
+  void addComponent(ExprPtr e) {
+    variables_.push_back(std::move(e));
+  }
+  template <class... Es>
+  void addComponent(ExprPtr e, Es&&... es) {
+    addComponent(std::move(e));
+    addComponent(std::forward<Es>(es)...);
+  }
+
+  // Uniquefy the terms using their hash.
+  void uniquefy();
+};
+
+// Context-sensitive IR simplification
+using VarBoundInfo = std::unordered_map<VarPtr, analysis::Bound>;
+
+class TORCH_API SimplifierUnderContext : public IRMutator {
+ public:
+  ~SimplifierUnderContext() override = default;
+  // Add boundary info for index variables in for-loops
+  StmtPtr mutate(ForPtr v) override;
+
+  ExprPtr mutate(DivPtr v) override;
+  ExprPtr mutate(ModPtr v) override;
+  ExprPtr mutate(CompareSelectPtr v) override;
+  ExprPtr mutate(IfThenElsePtr v) override;
+
+ protected:
+  bool getLoopBoundInfo(const ExprPtr& expr, analysis::Bound* loop_bound_info);
+
+ protected:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  HashProvider hasher_;
+  VarBoundInfo var_bound_info_;
+};
+
+// Stmt simplification should occur in both modes.
+class TORCH_API PolynomialBase : public IRMutator {
+ public:
+  ~PolynomialBase() override = default;
+
+  StmtPtr mutate(BlockPtr v) override;
+
+  StmtPtr mutate(CondPtr v) override;
+
+  StmtPtr mutate(ForPtr v) override;
+
+  // Trivially factorize terms by GCD of scalar components.
+  TermPtr factorizePolynomial(PolynomialPtr poly);
+
+  HashProvider& hasher() {
+    return hasher_;
+  }
+
+ protected:
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  HashProvider hasher_;
+};
+
+// Simplify the IR by combining arithmetic expressions over common terms.
+class TORCH_API PolynomialTransformer : public PolynomialBase {
+ public:
+  using PolynomialBase::mutate;
+  // Inserts term into the provided map, in the case of a hash collision
+  // combines the term with the existing and updates the map.
+  void addOrUpdateTerm(
+      std::unordered_map<SimplifierHashType, TermPtr>& varmap,
+      TermPtr term);
+
+  // Add Polynomial expressions, combining Terms representing the same
+  // variables.
+  ExprPtr addPolynomials(PolynomialPtr lhs, PolynomialPtr rhs);
+
+  // Insert a new Term into the provided polynomial. If the new term has
+  // common variables to an existing term it is combined.
+  ExprPtr insertTerm(PolynomialPtr poly, TermPtr term);
+
+  // Merge and simplify addition.
+  ExprPtr mutate(AddPtr v) override;
+
+  // Subtract one term from another, cancelling if necessary.
+  ExprPtr subTerms(TermPtr lhs, TermPtr rhs, bool negated);
+
+  // Subtract the RHS Polynomial from the LHS Polynomial, cancelling out where
+  // possible.
+  ExprPtr subPolynomials(PolynomialPtr lhs, PolynomialPtr rhs);
+
+  // Merge and simplify subtraction.
+  ExprPtr mutate(SubPtr v) override;
+
+  // Multiply two terms together, usually creating a new term with the variable
+  // lists concatenated.
+  TermPtr mulTerms(TermPtr lhs, TermPtr rhs);
+
+  // Multiply a Polynomial by a Term.
+  ExprPtr polyByTerm(PolynomialPtr poly, TermPtr term);
+
+  // Match a rounding pattern and create a RoundOff if found.
+  ExprPtr isRoundOff(ExprPtr lhs, ExprPtr rhs);
+
+  // Inserts a new component into a term, simplifying if possible.
+  ExprPtr insertIntoTerm(TermPtr term, ExprPtr expr);
+
+  // Merge and simplify multiplication.
+  ExprPtr mutate(MulPtr v) override;
+
+  ExprPtr mutate(DivPtr v) override;
+
+  ExprPtr mutate(ModPtr v) override;
+
+  ExprPtr mutate(AndPtr v) override;
+
+  ExprPtr mutate(XorPtr v) override;
+
+  ExprPtr mutate(LshiftPtr v) override;
+
+  ExprPtr mutate(RshiftPtr v) override;
+
+  ExprPtr mutate(MaxPtr v) override;
+
+  ExprPtr mutate(MinPtr v) override;
+
+  ExprPtr mutate(CompareSelectPtr v) override;
+
+  ExprPtr mutate(IntrinsicsPtr v) override;
+
+  ExprPtr mutate(CastPtr v) override;
+
+  ExprPtr mutate(IfThenElsePtr v) override;
+
+  static ExprPtr simplify(ExprPtr e);
+  static ExprHandle simplify(const ExprHandle& e);
+  static StmtPtr simplify(StmtPtr e);
+};
+
+// Expands Terms and Polynomial expressions into primitive operations.
+// Does some simple factorization and reordering.
+class TORCH_API TermExpander : public PolynomialBase {
+  PolynomialTransformer* simplifier_;
+  std::set<VarPtr> eliminated_allocations_;
+
+ public:
+  using PolynomialBase::mutate;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  TermExpander(PolynomialTransformer* simplifier) : simplifier_(simplifier) {}
+  bool check_safe() {
+    return eliminated_allocations_.empty();
+  }
+
+  // Expand Terms out to a series of Muls.
+  ExprPtr mutate(TermPtr v) override;
+
+  // Expand Polynomials out to a series of Adds.
+  ExprPtr mutate(PolynomialPtr v) override;
+
+  // Expand MaxTerms to a series of Max ops.
+  ExprPtr mutate(MaxTermPtr v) override;
+
+  // Expand MinTerms to a series of Min ops.
+  ExprPtr mutate(MinTermPtr v) override;
+
+  // Expand RoundOff to it's component: Mul(Div(lhs, rhs), rhs).
+  ExprPtr mutate(RoundOffPtr v) override;
+
+  // Eliminate zero length allocations.
+  StmtPtr mutate(AllocatePtr v) override;
+  StmtPtr mutate(FreePtr v) override;
+
+  // Override to enable condition fusing.
+  BlockPtr fuseConditions(BlockPtr v);
+  StmtPtr fuseSyncThreads(BlockPtr block);
+  StmtPtr mutate(BlockPtr v) override;
+};
+
+class TORCH_API IRSimplifier {
+ public:
+  static StmtPtr simplify(StmtPtr s);
+  static ExprPtr simplify(ExprPtr e);
+  static ExprHandle simplify(const ExprHandle& e) {
+    return ExprHandle(simplify(e.node()));
+  }
+};
+
+// Flattens the buf and performs the simplifier on the flattened dims.
+ExprPtr buf_flat_size(BufPtr v);
+// Returns true if expressions A and B can be simplified to an equal expression.
+TORCH_API bool exprEquals(ExprPtr A, ExprPtr B);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_verifier.h

@@ -0,0 +1,58 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class ExprHandle;
+class Mod;
+class And;
+class Or;
+class Xor;
+class Lshift;
+class Rshift;
+class CompareSelect;
+class Ramp;
+class Load;
+class IfThenElse;
+class Intrinsics;
+
+class Stmt;
+class ExternalCall;
+class Store;
+class For;
+class Block;
+
+class TORCH_API IRVerifier : public IRVisitor {
+ public:
+  IRVerifier() = default;
+
+  void visit(ModPtr v) override;
+  void visit(AndPtr v) override;
+  void visit(OrPtr v) override;
+  void visit(XorPtr v) override;
+  void visit(LshiftPtr v) override;
+  void visit(RshiftPtr v) override;
+  void visit(CompareSelectPtr v) override;
+  void visit(RampPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(IfThenElsePtr v) override;
+  void visit(IntrinsicsPtr v) override;
+
+  void visit(ExternalCallPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(ForPtr v) override;
+  void visit(BlockPtr v) override;
+};
+
+TORCH_API void verify(StmtPtr);
+TORCH_API void verify(ExprPtr);
+TORCH_API void verify(ExprHandle);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir_visitor.h

@@ -0,0 +1,64 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class TORCH_API IRVisitor {
+ public:
+  virtual ~IRVisitor() = default;
+  virtual void visit(AddPtr v);
+  virtual void visit(SubPtr v);
+  virtual void visit(MulPtr v);
+  virtual void visit(DivPtr v);
+  virtual void visit(ModPtr v);
+  virtual void visit(MaxPtr v);
+  virtual void visit(MinPtr v);
+  virtual void visit(AndPtr v);
+  virtual void visit(OrPtr v);
+  virtual void visit(XorPtr v);
+  virtual void visit(LshiftPtr v);
+  virtual void visit(RshiftPtr v);
+  virtual void visit(CompareSelectPtr v);
+
+#define IMM_PRINT_VISIT(Type, Name) virtual void visit(Name##ImmPtr v);
+
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_PRINT_VISIT)
+#undef IMM_PRINT_VISIT
+
+  virtual void visit(CastPtr v);
+  virtual void visit(BitCastPtr v);
+  virtual void visit(VarPtr v);
+  virtual void visit(BufPtr v);
+  virtual void visit(RampPtr v);
+  virtual void visit(LoadPtr v);
+  virtual void visit(ForPtr v);
+  virtual void visit(BlockPtr v);
+  virtual void visit(StorePtr v);
+  virtual void visit(BroadcastPtr v);
+  virtual void visit(IfThenElsePtr v);
+  virtual void visit(IntrinsicsPtr v);
+  virtual void visit(AllocatePtr v);
+  virtual void visit(FreePtr v);
+  virtual void visit(FreeExtPtr v);
+  virtual void visit(PlacementAllocatePtr v);
+  virtual void visit(LetPtr v);
+  virtual void visit(CondPtr v);
+  virtual void visit(TermPtr v);
+  virtual void visit(PolynomialPtr v);
+  virtual void visit(RoundOffPtr v);
+  virtual void visit(MaxTermPtr v);
+  virtual void visit(MinTermPtr v);
+  virtual void visit(ReduceOpPtr v);
+  virtual void visit(AtomicAddPtr v);
+  virtual void visit(SyncThreadsPtr v);
+  virtual void visit(ExternalCallPtr v);
+  virtual void visit(ExternalCallWithAllocPtr v);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/kernel.h

@@ -0,0 +1,382 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/passes/symbolic_shape_runtime_fusion.h>
+#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
+#include <torch/csrc/jit/runtime/interpreter.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/lowerings.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct SmallSizeTPairHash {
+ public:
+  std::size_t operator()(const std::pair<size_t, size_t>& x) const {
+    // hashing input index and then dim index
+    return x.first * 128 + x.second;
+  }
+};
+
+// Returns true if the TE fuser supports this conv2d.
+bool conv2dIsSupportedJit(const Node* node);
+// Returns true if the TE fuser supports this conv2d with mkldnn prepacked conv.
+bool mkldnnPrepackedConvIsSupportedJit(const Node* node);
+// Returns true if the TE _convolution node is Conv2d.
+bool isConv2d(const Node* node);
+// Returns true if the TE fuser supports this matmul.
+bool matmulIsSupported(const Node* node);
+template <typename T>
+inline std::vector<int64_t> bufferSizes(const T& t) {
+  std::vector<int64_t> sizes;
+  for (size_t i = 0; i < t->ndim(); i++) {
+    sizes.push_back(*intValue(t->dim(i)));
+  }
+  return sizes;
+}
+
+// Get the dimensions of a value.
+std::vector<ExprHandle> valueShape(const ArgValue& v);
+
+// If v is a tensor, broadcast it to match the shape of axes, or return
+// directly if v is a constant.
+ExprHandle tensorOrConstant(
+    const ArgValue& v,
+    const std::vector<ExprHandle>& axes);
+
+int64_t normalizeAndCheckIndex(int64_t idx, int64_t list_size);
+
+ExprHandle broadcast(BufHandle b, const std::vector<ExprHandle>& axes);
+
+ExprHandle constant(const ArgValue& v);
+
+std::vector<ExprHandle> computeIndicesToBroadcast(
+    const std::vector<ExprHandle>& outputAxes,
+    const std::vector<ExprHandle>& inputSizes);
+
+inline std::string getArgValueName(const ArgValue& a) {
+  if (std::holds_alternative<tensorexpr::BufHandle>(a)) {
+    return "BufHandle";
+  } else if (std::holds_alternative<tensorexpr::VarHandle>(a)) {
+    return "VarHandle";
+  } else if (std::holds_alternative<double>(a)) {
+    return "double";
+  } else if (std::holds_alternative<int64_t>(a)) {
+    return "int64_t";
+  } else if (std::holds_alternative<bool>(a)) {
+    return "bool";
+  } else if (std::holds_alternative<BufList>(a)) {
+    return "BufList";
+  } else if (std::holds_alternative<DoubleList>(a)) {
+    return "DoubleList";
+  } else if (std::holds_alternative<IntList>(a)) {
+    return "IntList";
+  } else if (std::holds_alternative<ArgNone>(a)) {
+    return "None";
+  } else {
+    throw std::runtime_error("ArgValue type not handled in string conversion");
+  }
+}
+
+template <class T>
+std::vector<T> convertVecArgValue(const std::vector<ArgValue>& v) {
+  std::vector<T> res;
+  for (auto& x : v) {
+    auto val = std::get_if<T>(&x);
+    if (val) {
+      res.push_back(*val);
+    } else {
+      throw std::runtime_error(
+          "vector type not homogeneous - found " + getArgValueName(x) +
+          ", expected " + getArgValueName(v[0]));
+    }
+  }
+  return res;
+}
+
+class TORCH_API TensorExprKernel {
+  struct ConstantDescr {
+    BufPtr buf;
+    // Only one of ptr and node is used at a time
+    // 1) ptr for the constant tensors
+    // 2) node for the constant custom class objects
+    void* ptr = nullptr;
+    Node* node = nullptr;
+  };
+
+ public:
+  // Constructor Params:
+  //  * subgraph
+  //      - the graph that needs to be compiled.
+  //  * kernel_func_name
+  //      - the name that should be used for the generated kernel.
+  //  * custom_lowerings
+  //      - map that represents custom lowering definitions for a set of ops.
+  //  * symbolic_shape_inputs
+  //      - a list of symbolic graph inputs that represent the symbolic dims of
+  //        the input tensors.
+  //  * pre_alloc
+  //      - a flag to control pre-allocation of buffers.
+  explicit TensorExprKernel(
+      const std::shared_ptr<Graph>& subgraph,
+      const std::string& kernel_func_name,
+      std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings =
+          {},
+      std::vector<int64_t> symbolic_shape_inputs = {},
+      bool pre_alloc = false,
+      std::unordered_map<
+          const torch::jit::Value*,
+          std::vector<torch::jit::StrideInput>> symbolic_strides = {});
+
+  explicit TensorExprKernel(
+      const std::shared_ptr<Graph>& subgraph,
+      std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings =
+          {},
+      std::vector<int64_t> symbolic_shape_inputs = {},
+      bool pre_alloc = false,
+      std::unordered_map<
+          const torch::jit::Value*,
+          std::vector<torch::jit::StrideInput>> symbolic_strides = {})
+      : TensorExprKernel(
+            subgraph,
+            SubgraphUtils::generateNameForGraph(subgraph),
+            custom_lowerings,
+            symbolic_shape_inputs,
+            pre_alloc,
+            symbolic_strides) {}
+
+  void run(Stack& stack) const;
+  void runFast(
+      const std::vector<void*>& inputs,
+      const std::vector<void*>& outputs) const;
+  // Expected format of stack:
+  //  ... <outputs> <inputs>
+  // i.e., output IValues must be below the input IValues in the stack.
+  void runWithAllocatedOutputs(Stack& stack) const;
+
+  void fallback(Stack& stack) const {
+    InterpreterState(code_).run(stack);
+  }
+  void recompile();
+
+  StmtPtr getCodeGenStmt();
+
+  std::string getCodeText(const std::string& attr = "") {
+    return codegen_->getCodeText(attr);
+  }
+
+  const std::shared_ptr<Graph> graph() {
+    return graph_;
+  }
+
+  const std::vector<ConstantDescr>& getConstantDescriptors() const {
+    return constants_;
+  }
+
+  const std::vector<CodeGen::BufferArg>& getBufferArgs() const {
+    return bufferArgs_;
+  }
+
+  const std::string& getKernelName() const {
+    return codegen_->kernel_func_name();
+  }
+
+  const std::vector<int64_t>& getSymbolicShapeInputs() const {
+    return symbolic_shape_inputs_;
+  }
+
+ private:
+  enum BackendType {
+    kUninitialized,
+    kSimpleIREval,
+    kLLVMCodeGen,
+    kCudaCodeGen,
+    kBlockCodeGen,
+  };
+
+  enum MemoryLayoutPolicy {
+    kContiguous,
+    kChannelsLastNdContiguous,
+  };
+
+  void compile();
+  void genInputDebugNames();
+  void runKernel(Stack& stack) const;
+
+  std::vector<ExprHandle> sizesForValue(const torch::jit::Value* v);
+
+  // These functions broadcast shape and also store a `hasBroadcast_` variable.
+  std::vector<ExprHandle> broadcastShapesMut(
+      const std::vector<ExprHandle>& a,
+      const std::vector<ExprHandle>& b);
+  std::vector<ExprHandle> broadcastShapesMut(
+      std::vector<std::vector<ExprHandle>> shapes);
+
+  ArgValue toArg(const torch::jit::Value* v) const;
+  ExprHandle constant(const torch::jit::Value* v);
+
+  Tensor computeValue(const torch::jit::Value* v);
+
+  void bindConstant(const torch::jit::Value* v);
+
+  StmtPtr transformLoops(BackendType backendType, StmtPtr st);
+
+  std::string getCodeGenName(BackendType backendType);
+
+  void getStaticOutputSizesAndStrides(
+      const at::ArrayRef<IValue>& inputs,
+      std::vector<std::vector<int64_t>>* static_sizes,
+      std::vector<std::vector<int64_t>>* static_strides) const;
+
+  std::vector<CodeGen::CallArg> prepareRunArgs(
+      const at::ArrayRef<IValue>& inputs,
+      std::vector<at::Tensor>& outputs) const;
+  BackendType inferBackendTypeFromDevice(at::Device device);
+
+  Tensor bindInput(const torch::jit::Value* input);
+  BlockPtr bindAllInputs();
+
+  // Deduce the memory layout policy to be propagated within
+  // NNC fusion group. The memory layout policy could be `kContiguous`
+  // or `kChannelsLastNdContiguous`.
+  //    `kContiguous`: Always convert the non-contiguous input tensors and
+  //        internal buffers to contiguous.
+  //    `kChannelsLastNdContiguous`: Always convert the input tensors and
+  //        internal buffers to channels-last contiguous.
+  // Currently, the rule is simple.
+  //    If all the input and out tensors of NNC fusion group are channels-last
+  //    contiguous, the policy is `kChannelsLastNdContiguous`. Otherwise, it
+  //    is always `kContiguous`.
+  void deduceMemoryLayoutPolicy();
+
+  Tensor convertSymbolicOutputToCorrectStrides(torch::jit::Value* v);
+  Tensor convertStaticShapeOutputToCorrectStrides(torch::jit::Value* v);
+  Tensor convertSymbolicOutputToCorrectStrides(
+      const std::vector<ExprHandle>& sizes,
+      const std::vector<size_t>& sorted_stride_indices_descending,
+      const std::vector<ExprPtr>& strides,
+      BufPtr& buf);
+
+  NNCLoweringFunction getCustomLoweringFor(c10::Symbol op) const;
+  std::unordered_map<c10::Symbol, NNCLoweringFunction> getCustomLowerings()
+      const {
+    return custom_lowerings_;
+  }
+
+  // Allocate memory for intermediate buffers at compile time.
+  // Specifically, we pre-allocate memory for intermediate buffers with static
+  // size and manage these buffers in the way we manage JIT constant tensors:
+  // push the buf args into the stack so NNC IR can access them at runtime.
+  std::vector<BufPtr> preAllocIntermediateBufs(
+      const std::vector<BufPtr>& interm_bufs);
+
+  struct UnpackedTensorOptions {
+    c10::optional<c10::ScalarType> dtype;
+    c10::optional<c10::Layout> layout;
+    c10::optional<c10::Device> device;
+    c10::optional<bool> pinned_memory;
+
+    UnpackedTensorOptions(const c10::TensorOptions& opts)
+        : dtype(c10::optTypeMetaToScalarType(opts.dtype_opt())),
+          layout(opts.layout_opt()),
+          device(opts.device_opt()),
+          pinned_memory(opts.pinned_memory_opt()) {}
+  };
+
+  ExprHandle getVarForShape(const c10::ShapeSymbol& ss);
+  std::vector<ExprHandle> computeInputTensorDims(
+      const torch::jit::Value* input);
+  ExprHandle getStrideArg(size_t tensor_input, size_t stride_index);
+  std::vector<ExprHandle> sizesFromSymbolicShape(
+      const c10::SymbolicShape& shape);
+  std::vector<ExprHandle> getInputStrides(
+      const torch::jit::Value* input,
+      const std::vector<ExprHandle>& inputTensorDims);
+  std::vector<torch::jit::StrideInput>& getSymbolicStrideDesc(
+      const torch::jit::Value* value);
+
+  // Apply the optimizations to the graph owned by the current fusion group,
+  // like concatenation optimization, post-op fusion, and some other graph-level
+  // optimizations.
+  void optimizeOwningGraph();
+
+  int64_t nInputs_ = 0;
+  int64_t nOutputs_ = 0;
+  std::vector<CodeGen::BufferArg> bufferArgs_;
+  std::vector<std::vector<int64_t>> tensorOutputSizes_;
+  std::vector<std::vector<int64_t>> tensorOutputStrides_;
+  std::vector<torch::jit::StrideInput> tensorOutputStrideDesc_;
+  std::vector<bool> isOutputScalar_;
+  std::vector<UnpackedTensorOptions> tensorOutputTensorOptions_;
+  std::unordered_set<BufPtr> bufOutputs_;
+  std::unordered_set<BufPtr> bufsToBeParallelized_;
+  std::unordered_map<const torch::jit::Value*, BufPtr> bufs_;
+  std::unordered_map<const torch::jit::Value*, VarHandle> scalars_;
+  std::unordered_map<const torch::jit::Value*, std::string> input_name_map_;
+  std::unique_ptr<CodeGen> codegen_;
+  at::Device device_ = at::kCPU;
+  std::shared_ptr<Graph> graph_;
+  Code code_;
+  bool allow_fallback_{false};
+  bool use_fallback_{false};
+  bool hasRandom_{false};
+  bool hasBroadcast_{false};
+  std::unordered_map<const torch::jit::Value*, std::vector<ExprHandle>>
+      known_sizes_;
+
+  std::vector<std::vector<ExprHandle>> tensorOutputSymbolicSizes_;
+  // A map from ShapeSymbol.value() to the corresponding Var.
+  std::unordered_map<int64_t, VarHandle> shapeSymbolToVar_;
+  std::unordered_map<ExprPtr, size_t> shapeSymbolInputPos_;
+  // List of values corresponding to the ShapeSymbols that are inputs to
+  // kernel being compiled. The order of these values correspond to the order
+  // of the symbolic inputs at the end of the list of inputs to the kernel.
+  std::vector<int64_t> symbolic_shape_inputs_;
+  bool has_symbolic_shapes_{false};
+
+  std::vector<at::Tensor> unpacked_constant_tensors_;
+  std::vector<ConstantDescr> constants_;
+
+  std::unordered_map<c10::Symbol, NNCLoweringFunction> custom_lowerings_;
+  StmtPtr stmt_ = nullptr;
+  bool pre_alloc_{false};
+  std::string kernel_func_name_;
+
+  // index of stack, stride index of tensor that will be appended as a codegen
+  // arg
+  std::vector<std::pair<size_t, size_t>> input_stride_args_;
+  // map from <input index, tensor dimension> to stride as arg VarHandle
+  std::unordered_map<std::pair<size_t, size_t>, VarHandle, SmallSizeTPairHash>
+      strideArgToVar_;
+  std::unordered_map<
+      const torch::jit::Value*,
+      std::vector<torch::jit::StrideInput>>
+      symbolic_strides_;
+
+  // Memory layout to be propagated with fusion group
+  MemoryLayoutPolicy memory_layout_policy_ = MemoryLayoutPolicy::kContiguous;
+};
+
+TORCH_API int& getTECudaPointwiseLoopLevels();
+TORCH_API int& getTECudaPointwiseBlockCount();
+TORCH_API int& getTECudaPointwiseBlockSize();
+TORCH_API bool& getTEGenerateBlockCode();
+TORCH_API bool& getTEMustUseLLVMOnCPU();
+TORCH_API bool fallbackAllowed();
+TORCH_API bool setFallbackAllowed(bool value);
+TORCH_API bool& getCatWoConditionals();
+TORCH_API bool& getOptConditionals();
+
+TORCH_API c10::optional<at::Device> pickDeviceType(
+    const at::ArrayRef<torch::jit::Value*>& inputs);
+
+bool isContiguous(
+    const torch::jit::Value* v,
+    at::MemoryFormat memory_format = at::MemoryFormat::Contiguous);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/llvm_codegen.h

@@ -0,0 +1,143 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <torch/csrc/Export.h>
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+
+#include <c10/util/Optional.h>
+
+#include <unordered_map>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class LLVMCodeGenImpl;
+class LLVMCodeGenCallee;
+
+class TORCH_API LLVMCodeGen : public CodeGen {
+ public:
+  explicit LLVMCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func",
+      Dtype dtype = kInt,
+      c10::optional<std::string> triple = c10::nullopt,
+      c10::optional<std::string> cpu = c10::nullopt,
+      c10::optional<std::string> attrs = c10::nullopt);
+  explicit LLVMCodeGen(StmtPtr stmt);
+
+  LLVMCodeGen() = delete;
+  ~LLVMCodeGen() override;
+
+  // Cleans up all the memory used during LLVM code generation pass except
+  // the generated kernel. After calling this method, users should not call
+  // methods like `getCodeText` that require the LLVMCodeGenImpl data. However,
+  // users can continue to call this kernel using `call` and `call_raw`.
+  void cleanup_memory();
+
+  TORCH_API void call(const std::vector<CallArg>& args) override;
+  TORCH_API void call_raw(const std::vector<void*>& args) override;
+  TORCH_API void call_with_numel(void** args, int64_t numel) override;
+
+  at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) override;
+
+  template <typename T>
+  T value() {
+    return value<T>(nullptr);
+  }
+
+  template <typename T>
+  T value(std::vector<void*>& args) {
+    return value<T>(args.data());
+  }
+
+  template <typename T>
+  T value(void** args) {
+    T (*fp)(void**) = (T(*)(void**))getKernelAddress(callee_.get());
+    T rv = fp(args);
+    return rv;
+  }
+
+  std::string getCodeText(const std::string& attr = "") override;
+
+ private:
+  void* getKernelAddress(LLVMCodeGenCallee* callee);
+
+  std::unique_ptr<LLVMCodeGenCallee> callee_;
+  std::unique_ptr<LLVMCodeGenImpl> impl_;
+};
+
+struct TORCH_API LLVMCodeGenBuilder {
+  using BufferArg = CodeGen::BufferArg;
+
+  LLVMCodeGenBuilder(StmtPtr stmt, std::vector<BufferArg> args)
+      : stmt_(stmt), args_(std::move(args)) {}
+
+  LLVMCodeGenBuilder& device(at::Device device) {
+    device_ = device;
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& kernelFuncName(std::string name) {
+    kernelFuncName_ = std::move(name);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& dtype(Dtype d) {
+    dtype_ = d;
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& triple(std::string triple) {
+    triple_ = std::move(triple);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& cpu(std::string cpu) {
+    cpu_ = std::move(cpu);
+    return *this;
+  }
+
+  LLVMCodeGenBuilder& attrs(std::string attrs) {
+    attrs_ = std::move(attrs);
+    return *this;
+  }
+
+  std::unique_ptr<LLVMCodeGen> build() {
+    return std::make_unique<LLVMCodeGen>(
+        stmt_, args_, device_, kernelFuncName_, dtype_, triple_, cpu_, attrs_);
+  }
+
+ private:
+  StmtPtr stmt_;
+  std::vector<BufferArg> args_;
+  at::Device device_ = at::kCPU;
+  std::string kernelFuncName_ = "func";
+  Dtype dtype_ = kInt;
+  c10::optional<std::string> triple_ = c10::nullopt;
+  c10::optional<std::string> cpu_ = c10::nullopt;
+  c10::optional<std::string> attrs_ = c10::nullopt;
+};
+
+TORCH_API c10::optional<std::string>& LLVMTargetTriple();
+TORCH_API c10::optional<std::string>& LLVMTargetCPU();
+TORCH_API c10::optional<std::string>& LLVMTargetAttrs();
+TORCH_API bool& LLVMAOTWorkflow();
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+#endif // TORCH_ENABLE_LLVM

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/llvm_jit.h

@@ -0,0 +1,77 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/Export.h>
+
+C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wsuggest-override")
+#include <llvm/ExecutionEngine/JITSymbol.h>
+C10_DIAGNOSTIC_POP()
+#include <llvm/ExecutionEngine/Orc/Core.h>
+#include <llvm/ExecutionEngine/Orc/ThreadSafeModule.h>
+#include <llvm/Target/TargetMachine.h>
+
+#include <memory>
+#include <string>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+inline std::string formatError(llvm::Error&& err, const char* msg) {
+  static constexpr const char* defaultErrorMsg =
+      "Unexpected failure in LLVM JIT";
+  std::string errorMsg(msg ? msg : defaultErrorMsg);
+  llvm::raw_string_ostream ss(errorMsg);
+  ss << ": " << err;
+  return ss.str();
+}
+
+template <typename T>
+T assertSuccess(llvm::Expected<T> valOrErr, const char* msg = nullptr) {
+  TORCH_INTERNAL_ASSERT(valOrErr, formatError(valOrErr.takeError(), msg));
+  return std::move(*valOrErr);
+}
+
+inline void assertSuccess(llvm::Error err, const char* msg = nullptr) {
+  TORCH_INTERNAL_ASSERT(!err, formatError(std::move(err), msg));
+}
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+namespace llvm {
+namespace orc {
+
+class PytorchLLVMJITImpl;
+
+class TORCH_API PytorchLLVMJIT {
+ public:
+  PytorchLLVMJIT(
+      c10::optional<std::string> triple,
+      c10::optional<std::string> cpu,
+      c10::optional<std::string> attrs);
+  ~PytorchLLVMJIT();
+
+  void addModule(std::unique_ptr<Module> M, std::unique_ptr<LLVMContext> C);
+
+  JITSymbol findSymbol(const std::string Name);
+
+  bool hasSymbol(const std::string& Name);
+
+  TargetMachine& getTargetMachine();
+
+  const DataLayout& getDataLayout();
+
+ private:
+  // Use the PImpl idiom here to hide the no-rtti parts of the JIT structure.
+  std::unique_ptr<PytorchLLVMJITImpl> impl_;
+};
+
+} // end namespace orc
+} // end namespace llvm
+
+#endif // ENABLE LLVM

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/loopnest.h

@@ -0,0 +1,606 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class Var;
+class Buf;
+class Tensor;
+class Function;
+class Stmt;
+class For;
+class Block;
+class Store;
+class Dtype;
+
+class TORCH_API LoopNest {
+ public:
+  // A constructor for building a LoopNest from a list of Tensors
+  LoopNest(
+      const std::vector<Tensor>& output_tensors,
+      const std::vector<Tensor>& tensors_to_compute);
+
+  // A convenience constructor for the case when all tensors are output tensors
+  LoopNest(const std::vector<Tensor>& output_tensors);
+
+  // A constructor for building a LoopNest from an Stmt and a list of output
+  // buffers.
+  LoopNest(StmtPtr stmt, std::unordered_set<BufPtr> output_bufs);
+
+  // A constructor for building a LoopNest from another loopnest. It clones the
+  // other loopnest's stmt.
+  LoopNest(const LoopNest& other);
+
+  StmtPtr root_stmt() const {
+    return root_stmt_;
+  }
+
+  std::vector<ForPtr> getLoopStmtsFor(Tensor) const;
+  std::vector<ForPtr> getLoopStmtsFor(BufPtr) const;
+  std::vector<ForPtr> getLoopStmtsFor(StmtPtr) const;
+  StmtPtr getLoopBodyFor(Tensor) const;
+  StmtPtr getLoopBodyFor(BufPtr) const;
+
+  // Returns the For stmt indexed by 'indices' in the 'root' For stmt.
+  //'indices' indicates the path to the returned loop from 'root' in AST, e.g.,
+  //
+  // root: for(int i...){
+  // j_loop: for (int j...){
+  // k1_loop:  for (int k1...){
+  //            A[i, j, k1] = ....
+  //          }
+  //          B[i, j] = ...
+  // k2_loop:  for (int k2...){
+  //            A[i, j, k2] = ...
+  //          }
+  //        }
+  //      }
+  //
+  // the path from 'root' to 'j_loop' is [0]
+  // the path from 'root' to 'k1_loop' is [0, 0]
+  // the path from 'root' to 'k2_loop' is [0, 2]
+  ForPtr getLoopAt(ForPtr root, const std::vector<int>& indices) const;
+
+  // Returns the For stmt that is immediately enclosing the given stmt.
+  static ForPtr getParentLoop(StmtPtr st);
+
+  // Returns the list of For stmts corresponding to the loopnest that is
+  // enclosing the given stmt.
+  static std::vector<ForPtr> getEnclosingLoopNest(StmtPtr st);
+
+  // Returns a list of all Stmts that write to the given buf.
+  std::vector<StmtPtr> getAllWritesToBuf(BufPtr) const;
+
+  // The following methods return the For loops that contain writes to
+  // the given buf.
+  //
+  // For example, consider the following code:
+  //   for i1
+  //     for j1
+  //       a[i1,j1] =
+  //   for i2
+  //     for j2
+  //       for k2
+  //         a[i2,j2] =
+  //     for j3
+  //       a[i2,j3] =
+
+  // Returns a list of For loops which directly contain a Stmt that writes
+  // to buf.
+  // For the above example:
+  //   getAllInnermostLoopsWritingToBuf(a) => {j1, k2, j3}
+  std::vector<ForPtr> getAllInnermostLoopsWritingToBuf(BufPtr) const;
+
+  // Returns a list of For loopnests which contain a Stmt that writes to
+  // the given buf. Each loopnest here is a vector For loops.
+  // For the above example:
+  //   getAllLoopNestsWritingToBuf(a) => {{i1,j1}, {i2,j2,k2}, {i2,j3}}
+  std::vector<std::vector<ForPtr>> getAllLoopNestsWritingToBuf(BufPtr) const;
+
+  StmtPtr simplify();
+
+  // Sanitize variables and buffer names.
+  // The pass assigns predefined names for loop index variables
+  // (i,j,k,l,m,n,o,p,i1,j1,k1,...) and ensures these names are not conflicting
+  // anywhere. It also removes duplicates from other Buf nad Var names as well
+  // as replaces illegal characters in them with underscores.
+  //
+  // Note: since it's currently technically possible to use the same variable
+  // as index in two different loops, this transformation finds such cases and
+  // introduces new variables to avoid duplication.
+  static StmtPtr sanitizeNames(StmtPtr s);
+
+  bool computeInline(StmtPtr s);
+  bool computeInline(BufPtr b);
+  void inlineIntermediateBufs(bool allow_duplicated_work);
+
+  // Optimizes conditionals.
+  //
+  // Currently, only the following pattern of conditionals is optimized.
+  // This corresponds to the conditional format that is generated to handle
+  // `aten::cat` op.
+  //
+  //   for (int i = 0; i < 20; i++) {
+  //     A[i] = IfThenElse(i<5 ? 1 : 0, B[i], C[i-5])
+  //   }
+  //
+  // Constraints that must be satisfied for this optimization:
+  //   * All conditions should be of the form "var < expr".
+  //   * All conditions should have the same variable, say v.
+  //   * The condition variable found should be the same as the inner-most
+  //     loop variable. TODO: Remove this constraint.
+  //   * If there are multiple stores that contain conditionals using the same
+  //     loop variable, only the first conditional will be optimized.
+  //     TODO: Remove this constraint.
+  bool optimizeConditionals();
+
+  // Splits the given loop into 2 nested loops with the given factor as the
+  // inner loop bound. If the factor does not evenly divide the loop bound,
+  // then the remaining iterations are extracted into a tail loop that is
+  // added after the given loop.
+  //
+  // For example, consider the following code:
+  //   for (int i = 0; i < 100; ++i) {
+  //     A[i] =
+  //   }
+  //
+  // splitWithTail(i, 8, ...) will result in:
+  //   for (int i_outer = 0; i_outer < 12; ++i_outer) {
+  //     for (int i_inner = 0; i_inner < 8; ++i_inner) {
+  //       A[i_outer * 8 + i_inner] =
+  //     }
+  //   }
+  //   for (int i_tail = 0; i_tail < 4; ++i_tail) {
+  //     A[i_tail + 96] =
+  //   }
+  //
+  // The given loop will be transformed to the outer loop after splitting.
+  // So, the pointer to the input loop should be valid after splitting and
+  // will point to the outer loop. The `inner` and `tail` parameters will be
+  // set to point to the inner and tail loops that are generated.
+  static void splitWithTail(ForPtr f, int factor, ForPtr* inner, ForPtr* tail);
+  // A convenience wrapper when the caller does not need to access the
+  // split loops.
+  static void splitWithTail(ForPtr f, int factor);
+
+  // Splits the given loop into 2 nested loops with the given factor as the
+  // inner loop bound. If the factor does not evenly divide the loop bound,
+  // then a conditional is inserted into the body to handle the remaining
+  // iterations appropriately.
+  //
+  // For example, consider the following code:
+  //   for (int i = 0; i < 100; ++i) {
+  //     A[i] =
+  //   }
+  //
+  // splitWithMask(i, 8, ...) will result in:
+  //   for (int i_outer = 0; i_outer < 13; ++i_outer) {
+  //     for (int i_inner = 0; i_inner < 8; ++i_inner) {
+  //       if (i_outer * 8 + i_inner < 100) {
+  //         A[i_outer * 8 + i_inner] =
+  //       }
+  //     }
+  //   }
+  //
+  // The given loop will be transformed to the outer loop after splitting.
+  // So, the pointer to the input loop should be valid after splitting and
+  // will point to the outer loop. The `inner` parameter will be set to point
+  // to the inner loop that is generated.
+  static void splitWithMask(ForPtr f, int factor, ForPtr* inner);
+  // A convenience wrapper when the caller does not need to access the
+  // split loops.
+  static void splitWithMask(ForPtr f, int factor);
+
+  // The following methods support loop distribution.
+  // For example, consider the following code. This will be used to
+  // demonstrate the methods below.
+  //
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+
+  // This method distributes the given loop over its body by splitting
+  // after every given pivot stmt.
+  //
+  // NOTE: Pivot stmts that are not in the given loop's body will be ignored.
+  //
+  // For the above example:
+  //   distributeLoop(S1, {S3, S5})
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoop(
+      ForPtr loop,
+      const std::unordered_set<StmtPtr>& pivots);
+
+  // This method distributes the given loop over every stmt in its body.
+  //
+  // For the above example:
+  //   distributeLoop(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  //   :    for i
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoop(ForPtr loop);
+  // Same as above, but also distribute parent loops.
+  // Returns the result of distributing the outermost loop.
+  //
+  // For the above example:
+  //   distributeLoopAndParents(S1) will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  //   :  for m
+  //   :    for i
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :  for m
+  //   :    for i
+  // S5:      B[i] = A[i]
+  //   :  for m
+  //   :    for i
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopAndParents(ForPtr loop);
+
+  // This method distributes the given loop over its body by splitting
+  // after every For stmt in its body.
+  //
+  // For the above example:
+  //   distributeLoopOverInnerLoops(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :    for i
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopOverInnerLoops(ForPtr loop);
+  // Same as above, but also distribute parent loops.
+  // Returns the result of distributing the outermost loop.
+  //
+  // For the above example:
+  //   distributeLoopAndParentsOverInnerLoops(S1)
+  // will result in:
+  // S0:  for m
+  // S1:    for i
+  // S2:      A[i] = 0
+  // S3:      for j
+  // S4:        A[i] = A[i] +
+  //   :  for m
+  //   :    for i
+  // S5:      B[i] = A[i]
+  // S6:      for k
+  // S7:        B[i] = B[i] +
+  static std::vector<ForPtr> distributeLoopAndParentsOverInnerLoops(
+      ForPtr loop);
+
+  // This method performs loop fusion.
+  // For example, consider the following code.
+  //
+  // S1:  for m
+  // S2:    A[m] = 0
+  // S3:    for j
+  // S4:      A[m] = A[m] +
+  // S5:  for n
+  // S5:    B[n] = A[n]
+  // S6:    for k
+  // S7:      B[n] = B[n] +
+  //
+  // fuseLoops({S1, S5}), will return the following loop:
+  // S1:  for m
+  // S2:    A[m] = 0
+  // S3:    for j
+  // S4:      A[m] = A[m] +
+  // S5:    B[m] = A[m]
+  // S6:    for k
+  // S7:      B[m] = B[m] +
+  //
+  // This transformation is unsafe as it simply add all loops into the body of
+  // the first loop for fusion without correctness checks.
+  //
+  // Below are the two requirements to apply unsafeFuseLoops:
+  //  * All the loops have the same parent.
+  //  * There are no statements between these loops in their parent body.
+  static bool unsafeFuseLoops(const std::vector<ForPtr>& loops, ForPtr* fused);
+
+  // Loop fusion is done only when all the conditions below are satisfied.
+  //  * All the loops have the same parent.
+  //  * There are no statements between these loops in their parent body.
+  //  * The start bounds are the same for all loops.
+  //  * The stop bounds are the same for all loops.
+  //  * Fusing the loops does not violate or add any dependencies.
+  static bool fuseLoops(const std::vector<ForPtr>& loops, ForPtr* fused);
+
+  static void reorderAxis(ForPtr a, ForPtr b);
+
+  // Reorder the given list of loops according to the permutation specified.
+  // Here `permutation[i]` represents the position of the loop in the input
+  // which will end up at position `i` after the reorder.
+  //
+  // For example, consider the following code:
+  //   for p
+  //     for q
+  //       for r
+  //         for s
+  //           A[p,q,r,s] =
+  //
+  // reorder({p, q, r, s}, {2, 3, 0, 1}) will return the list of loops in the
+  // following form:
+  //    for r
+  //      for s
+  //        for p
+  //          for q
+  //            A[p,q,r,s] =
+  static std::vector<ForPtr> reorder(
+      const std::vector<ForPtr>& loops,
+      const std::vector<size_t>& permutation);
+
+  // Tile takes a 2d domain (x, y) and splits it into small rectangular blocks
+  // each with shape (x_factor, y_factor). The traversal over the domain turns
+  // into an outer iteration over the blocks and an inner traversal over all
+  // points in the block.
+  // Note that if x dim % x_factor or y dim % y_factor does not equal to 0, the
+  // loop body will generate corresponding tailing loops.
+  // The transformation is in-place and returns 'xtail'.
+  //
+  // For example, consider the following code:
+  //   for i: [0, 64)
+  //     for j: [0, 64)
+  //       for k: [0, 32)
+  //         A[i, j] = B[i, k] + C[j, k]
+  //
+  // tile(i, j, 4, 8) will transform "i" for-stmt into the following nested
+  // loop:
+  //   for i_outer: [0, 16)
+  //     for j_outer: [0, 8)
+  //       for i_inner: [0, 4)
+  //         for j_inner: [0, 8)
+  //           for k: [0, 32)
+  //             A[i_outer * 4 + i_inner, j_outer * 8 + j_inner] =
+  //             B[i_outer * 4 + i_inner, k] + C[j_outer * 8 + j_inner, k]
+  //
+  // tile(i, j, 4, 9) will transform "i" for-stmt into the following nested
+  // loop:
+  //   for i_outer: [0, 16)
+  //     for j_outer: [0, 7)
+  //       for i_inner: [0, 4)
+  //         for j_inner: [0, 9)
+  //           for k: (0, 32)
+  //             A[i_outer * 4 + i_inner, j_outer * 9 + j_inner] =
+  //             B[i_outer * 4 + i_inner, k] + C[j_outer * 9 + j_inner, k]
+  //     for j_tail: [0, 1)
+  //       for i_inner: [0, 4)
+  //         for k: (0, 32)
+  //           A[i_outer * 4 + i_inner, 7 * 9 + j_tail] =
+  //           B[i_outer * 4 + i_inner, k] + C[7 * 9 + j_tail, k]
+  ForPtr tile(ForPtr x, ForPtr y, int x_factor, int y_factor);
+
+  // Returns true if the given loops are perfectly nested, i.e., every loop
+  // (except the innermost) should have exactly one statement in its body
+  // and that statement must be the next inner loop.
+  static bool areLoopsPerfectlyNested(const std::vector<ForPtr>& loops);
+
+  // Returns true if the given loop has a loop-carried dependence.
+  static bool hasLoopCarriedDependence(ForPtr loop);
+
+  // Unrolls all the iterations of the given loop.
+  // Requires that the loop bounds are constant.
+  static void fullUnroll(ForPtr f, StmtPtr* unrolled);
+  static void fullUnroll(ForPtr f);
+
+  // Unrolls the given loop for the specified factor.
+  // This does not require constant bounds for the loop being unrolled.
+  static void unroll(ForPtr f, int factor, ForPtr* tail);
+  static void unroll(ForPtr f, int factor);
+
+  static bool normalize(ForPtr f);
+  static bool isNormalized(ForPtr f);
+
+  static bool flatten(const std::vector<ForPtr>& f, ForPtr* flattened);
+  static bool flatten(const std::vector<ForPtr>& f);
+
+  // Compresses the given buffer based on its use in the given Stmts.
+  //
+  // NOTE: This API assumes that there are no accesses to the given buffer
+  // outside the given statement. So, this should be called with the entire
+  // kernel statement to avoid incorrect buffer compressions.
+  //
+  // For example, given the input:
+  //
+  // for (int i = 0; i < 100; ++i) {
+  //   for (int j = 0; j < 200; ++j) {
+  //     A[i,j] = sin(i*j)
+  //   }
+  //   for (int j = 0; j < 199; ++j) {
+  //     B[i,j] = A[i,j] + A[i, j+1]
+  //   }
+  // }
+  //
+  // compressBuffer(A, ...) will compress buffer A from
+  // [100, 200] to [1, 200] and modify the code as follows:
+  //
+  // for (int i = 0; i < 100; ++i) {
+  //   for (int j = 0; j < 200; ++j) {
+  //     A[0,j] = sin(i*j)
+  //   }
+  //   for (int j = 0; j < 199; ++j) {
+  //     B[i,j] = A[0,j] + A[0, j+1]
+  //   }
+  // }
+  static void compressBuffer(BufPtr buf, StmtPtr stmt);
+
+  // Compresses all buffers in the given statement.
+  //
+  // NOTE: This API assumes that there are no accesses to buffers outside
+  // the given statement. So, this should be called with the entire
+  // kernel statement to avoid incorrect buffer compressions.
+  //
+  // TODO: Add an IR verifier check to detect invalidly compressed buffers.
+  static void compressAllBuffers(StmtPtr stmt);
+
+  // Get 'num' loops from the loopnest starting at 'f'.
+  static std::vector<ForPtr> getLoopStmtsInLoopNest(ForPtr f, size_t num);
+
+  // LoopOptions are propagated to tail.
+  static void sliceHead(ForPtr f, int factor, ForPtr* head, ForPtr* tail);
+  static void sliceHead(ForPtr f, int factor);
+  // LoopOptions are propagated to head.
+  static void sliceTail(ForPtr f, int factor, ForPtr* head, ForPtr* tail);
+  static void sliceTail(ForPtr f, int factor);
+
+  using AccessResult = std::pair<BufPtr, StmtPtr>;
+  // Insert a cache for the consumer's usages of the buffer produced in
+  // consumer, and redirect reads and writes in the consumer to that cache.
+  // Returns a pair of the new cache buffer, and the new rewritten consumer.
+  static AccessResult cacheAccesses(
+      BufPtr producer,
+      const std::string& name,
+      StmtPtr consumer);
+
+  // Insert a temporary computation of statement S in the scope of loop AT.
+  // S is assumed to be a Store or a Block containing a Store. Along with the
+  // computation itself, this transformation inserts Alloc/Free statements for
+  // the temporary buffer used in the computation.
+  static void computeAt(StmtPtr s, ForPtr at);
+
+  // Rfactor a reduction axis into a normal axis.
+  //
+  // Requirements:
+  //  * S is the reduction store
+  //  * S is the only statement in the innermost loop
+  //  * There is at least two reduction arguments in S
+  //  * OUTER_REDUCTION_FOR loop corresponds to the outermost reduction variable
+  //  used in the store and all other reduction variables are index variables of
+  //  children loops of OUTER_REDUCTION_FOR
+  //  * OUTER_REDUCTION_FOR is a perfect loop nest, i.e. it has only loops
+  //  corresponding to the other reduction variables and the store, nested into
+  //  each other
+  //
+  // What it does:
+  //   * Introduce a new buffer with an extra dimension of a size equal to the
+  //   span of the loop OUTER_REDUCTION_FOR (the new buffer is returned via
+  //   RFAC_BUF_PTR)
+  //   * Insert an initialization store for the new buffer in
+  //   OUTER_REDUCTION_FOR before its nested loop
+  //   * Replace the reduction store to the original buffer with the reduction
+  //   store to the temp buffer, removing the index var of OUTER_REDUCTION_FOR
+  //   from reduction arguments
+  //   * Insert a final reduction store over the extra dimension of the new
+  //   buffer to the original buffer
+  //   * Returns TRUE if the transformation succeeded and FALSE otherwise
+  //
+  // Example:
+  // Original IR:
+  // S1: for i      # normal axis
+  // S2:   X[i] = 0
+  // S3:   for j    # reduction axis
+  // S4:     for k  # reduction axis
+  // S5:       X[i] = ReduceOp(X[i] + Y[i,j,k], reduce_axis={j,k})
+  //
+  // After RFACTOR(S5, S3)
+  // S1: for i               # normal axis
+  // S2:   X[i] = 0
+  // S3:   for j             # reduction axis for X, normal axis for X_rfac
+  //         X_rfac[i,j] = 0
+  // S4:     for k           # reduction axis
+  //           X_rfac[i,j] = ReduceOp(X_rfac[i,j] + Y[i,j,k], reduce_axis={k})
+  //         X[i] = ReduceOp(X[i] + X_rfac[i,j], reduce_axis={j})
+  static bool rfactor(StmtPtr s, ForPtr outer_reduction_for);
+  static bool rfactor(
+      StmtPtr s,
+      ForPtr outer_reduction_for,
+      BufPtr* rfac_buf_ptr);
+
+  // Vectorize the given loop. This method requires that the given loop
+  // does not perform a reduction.
+  // It returns true if vectorization is successful and false otherwise.
+  static bool vectorize(ForPtr);
+
+  // Find the inner-most loops and vectorize them. Currently, this only works
+  // for the LLVM backend, when no reductions are involved.
+  void vectorizeInnerLoops();
+
+  void eliminateDeadStores();
+
+  void prepareForCodegen();
+
+  const std::unordered_set<BufPtr> getInputBufs() const;
+  const std::unordered_set<BufPtr> getOutputBufs() const {
+    return output_bufs_;
+  }
+  std::vector<BufPtr> getIntermediateBufs() const;
+
+  // Finds which is the outer For between a and b for loops. If neither of the 2
+  // Fors is an ancestor of the other, it returns nullptr.
+  static ForPtr findOuterFor(ForPtr a, ForPtr b);
+
+ private:
+  void initialize(
+      const std::vector<Tensor>& output_tensors,
+      const std::vector<Tensor>& tensors_to_compute);
+
+  StmtPtr root_stmt_;
+
+  std::unordered_set<BufPtr> output_bufs_;
+};
+
+TORCH_API StmtPtr FlattenIndexes(StmtPtr s);
+
+// TODO: Revisit this once we decide on how dependencies analysis should look
+// like. Maybe we would choose to use a different API and BufUse would be
+// removed, or if we decide to keep it we need to properly document its API.
+struct BufLoadOrStoreUse {
+  StmtPtr s;
+  bool isStore;
+};
+
+/*
+ * Returns a map ( Buf -> uses of this Buf), uses are represented as vectors of
+ * BufUse elements, which are StmtPtr and a bool isStore flag. The order of uses
+ * in the vectors reflects the order in which the uses appear in the given
+ * statement.
+ */
+std::unordered_map<BufPtr, std::vector<BufLoadOrStoreUse>> findLoadOrStoreUses(
+    StmtPtr s);
+
+// replaces all invalid characters with underscore
+TORCH_API std::string sanitizeName(const std::string& input_name);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/loopnest_randomization.h

@@ -0,0 +1,13 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Applies a series of loop optimizations chosen randomly. This is only for
+// testing purposes. This allows automatic stress testing of NNC loop
+// transformations.
+void loopnestRandomization(int64_t seed, LoopNest& l);
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/lowerings.h

@@ -0,0 +1,49 @@
+// This file defines classes for registering standard lowerings from JIT to TE
+// IR.
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/runtime/interpreter.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+using ArgNone = std::monostate;
+using BufList = std::vector<tensorexpr::BufHandle>;
+using DoubleList = std::vector<double>;
+using IntList = std::vector<int64_t>;
+using ArgValue = std::variant<
+    tensorexpr::BufHandle,
+    tensorexpr::VarHandle,
+    double,
+    int64_t,
+    bool,
+    BufList,
+    DoubleList,
+    IntList,
+    std::string,
+    ArgNone>;
+
+using NNCLoweringFunction = std::function<Tensor(
+    const std::vector<ArgValue>&,
+    const std::vector<ExprHandle>&,
+    const std::vector<ExprHandle>&,
+    const c10::optional<ScalarType>&,
+    at::Device)>;
+
+TORCH_API FunctionSchemaMap<NNCLoweringFunction>& getNNCLoweringRegistry();
+TORCH_API NNCLoweringFunction getStandardLoweringFor(const std::string& op);
+
+struct RegisterNNCLoweringsFunction {
+  RegisterNNCLoweringsFunction(
+      const std::vector<std::string>& schemas,
+      NNCLoweringFunction fn);
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch