FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/core/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/core

Name	Size	Mode	Actions
boxing/	-	0755	rm
dispatch/	-	0755	rm
op_registration/	-	0755	rm
alias_info.h	2986	0644	edit dl rm
Array.h	768	0644	edit dl rm
ATenGeneral.h	45	0644	edit dl rm
ATenOpList.h	246	0644	edit dl rm
aten_interned_strings.h	25389	0644	edit dl rm
Backtrace.h	59	0644	edit dl rm
blob.h	5422	0644	edit dl rm
builtin_function.h	3649	0644	edit dl rm
DeprecatedTypeProperties.h	3773	0644	edit dl rm
DeprecatedTypePropertiesRegistry.h	795	0644	edit dl rm
Dict.h	13195	0644	edit dl rm
Dict_inl.h	7996	0644	edit dl rm
Dimname.h	1188	0644	edit dl rm
DimVector.h	247	0644	edit dl rm
DistributionsHelper.h	12594	0644	edit dl rm
Formatting.h	959	0644	edit dl rm
function.h	2145	0644	edit dl rm
functional.h	1460	0644	edit dl rm
function_schema.h	13577	0644	edit dl rm
function_schema_inl.h	9319	0644	edit dl rm
Generator.h	4935	0644	edit dl rm
grad_mode.h	210	0644	edit dl rm
interned_strings.h	25332	0644	edit dl rm
interned_strings_class.h	770	0644	edit dl rm
ivalue.h	38823	0644	edit dl rm
ivalue_inl.h	59963	0644	edit dl rm
ivalue_to.h	756	0644	edit dl rm
jit_type.h	75971	0644	edit dl rm
jit_type_base.h	6508	0644	edit dl rm
LegacyTypeDispatch.h	4626	0644	edit dl rm
List.h	15667	0644	edit dl rm
List_inl.h	11012	0644	edit dl rm
Macros.h	44	0644	edit dl rm
MT19937RNGEngine.h	6410	0644	edit dl rm
NamedTensor.h	5050	0644	edit dl rm
operator_name.h	3018	0644	edit dl rm
PhiloxRNGEngine.h	6496	0644	edit dl rm
PythonModeTLS.h	403	0644	edit dl rm
qualified_name.h	4358	0644	edit dl rm
QuantizerBase.h	2443	0644	edit dl rm
Range.h	418	0644	edit dl rm
Reduction.h	461	0644	edit dl rm
rref_interface.h	1144	0644	edit dl rm
Scalar.h	29	0644	edit dl rm
ScalarType.h	33	0644	edit dl rm
stack.h	6034	0644	edit dl rm
Tensor.h	1756	0644	edit dl rm
TensorAccessor.h	10296	0644	edit dl rm
TensorBase.h	32767	0644	edit dl rm
TensorBody.h	247555	0644	edit dl rm
TransformationHelper.h	6911	0644	edit dl rm
typeid.h	29	0644	edit dl rm
UndefinedTensorImpl.h	42	0644	edit dl rm
UnsafeFromTH.h	708	0644	edit dl rm
VariableHooksInterface.h	3312	0644	edit dl rm
Variadic.h	2257	0644	edit dl rm
Vitals.h	2305	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/core/ivalue.h (38823B)

#pragma once

#include <ATen/core/TensorBody.h>
#include <ATen/core/blob.h>
#include <ATen/core/ivalue_to.h>
#include <c10/util/C++17.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/WindowsTorchApiMacro.h>
#include <typeindex>

namespace torch {
class TORCH_API CustomClassHolder : public c10::intrusive_ptr_target {};
namespace jit {
using ::torch::CustomClassHolder;
struct Function;
struct CompilationUnit;
struct Module;
} // namespace jit
} // namespace torch
namespace c10 {
template <class Key, class Value>
class Dict;
template <class T>
class List;
struct IValue;
struct ClassType;
struct Type;
class RRefInterface;
using TypePtr = std::shared_ptr<Type>;

struct ClassType;
using ClassTypePtr = std::shared_ptr<ClassType>;

TORCH_API bool _fastEqualsForContainer(const IValue& lhs, const IValue& rhs);

TORCH_API torch::jit::Function* checkObjectSortSchema(
    const c10::ClassTypePtr& t,
    std::stringstream& why_not);

// A comparator that checks ordering of two IValues of same type.
typedef std::function<bool(const IValue& a, const IValue& b)> IValueComparator;

TORCH_API IValueComparator getLessThanComparator(const IValue& v);
TORCH_API IValueComparator getGreaterThanComparator(const IValue& v);

namespace ivalue {
struct Tuple;
struct Future;
struct ConstantString;
struct GenericDict;
struct Object;
struct PyObjectHolder;
struct EnumHolder;
// We need a ComplexHolder because currently the payloads in the Union
// only take 64 bits. Since ComplexDouble takes up 128 bits, and is too big
// to fit in the IValue directly, we indirect complex numbers through an intrusive
// pointer to ComplexHolder (which contains a c10::complex).
struct ComplexHolder : c10::intrusive_ptr_target {
  public:
    template <typename T>
    ComplexHolder(c10::complex<T> c) {
      val = convert<decltype(val), c10::complex<T>>(c);
    }
    ComplexHolder() {}
    c10::complex<double> val;
};
} // namespace ivalue

// This is an owning wrapper for a c10::optional<std::vector<T>>
// that can be implicitly converted to a (non-owning) optional<ArrayRef<T>>.
// Its purpose is to be used in generated code to keep the vector alive
// either until the end of a statement (as a temporary), or as a saved arg
// in autograd.
template <typename T>
struct OptionalArray {
  c10::optional<std::vector<T>> list;

OptionalArray(){}
  OptionalArray(std::vector<T> val) : list(std::move(val)) {}

// Used when saving an argument for the backwards pass.
  OptionalArray& operator=(c10::optional<ArrayRef<T>> ref) {
    if (ref) {
      list = std::vector<T>(ref->begin(), ref->end());
    } else {
      list = nullopt;
    }
    return *this;
  }

operator c10::optional<c10::ArrayRef<T>>() {
    if (!list) {
      return nullopt;
    }
    return *list;
  }
};

// Capsule is an internal implementation detail of custom C++ classes. We
// define it as an owning wrapper for
// c10::intrusive_ptr<torch::CustomClassHolder> This wrapper is here to serve as
// an abstraction of the type erased custom class object pointer. It also allow
// pybind11 to treat this as a standalone class to register as a separate type
// caster, instead of a custom pointer holder which the pointer holder type
// caster try to "unwrap" it automatically.
struct Capsule {
  c10::intrusive_ptr<torch::CustomClassHolder> obj_ptr;
  explicit Capsule(c10::intrusive_ptr<torch::CustomClassHolder> ptr)
      : obj_ptr(std::move(ptr)) {}
};

// IValue is the generic tagged union used by the interpreter to hold
// all value types.
// It is a 16-byte object with an 8-byte payload and an 8-byte tag.
// The tag is currently 4 bytes to determine the type, and 1 byte
// to mark whether that type is a subtype of c10::intrusive_ptr_target and needs
// retain/release calls.

#define TORCH_FORALL_TAGS(_) \
  _(None)                    \
  _(Tensor)                  \
  _(Storage)                 \
  _(Double)                  \
  _(ComplexDouble)           \
  _(Int)                     \
  _(Bool)                    \
  _(Tuple)                   \
  _(String)                  \
  _(Blob)                    \
  _(GenericList)             \
  _(GenericDict)             \
  _(Future)                  \
  _(Device)                  \
  _(Stream)                  \
  _(Object)                  \
  _(PyObject)                \
  _(Uninitialized)           \
  _(Capsule)                 \
  _(RRef)                    \
  _(Quantizer)               \
  _(Generator)               \
  _(Enum)

// [doxygen private]
// These methods are not actually private but we don't want to document them, so
// they are marked `@private`, which hides them on the doxygen documentation for
// this page.

/// IValue (Interpreter Value) is a tagged union over the types
/// supported by the TorchScript interpreter. IValues contain their
/// values as an `IValue::Payload`, which holds primitive types
/// (`int64_t`, `bool`, `double`, `Device`) and `Tensor` as values,
/// and all other types as a `c10::intrusive_ptr`. In order to
/// optimize performance of the destructor and related operations by
/// making the `Tensor` and `c10::intrusive_ptr` paths generate the
/// same code, we represent a null `c10::intrusive_ptr` as
/// `UndefinedTensorImpl::singleton()`, *not* `nullptr`.
///
/// IValues are used as inputs to and outputs from the TorchScript interpreter.
/// To retrieve the value contained within an IValue, use the `.toX()` methods,
/// where `X` is the type you are trying to get. Note that neither the `.toX()`
/// methods nor the templated `.to<T>` functions do any kind of casting, they
/// only unwrap the contained value. For example:
///
/// \rst
/// .. code-block:: cpp
///
///   // Make the IValue
///   torch::IValue my_ivalue(26);
///   std::cout << my_ivalue << "\n";
///
///   // Unwrap the IValue
///   int64_t my_int = my_ivalue.toInt();
///   std::cout << my_int << "\n";
///
///   // This will throw an error!
///   // `my_ivalue` is tagged as an int and cannot be used as another type
///   torch::Tensor my_tensor = my_ivalue.toTensor();
/// \endrst
struct TORCH_API IValue final {
  IValue(const IValue& rhs)
      : IValue(rhs.payload, rhs.tag, rhs.is_intrusive_ptr) {
    if (is_intrusive_ptr && payload.u.as_intrusive_ptr != c10::UndefinedTensorImpl::singleton()) {
      c10::raw::intrusive_ptr::incref(payload.u.as_intrusive_ptr);
    }
  }

IValue(IValue&& rhs) noexcept : tag(rhs.tag), is_intrusive_ptr(rhs.is_intrusive_ptr) {
    moveFrom(std::move(rhs));
  }

/// @private [doxygen private]
  ~IValue() {
    destroy();
  }

C10_ALWAYS_INLINE IValue& operator=(IValue&& rhs) & noexcept {
    if (&rhs == this) {
      return *this;
    }

destroy();
    moveFrom(std::move(rhs));
    return *this;
  }

IValue& operator=(IValue const& rhs) & {
    IValue(rhs).swap(*this);
    return *this;
  }

void dump() const;

/**
   * Equality comparison. The semantics are the same as Python's `==`:
   * 1. Numerical types are compared by value.
   * 2. Tensors compute element-wise equality, returning a BoolTensor (see:
   * `torch.eq()`)
   * 3. Strings are compared by value.
   * 4. Sequence types (list, tuple) are compared lexicographically by
   *    comparing their elements. Different sequence types never compare equal.
   * 5. Mappings (dict) must have equal (key, value) pairs.
   * 6. If not listed above, the default behavior for is to test identity
   * equality (e.g. pointer equality).
   *
   * Why does this return an IValue instead of a bool? Because in PyTorch,
   * `tensor1 == tensor2` returns a `BoolTensor`, not a bool.
   *
   * NOTE: we (like Python) assume that identity equality implies value equality
   * for efficiency.
   * TODO: need to support customizing equality
   */
  IValue equals(const IValue& rhs) const;
  /**
   * This implements the same semantics as `bool(lhs == rhs)` in Python. which
   * is the same as `equals()` except for Tensor types.
   */
  TORCH_API friend bool operator==(const IValue& lhs, const IValue& rhs);
  TORCH_API friend bool operator!=(const IValue& lhs, const IValue& rhs);

/**
   * Identity comparison. Checks if `this` is the same object as `rhs`. The
   * semantics are the same as Python's `is` operator.
   *
   * NOTE: Like in Python, this operation is poorly defined for primitive types
   * like numbers and strings. Prefer to use `==` unless you really want to
   * check identity equality.
   */
  bool is(const IValue& rhs) const;

/**
   * Hashing for IValues. Returns an IValue-boxed int.
   *
   * Some notes:
   * - Like eager, Tensors are hashed by looking at the pointer. This is not
   *   strictly correct because two value-equal tensors with different tensor
   *   pointers will hash differently, but we choose to reproduce the eager
   *   semantics.
   * - Hashing is not defined on all built-in IValue types (e.g. list and
   *   dict), following Python. Calling `hash()` on these types will throw.
   */
  IValue hash() const {
    return (int64_t)IValue::hash(*this);
  }
  // This is defined because `c10::hash` dispatches to a function of this
  // signature. See the member function `hash()`.
  static size_t hash(const IValue& iv);

/**
   * @private [doxygen private]
   * [container equality]
   * This is an equality implementation that assumes objects with the same
   * identity equal themselves, for efficiency reasons. We primarily have this
   * for consistency, because Python does the same thing. This actually
   * provokes user-visible changes in behavior due to quirks in torch:
   *      [tensor1] == [tensor1] -> True (because container equality will first
   * compare identity) [tensor1] == [tensor1_copy] -> RuntimeError:
   * Boolean value of Tensor with more than one value is ambiguous
   */
  TORCH_API friend bool _fastEqualsForContainer(
      const IValue& lhs,
      const IValue& rhs);

/// @private [doxygen private]
  bool isAliasOf(const IValue& rhs) const {
    if (this->tag != rhs.tag) {
      // Trivially don't alias if the type is different
      return false;
    }

// Tensors should be compared based on internal storage
    if (this->isTensor()) {
      const auto& thisTensor = this->toTensor();
      const auto& rhsTensor = rhs.toTensor();
      // mkldnn tensors dont have views or storage, so we compare
      // based on tensor impl. //TODO: find a way to use mkldnn storage
      if (thisTensor.is_mkldnn() || rhsTensor.is_mkldnn()) {
        return thisTensor.unsafeGetTensorImpl() ==
            rhsTensor.unsafeGetTensorImpl();
      }

return thisTensor.is_alias_of(rhsTensor);
    }

if (!this->is_intrusive_ptr) {
      // Primitive types don't alias anything
      return false;
    }

AT_ASSERT(rhs.is_intrusive_ptr);

// Other types can be compared by their ptr value
    return this->payload.u.as_intrusive_ptr == rhs.payload.u.as_intrusive_ptr;
  }

/// @private [doxygen private]
  size_t use_count() const noexcept {
    if (isTensor()) {
      return payload.as_tensor.use_count();
    }

if (!is_intrusive_ptr) {
      return 1;
    }

if (payload.u.as_intrusive_ptr == c10::UndefinedTensorImpl::singleton()) {
      return 0;
    }
    return c10::raw::intrusive_ptr::use_count(payload.u.as_intrusive_ptr);
  }

/// @private [doxygen private]
  void swap(IValue& rhs) noexcept {
    if (isTensor() && rhs.isTensor()) {
      std::swap(payload.as_tensor, rhs.payload.as_tensor);
    } else if (isTensor()) {
      at::Tensor t = std::move(payload.as_tensor);
      // As far as I can tell, omitting the usual explicit destructor call
      // is not UB in and of itself, and it's a slight perf win. The
      // destructor is a no-op, because the moved-from Tensor is
      // effectively an intrusive_ptr in the null state, so we don't need
      // the behavior for correctness reasons either. Leaving this
      // explanatory comment, including commented-out destructor call, to
      // make this abundantly clear.
      //
      // payload.as_tensor.~Tensor();
      payload.u = rhs.payload.u;
      new (&rhs.payload.as_tensor) at::Tensor(std::move(t));
    } else if (rhs.isTensor()) {
      rhs.swap(*this);
      return;
    } else {
      std::swap(payload.u, rhs.payload.u);
    }
    std::swap(is_intrusive_ptr, rhs.is_intrusive_ptr);
    std::swap(tag, rhs.tag);
  }

// Accessors for subtypes are arranged together below
  // While some of these accessors could be generated through templates,
  // we prefer to write them manually for clarity

IValue(at::Tensor t) : tag(Tag::Tensor), is_intrusive_ptr(false) {
    new (&payload.as_tensor) at::Tensor(std::move(t));
  }
  bool isTensor() const {
    return Tag::Tensor == tag;
  }

private:
  // Outlined error path so that toTensor() can be inlined.
  [[noreturn]] void reportToTensorTypeError() const;

public:
  at::Tensor toTensor() &&;
  at::Tensor& toTensor() &;
  const at::Tensor& toTensor() const&;
  at::TensorImpl* unsafeToTensorImpl() const {
    return payload.as_tensor.unsafeGetTensorImpl();
  }

IValue(at::Storage s) : tag(Tag::Storage), is_intrusive_ptr(static_cast<bool>(s)) {
    // Note: the undefined tensor is not refcounted, so while it
    // is tagged as a tensor, is_intrusive_ptr is set to false.
    // This is not an optional optimization: our incref call
    // *will not* do the right thing when called on an
    // undefined tensor.
    payload.u.as_intrusive_ptr = null_to_undefined_tensor(s.unsafeReleaseStorageImpl());
  }
  bool isStorage() const {
    return Tag::Storage == tag;
  }
  c10::Storage toStorage() &&;
  c10::Storage toStorage() const&;

const IValue& toIValue() const {
    return *this;
  }
  IValue& toIValue() {
    return *this;
  }

/// @private [doxygen private]
  IValue(intrusive_ptr<caffe2::Blob> blob)
      : tag(Tag::Blob), is_intrusive_ptr(true) {
    // TODO (after Tensor merge) If we pass in a Blob holding a Tensor, extract
    // and store it as a Tensor instead.
    payload.u.as_intrusive_ptr = null_to_undefined_tensor(blob.release());
  }

/// @private [doxygen private]
  bool isBlob() const {
    return Tag::Blob == tag;
  }

/// @private [doxygen private]
  c10::intrusive_ptr<caffe2::Blob> toBlob() &&;

/// @private [doxygen private]
  c10::intrusive_ptr<caffe2::Blob> toBlob() const&;

// Capsule. No new callsites of these APIs should
  // be introduced.
  static inline IValue make_capsule(
      intrusive_ptr<torch::CustomClassHolder> blob);
  bool isCapsule() const {
    return Tag::Capsule == tag;
  }
  c10::intrusive_ptr<torch::CustomClassHolder> toCapsule() &&;
  c10::intrusive_ptr<torch::CustomClassHolder> toCapsule() const&;

// Custom C++ classes
  template <
      typename T,
      std::enable_if_t<
          std::is_base_of<torch::CustomClassHolder, T>::value,
          int> = 0>
  IValue(intrusive_ptr<T> custom_class);
  bool isCustomClass() const;
  template <typename T>
  c10::intrusive_ptr<T> toCustomClass() &&;
  template <typename T>
  c10::intrusive_ptr<T> toCustomClass() const&;

// Tuple
  IValue(c10::intrusive_ptr<ivalue::Tuple> v);

template <
      typename... Args,
      std::enable_if_t<
          !guts::disjunction<
              std::is_lvalue_reference<Args>...,
              guts::negation<std::is_constructible<IValue, Args>>...>::value,
          std::nullptr_t> = nullptr>
  IValue(const std::tuple<Args...>& t);
  template <
      typename... Args,
      std::enable_if_t<
          !guts::disjunction<
              std::is_lvalue_reference<Args>...,
              guts::negation<std::is_constructible<IValue, Args>>...>::value,
          std::nullptr_t> = nullptr>
  IValue(std::tuple<Args...>&& t);
  bool isTuple() const {
    return Tag::Tuple == tag;
  }
  c10::intrusive_ptr<ivalue::Tuple> toTuple() &&;
  c10::intrusive_ptr<ivalue::Tuple> toTuple() const&;

// Double
  IValue(double d) : tag(Tag::Double), is_intrusive_ptr(false) {
    payload.u.as_double = d;
  }
  bool isDouble() const {
    return Tag::Double == tag;
  }
  double toDouble() const {
    AT_ASSERT(isDouble());
    return payload.u.as_double;
  }

// ComplexDouble
  template <typename T>
  IValue(c10::complex<T> c);
  bool isComplexDouble() const { return Tag::ComplexDouble == tag; }
  c10::complex<double> toComplexDouble() const;

// Future
  IValue(c10::intrusive_ptr<ivalue::Future> v);
  bool isFuture() const {
    return Tag::Future == tag;
  }
  c10::intrusive_ptr<ivalue::Future> toFuture() &&;
  c10::intrusive_ptr<ivalue::Future> toFuture() const&;

// RRef
  IValue(c10::intrusive_ptr<c10::RRefInterface> v);
  bool isRRef() const {
    return Tag::RRef == tag;
  }
  c10::intrusive_ptr<c10::RRefInterface> toRRef() &&;
  c10::intrusive_ptr<c10::RRefInterface> toRRef() const&;

// Quantizer
  IValue(c10::intrusive_ptr<at::Quantizer> v);
  bool isQuantizer() const {
    return Tag::Quantizer == tag;
  }
  c10::intrusive_ptr<at::Quantizer> toQuantizer() &&;
  c10::intrusive_ptr<at::Quantizer> toQuantizer() const&;

// Int
  IValue(int64_t i) : tag(Tag::Int), is_intrusive_ptr(false) {
    payload.u.as_int = i;
  }

// allow you to pass literals (3, 4) without ambiguity
  IValue(int32_t i) : IValue(static_cast<int64_t>(i)) {}

bool isInt() const {
    return Tag::Int == tag;
  }

int64_t toInt() const {
    AT_ASSERT(isInt());
    return payload.u.as_int;
  }

// Bool
  IValue(bool b) : tag(Tag::Bool), is_intrusive_ptr(false) {
#if defined(__clang__) && defined(__x86_64__)
    // Initializing entire payload stops valgrind's from reporting
    // "jump or move depends on uninitialised value" in IValue copy constructor
    // See https://github.com/pytorch/pytorch/issues/37117
    payload.u.as_int = b;
#else
    payload.u.as_bool = b;
#endif
  }
  bool isBool() const {
    return Tag::Bool == tag;
  }
  bool toBool() const {
    AT_ASSERT(isBool());
    return payload.u.as_bool;
  }

// IntList
  bool isIntList() const;
  c10::List<int64_t> toIntList() &&;
  c10::List<int64_t> toIntList() const&;
  std::vector<int64_t> toIntVector() const;

// ConstantString
  IValue(c10::intrusive_ptr<ivalue::ConstantString> v);
  IValue(std::string v);
  IValue(const char* v) : IValue(std::string(v)) {}
  IValue(c10::string_view v) : IValue(std::string(v)) {};
  bool isString() const {
    return Tag::String == tag;
  }
  c10::intrusive_ptr<ivalue::ConstantString> toString() &&;
  c10::intrusive_ptr<ivalue::ConstantString> toString() const&;
  const std::string& toStringRef() const;
  c10::optional<std::reference_wrapper<const std::string>> toOptionalStringRef()
      const;
  c10::string_view toStringView() const;

// DoubleList
  bool isDoubleList() const;
  c10::List<double> toDoubleList() &&;
  c10::List<double> toDoubleList() const&;
  std::vector<double> toDoubleVector() const;

// ComplexDoubleList
  bool isComplexDoubleList() const;
  c10::List<c10::complex<double>> toComplexDoubleList() &&;
  c10::List<c10::complex<double>> toComplexDoubleList() const&;
  std::vector<c10::complex<double>> toComplexDoubleVector() const;

// BoolList
  bool isBoolList() const;
  c10::List<bool> toBoolList() &&;
  c10::List<bool> toBoolList() const&;

// TensorList
  bool isTensorList() const;
  c10::List<at::Tensor> toTensorList() &&;
  c10::List<at::Tensor> toTensorList() const&;
  std::vector<at::Tensor> toTensorVector() const;

// GenericList
  IValue(c10::List<IValue> v);
  bool isList() const {
    return Tag::GenericList == tag;
  }
  c10::List<IValue> toList() &&;
  c10::List<IValue> toList() const&;
  c10::ArrayRef<IValue> toListRef() const;

// Some template constructors of IValue calls another constructor recursively.
  // This SNIFAEs the called constructor exists.
  template <class T>
  using enable_if_ivalue_constructible =
      std::enable_if_t<std::is_constructible<IValue, T>::value, std::nullptr_t>;

template <class T, enable_if_ivalue_constructible<T> = nullptr>
  IValue(c10::List<T>&& v);
  template <class T, enable_if_ivalue_constructible<T> = nullptr>
  IValue(const c10::List<T>& v);
  template <class T, enable_if_ivalue_constructible<T> = nullptr>
  IValue(at::ArrayRef<T> v);
  template <class T, enable_if_ivalue_constructible<T> = nullptr>
  IValue(const std::vector<T>& v);
  template <class T, size_t N>
  IValue(std::array<T, N> v);

// GenericDict
  IValue(c10::Dict<IValue, IValue> v);
  bool isGenericDict() const {
    return Tag::GenericDict == tag;
  }
  c10::Dict<IValue, IValue> toGenericDict() &&;
  c10::Dict<IValue, IValue> toGenericDict() const&;

template <class Key, class Value>
  IValue(c10::Dict<Key, Value> v);

template <class Key, class Value>
  /// \cond
  /// DOXYGEN_CANNOT_HANDLE_CONSTRUCTORS_WITH_MACROS_SO_EXCLUDE_THIS_LINE_FROM_DOXYGEN
  C10_DEPRECATED_MESSAGE(
      "IValues based on std::unordered_map<K, V> are slow and deprecated. Please use c10::Dict<K, V> instead.")
      /// \endcond
      IValue(std::unordered_map<Key, Value> v);

template <class T, enable_if_ivalue_constructible<T> = nullptr>
  IValue(c10::optional<T> v);
  IValue(c10::nullopt_t);

// ClassType
  IValue(c10::intrusive_ptr<ivalue::Object> v);
  bool isObject() const {
    return tag == Tag::Object;
  }
  c10::intrusive_ptr<ivalue::Object> toObject() &&;
  c10::intrusive_ptr<ivalue::Object> toObject() const&;
  const ivalue::Object& toObjectRef() const;

torch::jit::Module toModule() const;
  bool isModule() const;

// PyObject
  IValue(c10::intrusive_ptr<ivalue::PyObjectHolder> v);
  bool isPyObject() const {
    return tag == Tag::PyObject;
  }
  c10::intrusive_ptr<ivalue::PyObjectHolder> toPyObjectHolder() &&;
  c10::intrusive_ptr<ivalue::PyObjectHolder> toPyObjectHolder() const&;
  PyObject* toPyObject() const;

// Enum
  explicit IValue(c10::intrusive_ptr<ivalue::EnumHolder> v);
  bool isEnum() const {
    return tag == Tag::Enum;
  }
  c10::intrusive_ptr<ivalue::EnumHolder> toEnumHolder() &&;
  c10::intrusive_ptr<ivalue::EnumHolder> toEnumHolder() const&;

// None
  IValue() : tag(Tag::None), is_intrusive_ptr(false) {}
  bool isNone() const {
    return Tag::None == tag;
  }
  std::string toNone() const {
    AT_ASSERT(isNone());
    return "None";
  }

static IValue uninitialized() {
    auto i = IValue();
    i.tag = Tag::Uninitialized;
    return i;
  }

// Scalar, which gets encoded as either an Int, a Double or a ComplexDouble
  IValue(const at::Scalar& s) : IValue() {
    if (s.isFloatingPoint()) {
      *this = s.toDouble();
    } else if (s.isComplex()) {
      *this = s.toComplexDouble();
    } else if (s.isBoolean()) {
      *this = s.toBool();
    } else if (s.isIntegral(false)) {
      *this = s.toLong();
    } else {
      TORCH_CHECK(false, "Unknown type in Scalar");
    }
  }

bool isScalar() const {
    return isDouble() || isInt() || isComplexDouble() || isBool();
  }

at::Scalar toScalar() const {
    if (isDouble())
      return toDouble();
    else if (isInt())
      return toInt();
    else if (isComplexDouble())
      return toComplexDouble();
    else if (isBool())
      return toBool();
    throw std::runtime_error("IValue is not a Scalar");
  }

// Device
  IValue(c10::Device d) : tag(Tag::Device), is_intrusive_ptr(false) {
    payload.u.as_device.type = d.type();
    payload.u.as_device.index = d.index();
  }
  bool isDevice() const {
    return Tag::Device == tag;
  }
  c10::Device toDevice() const {
    AT_ASSERT(isDevice());
    return c10::Device(payload.u.as_device.type, payload.u.as_device.index);
  }

//Stream
  IValue(c10::Stream stream)
    : tag(Tag::Stream), is_intrusive_ptr(false) {
    payload.u.as_int = stream.pack();
  }
  c10::Stream toStream() &&;
  c10::Stream toStream() const &;
  bool isStream() const { return Tag::Stream == tag; }

// ScalarType
  IValue(ScalarType t)
      : IValue(static_cast<std::underlying_type<ScalarType>::type>(t)) {}
  at::ScalarType toScalarType() const {
    return static_cast<at::ScalarType>(toInt());
  }

// Layout
  IValue(Layout l)
      : IValue(static_cast<std::underlying_type<Layout>::type>(l)) {}
  at::Layout toLayout() const {
    return static_cast<at::Layout>(toInt());
  }

// MemoryFormat
  IValue(MemoryFormat m)
      : IValue(static_cast<std::underlying_type<MemoryFormat>::type>(m)) {}
  at::MemoryFormat toMemoryFormat() const {
    return static_cast<at::MemoryFormat>(toInt());
  }

// QScheme
  IValue(at::QScheme qscheme) : tag(Tag::Int), is_intrusive_ptr(false) {
    payload.u.as_int = static_cast<int64_t>(qscheme);
  }

at::QScheme toQScheme() const {
    return static_cast<at::QScheme>(toInt());
  }

// Dimname
  IValue(at::Dimname dimname) : IValue(dimname.symbol().toQualString()) {}

at::Dimname toDimname() const {
    return at::Dimname::fromSymbol(Symbol::fromQualString(toStringRef()));
  }

// Generator
  IValue(at::Generator g) : tag(Tag::Generator), is_intrusive_ptr(g.defined()) {
    // Note: the undefined generator is not refcounted, so while it
    // is tagged as a generator, is_intrusive_ptr is set to false.
    // This is not an optional optimization: our incref call
    // *will not* do the right thing when called on an
    // undefined generator.
    payload.u.as_intrusive_ptr = null_to_undefined_tensor(g.unsafeReleaseGeneratorImpl());
  }
  bool isGenerator() const {
    return Tag::Generator == tag;
  }
  at::Generator toGenerator() &&;
  at::Generator toGenerator() const&;

// for debugging
  std::string tagKind() const {
    switch (tag) {
#define DEFINE_CASE(x) \
  case Tag::x:         \
    return #x;
      TORCH_FORALL_TAGS(DEFINE_CASE)
#undef DEFINE_CASE
    }
    return "InvalidTag(" + c10::guts::to_string(static_cast<int>(tag)) + ")";
  }

// generic v.to<at::Tensor>() implementations
  // that can be used in special functions like pop/push
  // that use template meta-programming.
  // prefer the directly named methods when you can,
  // since they are simpler to understand

// Note: if you get linker errors saying one of these is missing,
  // change it to ... && = delete; and you will see better error messages for
  // why However, we cannot commit this because some compiler versions barf on
  // it.
  template <typename T>
  T to() &&;
  template <typename T>
  typename c10::detail::ivalue_to_const_ref_overload_return<T>::type to() const&;

// ToOptional: convert a IValue to the Optional obj that accepts both T and
  // None
  template <typename T>
  optional<T> toOptional();
  template <typename T>
  optional<T> toOptional() const;

/// @private [doxygen private]
  /// this is a shallow comparison of two IValues to test the object identity
  bool isSameIdentity(const IValue& rhs) const;

// Computes the "official" string representation of an IValue. This produces a
  // TorchScript expression that can be used to recreate an IValue with the same
  // value (e.g. when we are printing constants in the serializer).
  //
  // Callers can use `customFormatter` to override how `repr()` prints out an
  // IValue. This is useful if you have some other environment where you can
  // look up values, and you want to print a reference to that environment (like
  // the serializer's constant table).
  //
  // repr() is not necessarily defined on all objects!
  std::ostream& repr(
      std::ostream& stream,
      std::function<bool(std::ostream&, const IValue& v)> customFormatter)
      const;

// Computes an "informal" string representation of an IValue. This should be
  // used for debugging, or servicing `print()`-like functions.
  // This is different from `repr()` in that there is no expectation that we can
  // exactly reconstruct an IValue from the output; feel free to use a
  // concise/pretty form
  TORCH_API friend std::ostream& operator<<(
      std::ostream& out,
      const IValue& v);

bool isPtrType() const {
    return (isTensor() && payload.as_tensor.defined()) || is_intrusive_ptr;
  }

/// @private [doxygen private]
  const void* internalToPointer() const {
    TORCH_INTERNAL_ASSERT(
        isPtrType(), "Can only call internalToPointer() for pointer types");
    if (isTensor()) {
      return payload.as_tensor.unsafeGetTensorImpl();
    } else {
      return payload.u.as_intrusive_ptr != c10::UndefinedTensorImpl::singleton()
        ? payload.u.as_intrusive_ptr : nullptr;
    }
  }

TypePtr type() const;

// Detect aliased tensors.
  struct HashAliasedIValue {
    size_t operator()(const IValue& val) const {
      if (val.isTensor()) {
        if (val.toTensor().is_mkldnn()) {
          // MKLDNN tensors dont have storage and dont create views
          // or aliasing so we can just use Tensor pointer, TODO: find way
          // to use mkldnn storage
          return reinterpret_cast<size_t>(val.toTensor().unsafeGetTensorImpl());
        } else {
          return reinterpret_cast<size_t>(
              val.toTensor().storage().unsafeGetStorageImpl());
        }
      }
      // If it is not a Tensor, then two mutable IValues alias each other only
      // if they are the same pointer.
      return val.payload.u.as_int;
    }
  };

struct CompAliasedIValues {
    bool operator()(const IValue& lhs, const IValue& rhs) const {
      return lhs.isAliasOf(rhs);
    }
  };

using HashAliasedIValues =
      std::unordered_set<IValue, HashAliasedIValue, CompAliasedIValues>;
  using HashAliasedIValueMap =
      std::unordered_map<IValue, IValue, HashAliasedIValue, CompAliasedIValues>;

// Chechs if this and rhs has a subvalues in common.
  // [t1,t2] and [t2, t3] returns true.
  bool overlaps(const IValue& rhs) const;

// Inserts all subvalues of this in subValues.
  void getSubValues(HashAliasedIValues& subValues) const;

// Apply visitor to every subvalue.
  // TODO: There are several places that recurse over IValue. This is fragile.
  // This visitor should be used to recurse over ivalues.
  void visit(const std::function<bool(const IValue&)>& visitor) const;
  IValue deepcopy() const;
  IValue deepcopy(HashAliasedIValueMap& memo) const;

private:
  static c10::intrusive_ptr_target* null_to_undefined_tensor(c10::intrusive_ptr_target* p) {
    return p ? p : static_cast<c10::intrusive_ptr_target*>(c10::UndefinedTensorImpl::singleton());
  }

static bool ptrEqual(const IValue& lhs, const IValue& rhs);
  // NOTE: IValue tags are intentionally private. In the future we may encode
  // this value different (e.g. using NaN boxing), and this would make it more
  // costly to determine the tag for all types vs just determining if something
  // is a particular type. Instead we want clients to use the `isX` methods when
  // possible. If for perf. reasons you really, absolutely, must have a jump
  // table, then we can revisit this.
  enum class Tag : uint32_t {
#define DEFINE_TAG(x) x,
    TORCH_FORALL_TAGS(DEFINE_TAG)
#undef DEFINE_TAG
  };

template <
      class T,
      class NullType = c10::detail::intrusive_target_default_null_type<T>>
  c10::intrusive_ptr<T, NullType> moveToIntrusivePtr();
  template <
      typename T,
      class NullType = c10::detail::intrusive_target_default_null_type<T>>
  c10::intrusive_ptr<T, NullType> toIntrusivePtr() const;

void destroy() {
    // We carefully construct this call to both 1) avoid UB by using
    // the "wrong" one of as_tensor and as_intrusive_ptr and 2) enable
    // the compiler to generate the same code for each case. It is
    // surprisingly difficult to get this right.
    if (isTensor() || is_intrusive_ptr) {
      c10::intrusive_ptr_target* p = isTensor() ? payload.as_tensor.unsafeGetTensorImpl() : payload.u.as_intrusive_ptr;
      c10::intrusive_ptr<intrusive_ptr_target, c10::UndefinedTensorImpl>::reclaim(p);
      // No need to make this destructor call!
      // payload.as_tensor.~Tensor();
    }
  }

C10_ALWAYS_INLINE void moveFrom(IValue&& rhs) noexcept {
    if (rhs.isTensor()) {
      new (&payload.as_tensor) at::Tensor(std::move(rhs.payload.as_tensor));
      // As far as I can tell, omitting the usual explicit destructor call
      // is not UB in and of itself, and it's a slight perf win. The
      // destructor is a no-op, because the moved-from Tensor is
      // effectively an intrusive_ptr in the null state, so we don't need
      // the behavior for correctness reasons either. Leaving this
      // explanatory comment, including commented-out destructor call, to
      // make this abundantly clear.
      //
      // rhs.payload.as_tensor.~Tensor();
    } else {
      payload.u = rhs.payload.u;
    }
    tag = rhs.tag;
    is_intrusive_ptr = rhs.is_intrusive_ptr;
    rhs.clearToNone();
  }

void clearToNone() noexcept {
    payload.u.as_int = 0;
    tag = Tag::None;
    is_intrusive_ptr = false;
  }

union Payload {
    // We use a nested union here so that we can make the copy easy
    // and efficient in the non-tensor (i.e., trivially copyable)
    // case. Specifically, we do not have to do a switch-on-tag to
    // figure out which union member to assign; we can just use
    // TriviallyCopyablePayload::operator=.
    union TriviallyCopyablePayload {
      TriviallyCopyablePayload() : as_int(0) {}
      int64_t as_int;
      double as_double;
      bool as_bool;
      // Invariant: never nullptr; null state is represented as
      // c10::UndefinedTensorImpl::singleton() for consistency of
      // representation with Tensor.
      c10::intrusive_ptr_target* as_intrusive_ptr;
      struct {
        DeviceType type;
        DeviceIndex index;
      } as_device;
    } u;
    at::Tensor as_tensor;
    Payload() : u() {}
    ~Payload() {}
  };

IValue(const Payload& p, Tag t, bool i) : tag(t), is_intrusive_ptr(i) {
    if (isTensor()) {
      new (&payload.as_tensor) at::Tensor(p.as_tensor);
    } else {
      payload.u = p.u;
    }
  }

Payload payload;
  Tag tag;
  bool is_intrusive_ptr;
  friend struct WeakIValue;
};

struct TORCH_API WeakIValue final {
  WeakIValue() : tag(IValue::Tag::None), is_intrusive_ptr(false) {}

WeakIValue(const WeakIValue& rhs)
      : payload(rhs.payload),
        tag(rhs.tag),
        is_intrusive_ptr(rhs.is_intrusive_ptr) {
    if (is_intrusive_ptr && payload.as_intrusive_ptr != c10::UndefinedTensorImpl::singleton()) {
      c10::raw::weak_intrusive_ptr::incref(payload.as_intrusive_ptr);
    }
  }
  WeakIValue(const IValue& rhs)
      : tag(rhs.tag),
        is_intrusive_ptr(rhs.is_intrusive_ptr) {
    if (rhs.isTensor()) {
      payload.as_intrusive_ptr = rhs.unsafeToTensorImpl();
      is_intrusive_ptr = true;
    } else {
      payload = rhs.payload.u;
    }
    if (is_intrusive_ptr) {
      if (payload.as_intrusive_ptr != c10::UndefinedTensorImpl::singleton()) {
        c10::raw::weak_intrusive_ptr::incref(payload.as_intrusive_ptr);
      }
    }
  }
  WeakIValue(WeakIValue&& rhs) noexcept : WeakIValue() {
    swap(rhs);
  }
  ~WeakIValue() {
    if (is_intrusive_ptr && payload.as_intrusive_ptr != c10::UndefinedTensorImpl::singleton()) {
      c10::raw::weak_intrusive_ptr::decref(payload.as_intrusive_ptr);
    }
  }
  WeakIValue& operator=(WeakIValue&& rhs) & noexcept {
    WeakIValue(std::move(rhs)).swap(*this); // this also sets rhs to None
    return *this;
  }
  WeakIValue& operator=(WeakIValue const& rhs) & {
    WeakIValue(rhs).swap(*this);
    return *this;
  }
  void swap(WeakIValue& rhs) noexcept {
    std::swap(payload, rhs.payload);
    std::swap(is_intrusive_ptr, rhs.is_intrusive_ptr);
    std::swap(tag, rhs.tag);
  }

bool isSameIdentity(const WeakIValue& rhs) const {
    return payload.as_int == rhs.payload.as_int && tag == rhs.tag &&
        is_intrusive_ptr == rhs.is_intrusive_ptr;
  }

IValue lock() const {
    if (!is_intrusive_ptr) {
      IValue::Payload newPayload;
      newPayload.u = payload;
      return IValue(newPayload, tag, false);
    }
    if (IValue::Tag::Tensor == tag) {
      auto temp = c10::weak_intrusive_ptr<at::TensorImpl, c10::UndefinedTensorImpl>::reclaim(
          static_cast<at::TensorImpl*>(payload.as_intrusive_ptr));
      c10::intrusive_ptr<at::TensorImpl, c10::UndefinedTensorImpl> ip(temp.lock());
      temp.release();
      if (!ip) {
        return IValue();
      } else {
        return IValue(at::Tensor(std::move(ip)));
      }
    } else {
      auto temp = c10::weak_intrusive_ptr<c10::intrusive_ptr_target>::reclaim(
          payload.as_intrusive_ptr == c10::UndefinedTensorImpl::singleton()
          ? nullptr
          : payload.as_intrusive_ptr);
      IValue::Payload pl;
      pl.u.as_intrusive_ptr = temp.lock().release();
      temp.release();
      if (!pl.u.as_intrusive_ptr) {
        return IValue();
      } else {
        return IValue(pl, tag, true);
      }
    }
  }

size_t use_count() const noexcept {
    if (!is_intrusive_ptr) {
      return 1;
    }
    auto temp = c10::weak_intrusive_ptr<c10::intrusive_ptr_target, c10::UndefinedTensorImpl>::reclaim(
        payload.as_intrusive_ptr);
    size_t result = temp.use_count();
    temp.release();
    return result;
  }

size_t weak_use_count() const noexcept {
    if (!is_intrusive_ptr) {
      return 1;
    }
    auto temp = c10::weak_intrusive_ptr<c10::intrusive_ptr_target, c10::UndefinedTensorImpl>::reclaim(
        payload.as_intrusive_ptr);
    size_t result = temp.weak_use_count();
    temp.release();
    return result;
  }
  size_t hash() const {
    return payload.as_int;
  }

private:
  using Payload = IValue::Payload::TriviallyCopyablePayload;
  Payload payload;
  IValue::Tag tag;
  bool is_intrusive_ptr;
};

// An owning pointer to a type. When the type is class type, it requires a pair
// of shared_ptrs to the class type and its owning CU, so that the class type is
// guaranteed to stay alive as long as we hold this object.
struct TORCH_API StrongTypePtr {
  StrongTypePtr(
      std::shared_ptr<torch::jit::CompilationUnit> cu,
      std::shared_ptr<Type> type);

std::shared_ptr<torch::jit::CompilationUnit> cu_;
  std::shared_ptr<Type> type_;
};

TORCH_API ska::flat_hash_map<std::type_index, c10::ClassTypePtr>&
getCustomClassTypeMap();

template <typename T>
c10::ClassTypePtr getCustomClassTypeImpl() {
  auto& tmap = c10::getCustomClassTypeMap();
  auto res = tmap.find(std::type_index(typeid(T)));
  if (res == tmap.end()) {
    throw c10::Error("Can't find class id in custom class type map", "");
  }
  return res->second;
}

template <typename T>
const c10::ClassTypePtr& getCustomClassType() {
  // Classes are never unregistered from getCustomClassTypeMap and the
  // hash lookup can be a hot path, so just cache.
  // For the same reason, it's fine If this ends up getting duplicated across
  // DSO boundaries for whatever reason.
  static c10::ClassTypePtr cache = getCustomClassTypeImpl<T>();
  return cache;
}

TORCH_API std::unordered_map<std::string, std::function<PyObject*(void*)>>&
getClassConverter();
} // namespace c10

#include <ATen/core/ivalue_inl.h>

cmd: