#pragma once

#include <c10/core/Backend.h>
#include <c10/core/CopyBytes.h>
#include <c10/core/DispatchKeySet.h>
#include <c10/core/InferenceMode.h>
#include <c10/core/MemoryFormat.h>
#include <c10/core/Storage.h>
#include <c10/core/SymIntArrayRef.h>
#include <c10/core/TensorOptions.h>
#include <c10/core/WrapDimMinimal.h>
#include <c10/core/impl/LocalDispatchKeySet.h>
#include <c10/core/impl/PyInterpreter.h>
#include <c10/core/impl/SizesAndStrides.h>
#include <c10/util/DimVector.h>
#include <c10/util/Exception.h>
#include <c10/util/Flags.h>
#include <c10/util/Logging.h>
#include <c10/util/Optional.h>
#include <c10/util/accumulate.h>
#include <c10/util/irange.h>
#include <c10/util/python_stub.h>
#include <c10/util/safe_numerics.h>
#include <c10/util/strong_type.h>

#include <algorithm>
#include <atomic>
#include <limits>
#include <memory>
#include <numeric>

// A global boolean variable to control whether we free memory when a Tensor
// is shrunk to a smaller size. As a result, a Tensor is always going to
// keep the memory allocated for its maximum capacity reshaped to so far.
//
// This parameter is respected "upper-case" methods which call Resize()
// (e.g., CopyFrom, ResizeLike); it is NOT respected by Tensor::resize_
// or ShrinkTo, both of which guarantee to never to free memory.
C10_DECLARE_bool(caffe2_keep_on_shrink);

// Since we can have high variance in blob memory allocated across different
// inputs in the same run, we will shrink the blob only if the memory gain
// is larger than this flag in bytes.  This only applies to functions which
// respect caffe2_keep_on_shrink.
C10_DECLARE_int64(caffe2_max_keep_on_shrink_memory);

C10_CLANG_DIAGNOSTIC_PUSH()
#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
#endif

namespace at {
class Tensor;
class TensorBase;
} // namespace at

namespace c10 {
class Scalar;
struct Storage;
} // namespace c10

namespace c10 {

/**
 * A utility function to convert vector<int> to vector<int64_t>.
 */
inline std::vector<int64_t> ToVectorint64_t(const ArrayRef<int>& src) {
  return std::vector<int64_t>(src.begin(), src.end());
}

/**
 * Return product of all dimensions starting from k
 */
inline int64_t size_from_dim_(int k, IntArrayRef dims) {
  int64_t r = 1;
  for (const auto i : c10::irange(k, dims.size())) {
    r *= dims[i];
  }
  return r;
}

// Product of all dims up to k (not including dims[k])
inline int64_t size_to_dim_(int k, IntArrayRef dims) {
  TORCH_CHECK((unsigned)k <= dims.size());
  int64_t r = 1;
  for (const auto i : c10::irange(k)) {
    r *= dims[i];
  }
  return r;
}

// Product of all dims between k and l (not including dims[k] and dims[l])
inline int64_t size_between_dim_(int k, int l, IntArrayRef dims) {
  TORCH_CHECK((unsigned)l < dims.size() && (unsigned)k < dims.size());
  int64_t r = 1;
  if (k < l) {
    for (int i = k + 1; i < l; ++i) {
      r *= dims[i];
    }
  } else {
    for (int i = l + 1; i < k; ++i) {
      r *= dims[i];
    }
  }
  return r;
}

// Wrap around axis_index if it is negative, s.t., -1 is the last dim
inline int canonical_axis_index_(int axis_index, int ndims) {
  TORCH_CHECK(axis_index >= -ndims);
  TORCH_CHECK(axis_index < ndims);
  if (axis_index < 0) {
    return axis_index + ndims;
  }
  return axis_index;
}

using PlacementDtor = void (*)(void*, size_t);

/*
 * A Context that will call extra placement deleter during
 * deconstruction.
 *
 * Accept a already constructed DataPtr and store it as member
 * during destruction, we'll call extra deleter on the underlying
 * data pointer before the DataPtr is destructed.
 * `data_ptr_` owns the memory.
 */
struct C10_API PlacementDeleteContext {
  DataPtr data_ptr_;
  PlacementDtor placement_dtor_;
  size_t size_;
  PlacementDeleteContext(
      DataPtr&& data_ptr,
      PlacementDtor placement_dtor,
      size_t size)
      : data_ptr_(std::move(data_ptr)),
        placement_dtor_(placement_dtor),
        size_(size) {}
  static DataPtr makeDataPtr(
      DataPtr&& data_ptr,
      PlacementDtor placement_dtor,
      size_t size,
      Device device);
  ~PlacementDeleteContext() {
    placement_dtor_(data_ptr_.get(), size_);
    // original memory will be freed when data_ptr_ is destructed
  }
};

struct TensorImpl;

struct C10_API AutogradMetaInterface {
  virtual void set_requires_grad(
      bool requires_grad,
      at::TensorImpl* self_impl) = 0;
  virtual bool requires_grad() const = 0;
  virtual at::Tensor& mutable_grad() = 0;
  virtual const at::Tensor& grad() const = 0;
  virtual const at::Tensor& fw_grad(uint64_t level, const at::TensorBase& self)
      const = 0;
  virtual void set_fw_grad(
      const at::TensorBase& new_grad,
      const at::TensorBase& self,
      uint64_t level,
      bool is_inplace_op) = 0;
  virtual ~AutogradMetaInterface();
};

namespace impl {

// Unfortunately, the definition of AutogradMeta lives in a separate
// compilation unit than TensorImpl (libtorch.so versus libc10.so)
// which means that we cannot construct an AutogradMeta from TensorImpl,
// not even from the cpp file.  So we have to indirect it through a factory
// function which will be initialized when we load libtorch.so.

struct C10_API AutogradMetaFactory {
  virtual ~AutogradMetaFactory() = default;
  virtual std::unique_ptr<AutogradMetaInterface> make() const = 0;
  // This method is the dumbest method.  But I don't have access
  // to Tensor (not TensorImpl) which is undefined in this header.
  virtual const at::Tensor& undefined_tensor() const = 0;
};

C10_API void SetAutogradMetaFactory(AutogradMetaFactory* factory);
C10_API AutogradMetaFactory* GetAutogradMetaFactory();

struct C10_API AutogradMetaFactoryRegisterer {
  explicit AutogradMetaFactoryRegisterer(AutogradMetaFactory* factory) {
    SetAutogradMetaFactory(factory);
  }
};

// PyInterpreterStatus describes what the state of its interpreter tag
// is, relative to the thread currently holding the GIL.
enum class PyInterpreterStatus {
  // We just allocated the Tensor, it hasn't escaped to other threads,
  // we know that it definitely hasn't been tagged to be associated
  // with an interpreter.
  DEFINITELY_UNINITIALIZED,
  // We queried the interpreter field and it looked uninitialized.  But
  // another thread may have raced with us to tag it with some other
  // interpreter id.  So we will have to do a CEX to make sure we can
  // actually nab it.
  MAYBE_UNINITIALIZED,
  // We queried the interpreter field and it was tagged to belong to us.
  // This means we have sole write access (as we hold the GIL for this
  // interpreter)
  TAGGED_BY_US,
  // Someone else tagged this.  We can't use this TensorImpl from Python.
  TAGGED_BY_OTHER,
};

} // namespace impl

struct C10_API NamedTensorMetaInterface {
  virtual ~NamedTensorMetaInterface(){};
  virtual std::unique_ptr<NamedTensorMetaInterface> clone() const {
    TORCH_INTERNAL_ASSERT(
        false, "Not implemented: NamedTensorMetaInterface::clone");
  };
  virtual int64_t slow_dim() const {
    TORCH_INTERNAL_ASSERT(
        false, "Not implemented: NamedTensorMetaInterface::slow_dim");
  };
};

template <typename T>
using strong_bool = strong::
    type<bool, T, strong::regular, strong::iostreamable, strong::boolean>;

// For ease of copy pasting
#if 0
is_contiguous
is_channels_last_contiguous
is_channels_last_3d_contiguous
is_channels_last
is_channels_last_3d
is_non_overlapping_and_dense
#endif

using bool_is_contiguous = strong_bool<struct bool_is_contiguous_>;
using bool_is_channels_last_contiguous =
    strong_bool<struct bool_is_channels_last_contiguous_>;
using bool_is_channels_last_3d_contiguous =
    strong_bool<struct bool_is_channels_last_3d_contiguous_>;
using bool_is_channels_last = strong_bool<struct bool_is_channels_last_>;
using bool_is_channels_last_3d = strong_bool<struct bool_is_channels_last_3d_>;
using bool_is_non_overlapping_and_dense =
    strong_bool<struct bool_is_non_overlapping_and_dense_>;

struct C10_API ExtraMeta {
  SymDimVector sizes_ = {0};
  SymDimVector strides_ = {1};
  SymInt numel_ = 1;
  SymInt storage_offset_ = 0;
  // TODO: make these all SymBool
  bool_is_contiguous is_contiguous_{true};
  bool_is_channels_last_contiguous is_channels_last_contiguous_{false};
  bool_is_channels_last_3d_contiguous is_channels_last_3d_contiguous_{false};
  bool_is_channels_last is_channels_last_{false};
  bool_is_channels_last_3d is_channels_last_3d_{false};
  bool_is_non_overlapping_and_dense is_non_overlapping_and_dense_{true};
  std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta_ = nullptr;

  ExtraMeta() {}

  ExtraMeta(
      SymDimVector sizes,
      SymDimVector strides,
      SymInt numel,
      SymInt storage_offset,
      bool_is_contiguous is_contiguous,
      bool_is_channels_last_contiguous is_channels_last_contiguous,
      bool_is_channels_last_3d_contiguous is_channels_last_3d_contiguous,
      bool_is_channels_last is_channels_last,
      bool_is_channels_last_3d is_channels_last_3d,
      bool_is_non_overlapping_and_dense is_non_overlapping_and_dense,
      std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta)
      : sizes_(std::move(sizes)),
        strides_(std::move(strides)),
        numel_(std::move(numel)),
        storage_offset_(std::move(storage_offset)),
        is_contiguous_(is_contiguous),
        is_channels_last_contiguous_(is_channels_last_contiguous),
        is_channels_last_3d_contiguous_(is_channels_last_3d_contiguous),
        is_channels_last_(is_channels_last),
        is_channels_last_3d_(is_channels_last_3d),
        is_non_overlapping_and_dense_(is_non_overlapping_and_dense),
        named_tensor_meta_(std::move(named_tensor_meta)) {}

  std::unique_ptr<ExtraMeta> clone() const {
    return std::make_unique<ExtraMeta>(
        sizes_,
        strides_,
        numel_,
        storage_offset_,
        is_contiguous_,
        is_channels_last_contiguous_,
        is_channels_last_3d_contiguous_,
        is_channels_last_,
        is_channels_last_3d_,
        is_non_overlapping_and_dense_,
        named_tensor_meta_ ? named_tensor_meta_->clone() : nullptr);
  }
};

// NOTE [ Version Counter Sharing ]
//
// Every Tensor has a version counter. Version counters are incremented whenever
// the data or size of a tensor changes through in-place Variable operations.
// Version counters are used to detect modifications to saved variables which
// would result in incorrect gradient calculations. Version counters may be
// shared between Variables:
//
// 1. A view shares the version counter of the base Variable,
// 2. `x.detach()` shares the version counter of `x`,
// 3. Unpacked saved variables share the version counter of the source.
//
// Version counters are not shared in these scenarios:
//
// 1. When we replace a `Variable`'s underlying `Tensor` by calling
// `set_data(...)`,
// 2. `x.data` does not share the version counter of `x`. (See discussion at
// https://github.com/pytorch/pytorch/issues/5396)
//
// Question: Why do we put the version counter in TensorImpl instead of
// AutogradMeta?
//
// Answer: After the Variable/Tensor merge, a tensor will not have AutogradMeta
// when its `requires_grad_` is false, but when we use this tensor in the
// forward pass of a function that requires saving this tensor for backward, we
// need to keep track of this tensor's version to make sure it's always valid in
// the autograd graph.
//
// To achieve this goal, we put the version counter in TensorImpl instead of
// AutogradMeta, and have it always be available. This allows us to have the
// optimization of not carrying AutogradMeta when a tensor doesn't require
// gradient.
//
// A hypothetical alternative way to achieve this goal is to initialize
// AutogradMeta and create the version counter for the non-requires-grad tensor
// only when it's saved for backward. However, since saving a tensor for
// backward happens in the forward pass, and our invariant is that forward pass
// needs to be thread-safe, lazy-initializing AutogradMeta when saving a tensor
// can introduce race conditions when we are running the forward pass in
// multi-thread scenarios, thus making the forward pass not thread-safe anymore,
// which breaks the invariant.
struct C10_API VariableVersion {
 private:
  struct VersionCounter : intrusive_ptr_target {
    VersionCounter(uint32_t version) : version_(version) {}
    std::atomic<uint32_t> version_;
  };
  c10::intrusive_ptr<VersionCounter> version_counter_;

 public:
  // Note [Disabled VariableVersion]
  // VariableVersion struct has an intrusive_ptr pointing VersionCounter struct
  // with an atomic variable. Thus `VariableVersion(/*version=*/0)` is not as
  // cheap as we expected. In some cases constructing a VariableVersion with
  // version 0 is not necessary so we add a cheap constructor which
  // doesn't allocate the intrusive_ptr.
  // Example use cases are:
  //  - Inference tensors don't track version counter, so they'll just always
  //    have disbaled VariableVersion.
  //  - In SavedVariable class we override version_counter_ inside its
  //  construtor
  //    so that we can use the cheap constructor there.
  enum Disabled { DISABLED };
  // It's okay to return true even for inference tensor which
  // doesn't have version counter enabled.
  // We want to be permissive here since in many cases (e.g. make_variable)
  // we can std::move a TensorImpl if there's no other uses which saves us
  // an additional TensorImpl allocation.
  bool unique() const {
    return version_counter_ ? 1 == version_counter_.use_count() : true;
  }
  // NOTE: As of C++11 and 14, default-constructing a std::atomic variable
  // leaves it in a persistently undefined state. See
  // https://cplusplus.github.io/LWG/issue2334.
  VariableVersion(uint32_t version)
      : version_counter_(c10::make_intrusive<VersionCounter>(version)) {}
  VariableVersion(Disabled = DISABLED) {}

  bool enabled() const {
    return version_counter_;
  }

  // Note [Inplace update inference tensor]
  // 1. Inplace update to inference tensor is forbidden in normal mode.
  //   For example:
  //     inference_tensor.copy_(normal_tensor_requires_grad)
  //   This inplace makes inference_tensor have requires_grad=True and
  //   have a grad_fn.  This is bad because views of `inference_tensor`
  //   created in InferenceMode won't be able to know the grad_fn since
  //   their ViewMeta were not recorded. To match NoGradMode behavior
  //   that "inplace update to a view created in NoGradMode raise an error",
  //   we just ban inplace update to inference tensor since we can't tell
  //   if an inference tensor is a view created in InferenceMode.
  //
  //   Note that views of normal tensor created in InferenceMode has proper
  //   ViewMeta so that they're aware of the grad_fn correctly.
  //
  // 2. Inplace update to inference tensor in inference tensor doesn't bump
  //    version counter.
  //    * It either doesn't call bump() by skipping ADInplaceOrView kernel,
  //      - e.g. inference_tensor.add_(1)
  //    * or bump() is a no-op for inference tensor.
  //      - e.g. inference_tensor.add_(normal_tensor)
  void bump() {
    // TODO: Replace the link to the documentation once it's available.
    TORCH_CHECK(
        version_counter_ || InferenceMode::is_enabled(),
        "Inplace update to inference tensor outside InferenceMode is not allowed."
        "You can make a clone to get a normal tensor before doing inplace update."
        "See https://github.com/pytorch/rfcs/pull/17 for more details.");
    if (version_counter_) {
      ++version_counter_->version_;
    }
  }

  // Inference tensor doesn't have version counter so it shouldn't be
  // accessed.
  uint32_t current_version() const {
    TORCH_CHECK(
        version_counter_, "Inference tensors do not track version counter.");
    return version_counter_->version_;
  }
};

// Forward declaration of TensorImpl needed for forward declaration of
// C10_TensorImpl_Size_Check_Dummy_Class
struct C10_API TensorImpl;

// Forward declaration needed because TensorImpl needs to be friends with
// C10_TensorImpl_Size_Check_Dummy_Class in order to check the size
// of its private fields.
template <
    size_t cplusplus,
    size_t clang_ver_major,
    size_t gcc_ver,
    size_t gcc_ver_minor,
    size_t nvcc,
    size_t cuda_version,
    size_t cuda_version_major,
    size_t ptr_size>
class C10_TensorImpl_Size_Check_Dummy_Class;

/**
 * NOTE: Some TensorImpl methods are small and not overridden in the
 * PyTorch codebase itself, but may theoretically need to be
 * overridden by third-party TensorImpl subclasses. This macro allows
 * users that need maximum performance and don't need these extension
 * points to disable them with a build-time flag. (In particular,
 * XLA's XLATensorImpl currently overrides these methods, so we can't
 * enable this flag by default.)
 */
#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
#define TENSORIMPL_MAYBE_VIRTUAL
#else
#define TENSORIMPL_MAYBE_VIRTUAL virtual
#endif

/**
 * The low-level representation of a tensor, which contains a pointer
 * to a storage (which contains the actual data) and metadata (e.g., sizes and
 * strides) describing this particular view of the data as a tensor.
 *
 * Some basic characteristics about our in-memory representation of
 * tensors:
 *
 *  - It contains a pointer to a storage struct (Storage/StorageImpl)
 *    which contains the pointer to the actual data and records the
 *    data type and device of the view.  This allows multiple tensors
 *    to alias the same underlying data, which allows to efficiently
 *    implement differing *views* on a tensor.
 *
 *  - The tensor struct itself records view-specific metadata about
 *    the tensor, e.g., sizes, strides and offset into storage.
 *    Each view of a storage can have a different size or offset.
 *
 *  - This class is intrusively refcounted.  It is refcounted so that
 *    we can support prompt deallocation of large tensors; it is
 *    intrusively refcounted so that we can still perform reference
 *    counted operations on raw pointers, which is often more convenient
 *    when passing tensors across language boundaries.
 *
 *  - For backwards-compatibility reasons, a tensor may be in an
 *    uninitialized state.  A tensor may be uninitialized in the following
 *    two ways:
 *
 *      - A tensor may be DTYPE UNINITIALIZED.  A tensor of this
 *        form has an uninitialized dtype.  This situation most
 *        frequently arises when a user writes Tensor x(CPU).  The dtype
 *        is subsequently initialized when mutable_data<T>() is
 *        invoked for the first time.
 *
 *      - A tensor may be STORAGE UNINITIALIZED.  A tensor of this form
 *        has non-zero size, but has a storage with a null data pointer.
 *        This situation most frequently arises when a user calls
 *        Resize() or FreeMemory().  This is because Caffe2 historically
 *        does lazy allocation: allocation of data doesn't occur until
 *        mutable_data<T>() is invoked.  A tensor with zero size is
 *        always storage initialized, because no allocation is necessary
 *        in this case.
 *
 *    All combinations of these two uninitialized states are possible.
 *    Consider the following transcript in idiomatic Caffe2 API:
 *
 *      Tensor x(CPU); // x is storage-initialized, dtype-UNINITIALIZED
 *      x.Resize(4); // x is storage-UNINITIALIZED, dtype-UNINITIALIZED
 *      x.mutable_data<float>(); // x is storage-initialized, dtype-initialized
 *      x.FreeMemory(); // x is storage-UNINITIALIZED, dtype-initialized.
 *
 *    All other fields on tensor are always initialized.  In particular,
 *    size is always valid. (Historically, a tensor declared as Tensor x(CPU)
 *    also had uninitialized size, encoded as numel == -1, but we have now
 *    decided to default to zero size, resulting in numel == 0).
 *
 *    Uninitialized storages MUST be uniquely owned, to keep our model
 *    simple.  Thus, we will reject operations which could cause an
 *    uninitialized storage to become shared (or a shared storage to
 *    become uninitialized, e.g., from FreeMemory).
 *
 *    In practice, tensors which are storage-UNINITIALIZED and
 *    dtype-UNINITIALIZED are *extremely* ephemeral: essentially,
 *    after you do a Resize(), you basically always call mutable_data()
 *    immediately afterwards.  Most functions are not designed to
 *    work if given a storage-UNINITIALIZED, dtype-UNINITIALIZED tensor.
 *
 *    We intend to eliminate all uninitialized states, so that every
 *    tensor is fully initialized in all fields.  Please do not write new code
 *    that depends on these uninitialized states.
 */
struct C10_API TensorImpl : public c10::intrusive_ptr_target {
  TensorImpl() = delete;
  virtual ~TensorImpl() override;
  // Note [Enum ImplType]
  // This enum is temporary. In the followup refactor we should
  // think about how to specialize TensorImpl creation for view
  // tensors. Currently we only special case its key_set_ but
  // there's also potential to share version_counter_ directly
  // without creating first and then override in as_view.
  enum ImplType { VIEW };

  /**
   * Construct a 1-dim 0-size tensor backed by the given storage.
   */
  TensorImpl(
      Storage&& storage,
      DispatchKeySet,
      const caffe2::TypeMeta data_type);

  // See Note [Enum ImplType]
  TensorImpl(
      ImplType,
      Storage&& storage,
      DispatchKeySet,
      const caffe2::TypeMeta data_type);

  /**
   * Construct a 1-dim 0 size tensor that doesn't have a storage.
   */
  TensorImpl(
      DispatchKeySet,
      const caffe2::TypeMeta data_type,
      c10::optional<c10::Device> device_opt);

  // Legacy constructors so I don't have to go update call sites.
  // TODO: When Variable is added, delete these constructors
  TensorImpl(
      Storage&& storage,
      DispatchKey dispatch_key,
      const caffe2::TypeMeta data_type)
      : TensorImpl(
            std::move(storage),
            DispatchKeySet(dispatch_key),
            data_type) {}
  TensorImpl(
      DispatchKey dispatch_key,
      const caffe2::TypeMeta data_type,
      c10::optional<c10::Device> device_opt)
      : TensorImpl(DispatchKeySet(dispatch_key), data_type, device_opt) {}

 private:
  // This constructor is private, because the data_type is redundant with
  // storage.  Still, we pass it in separately because it's easier to write
  // the initializer list if we're not worried about storage being moved out
  // from under us.
  TensorImpl(
      Storage&& storage,
      DispatchKeySet,
      const caffe2::TypeMeta data_type,
      c10::optional<c10::Device>);

 public:
  TensorImpl(const TensorImpl&) = delete;
  TensorImpl& operator=(const TensorImpl&) = delete;
  TensorImpl(TensorImpl&&) = delete;
  TensorImpl& operator=(TensorImpl&&) = delete;

  /**
   * Release (decref) storage, and any other external allocations.  This
   * override is for `intrusive_ptr_target` and is used to implement weak
   * tensors.
   */
  void release_resources() override;

 private:
  void destroy_pyobj_if_needed();

 public:
  /**
   * Return the DispatchKeySet corresponding to this Tensor, specifying
   * all of the DispatchKeys that this Tensor identifies as.  This is the
   * information used to dispatch operations on this tensor.
   */
  DispatchKeySet key_set() const {
    return key_set_;
  }

  // NOTE: The general recipe for customizable methods is that the fastpath
  // function (e.g., sizes()) does an unlikely policy test, and if doesn't
  // trigger, it does the fast path implementation with no checks and going
  // directly to on-TensorImpl fields.  In particular, you never need to
  // check ExtraMeta if the policy doesn't trigger, as non-trivial ExtraMeta
  // implies the policy will always match.
  //
  // The default implementations of methods are "safe": they do extra tests
  // to make sure the internal state is consistent no matter if you are
  // doing symbolic shapes or not.  If you don't want the tests, directly
  // override the custom method (e.g., custom_sizes()) to do your preferred
  // behavior.

 public:
  /**
   * Return a reference to the sizes of this tensor.  This reference remains
   * valid as long as the tensor is live and not resized.
   */
  IntArrayRef sizes() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sizes_custom();
    }
    return sizes_and_strides_.sizes_arrayref();
  }

  SymIntArrayRef sym_sizes() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_sizes_custom();
    }
    // Sizes guaranteed to be non-negative, so unchecked cast is OK
    return c10::fromIntArrayRefKnownNonNegative(
        sizes_and_strides_.sizes_arrayref());
  }

  IntArrayRef sizes_default() const {
    // TODO: force backtrace to be printed on this error
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "Cannot call sizes() on tensor with symbolic sizes/strides");
    return sizes_and_strides_.sizes_arrayref();
  }

  SymIntArrayRef sym_sizes_default() const {
    if (has_symbolic_sizes_strides_) {
      return extra_meta_->sizes_;
    } else {
      // Sizes guaranteed to be non-negative, so unchecked cast is OK
      return c10::fromIntArrayRefKnownNonNegative(sizes_default());
    }
  }

  /**
   * The number of elements in a tensor.
   *
   * WARNING: Previously, if you were using the Caffe2 API, you could
   * test numel() == -1 to see if a tensor was uninitialized.  This
   * is no longer true; numel always accurately reports the product
   * of sizes of a tensor.
   */
  int64_t numel() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return numel_custom();
    }
    return numel_;
  }

  c10::SymInt sym_numel() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_numel_custom();
    }
    return c10::SymInt(SymInt::UNCHECKED, numel_);
  }

  int64_t numel_default() const {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "Cannot call numel() on tensor with symbolic sizes/strides");
    return numel_;
  }

  c10::SymInt sym_numel_default() const {
    if (has_symbolic_sizes_strides_) {
      return extra_meta_->numel_;
    } else {
      return c10::SymInt(SymInt::UNCHECKED, numel_);
    }
  }

  /**
   * Return the number of dimensions of this tensor.  Note that 0-dimension
   * represents a Tensor that is a Scalar, e.g., one that has a single element.
   */
  int64_t dim() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return dim_custom();
    }
    return sizes_and_strides_.size();
  }

  int64_t dim_default() const {
    if (has_symbolic_sizes_strides_) {
      return extra_meta_->sizes_.size();
    } else {
      return sizes_and_strides_.size();
    }
  }

  /**
   * Return the offset in number of elements into the storage that this
   * tensor points to.  Most tensors have storage_offset() == 0, but,
   * for example, an index into a tensor will have a non-zero storage_offset().
   *
   * WARNING: This is NOT computed in bytes.
   */
  int64_t storage_offset() const {
    // TODO: maybe this should be toggled by strides
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return storage_offset_custom();
    }
    return storage_offset_;
  }

  c10::SymInt sym_storage_offset() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_storage_offset_custom();
    }
    return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
  }

  int64_t storage_offset_default() const {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "Cannot call storage_offset() on tensor with symbolic sizes/strides");
    return storage_offset_;
  }

  c10::SymInt sym_storage_offset_default() const {
    if (has_symbolic_sizes_strides_) {
      return extra_meta_->storage_offset_;
    } else {
      return c10::SymInt(SymInt::UNCHECKED, storage_offset_);
    }
  }

  /**
   * Return a reference to the strides of this tensor.  This reference remains
   * valid as long as the tensor is live and not restrided.
   */
  IntArrayRef strides() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return strides_custom();
    }
    return sizes_and_strides_.strides_arrayref();
  }

  c10::SymIntArrayRef sym_strides() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return sym_strides_custom();
    }
    return c10::fromIntArrayRefKnownNonNegative(strides_default());
  }

  IntArrayRef strides_default() const {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "Cannot call strides() on tensor with symbolic sizes/strides");
    return sizes_and_strides_.strides_arrayref();
  }

  c10::SymIntArrayRef sym_strides_default() const {
    if (has_symbolic_sizes_strides_) {
      return extra_meta_->strides_;
    } else {
      return c10::fromIntArrayRefKnownNonNegative(strides_default());
    }
  }

  /**
   * Whether or not a tensor is laid out in contiguous memory.
   *
   * Tensors with non-trivial strides are not contiguous.  See
   * compute_contiguous() for the exact definition of whether or not
   * a tensor is contiguous or not.
   */
  bool is_contiguous(
      at::MemoryFormat memory_format = at::MemoryFormat::Contiguous) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_contiguous_custom(memory_format);
    }
    return is_contiguous_default(memory_format);
  }

  // These are factored into separate functions in case subclasses
  // want to use them
  bool is_contiguous_default(at::MemoryFormat memory_format) const {
    if (has_symbolic_sizes_strides_) {
      if (memory_format == at::MemoryFormat::ChannelsLast) {
        return bool(extra_meta_->is_channels_last_contiguous_);
      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
        return bool(extra_meta_->is_channels_last_3d_contiguous_);
      }
      return bool(extra_meta_->is_contiguous_);
    }

    if (memory_format == at::MemoryFormat::ChannelsLast) {
      return is_channels_last_contiguous_;
    } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
      return is_channels_last_3d_contiguous_;
    }
    return is_contiguous_;
  }

  bool is_strides_like_default(at::MemoryFormat memory_format) const {
    if (has_symbolic_sizes_strides_) {
      if (memory_format == at::MemoryFormat::ChannelsLast) {
        return bool(extra_meta_->is_channels_last_);
      } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
        return bool(extra_meta_->is_channels_last_3d_);
      } else {
        return false;
      }
    }

    if (memory_format == at::MemoryFormat::ChannelsLast) {
      return is_channels_last_;
    } else if (memory_format == at::MemoryFormat::ChannelsLast3d) {
      return is_channels_last_3d_;
    } else {
      return false;
    }
  }

  bool is_non_overlapping_and_dense_default() const {
    if (has_symbolic_sizes_strides_) {
      return bool(extra_meta_->is_non_overlapping_and_dense_);
    } else {
      return is_non_overlapping_and_dense_;
    }
  }

  // NB: these dim accessor functions don't have _default(), as you can use
  // sizes_default/strides_default
  /**
   * Return the size of a tensor at some dimension, wrapping the dimension if
   * necessary.
   *
   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
   * be faster
   */
  int64_t size(int64_t d) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return size_custom(d);
    }
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sizes_and_strides_.size_at_unchecked(d);
  }

  c10::SymInt sym_size(int64_t d) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomSizes))) {
      return sym_size_custom(d);
    }
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    const auto sizes = this->sym_sizes();
    return sizes[d];
  }

  /**
   * Return the stride of a tensor at some dimension, wrapping the dimension
   * if necessary.
   *
   * NOTE: if you know wrapping is unnecessary, do sizes()[d] instead; it will
   * be faster
   */
  int64_t stride(int64_t d) const {
    d = maybe_wrap_dim(d, dim(), false);
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      // TODO: provide stride_custom, symmetrically with size_custom.
      // There is presently no user for it; only NestedTensor is using
      // size_custom overrideability
      return strides_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
    }
    // Intentionally don't call default, which also handles symbolic
    return sizes_and_strides_.stride_at_unchecked(d);
  }

  enum class SizesStridesPolicy : uint8_t {
    // Default behavior, e.g., dense tensor.
    //
    // Can override: nothing
    Default = 0,
    // Customizable strides behavior, e.g., sparse tensor,
    // mkldnn tensor.
    //
    // Can override: strides(), is_contiguous()
    CustomStrides = 1,
    // Customizable sizes behavior, e.g., nested tensor
    //
    // Can override: strides(), is_contiguous(), sizes(), dim(), numel()
    CustomSizes = 2
  };

 protected:
  inline bool matches_policy(SizesStridesPolicy policy) const {
    return sizes_strides_policy_ >= static_cast<uint8_t>(policy);
  }

  inline bool matches_custom(SizesStridesPolicy policy) const {
    return custom_sizes_strides_ >= static_cast<uint8_t>(policy);
  }

  inline bool matches_python_custom(SizesStridesPolicy policy) const {
    auto r = python_custom_sizes_strides_ >= static_cast<uint8_t>(policy);
    if (r) {
      TORCH_INTERNAL_ASSERT(is_python_dispatch())
    }
    return r;
  }

  /**
   * Customization points for the functions above.  sizes_strides_policy_
   * must be set to enable these.
   *
   * NB: dim is overrideable separately from sizes because it is possible
   * for a tensor to have rank, but not well defined sizes.
   */
  // sizes_strides_policy_ >= CustomStrides
  virtual bool is_contiguous_custom(at::MemoryFormat memory_format) const;
  virtual bool is_strides_like_custom(at::MemoryFormat memory_format) const;
  virtual bool is_non_overlapping_and_dense_custom() const;
  // sizes_strides_policy_ >= CustomSizes
  // Currently this method only exists to be overwritten by subclasses such as
  // NestedTensorImpl.
  virtual int64_t size_custom(int64_t d) const {
    // TODO: We could add support to Python dispatch here.
    // TODO: We could call into aten::size.int instead of
    // sizes_custom()[d] and enable use of the dispatcher.
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
  }

  virtual c10::SymInt sym_size_custom(int64_t d) const {
    // TODO: We could add support to Python dispatch here.
    // TODO: We could call into aten::size.int instead of
    // sym_sizes_custom()[d] and enable use of the dispatcher.
    d = maybe_wrap_dim(d, dim(), /*wrap_scalar=*/false);
    return sym_sizes_custom()[d]; // unchecked (maybe_wrap_dim enforces bounds)
  }

  virtual IntArrayRef sizes_custom() const;
  virtual IntArrayRef strides_custom() const;
  virtual int64_t numel_custom() const;
  virtual int64_t storage_offset_custom() const;
  virtual int64_t dim_custom() const;
  virtual Device device_custom() const;
  virtual Layout layout_custom() const;

  virtual c10::SymIntArrayRef sym_sizes_custom() const;
  virtual c10::SymIntArrayRef sym_strides_custom() const;
  virtual c10::SymInt sym_numel_custom() const;
  virtual c10::SymInt sym_storage_offset_custom() const;

 public:
  /**
   * True if this tensor has storage. See storage() for details.
   */
#ifdef DEBUG
  // Allow subclasses to check that their storage_ is never getting set in debug
  // builds.
  virtual
#else
  TENSORIMPL_MAYBE_VIRTUAL
#endif
      bool
      has_storage() const
  // NOTE: we devirtualize this because it arguably shouldn't be an
  // error just to ask subclasses if they have storage.
  // This used to throw for most subclasses, but OpaqueTensorImpl
  // wanted it to successfully return false, so we went ahead and made
  // it a non-error.
#ifdef C10_DISABLE_TENSORIMPL_EXTENSIBILITY
  {
    return storage_;
  }
#else
      ;
#endif

  /**
   * Return the underlying storage of a Tensor.  Multiple tensors may share
   * a single storage.  A Storage is an impoverished, Tensor-like class
   * which supports far less operations than Tensor.
   *
   * Avoid using this method if possible; try to use only Tensor APIs to perform
   * operations.
   */
  TENSORIMPL_MAYBE_VIRTUAL const Storage& storage() const {
    if (C10_UNLIKELY(storage_access_should_throw_)) {
      throw_storage_access_error();
    }
    return storage_;
  }

  /**
   * Return the underlying storage, unsafely assuming this is a basic strided
   * tensor. In cases where `storage` access would throw, this returns a
   * default-constructed Storage.
   */
  inline const Storage& unsafe_storage() const {
    return storage_;
  }

  bool unique_version() const {
    return version_counter_.unique();
  }

 protected:
  virtual Layout layout_impl() const {
    TORCH_CHECK(
        false, "layout_impl is only implemented for TensorImpl subclasses.");
  }

 public:
  // Whether a tensor is sparse COO or not.
  bool is_sparse() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    return key_set_.has_all(c10::sparse_ks);
  }

  // Whether a tensor is sparse CSR or not.
  bool is_sparse_csr() const {
    return layout() == kSparseCsr;
  }

  bool is_quantized() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    constexpr auto quantized_ks = DispatchKeySet(DispatchKey::Quantized);
    return key_set_.has_all(quantized_ks);
  }

  bool is_meta() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_meta();
    }
    return device_opt_.has_value() && device_opt_->type() == kMeta;
  }

  bool is_cpu() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_cpu();
    }
    // Note: we cannot rely on dispatch keys to determine the device type
    // of a tensor, because "wrapper" tensors (like FunctionalTensorWrapper)
    // don't include backend dispatch keys.
    return device_opt_.has_value() && device_opt_->type() == kCPU;
  }

  bool is_cuda() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_cuda();
    }
    return device_opt_.has_value() && device_opt_->type() == kCUDA;
  }

  bool is_xpu() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_xpu();
    }
    return device_opt_.has_value() && device_opt_->type() == kXPU;
  }

  bool is_ipu() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_ipu();
    }
    return device_opt_.has_value() && device_opt_->type() == kIPU;
  }

  bool is_xla() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_xla();
    }
    return device_opt_.has_value() && device_opt_->type() == kXLA;
  }

  bool is_hpu() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_hpu();
    }
    return device_opt_.has_value() && device_opt_->type() == kHPU;
  }

  bool is_lazy() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_lazy();
    }
    return device_opt_.has_value() && device_opt_->type() == kLazy;
  }

  bool is_hip() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_hip();
    }
    return device_opt_.has_value() && device_opt_->type() == kHIP;
  }

  bool is_ve() const {
    // NB: This method is not virtual and avoid dispatches for performance
    // reasons.
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_ve();
    }
    return device_opt_.has_value() && device_opt_->type() == kVE;
  }

  bool is_mkldnn() const {
    return key_set_.has_all(c10::mkldnn_ks);
  }

  bool is_vulkan() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_vulkan();
    }
    return device_opt_.has_value() && device_opt_->type() == kVulkan;
  }

  bool is_metal() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_metal();
    }
    return device_opt_.has_value() && device_opt_->type() == kMetal;
  }

  bool is_mps() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_mps();
    }
    return device_opt_.has_value() && device_opt_->type() == kMPS;
  }

  bool is_ort() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().is_ort();
    }
    return device_opt_.has_value() && device_opt_->type() == kORT;
  }

  bool is_nested() const {
    return key_set_.has(DispatchKey::NestedTensor);
  }

  // TODO: remove this once we don't automatically enabled Autograd dispatch
  // keys
  //       in TensorImpl constructor.
  // DON'T USE THIS API!! It's only created for testing purpose in
  // file aten/src/ATen/core/boxing/impl/test_helpers.h
  void remove_autograd_key() {
    key_set_ = key_set_ - autograd_dispatch_keyset;
  }

  // Inference tensor doesn't have autograd or ADInplaceOrView key.
  // Invariant:
  //   Inference tensor has version_counter_.enabled() == false
  bool is_inference() {
    bool no_ADInplaceOrView = !key_set_.has_any(c10::inplace_or_view_ks);
    bool no_Autograd = !key_set_.has_any(c10::autograd_dispatch_keyset);
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
        no_ADInplaceOrView == no_Autograd,
        "ADInplaceOrView and Autograd keys must be on/off at the same time.");
    return no_ADInplaceOrView && no_Autograd;
  }

  int64_t get_device() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom().index();
    }
    return device_default().index();
  }

  Device device() const {
    if (C10_UNLIKELY(device_policy_)) {
      return device_custom();
    }
    return device_default();
  }

 protected:
  c10::Device device_default() const {
    TORCH_CHECK(device_opt_.has_value(), "tensor does not have a device");
    // See NOTE [c10::optional operator usage in CUDA]
    return *device_opt_;
  }

 public:
  Layout layout() const {
    if (C10_UNLIKELY(layout_policy_)) {
      return layout_custom();
    }

    // NB: This method is not virtual and avoid dispatches for perf.
    // strided is also the most common layout type, so we check for
    // strided case first.
    // This keyset must also be kept in sync with the logic in
    // is_sparse() / is_sparse_csr() / is_mkldnn()
    constexpr auto sparse_and_sparsecsr_and_mkldnn_ks =
        c10::sparse_ks | c10::sparse_csr_ks | c10::mkldnn_ks;
    if (!key_set_.has_any(sparse_and_sparsecsr_and_mkldnn_ks)) {
      return kStrided;
    } else if (is_sparse()) {
      return kSparse;
    } else if (key_set_.has_any(c10::sparse_csr_ks)) {
      // Typically, the tensor dispatch keys define the tensor layout
      // uniquely. This allows using non-virtual layout method for
      // better performance. However, when tensor's layout depends,
      // say, on tensor attributes, one must use this execution path
      // where the corresponding tensor impl class overwrites virtual
      // layout_impl() method.
      //
      // TODO: implement layout() as native function/method so that
      // __torch_dispatch__ users will be able to redefine the
      // layout() method.
      return layout_impl();
    } else {
      TORCH_INTERNAL_ASSERT(
          is_mkldnn(), "There is an error in the layout calculation logic.");
      return kMkldnn;
    }
  }

  /**
   * True if a tensor was auto-wrapped from a C++ or Python number.
   * For example, when you write 't + 2', 2 is auto-wrapped into a Tensor
   * with `is_wrapped_number_` set to true.
   *
   * Wrapped numbers do not participate in the result type computation for
   * mixed-type operations if there are any Tensors that are not wrapped
   * numbers.  This is useful, because we want 't + 2' to work with
   * any type of tensor, not just LongTensor (which is what integers
   * in Python represent).
   *
   * Otherwise, they behave like their non-wrapped equivalents.
   * See [Result type computation] in TensorIterator.h.
   *
   * Why did we opt for wrapped numbers, as opposed to just having
   * an extra function add(Tensor, Scalar)?  This helps greatly reduce
   * the amount of code we have to write for add, when actually
   * a Tensor-Scalar addition is really just a Tensor-Tensor
   * addition when the RHS is 0-dim (except for promotion behavior.)
   */
  bool is_wrapped_number() const {
    return is_wrapped_number_;
  }

  /**
   * Set whether or not a tensor was auto-wrapped from a C++ or Python
   * number.  You probably don't want to call this, unless you are
   * writing binding code.
   */
  void set_wrapped_number(bool value) {
    TORCH_INTERNAL_ASSERT(dim() == 0);
    is_wrapped_number_ = value;
  }

  /**
   * Returns true if Tensor supports as_strided and as_strided_backward.
   * This is used in autograd to perform inplace update on view Tensors.
   * See Note [View + Inplace update for base tensor] and
   * [View + Inplace update for view tensor] for details.
   * Note this method only returns true for XLA backend, where it
   * simulates strided Tensor to support most view ops, but it cannot
   * fully support general `as_strided` case.
   * It can be expanded as needed in the future, e.g sparse Tensor.
   */
  inline bool support_as_strided() const {
    return is_nested() ? false : device().supports_as_strided();
  }

  // ~~~~~ Autograd API ~~~~~
  // Some methods below are defined in TensorImpl.cpp because Tensor is an
  // incomplete type.

  /**
   * Set whether or not a tensor requires gradient.
   */
  void set_requires_grad(bool requires_grad);

  /**
   * True if a tensor requires gradient.  Tensors which require gradient
   * have history tracked for any operations performed on them, so that
   * we can automatically differentiate back to them.  A tensor that
   * requires gradient and has no history is a "leaf" tensor, which we
   * accumulate gradients into.
   */
  bool requires_grad() const;

  /**
   * Return a mutable reference to the gradient.  This is conventionally
   * used as `t.grad() = x` to set a gradient to a completely new tensor.
   */
  at::Tensor& mutable_grad();

  /**
   * Return the accumulated gradient of a tensor.  This gradient is written
   * into when performing backwards, when this tensor is a leaf tensor.
   */
  const at::Tensor& grad() const;

  /**
   * Whether or not the imaginary part of the tensor should be negated
   */
  inline bool is_conj() const {
    constexpr auto conjugate_ks = DispatchKeySet(DispatchKey::Conjugate);
    return key_set_.has_all(conjugate_ks);
  }

  /**
   * Set whether or not to take the conjugate of the tensor (flip the imaginary
   * bit).
   */
  void _set_conj(bool value) {
    if (value) {
      key_set_ = key_set_.add(DispatchKey::Conjugate);
      TORCH_INTERNAL_ASSERT(isComplexType(typeMetaToScalarType(dtype())));
    } else {
      key_set_ = key_set_.remove(DispatchKey::Conjugate);
    }
  }

  /**
   * XXX: do not use, private api!
   * Update the backend component related keys to the backend component
   * corresponding to this device.
   */
  void _change_backend_component_keys(c10::Device device);

  /**
   * Whether or not the tensor is a zerotensor
   */
  inline bool _is_zerotensor() const {
    constexpr auto zerotensor_ks = DispatchKeySet(DispatchKey::ZeroTensor);
    return key_set_.has_all(zerotensor_ks);
  }

  /**
   Set whether or not the tensor is a zero tensor
  */
  void _set_zero(bool value) {
    if (value) {
      TORCH_INTERNAL_ASSERT(
          false,
          "Please call `torch._efficientzerotensor` if you want to create a tensor with no storage.");
    } else {
      key_set_ = key_set_.remove(DispatchKey::ZeroTensor);
    }
  }

  /**
   * Whether or not the tensor should be negated
   */
  inline bool is_neg() const {
    constexpr auto negative_ks = DispatchKeySet(DispatchKey::Negative);
    return key_set_.has_all(negative_ks);
  }

  /**
   * Set whether or not to take the conjugate of the tensor (flip the imaginary
   * bit).
   */
  void _set_neg(bool value) {
    if (value) {
      key_set_ = key_set_.add(DispatchKey::Negative);
    } else {
      key_set_ = key_set_.remove(DispatchKey::Negative);
    }
  }

  /**
   * Return the accumulated gradient of a tensor. This gradient is computed
   * using forward mode AD.
   *
   * This is an internal API that should never be used by end users.
   *
   * The API is as follows:
   *   - "level" allows to specify the level of forward AD nesting for which the
   *     gradient should be returned. Note that since levels are not fully
   *     supported yet, this argument should be 0. See documentation for
   *     torch::autograd::enter_dual_level for more details about forward AD
   * nesting.
   *   - "self" should represent the Tensor whose forward grad is accessed. It
   * is required when dealing with view.
   */
  const at::Tensor& _fw_grad(uint64_t level, const at::TensorBase& self) const;

  /**
   * Sets the forward gradient for this Tensor.
   * The given Tensor might not be used directly and its content will be copied.
   *
   * This is an internal API that should never be used by end users.
   *
   * The API is as follows:
   *   - "new_grad" is a Tensor containing the new value of the gradient that
   * should be set
   *   - "self" should represent the Tensor whose forward grad is accessed. It
   * is required when dealing with view.
   *   - "level" allows to specify the level of forward AD nesting for which the
   *     gradient should be set. Note that since levels are not fully supported
   *     yet, this argument should be 0. See documentation for
   * torch::autograd::enter_dual_level for more details about forward AD
   * nesting.
   *   - "is_inplace_op" is a boolean flag that tells if this gradient was
   * generated by an inplace operation or an out of place one. This allows
   * better error checking.
   */
  void _set_fw_grad(
      const at::TensorBase& new_grad,
      const at::TensorBase& self,
      uint64_t level,
      bool is_inplace_op);

  /**
   * Return a typed data pointer to the actual data which this tensor refers to.
   * This checks that the requested type (from the template parameter) matches
   * the internal type of the tensor.
   *
   * It is invalid to call data() on a dtype-uninitialized tensor, even if
   * the size is 0.
   *
   * WARNING: If a tensor is not contiguous, you MUST use strides when
   * performing index calculations to determine the location of elements in
   * the tensor.  We recommend using 'TensorAccessor' to handle this computation
   * for you; this class is available from 'Tensor'.
   */
  template <typename T>
  inline T* data() const {
    TORCH_CHECK(
        data_type_.Match<T>(),
        "Tensor type mismatch, caller expects elements to be ",
        caffe2::TypeMeta::TypeName<T>(),
        ", while tensor contains ",
        data_type_.name(),
        ". ");
    return data_ptr_impl<T>();
  }

  /**
   * More efficient helper for Tensor::data_ptr(). Like data<T>(), but
   * does not do a type check. Unlike the untemplated data(), does
   * check has_storage() and storage_initialized().
   */
  template <typename T>
  inline T* data_ptr_impl() const {
    TORCH_CHECK(
        has_storage(),
        "Cannot access data pointer of Tensor that doesn't have storage");
    TORCH_CHECK(
        storage_initialized(),
        "The tensor has a non-zero number of elements, but its data is not allocated yet. "
        "Caffe2 uses a lazy allocation, so you will need to call "
        "mutable_data() or raw_mutable_data() to actually allocate memory.");
    // Caller does the type check.
    return storage_.unsafe_data<T>() + storage_offset_;
  }

  /**
   * Return a void* data pointer to the actual data which this tensor refers to.
   *
   * It is invalid to call data() on a dtype-uninitialized tensor, even if the
   * size is 0.
   *
   * WARNING: The data pointed to by this tensor may not contiguous; do NOT
   * assume that itemsize() * numel() is sufficient to compute the bytes that
   * can be validly read from this tensor.
   */
  inline void* data() const {
    TORCH_CHECK(
        has_storage(),
        "Cannot access data pointer of Tensor that doesn't have storage");
    TORCH_CHECK(
        dtype_initialized(),
        "Cannot access data pointer of Tensor that doesn't have initialized dtype "
        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
    // Computing an offset into an empty tensor would be UB, since an empty
    // tensor's storage will be nullptr, and adding a nonzero offset to nullptr
    // is UB.  So we skip the offset computation in this case.
    if (is_empty()) {
      return nullptr;
    }
    return static_cast<void*>(
        static_cast<char*>(storage_.data()) +
        data_type_.itemsize() * storage_offset_);
  }

  /**
   * Like data<T>(), but performs no checks.  You are responsible for ensuring
   * that all invariants required by data() are upheld here.
   */
  template <typename T>
  inline T* unsafe_data() const {
    return storage_.unsafe_data<T>() + storage_offset_;
  }

  /**
   * Returns the TypeMeta of a tensor, which describes what data type
   * it is (e.g., int, float, ...)
   */
  const caffe2::TypeMeta dtype() const {
    return data_type_;
  }

  /**
   * Return the size of a single element of this tensor in bytes.
   */
  size_t itemsize() const {
    TORCH_CHECK(
        dtype_initialized(),
        "Cannot report itemsize of Tensor that doesn't have initialized dtype "
        "(e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)");
    return data_type_.itemsize();
  }

 protected:
  /**
   * Returns the human-readable name of the actual type of this object (e.g.,
   * TensorImpl, BatchedTensorImpl, etc.). Used for error messages.
   */
  virtual const char* tensorimpl_type_name() const {
    return "TensorImpl";
  }

 private:
  [[noreturn]] void throw_storage_access_error() const;

 public:
  /**
   * True if a tensor has no elements (e.g., numel() == 0).
   */
  inline bool is_empty() const {
    return numel() == 0;
  }

  // if we are going to use sym sizes, we should be setting sym strides at the
  // same time, otherwise it's very easy to misuse this API
  void set_sizes_and_strides(
      c10::SymIntArrayRef sizes,
      c10::SymIntArrayRef strides,
      c10::optional<c10::SymInt> storage_offset = c10::nullopt);

  /**
   * Change the size at some dimension.  This DOES NOT update strides;
   * thus, most changes to size will not preserve contiguity.  You probably
   * also want to call set_stride() when you call this.
   *
   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
   * which is harder to misuse.
   */
  virtual void set_size(int64_t dim, int64_t new_size) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_size ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !matches_policy(SizesStridesPolicy::CustomSizes),
        "set_size() called on tensor with dynamic shapes or customized size behavior")
    sizes_and_strides_.size_at(dim) = new_size;
    refresh_numel();
    refresh_contiguous();
  }

  /**
   * Change the stride at some dimension.
   *
   * TODO: This should be jettisoned in favor of `set_sizes_and_strides`,
   * which is harder to misuse.
   */
  virtual void set_stride(int64_t dim, int64_t new_stride) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_stride ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_stride() called on tensor with symbolic shape")
    sizes_and_strides_.stride_at_unchecked(dim) = new_stride;
    refresh_contiguous();
  }

  /**
   * Set the offset into the storage of this tensor.
   *
   * WARNING: This does NOT check if the tensor is in bounds for the new
   * location at the storage; the caller is responsible for checking this
   * (and resizing if necessary.)
   */
  virtual void set_storage_offset(int64_t storage_offset) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_storage_offset ",
        err_msg_tensor_metadata_change_not_allowed);
    // TODO: this should probably consult policy
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_storage_offset() called on tensor with symbolic shape")
    storage_offset_ = storage_offset;
  }

  /**
   * Like set_sizes_and_strides but assumes contiguous strides.
   *
   * WARNING: This function does not check if the requested
   * sizes/strides are in bounds for the storage that is allocated;
   * this is the responsibility of the caller
   */
  void set_sizes_contiguous(IntArrayRef new_size) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_sizes_contiguous ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !matches_policy(SizesStridesPolicy::CustomStrides),
        "tried to directly modify sizes for customized tensor");
    sizes_and_strides_.set_sizes(new_size);

    refresh_numel();
    empty_tensor_restride(
        MemoryFormat::Contiguous); // calls refresh_contiguous()
  }

  /**
   * Set the sizes and strides of a tensor.
   *
   * WARNING: This function does not check if the requested
   * sizes/strides are in bounds for the storage that is allocated;
   * this is the responsibility of the caller
   */
  void set_sizes_and_strides(
      IntArrayRef new_size,
      IntArrayRef new_stride,
      c10::optional<int64_t> storage_offset = c10::nullopt) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_sizes_and_strides ",
        err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "set_sizes_and_strides() called on tensor with symbolic shape")
    TORCH_CHECK(
        new_size.size() == new_stride.size(),
        "dimensionality of sizes (",
        new_size.size(),
        ") must match dimensionality of strides (",
        new_stride.size(),
        ")");
    const auto new_dim = new_size.size();

    sizes_and_strides_.set_sizes(new_size);

    if (new_dim > 0) {
      for (size_t dim = new_dim - 1;; dim--) {
        if (new_stride[dim] >= 0) {
          sizes_and_strides_.stride_at_unchecked(dim) = new_stride[dim];
        } else {
          // XXX: This behavior is surprising and may need to be removed to
          // support negative strides. Some pytorch functions rely on it:
          // for example, torch.cat (run TestTorch.test_cat_empty).
          if (dim == new_dim - 1) {
            sizes_and_strides_.stride_at_unchecked(dim) = 1;
          } else {
            // Keep stride monotonically increasing to match NumPy.
            sizes_and_strides_.stride_at_unchecked(dim) =
                std::max<int64_t>(
                    sizes_and_strides_.size_at_unchecked(dim + 1), 1) *
                sizes_and_strides_.stride_at_unchecked(dim + 1);
          }
        }
        if (dim == 0)
          break;
      }
    }

    refresh_numel();
    refresh_contiguous();

    if (storage_offset.has_value()) {
      storage_offset_ = *storage_offset;
    }
  }

  /**
   * Set whether a tensor allows changes to its metadata (e.g. sizes / strides /
   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
   * ] for details.
   */
  void set_allow_tensor_metadata_change(bool value) {
    // TODO: at some point, we should kill this field completely.
    allow_tensor_metadata_change_ = true;
  }

  /**
   * True if a tensor allows changes to its metadata (e.g. sizes / strides /
   * storage / storage_offset). See NOTE [ Metadata Change for a Detached Tensor
   * ] for details.
   */
  bool allow_tensor_metadata_change() const {
    return allow_tensor_metadata_change_;
  }

  /**
   * Set the pointer to autograd metadata.
   */
  void set_autograd_meta(
      std::unique_ptr<c10::AutogradMetaInterface> autograd_meta);

  /**
   * Return the pointer to autograd metadata.  May return nullptr if the
   * tensor does not track gradients.
   */
  c10::AutogradMetaInterface* autograd_meta() const;

  /**
   * Set the pointer to named tensor metadata.
   */
  void set_named_tensor_meta(
      std::unique_ptr<c10::NamedTensorMetaInterface> named_tensor_meta) {
    TORCH_WARN_ONCE(
        "Named tensors and all their associated APIs are an experimental feature ",
        "and subject to change. Please do not use them for anything important ",
        "until they are released as stable.");
#ifdef DEBUG
    if (named_tensor_meta) {
      TORCH_INTERNAL_ASSERT(named_tensor_meta->slow_dim() == dim());
    }
#endif
    if (named_tensor_meta) {
      if (!extra_meta_) {
        extra_meta_ = std::make_unique<ExtraMeta>();
      }
      extra_meta_->named_tensor_meta_ = std::move(named_tensor_meta);
      key_set_ = key_set_.add(DispatchKey::Named);
    } else {
      if (extra_meta_) {
        extra_meta_->named_tensor_meta_ = nullptr;
      }
      key_set_ = key_set_.remove(DispatchKey::Named);
    }
  }

  void set_python_dispatch(bool k) {
    if (k) {
      key_set_ = key_set_.add(c10::python_ks);
    } else {
      key_set_ = key_set_ - c10::python_ks;
    }
  }

  bool is_python_dispatch() const {
    return key_set_.has_all(c10::python_ks);
  }

  /**
   * Return the pointer to named tensor metadata.
   */
  const c10::NamedTensorMetaInterface* named_tensor_meta() const {
    if (!extra_meta_) {
      return nullptr;
    }
    return extra_meta_->named_tensor_meta_.get();
  }

  c10::NamedTensorMetaInterface* named_tensor_meta() {
    if (!extra_meta_) {
      return nullptr;
    }
    return extra_meta_->named_tensor_meta_.get();
  }

  bool has_named_tensor_meta() const {
    if (!extra_meta_) {
      return false;
    }
    return extra_meta_->named_tensor_meta_ != nullptr;
  }

  // NOTE [ TensorImpl Shallow-Copying ]
  //
  // TensorImpl shallow-copying is used when we want to have two Variables share
  // the same tensor metadata (e.g. sizes / strides / storage pointer /
  // storage_offset), but each with a different autograd history. Example call
  // sites:
  //
  // 1. `var_detached = var.detach()` uses `shallow_copy_and_detach()` to create
  // `var_detached` that shares the same tensor metadata with `var`, but with a
  // completely new autograd history.
  // 2. `var.set_data(tensor)` uses `shallow_copy_from()` to copy tensor
  // metadata from `tensor` into `var`, while keeping `var`'s original
  // AutogradMeta.
  //
  // Functions that shallow-copy a TensorImpl (such as
  // `shallow_copy_and_detach()` / `shallow_copy_from()` /
  // `copy_tensor_metadata()`) copy the tensor metadata fields (e.g. sizes /
  // strides / storage pointer / storage_offset) by value. However, the
  // following fields are not copied:
  //
  // 1. the AutogradMeta pointer, because it is unique for each Variable.
  // 2. the version counter, because the destination TensorImpl's version
  // counter is either set to the passed-in `version_counter` (in
  // `shallow_copy_and_detach()` and `copy_tensor_metadata()`), or it is kept
  // intact (in `shallow_copy_from()`). See NOTE [ Version Counter Sharing ] for
  // details.
  //
  // In `shallow_copy_and_detach()` and `copy_tensor_metadata()`, the passed-in
  // `allow_tensor_metadata_change` determines whether the TensorImpl
  // shallow-copy allows changes to its metadata (e.g. sizes / strides / storage
  // / storage_offset). See NOTE [ Metadata Change for a Detached Tensor ] for
  // details.
  //
  // In `shallow_copy_from()`, we don't check the destination TensorImpl's
  // `allow_tensor_metadata_change_`, because `shallow_copy_from()` is used for
  // implementing functions such as `var.set_data(tensor)`, which changes
  // `var`'s tensor metadata and expects its `allow_tensor_metadata_change_` to
  // be ignored.

  /**
   * One TensorImpl can be copied to another TensorImpl if they have the same
   * DispatchKeySet. The only two special cases (for legacy reason) are:
   * CPU is compatible with CUDA and SparseCPU is
   * compatible with SparseCUDA.
   */
  inline bool has_compatible_shallow_copy_type(DispatchKeySet from) {
    auto is_dense = [](DispatchKeySet ts) {
      constexpr auto dense_backends = DispatchKeySet(
          {BackendComponent::CPUBit,
           BackendComponent::CUDABit,
           BackendComponent::MPSBit,
           BackendComponent::HIPBit,
           BackendComponent::XPUBit});
      constexpr auto dense_k = DispatchKeySet(DispatchKey::Dense);
      return ts.has_any(dense_k) && ts.has_any(dense_backends);
    };
    auto is_sparse = [](DispatchKeySet ts) {
      constexpr auto sparse_backends = DispatchKeySet(
          {BackendComponent::CPUBit,
           BackendComponent::CUDABit,
           BackendComponent::HIPBit,
           BackendComponent::XPUBit});
      constexpr auto sparse_k = DispatchKeySet(DispatchKey::Sparse);
      return ts.has_any(sparse_k) && ts.has_any(sparse_backends);
    };
    return (key_set_ == from) || (is_dense(key_set_) && is_dense(from)) ||
        (is_sparse(key_set_) && is_sparse(from));
  }

 private:
  template <typename VariableVersion>
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach_core(
      VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const;

 public:
  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) const;

  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const;

  /**
   * Shallow-copies data from another TensorImpl into this TensorImpl.
   *
   * For why this function doesn't check this TensorImpl's
   * `allow_tensor_metadata_change_`, see NOTE [ TensorImpl Shallow-Copying ].
   */
  virtual void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) {
    copy_tensor_metadata(
        /*src_impl=*/impl.get(),
        /*dest_impl=*/this,
        /*version_counter=*/version_counter(),
        /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
    refresh_numel();
    refresh_contiguous();
  }

  // Inference tensor doesn't have version counter,
  // set_version_counter is no-op for them.
  void set_version_counter(const c10::VariableVersion& version_counter) {
    TORCH_CHECK(
        !(is_inference() && version_counter.enabled()),
        "Cannot set version_counter for inference tensor");
    version_counter_ = version_counter;
  }

  void set_version_counter(c10::VariableVersion&& version_counter) {
    TORCH_CHECK(
        !(is_inference() && version_counter.enabled()),
        "Cannot set version_counter for inference tensor");
    version_counter_ = std::move(version_counter);
  }

  const c10::VariableVersion& version_counter() const noexcept {
    return version_counter_;
  }

  void bump_version() {
    version_counter_.bump();
  }

  // Associate the TensorImpl with the specified PyObject, and, if necessary,
  // also tag the interpreter.
  //
  // NB: This lives in a header so that we can inline away the switch on status
  //
  // NB: THIS FUNCTION CAN RAISE AN EXCEPTION.  Make sure to clean up after
  // PyObject if necessary!
  void init_pyobj(
      impl::PyInterpreter* self_interpreter,
      PyObject* pyobj,
      c10::impl::PyInterpreterStatus status) {
    impl::PyInterpreter* expected = nullptr;
    switch (status) {
      case impl::PyInterpreterStatus::DEFINITELY_UNINITIALIZED:
        // caller guarantees there is no multithreaded access; if there is
        // no data race OK to do a relaxed store
        pyobj_interpreter_.store(self_interpreter, std::memory_order_relaxed);
        break;
      case impl::PyInterpreterStatus::TAGGED_BY_US:
        // no tagging is necessary, the tag is already correct
        break;
      case impl::PyInterpreterStatus::MAYBE_UNINITIALIZED:
        // attempt to claim this TensorImpl with the specified interpreter
        // tag
        if (pyobj_interpreter_.compare_exchange_strong(
                expected, self_interpreter, std::memory_order_acq_rel)) {
          break;
        }
        // test if, actually, it was already tagged by us!  this situation can't
        // be caused by a race, but it could be caused by a situation
        // where someone conservatively tagged the tensor as MAYBE_UNINITIALIZED
        // (because they didn't pre-check the tag) when actually it was
        // owned by the interpreter
        if (expected == self_interpreter) {
          break;
        }
        // fallthrough, we lost the race.  We are guaranteed not to lose the
        // race with ourself, as calls to init_pyobj with the same interpreter
        // ID must be sequentialized by the GIL
        C10_FALLTHROUGH;
      case impl::PyInterpreterStatus::TAGGED_BY_OTHER:
        TORCH_CHECK(
            false,
            "cannot allocate PyObject for Tensor on interpreter ",
            self_interpreter,
            " that has already been used by another torch deploy interpreter ",
            pyobj_interpreter_.load());
    }

    // we are the ONLY thread that can have gotten to this point.  It is not
    // possible to conflict with another zero interpreter as access is protected
    // by GIL
    // NB: owns_pyobj tag is initially false
    pyobj_ = pyobj;
  }

  // Query the PyObject interpreter.  This may return null if there is no
  // interpreter.  This is racy!
  impl::PyInterpreter* pyobj_interpreter() {
    return pyobj_interpreter_.load(std::memory_order_acquire);
  }

  PyObject* _unchecked_untagged_pyobj() const {
    return reinterpret_cast<PyObject*>(
        reinterpret_cast<uintptr_t>(pyobj_) & ~0x1ULL);
  }

  // Test the interpreter tag.  If tagged for the current interpreter, return
  // a non-nullopt (but possibly null) PyObject.  If (possibly) untagged,
  // returns a nullopt.  If it is definitely invalid, raises an error.
  //
  // NB: this lives in header so that we can avoid actually creating the
  // c10::optional
  c10::optional<PyObject*> check_pyobj(
      impl::PyInterpreter* self_interpreter) const {
    // Note [Memory ordering on Python interpreter tag]
    impl::PyInterpreter* interpreter =
        pyobj_interpreter_.load(std::memory_order_acquire);
    if (interpreter == nullptr) {
      // NB: This never returns DEFINITELY_UNINITIALIZED because there is
      // always the possibility that another thread races to initialize
      // after we query here.  The only time when we can conclude a tensor
      // is definitely uninitialized is when we have just allocated it and
      // it cannot have escaped to other threads yet
      return c10::nullopt;
    } else if (interpreter == self_interpreter) {
      // NB: pyobj_ could still be null!
      return c10::make_optional(_unchecked_untagged_pyobj());
    } else {
      TORCH_CHECK(
          false,
          "cannot access PyObject for Tensor on interpreter ",
          (*self_interpreter)->name(),
          " that has already been used by another torch deploy interpreter ",
          (*pyobj_interpreter_.load())->name());
    }
  }

  // Clear the PyObject field for an interpreter, in situations where we
  // statically know the tensor is tagged with our interpreter.
  void unchecked_clear_pyobj(impl::PyInterpreter* interpreter) {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(interpreter == pyobj_interpreter_.load());
    pyobj_ = nullptr;
  }

 private:
  // See NOTE [c10::optional operator usage in CUDA]
  // We probably don't want to expose this publicly until
  // the note is addressed.
  c10::optional<c10::Device> device_opt() const {
    return device_opt_;
  }

  impl::PyInterpreter& load_pyobj_interpreter() const;

 public:
  /**
   * The device type of a Tensor, e.g., DeviceType::CPU or DeviceType::CUDA.
   */
  DeviceType device_type() const {
    // TODO: A useful internal assert would be to show that device_opt_ is null
    // only if you are an undefined tensor
    TORCH_CHECK(
        device_opt_.has_value(),
        "device_type cannot be run on undefined Tensor");
    // See NOTE [c10::optional operator usage in CUDA]
    return (*device_opt_).type();
  }

  /**
   * @brief Extends the outer-most dimension of this tensor by num elements,
   * preserving the existing data.
   *
   * The underlying data may be reallocated in order to accommodate the new
   * elements, in which case this tensors' capacity is grown at a factor of
   * growthPct. This ensures that Extend runs on an amortized O(1) time
   * complexity.
   *
   * This op is auto-asynchronous if the underlying device (CUDA) supports it.
   */
  void Extend(int64_t num, float growthPct);

  /**
   * @brief Reserve space for the underlying tensor.
   *
   * This must be called after Resize(), since we only specify the first
   * dimension This does not copy over the old data to the newly allocated space
   */
  void ReserveSpace(int64_t outer_dim);

  /**
   * @brief Resizes a tensor.
   *
   * Resize takes in a vector of ints specifying the dimensions of the tensor.
   * You can pass in an empty vector to specify that it is a scalar (i.e.
   * containing one single item).
   *
   * The underlying storage may be deleted after calling Resize: if the new
   * shape leads to a different number of items in the tensor, the old memory
   * is deleted and new memory will be allocated next time you call
   * mutable_data(). However, if the shape is different but the total number of
   * items is the same, the underlying storage is kept.
   *
   * This method respects caffe2_keep_on_shrink.  Consult the internal logic
   * of this method to see exactly under what circumstances this flag matters.
   */
  template <typename... Ts>
  void Resize(Ts... dim_source) {
    bool size_changed = SetDims(dim_source...);
    if (size_changed) {
      HandleResize();
    }
  }

  template <typename T>
  void Resize(const std::vector<T>& dim_source) {
    Resize(ArrayRef<T>(dim_source));
  }

  /**
   * Resizes the tensor without touching underlying storage.
   * This requires the total size of the tensor to remains constant.
   */
  void Reshape(const std::vector<int64_t>& dims);

  /**
   * Release whatever memory the tensor was holding but keep size and type
   * information. Subsequent call to mutable_data will trigger new memory
   * allocation.
   */
  void FreeMemory();

  /**
   * @brief Shares the data with another tensor.
   *
   * To share data between two tensors, the sizes of the two tensors must be
   * equal already. The reason we do not implicitly do a Resize to make the two
   * tensors have the same shape is that we want to allow tensors of different
   * shapes but the same number of items to still be able to share data. This
   * allows one to e.g. have a n-dimensional Tensor and a flattened version
   * sharing the same underlying storage.
   *
   * The source tensor should already have its data allocated.
   */
  // To be deprecated
  void ShareData(const TensorImpl& src);

  void ShareExternalPointer(
      DataPtr&& data_ptr,
      const caffe2::TypeMeta data_type,
      size_t size_bytes);

  /**
   * Returns a mutable raw pointer of the underlying storage. Since we will need
   * to know the type of the data for allocation, a TypeMeta object is passed in
   * to specify the necessary information. This is conceptually equivalent of
   * calling mutable_data<T>() where the TypeMeta parameter meta is derived from
   * the type T. This function differs from mutable_data<T>() in the sense that
   * the type T can be specified during runtime via the TypeMeta object.
   *
   * If the existing data does not match the desired type, it will be deleted
   * and a new storage will be created.
   */
  inline void* raw_mutable_data(const caffe2::TypeMeta meta) {
    // For 0-size tensors it's fine to return any pointer (including nullptr)
    if (data_type_ == meta && storage_initialized()) {
      return static_cast<void*>(
          static_cast<char*>(storage_.data()) +
          storage_offset_ * meta.itemsize());
    } else {
      bool had_special_dtor = data_type_.placementDelete() != nullptr;
      storage_offset_ = 0;
      data_type_ = meta;
      // NB: device is not changed

      // We can reuse the existing buffer if the current data does not have
      // a special destructor and the new data doesn't have a special
      // constructor.
      if (numel_ == 0 ||
          (meta.placementNew() == nullptr && !had_special_dtor &&
           (storage_.nbytes() >= (numel_ * data_type_.itemsize())))) {
        TORCH_INTERNAL_ASSERT(
            storage_offset_ == 0); // because we just reallocated
        return storage_.data();
      }
      const Allocator* allocator = storage_.allocator();
      // Storage might have nullptr allocator in rare cases, for example, if
      // an external memory segment has been wrapped with Tensor and we don't
      // know how to reallocate it. However, in order to preserve legacy C2
      // behavior, we allow reallocating the memory using default allocator.
      if (allocator == nullptr) {
        allocator = GetAllocator(storage_.device_type());
      }
      if (meta.placementNew()) {
        // For types that need placement new, we will call it, as well as
        // making sure that when the data is freed, it calls the right
        // destruction procedure.
        auto size = numel_;
        auto dtor = data_type_.placementDelete();
        auto data_ptr = allocator->allocate(numel_ * data_type_.itemsize());
        storage_.set_data_ptr_noswap(PlacementDeleteContext::makeDataPtr(
            std::move(data_ptr), dtor, size, storage_.device()));
        data_type_.placementNew()(storage_.data(), numel_);
      } else {
        // For fundamental type, new and delete is easier.
        storage_.set_data_ptr_noswap(
            allocator->allocate(numel_ * data_type_.itemsize()));
      }
      storage_.set_nbytes(numel_ * data_type_.itemsize());
      TORCH_INTERNAL_ASSERT(
          storage_offset_ == 0); // because we just reallocated
      device_opt_ = storage_.device();
      return storage_.data();
    }
  }

  /**
   * Returns a typed pointer of the underlying storage.
   *
   * For fundamental types, we reuse possible existing storage if there
   * is sufficient capacity.
   */
  template <typename T>
  inline T* mutable_data() {
    if (storage_initialized() && data_type_.Match<T>()) {
      return static_cast<T*>(storage_.data()) + storage_offset_;
    }
    // Check it here statically - otherwise TypeMeta would throw the runtime
    // error in attempt to invoke TypeMeta::ctor()
    static_assert(
        std::is_default_constructible<T>::value,
        "Tensor can't hold non-default-constructable types");
    return static_cast<T*>(raw_mutable_data(caffe2::TypeMeta::Make<T>()));
  }

  /**
   * True if a tensor is storage initialized.  A tensor may become
   * storage UNINITIALIZED after a Resize() or FreeMemory()
   */
  bool storage_initialized() const {
    TORCH_CHECK(
        has_storage(),
        "cannot call storage_initialized on tensor that does not have storage");
    return storage_.data() || numel_ == 0;
  }

  /**
   * True if a tensor is dtype initialized.  A tensor allocated with
   * Caffe2-style constructors is dtype uninitialized until the
   * first time mutable_data<T>() is called.
   */
  bool dtype_initialized() const noexcept {
    return data_type_ != caffe2::TypeMeta();
  }

  void set_storage_keep_dtype(at::Storage storage) {
    TORCH_CHECK(
        allow_tensor_metadata_change(),
        "set_storage ",
        err_msg_tensor_metadata_change_not_allowed);
    storage_ = std::move(storage);
    device_opt_ = storage_.device();
  }

  void set_storage_and_dtype(
      at::Storage storage,
      const caffe2::TypeMeta data_type) {
    set_storage_keep_dtype(storage);
    data_type_ = data_type;
  }

  /**
   * Set the strides of the tensor to match memory_format
   *
   * WARNING: This function doesn't rearrange data and assumes tensor is a
   * memory contiguous
   */
  void empty_tensor_restride(MemoryFormat memory_format) {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "empty_tensor_restride() called on tensor with symbolic shape")
#ifdef DEBUG
    TORCH_INTERNAL_ASSERT(
        compute_numel() == numel_,
        "If you are seeing this error, that means empty_tensor_restride was "
        "called before setting correct numel");
#endif
    switch (memory_format) {
      case MemoryFormat::Contiguous: {
        // dim_ is a virtual call, don't repeat it
        const auto dim_ = dim();
        sizes_and_strides_.resize(dim_);
        if (dim_ > 0) {
          const auto last_idx = dim_ - 1;
          sizes_and_strides_.stride_at_unchecked(last_idx) = 1;
          for (auto i = last_idx - 1; i >= 0; --i) {
            sizes_and_strides_.stride_at_unchecked(i) =
                sizes_and_strides_.stride_at_unchecked(i + 1) *
                std::max<int64_t>(
                    sizes_and_strides_.size_at_unchecked(i + 1), 1);
          }
        }
        break;
      }
      case MemoryFormat::ChannelsLast: {
        TORCH_CHECK(
            dim() == 4, "required rank 4 tensor to use channels_last format");
        set_sizes_and_strides(sizes(), get_channels_last_strides_2d(sizes()));
        break;
      }
      case MemoryFormat::ChannelsLast3d: {
        TORCH_CHECK(
            dim() == 5,
            "required rank 5 tensor to use channels_last_3d format");
        set_sizes_and_strides(sizes(), get_channels_last_strides_3d(sizes()));
        break;
      }
      case MemoryFormat::Preserve:
        TORCH_CHECK(false, "unsupported memory format ", memory_format);
        // Cleaning warning messages, no need to break as TORCH_CHECK(false)
        // terminates flow.
        // break;
      case MemoryFormat::NumOptions:
        TORCH_INTERNAL_ASSERT(false, "invalid memory format ", memory_format);
    }
    // recompute contiguous flag, as currently NHWC/NCHW flags are not mutually
    // exclusive see #24090
    refresh_contiguous();
  }

  bool is_strides_like(at::MemoryFormat memory_format) const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_strides_like_custom(memory_format);
    }
    return is_strides_like_default(memory_format);
  }

  bool is_strides_like_channels_last() const {
    return is_strides_like(at::MemoryFormat::ChannelsLast);
  }

  bool is_strides_like_channels_last_3d() const {
    return is_strides_like(at::MemoryFormat::ChannelsLast3d);
  }

  bool is_non_overlapping_and_dense() const {
    if (C10_UNLIKELY(matches_policy(SizesStridesPolicy::CustomStrides))) {
      return is_non_overlapping_and_dense_custom();
    }
    return is_non_overlapping_and_dense_default();
  }

  bool has_symbolic_sizes_strides() const {
    return has_symbolic_sizes_strides_;
  }

 private:
  void HandleResize();

  // The Caffe2 Resize() method supports being called both as Resize({2,2}) as
  // well as variadic with Resize(2, 2).  These overloads provide all of the
  // supported calling configurations, while being overloads (and not templates)
  // so that implicit conversions still work.
  //
  // SetDims on ArrayRef is internally implemented as a template, so we can
  // handle both ArrayRefs of different types (there are some uses of
  // Resize in Caffe2 which pass in int, not int64_t.)

  template <
      typename T,
      typename = typename std::enable_if<std::is_integral<T>::value>::type>
  bool SetDimsTemplate(ArrayRef<T> src) {
    TORCH_CHECK(
        !has_symbolic_sizes_strides_,
        "SetDims() called on tensor with symbolic shape")

    auto old_numel = numel_;
    sizes_and_strides_.resize(src.size());
    int64_t new_numel = 1;
    for (const auto i : c10::irange(src.size())) {
      new_numel *= src[i];
      sizes_and_strides_.size_at_unchecked(i) = src[i];
    }
    numel_ = new_numel;
    empty_tensor_restride(MemoryFormat::Contiguous);
    return numel_ != old_numel;
  }

  bool SetDims(ArrayRef<int64_t> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims(ArrayRef<int> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims(ArrayRef<size_t> s) {
    return SetDimsTemplate(s);
  }

  bool SetDims() {
    return SetDims(IntArrayRef{});
  }

  bool SetDims(const int64_t d0) {
    return SetDims(IntArrayRef{d0});
  }

  bool SetDims(const int64_t d0, const int64_t d1) {
    return SetDims(IntArrayRef{d0, d1});
  }

  bool SetDims(const int64_t d0, const int64_t d1, const int64_t d2) {
    return SetDims(IntArrayRef{d0, d1, d2});
  }

  bool SetDims(
      const int64_t d0,
      const int64_t d1,
      const int64_t d2,
      const int64_t d3) {
    return SetDims(IntArrayRef{d0, d1, d2, d3});
  }

  /**
   * Compute the number of elements based on the sizes of a tensor.
   */
  // NB: This is ONLY called when sizes_and_strides_ is used directly; if
  // we are virtualizing, then numel calls are virtualized as well, and this
  // should never get called
  int64_t compute_numel() const {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
#if C10_HAS_BUILTIN_OVERFLOW() && !defined(C10_MOBILE)
    // Use overflow checks if supported by the compiler
    return safe_compute_numel();
#else
    return c10::multiply_integers(sizes_and_strides_.sizes_arrayref());
#endif
  }

  /**
   * Compute the number of elements based on the sizes of a
   * tensor. Catches integer overflow that may occur when a tensor
   * using a sparse layout has multiple dimensions with large sizes.
   */
  int64_t safe_compute_numel() const {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!has_symbolic_sizes_strides_);
    uint64_t n = 1;
    bool overflows =
        c10::safe_multiplies_u64(sizes_and_strides_.sizes_arrayref(), &n);
    constexpr auto numel_max = std::min(
        static_cast<uint64_t>(std::numeric_limits<int64_t>::max()),
        static_cast<uint64_t>(std::numeric_limits<size_t>::max()));

    overflows |= (n > numel_max);
    TORCH_CHECK(!overflows, "numel: integer multiplication overflow");
    return static_cast<int64_t>(n);
  }

  SymInt compute_sym_numel() const {
    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(has_symbolic_sizes_strides_);
    SymInt numel = 1;
    for (const auto& s : extra_meta_->sizes_) {
      numel *= s;
    }
    return numel;
  }

  /**
   * Compute whether or not a tensor is contiguous based on the sizes and
   * strides of a tensor.
   */
  bool_is_contiguous compute_contiguous() const;

  bool_is_channels_last_contiguous compute_channels_last_contiguous_2d() const;

  bool_is_channels_last_3d_contiguous compute_channels_last_contiguous_3d()
      const;

  bool_is_channels_last compute_strides_like_channels_last_2d() const;

  bool_is_channels_last_3d compute_strides_like_channels_last_3d() const;

  bool_is_non_overlapping_and_dense compute_non_overlapping_and_dense() const;

 protected:
  /**
   * Recompute the cached numel of a tensor.  Call this if you modify
   * sizes.
   *
   * For tensors with sparse layouts, use safe_refresh_numel() instead
   * because it will catch integer overflow that may occur for tensors
   * with sparse layouts and large dimensions.
   *
   * NB: We may uselessly recompute cached numel even in situations where
   * it is completely never used (e.g., if CustomSizes for Python).  However,
   * we still must keep it up to date in case the Python overload
   * returns None (in which case we will consult the field here).  This also
   * implies that sizes/strides will never be complete garbage; in the
   * very worst case scenario, it will reflect a 1-dim zero size tensor.
   */
  void refresh_numel() {
    if (has_symbolic_sizes_strides_) {
      extra_meta_->numel_ = compute_sym_numel();
    } else {
      numel_ = compute_numel();
    }
  }

  /**
   * Recompute the cached numel of a tensor.  Call this if you modify
   * sizes. Use only for tensors with sparse layouts because only
   * sparse tensor are likely to have sizes that may lead to integer
   * overflow when computing numel.
   */
  void safe_refresh_numel() {
    if (has_symbolic_sizes_strides_) {
      // NB: sym numel is done with symbolic integers, which handle overflow
      // checking
      extra_meta_->numel_ = compute_sym_numel();
    } else {
      numel_ = safe_compute_numel();
    }
  }

  /**
   * Recompute the cached contiguity of a tensor.  Call this if you modify sizes
   * or strides.
   */
  void refresh_contiguous() {
    auto set_fields =
        [&](bool_is_contiguous is_contiguous,
            bool_is_channels_last_contiguous is_channels_last_contiguous,
            bool_is_channels_last_3d_contiguous is_channels_last_3d_contiguous,
            bool_is_channels_last is_channels_last,
            bool_is_channels_last_3d is_channels_last_3d,
            bool_is_non_overlapping_and_dense is_non_overlapping_and_dense) {
          if (has_symbolic_sizes_strides_) {
            extra_meta_->is_contiguous_ = is_contiguous;
            extra_meta_->is_channels_last_contiguous_ =
                is_channels_last_contiguous;
            extra_meta_->is_channels_last_3d_contiguous_ =
                is_channels_last_3d_contiguous;
            extra_meta_->is_channels_last_ = is_channels_last;
            extra_meta_->is_channels_last_3d_ = is_channels_last_3d;
            extra_meta_->is_non_overlapping_and_dense_ =
                is_non_overlapping_and_dense;
          } else {
            is_contiguous_ = bool(is_contiguous);
            is_channels_last_contiguous_ = bool(is_channels_last_contiguous);
            is_channels_last_3d_contiguous_ =
                bool(is_channels_last_3d_contiguous);
            is_channels_last_ = bool(is_channels_last);
            is_channels_last_3d_ = bool(is_channels_last_3d);
            is_non_overlapping_and_dense_ = bool(is_non_overlapping_and_dense);
          }
        };

    auto is_contiguous = compute_contiguous();
    // Note:
    // Dim 0, 1, 2 will never be a channels last 2d/3d format
    // Dim 3+ is possibly be a channels last 2d format (Dim 4 only at this
    // point) Dim 4+ is possibly be a channels last 3d format (Dim 5 only at
    // this point)
    switch (dim()) {
      case 4: {
        auto is_channels_last_contiguous =
            compute_channels_last_contiguous_2d();
        set_fields(
            is_contiguous,
            is_channels_last_contiguous,
            bool_is_channels_last_3d_contiguous(false),
            compute_strides_like_channels_last_2d(),
            bool_is_channels_last_3d(false),
            bool_is_non_overlapping_and_dense(
                is_contiguous || is_channels_last_contiguous ||
                compute_non_overlapping_and_dense()));
        break;
      }
      case 5: {
        auto is_channels_last_contiguous =
            compute_channels_last_contiguous_2d();
        auto is_channels_last_3d_contiguous =
            bool_is_channels_last_3d_contiguous(
                !is_channels_last_contiguous &&
                compute_channels_last_contiguous_3d());
        auto is_channels_last = bool_is_channels_last(
            !is_channels_last_3d_contiguous &&
            compute_strides_like_channels_last_2d());
        auto is_channels_last_3d = bool_is_channels_last_3d(
            !is_channels_last && compute_strides_like_channels_last_3d());
        auto is_non_overlapping_and_dense = bool_is_non_overlapping_and_dense(
            is_contiguous || is_channels_last_contiguous ||
            is_channels_last_3d_contiguous ||
            compute_non_overlapping_and_dense());
        set_fields(
            is_contiguous,
            is_channels_last_contiguous,
            is_channels_last_3d_contiguous,
            is_channels_last,
            is_channels_last_3d,
            is_non_overlapping_and_dense);
        break;
      }
      default:
        // is_channels_last_ and is_channels_last_3d_ are suggested
        // memory_format. Being channels_last_contiguous doesn't necessarily
        // mean the tensor is strided like channels_last: for strides on channel
        // dimension could suggest desired memory_layout, but it doesn't affect
        // memory storage
        set_fields(
            is_contiguous,
            bool_is_channels_last_contiguous(false),
            bool_is_channels_last_3d_contiguous(false),
            bool_is_channels_last(false),
            bool_is_channels_last_3d(false),
            bool_is_non_overlapping_and_dense(
                is_contiguous || compute_non_overlapping_and_dense()));
    }
  }

  /**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
   * storage_offset) from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
   * [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change);

  /**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer /
   * storage_offset) from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE
   * [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change);

 private:
  static void copy_tensor_metadata_except_version_counter(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl,
      bool allow_tensor_metadata_change);

 protected:
  // Error message to show when the user tries to change tensor metadata on
  // Tensor created from .data or .detach().
  //
  // See NOTE [ Metadata Change for a Detached Tensor ] for details.
  static const char* const err_msg_tensor_metadata_change_not_allowed;

  static void copy_generic_tensor_metadata(
      const TensorImpl* src_impl,
      TensorImpl* dest_impl);

 public:
  void set_storage_access_should_throw() {
    storage_access_should_throw_ = true;
  }

  bool owns_pyobj() {
    return reinterpret_cast<uintptr_t>(pyobj_) & 1;
  }

  void set_owns_pyobj(bool b) {
    pyobj_ = reinterpret_cast<PyObject*>(
        reinterpret_cast<uintptr_t>(_unchecked_untagged_pyobj()) | b);
  }

 public:
  void set_custom_sizes_strides(SizesStridesPolicy policy) {
    custom_sizes_strides_ = static_cast<uint8_t>(policy);
    refresh_sizes_strides_policy();
  }

  void set_python_custom_sizes_strides(SizesStridesPolicy policy) {
    python_custom_sizes_strides_ = static_cast<uint8_t>(policy);
    refresh_sizes_strides_policy();
  }

  void set_custom_device(bool custom_device) {
    custom_device_ = custom_device;
    refresh_device_policy();
  }

  void set_custom_layout(bool custom_layout) {
    custom_layout_ = custom_layout;
    refresh_layout_policy();
  }

  void set_python_custom_device(bool custom_device) {
    python_custom_device_ = custom_device;
    refresh_device_policy();
  }

  void set_python_custom_layout(bool custom_layout) {
    python_custom_layout_ = custom_layout;
    refresh_layout_policy();
  }

 protected:
  void refresh_sizes_strides_policy() {
    if (has_symbolic_sizes_strides_) {
      sizes_strides_policy_ =
          static_cast<uint8_t>(SizesStridesPolicy::CustomSizes);
    } else {
      sizes_strides_policy_ =
          std::max(custom_sizes_strides_, python_custom_sizes_strides_);
    }
  }

  void refresh_device_policy() {
    device_policy_ = custom_device_ || python_custom_device_;
  }

  void refresh_layout_policy() {
    layout_policy_ = custom_layout_ || python_custom_layout_;
  }

 protected:
  Storage storage_;

 private:
  // This pointer points to an AutogradMeta struct that stores autograd-specific
  // fields (such as grad_ / grad_fn_ / grad_accumulator_). This pointer always
  // has unique ownership (meaning only one TensorImpl can own it at a time).
  //
  // autograd_meta_ can be nullptr, as an optimization.  When this occurs, it is
  // equivalent to having an autograd_meta_ pointing to a default constructed
  // AutogradMeta; intuitively, tensors which don't require grad will have this
  // field set to null.
  //
  // This means accessors on autograd_meta_ have to be careful to test if they
  // got a nullptr, and handle default behavior appropriately in that case.
  //
  // Note that we don't enforce the invariant that if the AutogradMeta is
  // default constructed, it is nullptr (to do this, we'd have to continuously
  // check if an AutogradMeta became, by mutation, equal to the default
  // constructed form.  (This might be useful, but it seems rare enough that
  // a requires_grad=True variable will turn back into the requires_grad=False
  // version.)  So there are three representable states:
  //
  //    1. autograd_meta_ == nullptr
  //    2. autograd_meta_ is default constructed (semantically, same as (1))
  //    3. autograd_meta_ has nontrivial information content
  //
  std::unique_ptr<c10::AutogradMetaInterface> autograd_meta_ = nullptr;

 protected:
  std::unique_ptr<c10::ExtraMeta> extra_meta_ = nullptr;

  c10::VariableVersion version_counter_;

  // This field contains the interpreter tag for this object.  See
  // Note [Python interpreter tag] for general context
  //
  // Note [Memory ordering on Python interpreter tag]
  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  // What memory_order do we need when accessing this atomic?  We don't
  // need a single total modification order (as provided by
  // memory_order_seq_cst) as pyobj_interpreter_ is monotonic: it can only
  // transition from -1 to some positive integer and never changes afterwards.
  // Because there is only one modification, it trivially already has a total
  // modification order (e.g., we don't need fences or locked instructions on
  // x86)
  //
  // In fact, one could make a reasonable argument that relaxed reads are OK,
  // due to the presence of external locking (GIL) to ensure that interactions
  // with other data structures are still correctly synchronized, so that
  // we fall in the "Single-Location Data Structures" case as described in
  // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
  // However, on x86, it doesn't matter if I use acquire or relaxed on the load
  // as I get the same assembly in both cases.  So I just use the more
  // conservative acquire (which will impede compiler optimizations but I don't
  // care)
  std::atomic<impl::PyInterpreter*> pyobj_interpreter_;

  // This field contains a reference to a PyObject representing this Tensor.
  // If pyobj is nullptr, when we transfer Tensor to Python, we allocate a new
  // PyObject for it and set this field.  This field does not have to be
  // protected by an atomic as it is only allowed to be accessed when you hold
  // the GIL, or during destruction of the tensor.
  //
  // When a PyObject dies, you are obligated to clear this field
  // (otherwise, you will try to use-after-free the pyobj); this currently
  // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp
  //
  // NB: Ordinarily, this should not be a strong reference, as if the
  // PyObject owns the Tensor, this would create a reference cycle.
  // However, sometimes this ownership flips.  To track who owns
  // who, this has a single pointer tag indicating whether or not the
  // C++ object owns the PyObject (the common case, zero, means PyObject
  // owns the C++ object); see _unchecked_untagged_pyobj for raw access
  // or check_pyobj for checked access.  See references to PyObject
  // resurrection in torch/csrc/autograd/python_variable.cpp
  PyObject* pyobj_;

  c10::impl::SizesAndStrides sizes_and_strides_;

  int64_t storage_offset_ = 0;
  // If sizes and strides are empty, the numel is 1!!  However, most of the
  // time, we will immediately set sizes to {0} and reset numel to 0.
  // (Can't do that in the default initializers, because there's no way to
  // spell "allocate a one-element array" for strides_).
  int64_t numel_ = 1;

  // INVARIANT: When storage is non-null, this type meta must
  // agree with the type meta in storage
  caffe2::TypeMeta data_type_;

  // NOTE [c10::optional operator usage in CUDA]
  // Our optional definition doesn't compile in .cu file if `value()` or
  // `operator->` are used.  Instead, we always use `operator*`.
  // See https://github.com/pytorch/pytorch/issues/18496 for more info.
  // If this is too burdensome to maintain, we can just
  // manually implement this with an additional bool.

  // INVARIANT: When storage is non-null, this Device must
  // agree with the type meta in storage.
  //
  // INVARIANT: device_opt_ is only nullopt for undefined tensors
  // (which do not have a device.)
  c10::optional<c10::Device> device_opt_;

  // default member initializers for bit-fields only available with -std=c++2a
  // or -std=gnu++2a
  inline void init_bitfields() {
    is_contiguous_ = true;
    is_channels_last_ = false;
    is_channels_last_contiguous_ = false;
    is_channels_last_3d_ = false;
    is_channels_last_3d_contiguous_ = false;
    is_non_overlapping_and_dense_ = true;
    is_wrapped_number_ = false;
    allow_tensor_metadata_change_ = true;
    reserved_ = false;
    sizes_strides_policy_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
    custom_sizes_strides_ = static_cast<uint8_t>(SizesStridesPolicy::Default);
    python_custom_sizes_strides_ =
        static_cast<uint8_t>(SizesStridesPolicy::Default);
    python_custom_device_ = false;
    python_custom_layout_ = false;
    custom_device_ = false;
    custom_layout_ = false;
    device_policy_ = false;
    layout_policy_ = false;
    storage_access_should_throw_ = false;
    has_symbolic_sizes_strides_ = false;
  }

  // Tensor is contiguous
  bool is_contiguous_ : 1;

  // Tensor is a subclass that does not permit storage access.
  bool storage_access_should_throw_ : 1;

  // Tensor is stored in the channels last 2d memory format, when dimensions
  // order is (N)CHW and C-strides < W-strides < H-strides (< N-strides)
  // (If size of any dimension is equal to 1, this dimension strides value
  // is not taken into account).
  bool is_channels_last_ : 1;

  // Channels last contiguous tensor is channel last tensor which occupies
  // contiguous memory block.
  bool is_channels_last_contiguous_ : 1;

  // Tensor is stored in the channels last 3d memory format, when dimensions
  // order is (N)CDHW and C-strides < W-strides < H-strides < D - strides (<
  // N-strides) (If size of any dimension is equal to 1, this dimension strides
  // value is not taken into account).
  bool is_channels_last_3d_ : 1;

  // Channels last 3d contiguous tensor is channel last 3d tensor which occupies
  // contiguous memory block.
  bool is_channels_last_3d_contiguous_ : 1;

  // Dense tensor is the tensor that store values in a contiguous block of
  // memory. Non-overlapping tensor is the tensor in which elements occupy
  // individual non-repetitive memory.
  bool is_non_overlapping_and_dense_ : 1;

  bool is_wrapped_number_ : 1;

  // NOTE [ Metadata Change for a Detached Tensor ]
  //
  // Normally, a user is allowed to change the tensor metadata
  // (e.g. sizes / strides / storage / storage_offset) of a tensor.
  // However, if the tensor is created by `t1_detached = t1.data` in Python
  // or `t1_detached = t1.detach()` in Python/C++, those changes to the
  // tensor metadata of `t1_detached` will not be propagated back to the
  // original tensor `t1`. In order to make such changes explicitly illegal,
  // we created the `allow_tensor_metadata_change_` flag, to prevent users
  // from changing metadata of the detached tensor and expecting the original
  // tensor to also be updated.
  //
  // NOTE: For a full list of tensor metadata fields, please see
  // `copy_tensor_metadata()` in TensorImpl and its subclasses to find
  // which fields are copied by value.
  bool allow_tensor_metadata_change_ : 1;

  // we decide to keep reserved_ and it will
  // live in Tensor after the split
  // The logic is that if Extend() or ReserveSpace() were ever called,
  // then subsequent Resize()s will not free up Storage.
  bool reserved_ : 1;

  // Call _custom() virtual methods for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  // This is a combination of sizes_strides_custom_dispatch_
  // and has_symbolic_sizes_strides_
  uint8_t sizes_strides_policy_ : 2;

  // Whether or not sizes_and_strides_ contains a symbolic value.
  bool has_symbolic_sizes_strides_ : 1;

  // Call _custom() virtual method for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  uint8_t custom_sizes_strides_ : 2;

  // Combo of custom_ and python_custom_
  bool device_policy_ : 1;
  bool layout_policy_ : 1;

  // Call _custom() virtual method for device()
  bool custom_device_ : 1;

  // Call _custom() virtual method for layout()
  bool custom_layout_ : 1;

  // Call into Python for
  // strides()/is_contiguous()/sizes()/dim()/numel()
  uint8_t python_custom_sizes_strides_ : 2;

  // Call into Python for device()
  bool python_custom_device_ : 1;

  // Call into Python for layout()
  bool python_custom_layout_ : 1;

  // The set of DispatchKeys which describe this tensor.  NB: this
  // does NOT include Autograd (historically, it did, but
  // not anymore!)
  //
  // INVARIANT: extra_meta_->named_tensor_meta_ != nullptr  <==>
  // key_set_.has(DispatchKey::Named)
  DispatchKeySet key_set_;

 private:
  // C10_TensorImpl_Size_Check_Dummy_Class needs to be friends with
  // TensorImpl so it can inspect the size of private fields
  template <
      size_t cplusplus,
      size_t clang_ver_major,
      size_t gcc_ver,
      size_t gcc_ver_minor,
      size_t nvcc,
      size_t cuda_version,
      size_t cuda_version_major,
      size_t ptr_size>
  friend class C10_TensorImpl_Size_Check_Dummy_Class;
};

// Note [TensorImpl size constraints]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Changed the size of TensorImpl?  If the size went down, good for
// you!  Adjust the documentation below and the expected size.
// Did it go up?  Read on...
//
// Struct size matters.  In some production systems at Facebook, we have
// 400M live tensors during a training run.  Do the math: every 64-bit
// word you add to Tensor is an extra 3.2 gigabytes in RAM.
//
// If you are a Facebook employee, you can check if the run in question
// has tipped you over the point using the command here:
// https://fburl.com/q5enpv98
//
// For reference, we OOMed at 160 bytes (20 words) per TensorImpl.
// This is not counting overhead from strides out-of-line allocation and
// StorageImpl space and this is from before we inlined sizes and strides
// directly into TensorImpl as SmallVectors.
//
// Our memory usage on 32-bit systems is suboptimal, but we're not checking
// for it at the moment (to help avoid rage inducing cycles when the
// 32-bit number is wrong).
//
// Current breakdown:
//
//    vtable pointer
//    strong refcount           TODO: pack these into one word
//    weak refcount
//    storage pointer
//    autograd metadata pointer
//    named tensor metadata pointer
//    version counter pointer
//    Python interpreter pointer
//    PyObject pointer
//    SizesAndStrides size/pointer
//    SizesAndStrides sizes (pre-allocated 0)
//    SizesAndStrides sizes (pre-allocated 1)
//    SizesAndStrides sizes (pre-allocated 2)
//    SizesAndStrides sizes (pre-allocated 3)
//    SizesAndStrides sizes (pre-allocated 4)
//    SizesAndStrides strides (pre-allocated 0)
//    SizesAndStrides strides (pre-allocated 1)
//    SizesAndStrides strides (pre-allocated 2)
//    SizesAndStrides strides (pre-allocated 3)
//    SizesAndStrides strides (pre-allocated 4)
//    storage offset
//    numel
//    data type, device, is_contiguous, storage_access_should_throw_, bitfields
//    DispatchKeySet
//

// Various preprocessor macros we use to check that the
// TensorImpl size hasn't changed unexpectedly. We undef
// these later.
#ifndef __NVCC__
#define C10_NVCC 0
#else
#define C10_NVCC __NVCC__
#endif

#ifndef __CUDA_VER_MAJOR__
#define C10_CUDA_VERSION_MAJOR 0
#else
#define C10_CUDA_VERSION_MAJOR __CUDA_VER_MAJOR__
#endif

#ifndef CUDA_VERSION
#define C10_CUDA_VERSION 0
#else
#define C10_CUDA_VERSION CUDA_VERSION
#endif

#ifndef __clang_major__
#define C10_CLANG_MAJOR_VERSION 0
#else
#define C10_CLANG_MAJOR_VERSION __clang_major__
#endif

#ifndef __GNUC__
#define C10_GCC_VERSION 0
#else
#define C10_GCC_VERSION __GNUC__
#endif

#ifndef __GNUC_MINOR__
#define C10_GCC_VERSION_MINOR 0
#else
#define C10_GCC_VERSION_MINOR __GNUC_MINOR__
#endif

// We use a templatized class to both contain the logic of checking the sizes
// as well as to provide compile-time information that might be useful in
// figuring out why sizes may have changed.
// All the compile time information is given by the template fields that are
// always printed by the compiler when the static_assert fails.
template <
    size_t cplusplus = __cplusplus,
    size_t clang_ver_major = C10_CLANG_MAJOR_VERSION,
    size_t gcc_ver = C10_GCC_VERSION,
    size_t gcc_ver_minor = C10_GCC_VERSION_MINOR,
    size_t nvcc = C10_NVCC,
    size_t cuda_version = C10_CUDA_VERSION,
    size_t cuda_version_major = C10_CUDA_VERSION_MAJOR,
    size_t ptr_size = sizeof(void*)>
class C10_TensorImpl_Size_Check_Dummy_Class : private TensorImpl {
  // Names of (non-bitfield) fields in TensorImpl; used to provide
  // compile-time info about fields whose size changes unexpectedly.
  enum class FieldNameEnum {
    storage_,
    autograd_meta_,
    extra_meta_,
    version_counter_,
    pyobj_interpreter_,
    pyobj_,
    sizes_and_strides_,
    storage_offset_,
    numel_,
    data_type_,
    device_opt_,
    key_set_,
    TOTAL_SIZE
  };

  // Provides compile-time equality check that reveals what numbers
  // were used and on which quantity
  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
  constexpr static bool are_equal() {
    static_assert(
        Actual == Expected,
        "Actual and Expected sizes of a field did not match!");
    return true;
  }

  // Provides compile-time <= check that reveals what numbers
  // were used and on which quantity
  template <size_t Actual, size_t Expected, FieldNameEnum FiledName>
  constexpr static bool is_le() {
    static_assert(
        Actual <= Expected,
        "Actual and Expected sizes of a field did not match!");
    return true;
  }

 public:
  // Compile-time check that TensorImpl field sizes are as expected
  //
  // Observed total sizes and associated versions
  // If you find a flag that predicts when unique_ptr has 16 bytes
  // on 64-bit systems or when sizes_and_strides_ is 84 vs 88 bytes
  // on 32-bit systems you get a cookie!
  // Length | LLVM | GCC  |    C++ |  CUDA
  //    192 |    ? | 11.2 | 201703 | 11040
  //    208 |    ? | 11.2 | 201703 | 11040
  //    208 |    ? | 11.2 | 201402 | 11040
  //    192 |    ? | 11.2 | 201402 | 11040
  //    160 |   12 |  4.2 | 201703 |     0
  //
  // To keep things clean, we split on systems here.

#if UINTPTR_MAX == 0xFFFFFFFF
  // This is a 32-bit system
  static constexpr bool check_sizes() {
    constexpr size_t tsize = 20 * sizeof(int64_t);

    // clang-format off
    are_equal<sizeof(storage_),            4,  FieldNameEnum::storage_>();
    are_equal<sizeof(autograd_meta_),      4,  FieldNameEnum::autograd_meta_>();
    are_equal<sizeof(extra_meta_),         4,  FieldNameEnum::extra_meta_>();
    are_equal<sizeof(version_counter_),    4,  FieldNameEnum::version_counter_>();
    are_equal<sizeof(pyobj_interpreter_),  4,  FieldNameEnum::pyobj_interpreter_>();
    are_equal<sizeof(pyobj_),              4,  FieldNameEnum::pyobj_>();
    is_le<sizeof(sizes_and_strides_),     88, FieldNameEnum::sizes_and_strides_>();
    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
    // clang-format on

    return true;
  }
#else
  // This is a 64-bit system
  static constexpr bool check_sizes() {
    constexpr size_t tsize = 26 * sizeof(int64_t);

    // clang-format off
    are_equal<sizeof(storage_),            8,  FieldNameEnum::storage_>();
    // On some systems involving NVCC the size of unique_ptr is 16 bytes. We haven't
    // figured out how to detect those via macro preprocessors yet, so we use <=
    // comparisons for the relevant fields.
    is_le<sizeof(autograd_meta_),         16,  FieldNameEnum::autograd_meta_>();
    is_le<sizeof(extra_meta_),            16,  FieldNameEnum::extra_meta_>();
    are_equal<sizeof(version_counter_),    8,  FieldNameEnum::version_counter_>();
    are_equal<sizeof(pyobj_interpreter_),  8,  FieldNameEnum::pyobj_interpreter_>();
    are_equal<sizeof(pyobj_),              8,  FieldNameEnum::pyobj_>();
    are_equal<sizeof(sizes_and_strides_), 88,  FieldNameEnum::sizes_and_strides_>();
    are_equal<sizeof(storage_offset_),     8,  FieldNameEnum::storage_offset_>();
    are_equal<sizeof(numel_),              8,  FieldNameEnum::numel_>();
    are_equal<sizeof(data_type_),          2,  FieldNameEnum::data_type_>();
    are_equal<sizeof(device_opt_),         3,  FieldNameEnum::device_opt_>();
    are_equal<sizeof(key_set_),            8,  FieldNameEnum::key_set_>();
    is_le<sizeof(TensorImpl),          tsize,  FieldNameEnum::TOTAL_SIZE>();
    // clang-format on

    return true;
  }
#endif
};

// We use a class to encapsulate size-checking logic with
// templates to capture sizes and flags. We call this within
// a static assert to prove there is no run-time behaviour.
// Since the methods we call return either true or fail their
// own static_asserts, we should never see the error messages
// below. We have to provide it though for c++ <17.
static_assert(
    C10_TensorImpl_Size_Check_Dummy_Class<>::check_sizes(),
    "You should not see this message.");

// Clean up after ourselves
#undef C10_NVCC
#undef C10_CUDA_VERSION_MAJOR
#undef C10_CUDA_VERSION
#undef C10_CLANG_MAJOR_VERSION
#undef C10_GCC_VERSION
#undef C10_GCC_VERSION_MINOR

} // namespace c10

C10_CLANG_DIAGNOSTIC_POP()