FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/c10/core/

/usr/local/lib64/python3.6/site-packages/torch/include/c10/core

Name	Size	Mode	Actions
impl/	-	0755	rm
Allocator.h	8725	0644	edit dl rm
AutogradState.h	994	0644	edit dl rm
Backend.h	8487	0644	edit dl rm
CompileTimeFunctionPointer.h	1677	0644	edit dl rm
CopyBytes.h	1229	0644	edit dl rm
CPUAllocator.h	2267	0644	edit dl rm
DefaultDtype.h	394	0644	edit dl rm
DefaultTensorOptions.h	1032	0644	edit dl rm
Device.h	5396	0644	edit dl rm
DeviceGuard.h	7555	0644	edit dl rm
DeviceType.h	2994	0644	edit dl rm
DispatchKey.h	18069	0644	edit dl rm
DispatchKeySet.h	12951	0644	edit dl rm
Event.h	4169	0644	edit dl rm
GeneratorImpl.h	3713	0644	edit dl rm
GradMode.h	1261	0644	edit dl rm
InferenceMode.h	3471	0644	edit dl rm
Layout.h	1225	0644	edit dl rm
MemoryFormat.h	8571	0644	edit dl rm
OptionalRef.h	521	0644	edit dl rm
QEngine.h	861	0644	edit dl rm
QScheme.h	1562	0644	edit dl rm
Scalar.h	6019	0644	edit dl rm
ScalarType.h	17073	0644	edit dl rm
ScalarTypeToTypeMeta.h	1365	0644	edit dl rm
Storage.h	4369	0644	edit dl rm
StorageImpl.h	5610	0644	edit dl rm
Stream.h	7373	0644	edit dl rm
StreamGuard.h	6315	0644	edit dl rm
TensorImpl.h	96152	0644	edit dl rm
TensorOptions.h	27593	0644	edit dl rm
thread_pool.h	2992	0644	edit dl rm
UndefinedTensorImpl.h	911	0644	edit dl rm
WrapDimMinimal.h	805	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/c10/core/TensorOptions.h (27593B)

#pragma once

#include <c10/core/Backend.h>
#include <c10/core/DefaultDtype.h>
#include <c10/core/Device.h>
#include <c10/core/DispatchKeySet.h>
#include <c10/core/Layout.h>
#include <c10/core/MemoryFormat.h>
#include <c10/core/ScalarType.h>
#include <c10/core/ScalarTypeToTypeMeta.h>

#include <c10/macros/Macros.h>
#include <c10/util/C++17.h>
#include <c10/util/Optional.h>

#include <cstddef>
#include <iosfwd>
#include <utility>

namespace c10 {

DispatchKey computeDispatchKey(
    c10::optional<ScalarType> dtype,
    c10::optional<Layout> layout,
    c10::optional<Device> device);

inline ScalarType dtype_or_default(c10::optional<ScalarType> dtype) {
  return value_or_else(dtype, [] { return get_default_dtype_as_scalartype(); });
}

inline caffe2::TypeMeta dtype_or_default(
    c10::optional<caffe2::TypeMeta> dtype) {
  return value_or_else(dtype, [] { return get_default_dtype(); });
}

inline Layout layout_or_default(c10::optional<Layout> layout) {
  return layout.value_or(kStrided);
}

inline Device device_or_default(c10::optional<Device> device) {
  return value_or_else(device, [] { return Device(kCPU); });
}

inline bool pinned_memory_or_default(c10::optional<bool> pinned_memory) {
  return pinned_memory.value_or(false);
}

/// A class to encapsulate construction axes of an Tensor.  TensorOptions was
/// designed to support the Python style API for specifying construction options
/// on factory functions, e.g.,
///
///     torch.zeros(2, 3, dtype=torch.int32)
///
/// Because C++ doesn't natively support keyword arguments, there must be
/// another way of specifying keyword-like arguments.  TensorOptions is a
/// builder class which can be used to construct this "dictionary" of keyword
/// arguments: functions which support TensorOptions conventionally take this
/// argument optionally as their last argument.
///
/// WARNING: In PyTorch, there are `torch::` variants of factory functions,
/// e.g., torch::zeros for at::zeros.  These return Variables (while the
/// stock ATen functions return plain Tensors).  If you mix these functions
/// up, you WILL BE SAD.
///
/// Rather than use the constructor of this class directly, you should prefer to
/// use the constructor functions, and then chain setter methods on top of them.
///
///     at::device(at::kCUDA).dtype(kInt)
///     at::dtype(at::kInt)
///
/// Additionally, anywhere a TensorOptions is expected, you can directly
/// pass at::kCUDA / at::kInt, and it will implicitly convert to a
/// TensorOptions.
///
/// Here are some recommended ways to create a 2x2 tensor of zeros
/// with certain properties.  These all *implicitly* make use of
/// TensorOptions, even if they don't mention the class explicitly:
///
///     at::zeros({2,2}, at::kCUDA);
///     at::zeros({2,2}, at::kLong);
///     at::zeros({2,2}, at::device(at::kCUDA).dtype(at::kLong()));
///     at::zeros({2,2}, at::device({at::kCUDA, 1})); // place on device 1
///     at::zeros({2,2}, at::requires_grad());
///

/// NOTE [ TensorOptions Constructors ]
///
/// TensorOptions is like a dictionary with entries from the set:
/// {requires_grad, device, dtype, layout}, where each entry may be
/// unspecified (i.e., is optional). It is used to specify the properties of
/// tensors in many places both in C++ internal and API, e.g., tensor factory
/// methods like `at::empty({10}, options)`, tensor conversions like
/// `tensor.to(...)`, etc.
///
/// To provide a simple API that is consistent with Python, where one can do
/// `torch.empty(sizes, X)` with `X` being a `torch.device`, `torch.dtype`, or a
/// `torch.layout`, we want TensorOptions to be implicitly convertible from
/// `ScalarType dtype`, `Layout layout` and `Device device`. Therefore, we have
/// three implicit constructors from each of these three types.
///
/// This is sufficient for `ScalarType` and `Layout` as they are simple Enum
/// classes. However, `Device` is an ordinary class with implicit constructors
/// `Device(DeviceType, DeviceIndex = -1)` and `Device(std::string)` to be
/// consistent with Python API, where strings are treated as equivalent with a
/// `torch.device` object (e.g., "cuda:1" can be passed to everywhere a
/// `torch.device("cuda:1")` is accepted). To support the syntax
/// `at::empty({10}, {kCUDA, 1})` and `tensor.to(kCUDA)`, we need to make sure
/// that `TensorOptions` is implicitly constructible with any argments that a
/// `Device` can constructed from. So we have,
///
///    /* implicit */ TensorOptions(T&& device) : TensorOptions() {
///      this->set_device(device);
///    }
///
///    template <typename... Args,
///             typename = std::enable_if_t<std::is_constructible<Device,
///             Args&&...>::value>>
///    /* implicit */  TensorOptions(Args&&... args)
///     : TensorOptions(Device(std::forward<Args>(args)...)) {}
///
///
/// But this will be problematic. Consider this: `TensorOptions({kCUDA, 1})`.
/// Compiler will compain about ambiguity between the copy constructor and the
/// `Device` constructor because `{kCUDA, 1}` can be converted to both a
/// `TensorOption` and a `Device`.
///
/// To get around this, we templatize the `Device` constructor. Since overload
/// resolution is done before template resolution, our problem is solved.

DispatchKey computeDispatchKey(
    optional<ScalarType> dtype,
    optional<Layout> layout,
    optional<Device> device);

struct C10_API TensorOptions {
  TensorOptions()
      : requires_grad_(false),
        pinned_memory_(false),
        has_device_(false),
        has_dtype_(false),
        has_layout_(false),
        has_requires_grad_(false),
        has_pinned_memory_(false),
        has_memory_format_(false) {}

/// Constructs a `TensorOptions` object with the given layout.
  /* implicit */ TensorOptions(Layout layout) : TensorOptions() {
    this->set_layout(layout);
  }

/// Constructs a `TensorOptions` object with the given device.
  /// See NOTE [ TensorOptions Constructors ] on why this is templatized.
  template <
      typename T,
      typename = std::enable_if_t<std::is_same<std::decay_t<T>, Device>::value>>
  /* implicit */ TensorOptions(T&& device) : TensorOptions() {
    this->set_device(std::forward<T>(device));
  }

/// Constructs a `TensorOptions` object from arguments allowed in `Device`
  /// constructors.
  ///
  /// See NOTE [ TensorOptions Constructors ].
  ///
  /// NB: Ideally we only allow implicit constructors here. But there is no easy
  ///     way to detect them. So we have this one that allows explicit
  ///     constructors too.
  template <
      typename... Args,
      typename =
          std::enable_if_t<std::is_constructible<Device, Args&&...>::value>>
  /* implicit */ TensorOptions(Args&&... args)
      : TensorOptions(Device(std::forward<Args>(args)...)) {}

/// Constructs a `TensorOptions` object with the given dtype.
  /* implicit */ TensorOptions(caffe2::TypeMeta dtype) : TensorOptions() {
    this->set_dtype(dtype);
  }

/// legacy constructor to support ScalarType
  /* implicit */ TensorOptions(ScalarType dtype) : TensorOptions() {
    this->set_dtype(dtype);
  }

/// Constructs a `TensorOptions` object with the given memory format.
  /* implicit */ TensorOptions(MemoryFormat memory_format) : TensorOptions() {
    set_memory_format(memory_format);
  }

/// Return a copy of `TensorOptions` with `device` set to the given one, or
  /// cleared if `device` is `nullopt`.
  C10_NODISCARD TensorOptions
  device(c10::optional<Device> device) const noexcept {
    TensorOptions r = *this;
    r.set_device(device);
    return r;
  }

/// Return a copy of `TensorOptions` with `device` set to the given one.
  /// (This overload ensures that variadic template c10::optional constructor
  /// for Device work correctly.)
  template <typename... Args>
  C10_NODISCARD TensorOptions device(Args&&... args) const noexcept {
    return device(
        c10::optional<Device>(c10::in_place, std::forward<Args>(args)...));
  }

/// Return a copy of `TensorOptions`, but with device set to CUDA, and the
  /// device index set to the given one.
  ///
  /// TODO: This function encourages bad behavior (assuming CUDA is
  /// the only device that matters).  Get rid of it / rename it.
  C10_NODISCARD TensorOptions
  device_index(c10::DeviceIndex device_index) const noexcept {
    return device(Device::Type::CUDA, device_index);
  }

/// Return a copy of `TensorOptions` with `dtype` set to the given one.
  C10_NODISCARD TensorOptions
  dtype(c10::optional<caffe2::TypeMeta> dtype) const noexcept {
    TensorOptions r = *this;
    r.set_dtype(dtype);
    return r;
  }

// legacy function to support ScalarType
  C10_NODISCARD TensorOptions
  dtype(c10::optional<ScalarType> dtype) const noexcept {
    TensorOptions r = *this;
    r.set_dtype(dtype);
    return r;
  }

// Since dtype is taken...
  template <typename T>
  TensorOptions& dtype() {
    dtype_ = caffe2::TypeMeta::Make<T>();
    has_dtype_ = true;
    return *this;
  }

/// Sets the layout of the `TensorOptions`.
  C10_NODISCARD TensorOptions
  layout(c10::optional<Layout> layout) const noexcept {
    TensorOptions r = *this;
    r.set_layout(layout);
    return r;
  }

/// Sets the `requires_grad` property of the `TensorOptions`.
  C10_NODISCARD TensorOptions
  requires_grad(c10::optional<bool> requires_grad) const noexcept {
    TensorOptions r = *this;
    r.set_requires_grad(requires_grad);
    return r;
  }

/// Sets the `pinned_memory` property on the `TensorOptions`.
  C10_NODISCARD TensorOptions
  pinned_memory(c10::optional<bool> pinned_memory) const noexcept {
    TensorOptions r = *this;
    r.set_pinned_memory(pinned_memory);
    return r;
  }

/// Sets the `memory_format` property on `TensorOptions`.
  C10_NODISCARD TensorOptions
  memory_format(c10::optional<MemoryFormat> memory_format) const noexcept {
    TensorOptions r = *this;
    r.set_memory_format(memory_format);
    return r;
  }

/// Returns the device of the `TensorOptions`.
  Device device() const noexcept {
    return device_or_default(device_opt());
  }

/// Returns whether the device is specified.
  bool has_device() const noexcept {
    return has_device_;
  }

/// Returns the device of the `TensorOptions`, or `c10::nullopt` if
  /// device is not specified.
  c10::optional<Device> device_opt() const noexcept {
    return has_device_ ? c10::make_optional(device_) : c10::nullopt;
  }

/// Returns the device index of the `TensorOptions`.
  int32_t device_index() const noexcept {
    return device().index();
  }

/// Returns the dtype of the `TensorOptions`.
  caffe2::TypeMeta dtype() const noexcept {
    return dtype_or_default(dtype_opt());
  }

/// Returns whether the dtype is specified.
  bool has_dtype() const noexcept {
    return has_dtype_;
  }

/// Returns the dtype of the `TensorOptions`, or `c10::nullopt` if
  /// device is not specified.
  c10::optional<caffe2::TypeMeta> dtype_opt() const noexcept {
    return has_dtype_ ? c10::make_optional(dtype_) : c10::nullopt;
  }

/// Returns the layout of the `TensorOptions`.
  Layout layout() const noexcept {
    return layout_or_default(layout_opt());
  }

/// Returns whether the layout is specified.
  bool has_layout() const noexcept {
    return has_layout_;
  }

/// Returns the layout of the `TensorOptions`, or `c10::nullopt` if
  /// layout is not specified.
  c10::optional<Layout> layout_opt() const noexcept {
    return has_layout_ ? c10::make_optional(layout_) : c10::nullopt;
  }

/// Returns the `requires_grad` property of the `TensorOptions`.
  bool requires_grad() const noexcept {
    return has_requires_grad_ ? requires_grad_ : false;
  }

/// Returns whether the `requires_grad` is specified.
  bool has_requires_grad() const noexcept {
    return has_requires_grad_;
  }

/// Returns the `requires_grad` property of the `TensorOptions`, or
  /// `c10::nullopt` if `requires_grad` is not specified.
  c10::optional<bool> requires_grad_opt() const noexcept {
    return has_requires_grad_ ? c10::make_optional(requires_grad_)
                              : c10::nullopt;
  }

/// Returns the `pinned_memory` property of the `TensorOptions`.
  bool pinned_memory() const noexcept {
    return pinned_memory_or_default(pinned_memory_opt());
  }

/// Returns whether the `pinned_memory` is specified.
  bool has_pinned_memory() const noexcept {
    return has_pinned_memory_;
  }

/// Returns if the layout is sparse
  bool is_sparse() const {
    return layout_ == c10::Layout::Sparse;
  }

bool is_sparse_csr() const {
    return layout_ == c10::Layout::SparseCsr;
  }

// For compatibility with legacy tensor.type() comparisons
  bool type_equal(const TensorOptions& other) const {
    return computeDispatchKey() == other.computeDispatchKey() &&
        typeMetaToScalarType(dtype_) == typeMetaToScalarType(other.dtype());
  }

/// Returns the `pinned_memory` property of the `TensorOptions`, or
  /// `c10::nullopt` if `pinned_memory` is not specified.
  c10::optional<bool> pinned_memory_opt() const noexcept {
    return has_pinned_memory_ ? c10::make_optional(pinned_memory_)
                              : c10::nullopt;
  }

/// Returns whether the `memory_layout` is specified
  bool has_memory_format() const noexcept {
    return has_memory_format_;
  }

// NB: memory_format() getter is PURPOSELY not defined, as the default
  // behavior of memory_format varies from function to function.

/// Returns the `memory_layout` property of `TensorOptions, or
  /// `c10::nullopt` if `memory_format` is not specified.
  c10::optional<MemoryFormat> memory_format_opt() const noexcept {
    return has_memory_format_ ? c10::make_optional(memory_format_)
                              : c10::nullopt;
  }

// Resolves the ATen backend specified by the current construction axes.
  // TODO: Deprecate this
  Backend backend() const noexcept {
    return at::dispatchKeyToBackend(computeDispatchKey());
  }

/// Return the right-biased merge of two TensorOptions.  This has the
  /// effect of overwriting settings from self with specified options
  /// of options.
  ///
  /// NB: This merging operation does NOT respect device merges.
  /// For example, if you device({kCUDA, 1}).merge_in(kCUDA)
  /// you will get kCUDA in the end!  Functions like Tensor.new_empty
  /// ensure the right device is selected anyway by way of a
  /// device guard.
  ///
  TensorOptions merge_in(TensorOptions options) const noexcept {
    TensorOptions merged = *this;
    if (options.has_device())
      merged.set_device(options.device_opt());
    if (options.has_dtype())
      merged.set_dtype(options.dtype_opt());
    if (options.has_layout())
      merged.set_layout(options.layout_opt());
    // NB: requires grad is right biased; not a logical AND/OR!
    if (options.has_requires_grad())
      merged.set_requires_grad(options.requires_grad_opt());
    if (options.has_pinned_memory())
      merged.set_pinned_memory(options.pinned_memory_opt());
    if (options.has_memory_format())
      merged.set_memory_format(options.memory_format_opt());
    return merged;
  }

// TODO remove after TensorOptions rationalization
  TensorOptions merge_memory_format(
      c10::optional<MemoryFormat> optional_memory_format) const noexcept {
    TensorOptions merged = *this;
    if (optional_memory_format.has_value()) {
      merged.set_memory_format(*optional_memory_format);
    }
    return merged;
  }

// INVARIANT: computeDispatchKey returns only the subset of dispatch keys for
  // which dispatchKeyToBackend is injective, if it is defined at all  (for
  // the most part, this just means that this function never returns an
  // Autograd key)
  DispatchKey computeDispatchKey() const {
    return c10::computeDispatchKey(
        optTypeMetaToScalarType(dtype_opt()), layout_opt(), device_opt());
  }

private:
  // These methods are currently private because I'm not sure if it's wise
  // to actually publish them.  They are methods because I need them in
  // the constructor and the functional API implementation.
  //
  // If you really, really need it, you can make these public, but check if you
  // couldn't just do what you need with the functional API.  Similarly, these
  // methods are not chainable, because if you wanted chaining, you probably
  // want to use the functional API instead.  (It's probably OK to make
  // these chainable, because these functions are all explicitly annotated
  // with a ref-qualifier, the trailing &, that makes them illegal to call
  // on temporaries.)

/// Mutably set the device of `TensorOptions`.
  void set_device(c10::optional<Device> device) & noexcept {
    if (device) {
      device_ = *device;
      has_device_ = true;
    } else {
      has_device_ = false;
    }
  }

/// Mutably set the dtype of `TensorOptions`.
  void set_dtype(c10::optional<caffe2::TypeMeta> dtype) & noexcept {
    if (dtype) {
      dtype_ = *dtype;
      has_dtype_ = true;
    } else {
      has_dtype_ = false;
    }
  }

// legacy function to support ScalarType
  void set_dtype(c10::optional<ScalarType> dtype) & noexcept {
    if (dtype) {
      dtype_ = scalarTypeToTypeMeta(*dtype);
      has_dtype_ = true;
    } else {
      has_dtype_ = false;
    }
  }

/// Mutably set the layout of `TensorOptions`.
  void set_layout(c10::optional<Layout> layout) & noexcept {
    if (layout) {
      layout_ = *layout;
      has_layout_ = true;
    } else {
      has_layout_ = false;
    }
  }

/// Mutably set the `requires_grad` property of `TensorOptions`.
  void set_requires_grad(c10::optional<bool> requires_grad) & noexcept {
    if (requires_grad) {
      requires_grad_ = *requires_grad;
      has_requires_grad_ = true;
    } else {
      has_requires_grad_ = false;
    }
  }

/// Mutably set the `pinned_memory` property of `TensorOptions`.
  void set_pinned_memory(c10::optional<bool> pinned_memory) & noexcept {
    if (pinned_memory) {
      pinned_memory_ = *pinned_memory;
      has_pinned_memory_ = true;
    } else {
      has_pinned_memory_ = false;
    }
  }

/// Mutably set the `memory_Format` property of `TensorOptions`.
  void set_memory_format(c10::optional<MemoryFormat> memory_format) & noexcept {
    if (memory_format) {
      memory_format_ = *memory_format;
      has_memory_format_ = true;
    } else {
      has_memory_format_ = false;
    }
  }

// WARNING: If you edit TensorOptions to add more options, you
  // may need to adjust the implementation of Tensor::options.
  // The criteria for whether or not Tensor::options must be adjusted
  // is whether or not the new option you added should preserved
  // by functions such as empty_like(); if it should be preserved,
  // you must adjust options().
  //
  // TODO: MemoryFormat is not implemented in this way

// NB: We didn't use c10::optional here, because then we can't pack
  // the has_***_ boolean fields.

Device device_ = at::kCPU; // 16-bit
  caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make<float>(); // 16-bit
  Layout layout_ = at::kStrided; // 8-bit
  MemoryFormat memory_format_ = MemoryFormat::Contiguous; // 8-bit

// Bitmask required here to get this to fit inside 32 bits (or even 64 bits,
  // for that matter)

bool requires_grad_ : 1;
  bool pinned_memory_ : 1;

bool has_device_ : 1;
  bool has_dtype_ : 1;
  bool has_layout_ : 1;
  bool has_requires_grad_ : 1;
  bool has_pinned_memory_ : 1;
  bool has_memory_format_ : 1;
};

// We should aspire to fit in one machine-size word; but a size greater than two
// words is too much.  (We are doing terribly on 32-bit archs, where we require
// three machine size words to store tensor options.  Eek!)
static_assert(
    sizeof(TensorOptions) <= sizeof(int64_t) * 2,
    "TensorOptions must fit in 128-bits");

/// Convenience function that returns a `TensorOptions` object with the `dtype`
/// set to the given one.
inline TensorOptions dtype(caffe2::TypeMeta dtype) {
  return TensorOptions().dtype(dtype);
}

// legacy function to support ScalarType
inline TensorOptions dtype(ScalarType dtype) {
  return TensorOptions().dtype(scalarTypeToTypeMeta(dtype));
}

/// Convenience function that returns a `TensorOptions` object with the `layout`
/// set to the given one.
inline TensorOptions layout(Layout layout) {
  return TensorOptions().layout(layout);
}

/// Convenience function that returns a `TensorOptions` object with the `device`
/// set to the given one.
inline TensorOptions device(Device device) {
  return TensorOptions().device(std::move(device));
}

/// Convenience function that returns a `TensorOptions` object with the
/// `device` set to CUDA and the `device_index` set to the given one.
inline TensorOptions device_index(int16_t device_index) {
  return TensorOptions().device_index(
      static_cast<c10::DeviceIndex>(device_index));
}

/// Convenience function that returns a `TensorOptions` object with the
/// `requires_grad` set to the given one.
inline TensorOptions requires_grad(bool requires_grad = true) {
  return TensorOptions().requires_grad(requires_grad);
}

/// Convenience function that returns a `TensorOptions` object with the
/// `memory_format` set to the given one.
inline TensorOptions memory_format(MemoryFormat memory_format) {
  return TensorOptions().memory_format(memory_format);
}

C10_API std::ostream& operator<<(
    std::ostream& stream,
    const TensorOptions& options);

template <typename T>
inline TensorOptions dtype() {
  return dtype(caffe2::TypeMeta::Make<T>());
}

inline std::string toString(const TensorOptions options) {
  std::ostringstream stream;
  stream << options;
  return stream.str();
}

// This is intended to be a centralized location by which we can determine
// what an appropriate DispatchKey for a tensor is.
inline DispatchKey computeDispatchKey(
    c10::optional<ScalarType> dtype,
    c10::optional<Layout> layout,
    c10::optional<Device> device) {
  const auto layout_ = layout_or_default(layout);
  const auto device_ = device_or_default(device);
  switch (layout_) {
    case Layout::Strided: {
      const auto dtype_ = dtype_or_default(dtype);
      switch (device_.type()) {
        case DeviceType::CPU: {
          if (isQIntType(dtype_)) {
            return DispatchKey::QuantizedCPU;
          }
          return DispatchKey::CPU;
        }
        case DeviceType::CUDA: {
          if (isQIntType(dtype_)) {
            return DispatchKey::QuantizedCUDA;
          }
          return DispatchKey::CUDA;
        }
        case DeviceType::XPU: {
          if (isQIntType(dtype_)) {
            return DispatchKey::QuantizedXPU;
          }
          return DispatchKey::XPU;
        }
        case DeviceType::MKLDNN:
        case DeviceType::OPENGL:
        case DeviceType::OPENCL:
        case DeviceType::IDEEP:
          TORCH_INTERNAL_ASSERT(
              0,
              "This is a grandfathered Caffe2 device type ",
              device_.type(),
              ", it shouldn't ever convert to a DispatchKey.  File a bug describing what you were doing if you think this is in error.");
        case DeviceType::HIP:
          return DispatchKey::HIP;
        case DeviceType::VE:
          return DispatchKey::VE;
        case DeviceType::FPGA:
          return DispatchKey::FPGA;
        case DeviceType::ORT:
          return DispatchKey::ORT;
        case DeviceType::XLA:
          return DispatchKey::XLA;
        case DeviceType::Lazy:
          return DispatchKey::Lazy;
        case DeviceType::MLC:
          return DispatchKey::MLC;
        case DeviceType::Vulkan:
          return DispatchKey::Vulkan;
        case DeviceType::Metal:
          return DispatchKey::Metal;
        case DeviceType::Meta:
          return DispatchKey::Meta;
        case DeviceType::HPU:
          return DispatchKey::HPU;
        default:
          TORCH_CHECK_NOT_IMPLEMENTED(
              false,
              "Unsupported device type for dense layout: ",
              device_.type());
      }
    }
    case Layout::Sparse:
      switch (device_.type()) {
        case DeviceType::CPU:
          return DispatchKey::SparseCPU;
        case DeviceType::CUDA:
          return DispatchKey::SparseCUDA;
        case DeviceType::HIP:
          return DispatchKey::SparseHIP;
        case DeviceType::VE:
          return DispatchKey::SparseVE;
        case DeviceType::XPU:
          return DispatchKey::SparseXPU;
        default:
          TORCH_CHECK_NOT_IMPLEMENTED(
              false,
              "Unsupported device type for sparse layout: ",
              device_.type());
      }
    case Layout::Mkldnn:
      switch (device_.type()) {
        case DeviceType::CPU:
          return DispatchKey::MkldnnCPU;
        default:
          TORCH_CHECK_NOT_IMPLEMENTED(
              false,
              "Unsupported device type for mkldnn layout: ",
              device_.type());
      }
    case Layout::SparseCsr:
      switch (device_.type()) {
        case DeviceType::CPU:
          return DispatchKey::SparseCsrCPU;
        case DeviceType::CUDA:
          return DispatchKey::SparseCsrCUDA;
        default:
          AT_ERROR(
              "Unsupported device type for sparse CSR layout: ",
              device_.type());
      }
    default:
      TORCH_CHECK(false, "Unsupported layout: ", layout_);
  }
}

inline Layout dispatchKeyToLayout(DispatchKey dispatch_key) {
  switch (dispatch_key) {
    case DispatchKey::SparseCPU:
    case DispatchKey::SparseCUDA:
    case DispatchKey::SparseHIP:
    case DispatchKey::SparseVE:
    case DispatchKey::SparseXPU:
    case DispatchKey::SparseCsrCPU:
    case DispatchKey::SparseCsrCUDA:
      return Layout::Sparse;
    case DispatchKey::MkldnnCPU:
      return Layout::Mkldnn;
    default:
      return Layout::Strided;
  }
}

inline DeviceType dispatchKeyToDeviceType(DispatchKey dispatch_key) {
  switch (dispatch_key) {
    // stuff that's real
    case DispatchKey::CPU:
    case DispatchKey::SparseCPU:
    case DispatchKey::MkldnnCPU:
    case DispatchKey::QuantizedCPU:
    case DispatchKey::AutogradCPU:
      return DeviceType::CPU;
    case DispatchKey::CUDA:
    case DispatchKey::SparseCUDA:
    case DispatchKey::QuantizedCUDA:
    case DispatchKey::AutogradCUDA:
      return DeviceType::CUDA;
    case DispatchKey::HIP:
    case DispatchKey::SparseHIP:
      return DeviceType::HIP;
    case DispatchKey::VE:
    case DispatchKey::SparseVE:
      return DeviceType::VE;
    case DispatchKey::XLA:
    case DispatchKey::AutogradXLA:
      return DeviceType::XLA;
    case DispatchKey::Lazy:
    case DispatchKey::AutogradLazy:
      return DeviceType::Lazy;
    case DispatchKey::Vulkan:
      return DeviceType::Vulkan;
    case DispatchKey::Meta:
      return DeviceType::Meta;

// stuff that people are actively developing
    case DispatchKey::XPU:
    case DispatchKey::SparseXPU:
    case DispatchKey::QuantizedXPU:
    case DispatchKey::AutogradXPU:
      return DeviceType::XPU;
    case DispatchKey::MLC:
    case DispatchKey::AutogradMLC:
      return DeviceType::MLC;
    case DispatchKey::HPU:
    case DispatchKey::AutogradHPU:
      return DeviceType::HPU;
    case DispatchKey::ORT:
      return DeviceType::ORT;
    default:
      TORCH_CHECK(
          false,
          "DispatchKey ",
          dispatch_key,
          " doesn't correspond to a device");
  }
}

inline TensorOptions dispatchKeyToTensorOptions(DispatchKey dispatch_key) {
  return TensorOptions()
      .layout(dispatchKeyToLayout(dispatch_key))
      .device(dispatchKeyToDeviceType(dispatch_key));
}

} // namespace c10

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