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/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/MemoryFormat.h (8571B)

#pragma once

#include <c10/core/Backend.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Exception.h>

#include <ostream>

// Memory format is not the property of a Tensor. It is the way to tell an
// operator how the result should be organized in memory and nothing more. That
// means memory format should never be used as return value for any tensor state
// interrogation functions (internally and externally).
//
// Possible options are:
//  Preserve:
//    If any of the input tensors is in channels_last format, operator output
//    should be in channels_last format
//
//  Contiguous:
//    Regardless of input tensors format, the output should be contiguous
//    Tensor.
//
//  ChannelsLast:
//    Regardless of input tensors format, the output should be in channels_last
//    format.

namespace c10 {
enum class MemoryFormat : int8_t {
  Contiguous,
  Preserve,
  ChannelsLast,
  ChannelsLast3d
};

// If you are seeing this, it means that this call site was not checked if
// the memory format could be preserved, and it was switched to old default
// behaviour of contiguous
#define LEGACY_CONTIGUOUS_MEMORY_FORMAT c10::get_contiguous_memory_format()

inline MemoryFormat get_contiguous_memory_format() {
  return MemoryFormat::Contiguous;
}

inline std::ostream& operator<<(
    std::ostream& stream,
    at::MemoryFormat memory_format) {
  switch (memory_format) {
    case MemoryFormat::Preserve:
      return stream << "Preserve";
    case MemoryFormat::Contiguous:
      return stream << "Contiguous";
    case MemoryFormat::ChannelsLast:
      return stream << "ChannelsLast";
    case MemoryFormat::ChannelsLast3d:
      return stream << "ChannelsLast3d";
    default:
      TORCH_CHECK(false, "Unknown memory format");
  }
}

// Note: Hardcoded the channel last stride indices here to get better
// performance
inline std::vector<int64_t> get_channels_last_strides_2d(IntArrayRef sizes) {
  std::vector<int64_t> strides(sizes.size());
  switch (sizes.size()) {
    case 4:
      strides[1] = 1;
      strides[3] = sizes[1];
      strides[2] = strides[3] * sizes[3];
      strides[0] = strides[2] * sizes[2];
      return strides;
    case 3:
      strides[0] = 1;
      strides[2] = sizes[0];
      strides[1] = strides[2] * sizes[2];
      return strides;
    default:
      TORCH_INTERNAL_ASSERT(
          false, "ChannelsLast2d doesn't support size ", sizes.size());
  }
}

inline std::vector<int64_t> get_channels_last_strides_3d(IntArrayRef sizes) {
  std::vector<int64_t> strides(sizes.size());
  switch (sizes.size()) {
    case 5:
      strides[1] = 1;
      strides[4] = sizes[1];
      strides[3] = strides[4] * sizes[4];
      strides[2] = strides[3] * sizes[3];
      strides[0] = strides[2] * sizes[2];
      return strides;
    case 4:
      strides[0] = 1;
      strides[3] = sizes[0];
      strides[2] = strides[3] * sizes[3];
      strides[1] = strides[2] * sizes[2];
      return strides;
    default:
      TORCH_INTERNAL_ASSERT(
          false, "ChannelsLast3d doesn't support size ", sizes.size());
  }
}

// NOTE:
// Below are Helper functions for is_channels_last_strides_xd.
// 1. Please do not combine these helper functions, each helper function handles
// exactly one case of sizes + memory_format, by doing this, the strides indices
// will be a constant array and we can access it using constant index number,
// the compiler will fully unroll the loop on strides indices to gain a better
// performance.
// 2. No error check in helper function, caller ensures the correctness of the
// input
// 3. All helper functions have similar comments, only 1st helper function is
// commented here.
inline bool is_channels_last_strides_2d_s4(
    const IntArrayRef sizes,
    const IntArrayRef strides) {
  int64_t min = 0;
  // special case for trivial C dimension. default to NCHW
  if (strides[1] == 0) {
    return false;
  }
  // loop strides indices
  for (auto& d : {1, 3, 2, 0}) {
    if (sizes[d] == 0) {
      return false;
    }
    if (strides[d] < min) {
      return false;
    }
    // Fallback to NCHW as default layout for ambiguous cases
    // This is the flaw of implicit memory_format from strides.
    // N111 tensor with identical strides for size 1 dimension;
    // Two cases could lead us here:
    // a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1])
    // b. N11W contiguous Tensor sliced on the W-dimension.
    // ([N,1,1,1]@[W,W,W,W])
    if (d == 0 && min == strides[1]) {
      return false;
    }
    // This is necessary to:
    // 1. distinguish the memory_format of N1H1;
    //     [H, 1, 1, 1] channels_last stride
    //     [H, H, 1, 1] contiguous stride
    // 2. permutation of 1C1W:
    //     [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3)
    //     [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as channels_last
    min = strides[d];
    if (sizes[d] > 1) {
      min *= sizes[d];
    }
  }
  return true;
}

inline bool is_channels_last_strides_3d_s5(
    const IntArrayRef sizes,
    const IntArrayRef strides) {
  int64_t min = 0;
  if (strides[1] == 0) {
    return false;
  }
  for (auto& d : {1, 4, 3, 2, 0}) {
    if (sizes[d] == 0) {
      return false;
    }
    if (strides[d] < min) {
      return false;
    }
    if (d == 0 && min == strides[1]) {
      return false;
    }
    min = strides[d];
    if (sizes[d] > 1) {
      min *= sizes[d];
    }
  }
  return true;
}

// Note [Ambiguous is_channels_last_strides_xd]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// The flaw of carrying memory_format implicitly through strides is very hard
// to WAR properly. issue #24090
// Without the history of permutation, we can't infer the memory_format of a
// tensor from the snapshot of its size & stride
// e.g.
//
// 1. We can NOT specify the memory_format of N111 tensor through strides in a
//  meaningful way;
//
// 2. Two path that ended up with identical size/stride
//  N11W contiguous tensor sliced at w-dimension becomes [N,1,1,1]@[W,W,W,W]
//  NC11 channels_last tensor sliced at c-dimension becomes [N,1,1,1]@[C,C,C,C]
//    So if we see a tensor [N,1,1,1]@[X,X,X,X], there's no way for us to infer
//    the memory_format of the original tensor.
//
// Due to the limitations, our temporary WAR `is_channels_last_strides` does the
// best effort to infer whether the original memory_format of a tensor is
// at::MemoryFormat::ChannelsLast. The two objectives of this function (ordered
// by their importance):
//   1. Ensure that normal shape manipulation does not accidentally change the
//      MemoryFormat of an existing tensor.
//   2. Allows user to mark MemoryFormat::ChannelsLast to tensors;
//
// The function does so via checking strides of the tensor, including strides of
// size-1 dimensions. Although conventionally PyTorch implies no restriction on
// trivial stride (stride for size-1 dimension).
//
// Note that this approach is a compromise. We did not solve the problem
// completely. Many cases we will not be able to infer the correct memory
// format.
// The implementation of `is_channels_last_strides` is to serve the objectives:
// MemoryFormat::ChannelsLast has to be explicitly opted-in (no accidental
// conversion); Best effort to maintain the ChannelsLast flag.
//
// Due to the fact that this is not a bulletproof solution, through testing
// (aten/src/ATen/test/memory_format_test.cpp)
//   a. we ensure that the common tasks are supported;
//   a. we identify corner cases where the implementation compromises on.
//
// By the time accumulated permutation is enabled to replace implicit
// memory_format through strides, we should be updating our tests and fix the
// issues in our tests.
//
// We use Channels Last 2d as an example above.
// This is a general problem for all the is_channels_last_strides_xd
// implementation. Please check the helper functions
// (is_channels_last_strides_*d_s*) for more details.

inline bool is_channels_last_strides_2d(
    const IntArrayRef sizes,
    const IntArrayRef strides) {
  switch (sizes.size()) {
    case 4:
      return is_channels_last_strides_2d_s4(sizes, strides);
    case 3:
      // TODO dim == 3 case will be enabled once it is fully tested
      return false;
    default:
      return false;
  }
}

inline bool is_channels_last_strides_3d(
    const IntArrayRef sizes,
    const IntArrayRef strides) {
  switch (sizes.size()) {
    case 5:
      return is_channels_last_strides_3d_s5(sizes, strides);
    case 4:
      // TODO dim == 4 case will be enabled once it is fully tested
      return false;
    default:
      return false;
  }
}

} // namespace c10

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