FM / native

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

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

Name	Size	Mode	Actions
cpu/	-	0755	rm
cuda/	-	0755	rm
quantized/	-	0755	rm
Activation.h	3069	0644	edit dl rm
AdaptivePooling.h	1165	0644	edit dl rm
BatchLinearAlgebra.h	8246	0644	edit dl rm
batch_norm.h	1285	0644	edit dl rm
BinaryOps.h	4916	0644	edit dl rm
BucketizationUtils.h	4248	0644	edit dl rm
ComplexHelper.h	3797	0644	edit dl rm
CompositeRandomAccessor.h	888	0644	edit dl rm
CompositeRandomAccessorCommon.h	6713	0644	edit dl rm
ConvUtils.h	5350	0644	edit dl rm
Copy.h	356	0644	edit dl rm
CPUBlas.h	4199	0644	edit dl rm
CPUFallback.h	2404	0644	edit dl rm
Cross.h	262	0644	edit dl rm
DilatedConvolutionUtils.h	6416	0644	edit dl rm
DispatchStub.h	7672	0644	edit dl rm
Distance.h	732	0644	edit dl rm
Distributions.h	21654	0644	edit dl rm
DistributionTemplates.h	18623	0644	edit dl rm
EmbeddingBag.h	1320	0644	edit dl rm
Fill.h	384	0644	edit dl rm
ForeachUtils.h	5962	0644	edit dl rm
FunctionOfAMatrixUtils.h	436	0644	edit dl rm
GridSampler.h	10525	0644	edit dl rm
group_norm.h	896	0644	edit dl rm
Histogram.h	492	0644	edit dl rm
im2col.h	2838	0644	edit dl rm
im2col_shape_check.h	6181	0644	edit dl rm
IndexingUtils.h	5373	0644	edit dl rm
layer_norm.h	2892	0644	edit dl rm
Lerp.h	553	0644	edit dl rm
LinearAlgebra.h	603	0644	edit dl rm
LinearAlgebraUtils.h	25236	0644	edit dl rm
LossMulti.h	2197	0644	edit dl rm
Math.h	91356	0644	edit dl rm
MathBitFallThroughLists.h	4086	0644	edit dl rm
MathBitsFallback.h	7326	0644	edit dl rm
MaxPooling.h	1234	0644	edit dl rm
Normalization.h	302	0644	edit dl rm
PointwiseOps.h	749	0644	edit dl rm
Pool.h	10922	0644	edit dl rm
Pow.h	1694	0644	edit dl rm
ReduceAllOps.h	378	0644	edit dl rm
ReduceOps.h	1745	0644	edit dl rm
ReduceOpsUtils.h	12245	0644	edit dl rm
Repeat.h	1286	0644	edit dl rm
Resize.h	6501	0644	edit dl rm
ResizeCommon.h	1321	0644	edit dl rm
RNN.h	2467	0644	edit dl rm
ScatterGatherChecks.h	3641	0644	edit dl rm
SegmentReduce.h	685	0644	edit dl rm
SharedReduceOps.h	15785	0644	edit dl rm
SobolEngineOpsUtils.h	1723	0644	edit dl rm
Sorting.h	536	0644	edit dl rm
SortingUtils.h	5722	0644	edit dl rm
SpectralOpsUtils.h	3146	0644	edit dl rm
StridedRandomAccessor.h	6847	0644	edit dl rm
TensorAdvancedIndexing.h	3072	0644	edit dl rm
TensorCompare.h	1333	0644	edit dl rm
TensorDimApply.h	1832	0644	edit dl rm
TensorFactories.h	3382	0644	edit dl rm
TensorIterator.h	46	0644	edit dl rm
TensorIteratorDynamicCasting.h	2025	0644	edit dl rm
TensorShape.h	1049	0644	edit dl rm
TensorTransformations.h	938	0644	edit dl rm
TriangularOpsUtils.h	2000	0644	edit dl rm
TypeProperties.h	496	0644	edit dl rm
UnaryOps.h	4464	0644	edit dl rm
Unfold2d.h	551	0644	edit dl rm
Unfold3d.h	852	0644	edit dl rm
UnfoldBackward.h	5398	0644	edit dl rm
UpSample.h	13599	0644	edit dl rm
vol2col.h	3642	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/native/Resize.h (6501B)

#pragma once

#include <ATen/ATen.h>
#include <ATen/native/ResizeCommon.h>
#include <ATen/TensorUtils.h>

#include <c10/core/CPUAllocator.h>

namespace at { namespace native {

// TODO: make all operations that resize given outputs use this function
//   for consistency and maintainability.
//   Some operations like `cat` might not be able to make the use of
//   resize_output directly. For more details to understand how it works in `cat`,
//   see https://github.com/pytorch/pytorch/pull/62560#discussion_r687363362
// Resizes outputs
// Functions accepting output tensors, like with the "out" kwarg, should
//   call this function to handle resizing their output tensor.
// Issues a warning if the output tensor has one or more elements and
//   needs resizing
// NOTE: In the future the warning will become an error
// Returns a bool saying whether or not the resize actually happened or not
TORCH_API bool resize_output(const Tensor& output, IntArrayRef shape);

// Utility for resize_output
//  Returns a bool saying resize should happen or not and
//  raises a warning if resizing for one or more elements
TORCH_API bool resize_output_check(const Tensor& output, IntArrayRef shape);

TORCH_API void resize_bytes_cpu(StorageImpl* storage, size_t size_bytes);

static inline void maybe_resize_storage_cpu(TensorImpl* self, uint64_t new_size) {
  // It does not make sense to try to resize a storage
  // to hold 0 elements, and this can break
  // if storage_offset is positive but
  // new_size is 0, so just bail in that case
  // (same comment is in Resize.cuh)
  if (new_size == 0) {
    return;
  }

const auto new_size_bytes_i =
      (new_size + self->storage_offset()) * self->dtype().itemsize();
  TORCH_CHECK(!overflows<size_t>(new_size_bytes_i), "Requested storage size (",
              new_size_bytes_i, ") cannot be represented as a size_t");
  const auto new_size_bytes = static_cast<size_t>(new_size_bytes_i);

const Storage& storage = self->unsafe_storage();
  if (!storage) {
    auto new_storage = c10::make_intrusive<StorageImpl>(
        StorageImpl::use_byte_size_t(),
        new_size_bytes,
        c10::GetCPUAllocator(),
        true);
    self->set_storage_keep_dtype(std::move(new_storage));
  } else if (new_size_bytes > storage.nbytes()) {
    resize_bytes_cpu(storage.unsafeGetStorageImpl(), new_size_bytes);
  }
}

inline TensorImpl* resize_impl_cpu_(
    TensorImpl* self,
    IntArrayRef size,
    c10::optional<IntArrayRef> stride,
    bool resize_storage = true) {
  if (self->sizes() == size && (!stride || self->strides() == stride)) {
    return self;
  }

int64_t storage_size = 1;
  if (stride) {
    self->set_sizes_and_strides(size, *stride);
    // NB: storage size can be different from numel.
    storage_size = storage_size_for(size, *stride);
  } else {
    self->set_sizes_contiguous(size);
    storage_size = self->numel();
  }
  if (resize_storage) {
    maybe_resize_storage_cpu(self, storage_size);
  }

return self;
}

static inline void checkInBoundsForStorage(
    IntArrayRef size,
    IntArrayRef stride,
    int64_t storage_offset,
    const caffe2::TypeMeta data_type,
    const Storage& new_storage) {
  int64_t storage_size_bytes =
      at::detail::computeStorageNbytes(size, stride, data_type.itemsize());
  int64_t storage_offset_bytes = storage_offset * data_type.itemsize();
  if (storage_size_bytes == 0) {
    // NB: (a tensor with arbitrary 0 dims)'s storage can have any numel.
    return;
  }
  int64_t new_storage_size_bytes = new_storage.nbytes();
  TORCH_CHECK(
      storage_size_bytes + storage_offset_bytes <= new_storage_size_bytes,
      "setStorage: sizes ",
      size,
      ", strides ",
      stride,
      ","
      " storage offset ",
      storage_offset,
      ", and itemsize ",
      data_type.itemsize(),
      " requiring a storage size of ",
      storage_size_bytes + storage_offset_bytes,
      " are out of bounds for storage of size ",
      new_storage_size_bytes);
}

static inline void checkSetStorage(Tensor& result, Storage storage, int64_t storage_offset,
                                   IntArrayRef size, IntArrayRef stride) {
  // FIXME: stride should be optional
  if (stride.data()) {
    TORCH_CHECK(size.size() == stride.size(), "unequal size length (", size.size(),
                                              ") and stride length (", stride.size(), ")");
  }

#ifdef DEBUG
  TORCH_CHECK(size.size() <= INT_MAX, "size length (", size.size(), ") greater than INT_MAX");
#endif

// storage: note this can't be replaced with result.set_(storage) as the semantics of that
  // function is to set the tensor size to be equal to the size of the storage.
  if (!result.storage().is_alias_of(storage)) {
    // Caffe2 might have tensors whose storages are null, but we
    // don't allow it in PyTorch.
    TORCH_INTERNAL_ASSERT(storage);
    TORCH_INTERNAL_ASSERT(result.storage());

// We used to allow this, but this breaks device caching.
    // Let's put an actual error message for this one.
    TORCH_CHECK(result.storage().device() == storage.device(),
                "Attempted to set the storage of a tensor on device \"", result.storage().device(),
                "\" to a storage on different device \"", storage.device(),
                "\".  This is no longer allowed; the devices must match.");
    result.unsafeGetTensorImpl()->set_storage_keep_dtype(storage);
  }

// storageOffset
  TORCH_CHECK(storage_offset >= 0, "Tensor: invalid storage offset ", storage_offset);
}

/**
 * Set self's sizes, strides, and storage_offset.
 * (size, stride, storage_offset) must be in bounds for self's storage.
 */
inline void setStrided(
    const Tensor& self,
    IntArrayRef size,
    IntArrayRef stride,
    int64_t storage_offset) {
  TORCH_CHECK(size.size() == stride.size(), "mismatch in length of strides and shape");
  auto* self_ = self.unsafeGetTensorImpl();
  checkInBoundsForStorage(
      size, stride, storage_offset, self_->dtype(), self_->storage());

/* storage offset */
  TORCH_CHECK(storage_offset >= 0, "Tensor: invalid storage offset ", storage_offset);
  self_->set_storage_offset(storage_offset);

/* size and stride */
  if (self_->sizes() == size && self_->strides() == stride) {
    return;
  }
  for (auto val : stride) {
    TORCH_CHECK(val >= 0,
                "as_strided: Negative strides are not supported at the moment, "
                "got strides: ", stride);
  }
  self_->set_sizes_and_strides(size, stride);
}

}}

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