FM / ATen

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

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

Name	Size	Mode	Actions
core/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
cudnn/	-	0755	rm
detail/	-	0755	rm
hip/	-	0755	rm
native/	-	0755	rm
quantized/	-	0755	rm
AccumulateType.h	4438	0644	edit dl rm
ArrayRef.h	44	0644	edit dl rm
ATen.h	998	0644	edit dl rm
autocast_mode.h	6716	0644	edit dl rm
Backend.h	43	0644	edit dl rm
Backtrace.h	46	0644	edit dl rm
BatchedFallback.h	965	0644	edit dl rm
BatchedTensorImpl.h	5383	0644	edit dl rm
CompositeExplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeExplicitAutogradFunctions_inl.h	54075	0644	edit dl rm
CompositeImplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeImplicitAutogradFunctions_inl.h	142082	0644	edit dl rm
Config.h	734	0644	edit dl rm
Context.h	12767	0644	edit dl rm
cpp_custom_type_hack.h	5326	0644	edit dl rm
CPUApplyUtils.h	12582	0644	edit dl rm
CPUFixedAllocator.h	830	0644	edit dl rm
CPUFunctions.h	1600	0644	edit dl rm
CPUFunctions_inl.h	171924	0644	edit dl rm
CPUGeneratorImpl.h	1431	0644	edit dl rm
CUDAFunctions.h	1601	0644	edit dl rm
CUDAFunctions_inl.h	185696	0644	edit dl rm
CUDAGeneratorImpl.h	4695	0644	edit dl rm
Device.h	42	0644	edit dl rm
DeviceGuard.h	1134	0644	edit dl rm
Dimname.h	31	0644	edit dl rm
DimVector.h	46	0644	edit dl rm
Dispatch.h	52137	0644	edit dl rm
div_rtn.h	204	0644	edit dl rm
DLConvertor.h	576	0644	edit dl rm
dlpack.h	5244	0644	edit dl rm
DynamicLibrary.h	369	0644	edit dl rm
ExpandUtils.h	14506	0644	edit dl rm
Formatting.h	34	0644	edit dl rm
Functions.h	846326	0644	edit dl rm
Generator.h	46	0644	edit dl rm
InferSize.h	2143	0644	edit dl rm
InitialTensorOptions.h	445	0644	edit dl rm
Layout.h	42	0644	edit dl rm
MapAllocator.h	2999	0644	edit dl rm
MatrixRef.h	3016	0644	edit dl rm
MemoryOverlap.h	1117	0644	edit dl rm
MetaFunctions.h	1601	0644	edit dl rm
MetaFunctions_inl.h	84006	0644	edit dl rm
NamedTensor.h	35	0644	edit dl rm
NamedTensorUtils.h	5747	0644	edit dl rm
NativeFunctions.h	354651	0644	edit dl rm
NativeMetaFunctions.h	35445	0644	edit dl rm
NumericUtils.h	2787	0644	edit dl rm
OpaqueTensorImpl.h	6080	0644	edit dl rm
Operators.h	1707199	0644	edit dl rm
OpMathType.h	460	0644	edit dl rm
Parallel.h	4875	0644	edit dl rm
ParallelNative.h	2443	0644	edit dl rm
ParallelNativeTBB.h	2934	0644	edit dl rm
ParallelOpenMP.h	3049	0644	edit dl rm
PTThreadPool.h	394	0644	edit dl rm
record_function.h	24044	0644	edit dl rm
RedispatchFunctions.h	1112886	0644	edit dl rm
RegistrationDeclarations.h	545777	0644	edit dl rm
SavedTensorHooks.h	328	0644	edit dl rm
Scalar.h	44	0644	edit dl rm
ScalarOps.h	2272	0644	edit dl rm
ScalarType.h	129	0644	edit dl rm
SequenceNumber.h	373	0644	edit dl rm
SmallVector.h	47	0644	edit dl rm
SparseCsrTensorImpl.h	2045	0644	edit dl rm
SparseCsrTensorUtils.h	523	0644	edit dl rm
SparseTensorImpl.h	12417	0644	edit dl rm
SparseTensorUtils.h	4219	0644	edit dl rm
Storage.h	43	0644	edit dl rm
Tensor.h	48	0644	edit dl rm
TensorAccessor.h	51	0644	edit dl rm
TensorGeometry.h	1855	0644	edit dl rm
TensorIndexing.h	21923	0644	edit dl rm
TensorIterator.h	29962	0644	edit dl rm
TensorIteratorInternal.h	1862	0644	edit dl rm
TensorMeta.h	2917	0644	edit dl rm
TensorNames.h	2519	0644	edit dl rm
TensorOperators.h	3275	0644	edit dl rm
TensorOptions.h	49	0644	edit dl rm
TensorUtils.h	5687	0644	edit dl rm
ThreadLocalState.h	3289	0644	edit dl rm
TracerMode.h	5576	0644	edit dl rm
TypeDefault.h	680	0644	edit dl rm
Utils.h	5993	0644	edit dl rm
Version.h	340	0644	edit dl rm
VmapMode.h	952	0644	edit dl rm
VmapTransforms.h	7654	0644	edit dl rm
WrapDimUtils.h	3438	0644	edit dl rm
WrapDimUtilsMulti.h	768	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/WrapDimUtils.h (3438B)

#pragma once

#include <c10/core/WrapDimMinimal.h>
#include <c10/core/TensorImpl.h>
#include <ATen/core/Tensor.h>

namespace at {

static inline int64_t maybe_wrap_dim(int64_t dim, int64_t dim_post_expr, bool wrap_scalar=true) {
  // if dim_post_expr is 0 and wrap_scalar is true, then dim must be in the range [-1, 0].
  // This is a special case for scalar tensors and manifests in e.g. torch.sum(scalar_tensor, 0)
  // Otherwise, dim should be in the range [-dim_post_expr, dim_post_expr-1].
  return c10::maybe_wrap_dim(dim, dim_post_expr, wrap_scalar);
}

static inline int64_t maybe_wrap_dim(int64_t dim, TensorImpl *tensor) {
  return maybe_wrap_dim(dim, tensor->dim());
}

static inline int64_t maybe_wrap_dim(int64_t dim, TensorList tensors) {
  if (tensors.size() == 0) {
    // can't wrap empty TensorList; rely on underlying implementation to throw error if necessary.
    return dim;
  }
  return maybe_wrap_dim(dim, tensors[0].dim());
}

static inline int64_t maybe_wrap_dim(int64_t dim, const std::vector<std::vector<int64_t>> & tensor_sizes) {
  if (tensor_sizes.size() == 0) {
    // can't wrap empty list; rely on underlying implementation to throw error if necessary
    return dim;
  }
  return maybe_wrap_dim(dim, tensor_sizes[0].size());
}

// wrap each dim in the dims array, taking dim_post_expr as the true number of dimensions
static inline void maybe_wrap_dims_n(int64_t* dims, int64_t ndims, int64_t dim_post_expr) {
  if (dim_post_expr <= 0) {
    dim_post_expr = 1; // this will make range [-1, 0]
  }
  int64_t min = -dim_post_expr;
  int64_t max = dim_post_expr - 1;
  for (int64_t i = 0; i < ndims; ++i) {
    auto &dim = dims[i];
    if (dim < min || dim > max) {
      TORCH_CHECK_INDEX(false,
        "Dimension out of range (expected to be in range of [",
        min, ", ", max, "], but got ", dim, ")");
    }
    if (dim < 0) dim += dim_post_expr;
  }
}

// Wrap each dim in a contiguous container, taking dim_post_expr as the true number of dimensions
// E.g. could also be std::array or c10::SmallVector
template <typename Container>
inline void maybe_wrap_dims(Container& dims, int64_t dim_post_expr) {
  return maybe_wrap_dims_n(dims.data(), dims.size(), dim_post_expr);
}

// previously, size [0] tensors were the only possible empty tensors; thus, it wasn't possible
// to cat empty tensors unless all the other tensors were 1-dimensional, so we allowed these tensors
// to be "skipped" (both for wrap dimension behavior and dimension size checking).
// We maintain this behavior for backwards compatibility, but only for this specific size
// (i.e. other empty sizes are not skipped).
static inline int64_t legacy_cat_wrap_dim(int64_t dim, const std::vector<std::vector<int64_t>>& tensor_sizes) {
  for (auto& sizes : tensor_sizes) {
    if (sizes == std::vector<int64_t>({0})) {
      continue;
    }
    return maybe_wrap_dim(dim, sizes.size());
  }
  return dim;
}

static inline int64_t legacy_cat_wrap_dim(int64_t dim, TensorList tensors) {
  for (auto& tensor : tensors) {
    if (tensor.dim() == 1 && tensor.sizes()[0] == 0) {
      continue;
    }
    return maybe_wrap_dim(dim, tensor.dim());
  }
  return dim;
}

// wrap negative dims in a vector
static inline void wrap_all_dims(std::vector<int64_t>& dims_to_wrap, int64_t tensor_total_dims) {
  for (size_t i = 0; i < dims_to_wrap.size(); i++) {
    dims_to_wrap[i] = maybe_wrap_dim(dims_to_wrap[i], tensor_total_dims);
  }
}

}

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