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/TensorUtils.h (5687B)

#pragma once

#include <ATen/DimVector.h>
#include <ATen/Tensor.h>
#include <ATen/TensorGeometry.h>
#include <ATen/Utils.h>

// These functions are NOT in Utils.h, because this file has a dep on Tensor.h

namespace at {

// The following are utility functions for checking that arguments
// make sense.  These are particularly useful for native functions,
// which do NO argument checking by default.

struct TORCH_API TensorArg {
  const Tensor& tensor;
  const char* name;
  int pos; // 1-indexed
  TensorArg(const Tensor& tensor, const char* name, int pos)
    : tensor(tensor), name(name), pos(pos) {}
  // Try to mitigate any possibility of dangling reference to temporaries.
  TensorArg(Tensor&& tensor, const char* name, int pos) = delete;
  const Tensor* operator->() const { return &tensor; }
  const Tensor& operator*() const { return tensor; }
};

struct TORCH_API TensorGeometryArg {
  TensorGeometry tensor;
  const char* name;
  int pos; // 1-indexed
  /* implicit */ TensorGeometryArg(TensorArg arg)
    : tensor(TensorGeometry{arg.tensor}), name(arg.name), pos(arg.pos) {}
  TensorGeometryArg(TensorGeometry tensor, const char* name, int pos)
    : tensor(tensor), name(name), pos(pos) {}
  const TensorGeometry* operator->() const { return &tensor; }
  const TensorGeometry& operator*() const { return tensor; }
};

// A string describing which function did checks on its input
// arguments.
// TODO: Consider generalizing this into a call stack.
using CheckedFrom = const char*;

// The undefined convention: singular operators assume their arguments
// are defined, but functions which take multiple tensors will
// implicitly filter out undefined tensors (to make it easier to perform
// tests which should apply if the tensor is defined, and should not
// otherwise.)
//
// NB: This means that the n-ary operators take lists of TensorArg,
// not TensorGeometryArg, because the Tensor to TensorGeometry
// conversion will blow up if you have undefined tensors.

TORCH_API std::ostream& operator<<(std::ostream& out, TensorGeometryArg t);
TORCH_API void checkDim(
    CheckedFrom c,
    const Tensor& tensor,
    const char* name,
    int pos, // 1-indexed
    int64_t dim);
TORCH_API void checkDim(
    CheckedFrom c,
    const TensorGeometryArg& t,
    int64_t dim);
// NB: this is an inclusive-exclusive range
TORCH_API void checkDimRange(
    CheckedFrom c,
    const TensorGeometryArg& t,
    int64_t dim_start,
    int64_t dim_end);
TORCH_API void checkSameDim(
    CheckedFrom c,
    const TensorGeometryArg& t1,
    const TensorGeometryArg& t2);
TORCH_API void checkContiguous(CheckedFrom c, const TensorGeometryArg& t);
TORCH_API void checkAllContiguous(CheckedFrom c, at::ArrayRef<TensorArg> ts);
TORCH_API void checkSize(
    CheckedFrom c,
    const TensorGeometryArg& t,
    IntArrayRef sizes);
TORCH_API void checkSize(
    CheckedFrom c,
    const TensorGeometryArg& t,
    int64_t dim,
    int64_t size);
TORCH_API void checkNumel(
    CheckedFrom c,
    const TensorGeometryArg& t,
    int64_t numel);
TORCH_API void checkSameNumel(
    CheckedFrom c,
    const TensorGeometryArg& t1,
    const TensorGeometryArg& t2);
TORCH_API void checkAllSameNumel(CheckedFrom c, ArrayRef<TensorArg> tensors);
TORCH_API void checkScalarType(
    CheckedFrom c,
    const TensorArg& t,
    ScalarType s);
TORCH_API void checkScalarTypes(
    CheckedFrom c,
    const TensorArg& t,
    at::ArrayRef<ScalarType> l);
TORCH_API void checkSameGPU(
    CheckedFrom c,
    const TensorArg& t1,
    const TensorArg& t2);
TORCH_API void checkAllSameGPU(CheckedFrom c, ArrayRef<TensorArg> tensors);
TORCH_API void checkSameType(
    CheckedFrom c,
    const TensorArg& t1,
    const TensorArg& t2);
TORCH_API void checkAllSameType(CheckedFrom c, ArrayRef<TensorArg> tensors);
TORCH_API void checkSameSize(
    CheckedFrom c,
    const TensorArg& t1,
    const TensorArg& t2);
TORCH_API void checkDefined(CheckedFrom c, const TensorArg& t);
TORCH_API void checkAllDefined(CheckedFrom c, at::ArrayRef<TensorArg> t);

// FixMe: does TensorArg slow things down?
TORCH_API void checkBackend(
    CheckedFrom c,
    at::ArrayRef<Tensor> t,
    at::Backend backend);

TORCH_API void checkDeviceType(
    CheckedFrom c,
    at::ArrayRef<Tensor> tensors,
    at::DeviceType device_type);

TORCH_API void checkLayout(CheckedFrom c, const Tensor& t, Layout layout);

TORCH_API void checkLayout(CheckedFrom c, at::ArrayRef<Tensor> tensors, at::Layout layout);

// Methods for getting data_ptr if tensor is defined
TORCH_API void* maybe_data_ptr(const Tensor& tensor);
TORCH_API void* maybe_data_ptr(const TensorArg& tensor);

// Return if the tensor geometry represented by `sizes` and `strides` is contiguous
// Although we cache is_contiguous in tensor now, this is till useful because it
// allows checking if a particular geometry is contiguous without explicitly
// constructing a tensor, e.g., when you want to choose a kernel strategy based
// on whether a subgeometry is contiguous.
TORCH_API bool geometry_is_contiguous(IntArrayRef sizes, IntArrayRef strides);

TORCH_API void check_dim_size(
    const Tensor& tensor,
    int64_t dim,
    int64_t dim_size,
    int64_t size);

namespace detail {
TORCH_API std::vector<int64_t> defaultStrides(IntArrayRef sizes);
TORCH_API size_t
computeStorageNbytes(IntArrayRef sizes, IntArrayRef strides, size_t itemsize);

TORCH_API c10::optional<std::vector<int64_t>> computeStride(
    IntArrayRef oldshape,
    IntArrayRef oldstride,
    IntArrayRef newshape);

TORCH_API c10::optional<DimVector> computeStride(
    IntArrayRef oldshape,
    IntArrayRef oldstride,
    const DimVector& newshape);

} // namespace detail
} // namespace at

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