/usr/local/lib64/python3.6/site-packages/torch/include/ATen
Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/TensorUtils.h (5687B)
#pragma once
#include
#include
#include
#include
// 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 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 tensors);
TORCH_API void checkScalarType(
CheckedFrom c,
const TensorArg& t,
ScalarType s);
TORCH_API void checkScalarTypes(
CheckedFrom c,
const TensorArg& t,
at::ArrayRef l);
TORCH_API void checkSameGPU(
CheckedFrom c,
const TensorArg& t1,
const TensorArg& t2);
TORCH_API void checkAllSameGPU(CheckedFrom c, ArrayRef tensors);
TORCH_API void checkSameType(
CheckedFrom c,
const TensorArg& t1,
const TensorArg& t2);
TORCH_API void checkAllSameType(CheckedFrom c, ArrayRef 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 t);
// FixMe: does TensorArg slow things down?
TORCH_API void checkBackend(
CheckedFrom c,
at::ArrayRef t,
at::Backend backend);
TORCH_API void checkDeviceType(
CheckedFrom c,
at::ArrayRef tensors,
at::DeviceType device_type);
TORCH_API void checkLayout(CheckedFrom c, const Tensor& t, Layout layout);
TORCH_API void checkLayout(CheckedFrom c, at::ArrayRef 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 defaultStrides(IntArrayRef sizes);
TORCH_API size_t
computeStorageNbytes(IntArrayRef sizes, IntArrayRef strides, size_t itemsize);
TORCH_API c10::optional> computeStride(
IntArrayRef oldshape,
IntArrayRef oldstride,
IntArrayRef newshape);
TORCH_API c10::optional computeStride(
IntArrayRef oldshape,
IntArrayRef oldstride,
const DimVector& newshape);
} // namespace detail
} // namespace at