ai-content-maker/.venv/Lib/site-packages/torch/include/ATen/TensorGeometry.h

145 lines
4.2 KiB
C++

#pragma once
#include <ATen/core/TensorBase.h>
#include <c10/core/WrapDimMinimal.h>
namespace at {
// 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);
struct TORCH_API TensorGeometry {
TensorGeometry() = default;
explicit TensorGeometry(c10::SymIntArrayRef sizes)
: sizes_(sizes.vec()),
strides_(sizes.size()),
has_symbolic_sizes_strides_(
!c10::asIntArrayRefSlowOpt(sizes).has_value()) {
int64_t dim = static_cast<int64_t>(sizes.size());
c10::SymInt expected_stride = 1;
for (int64_t i = dim - 1; i >= 0; i--) {
strides_[i] = expected_stride;
expected_stride *= sizes_[i];
}
numel_ = expected_stride;
}
explicit TensorGeometry(const TensorBase& t)
: sizes_(t.sym_sizes().vec()),
strides_(t.sym_strides().vec()),
storage_offset_(t.sym_storage_offset()),
numel_(t.sym_numel()),
has_symbolic_sizes_strides_(
t.unsafeGetTensorImpl()->has_symbolic_sizes_strides()) {}
// true if the tensor is contiguous
bool is_contiguous() const;
int64_t dim() const {
return static_cast<int64_t>(sizes_.size());
}
int64_t size(int64_t dim) const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
dim = c10::maybe_wrap_dim(dim, this->dim());
return sizes_.at(static_cast<size_t>(dim)).as_int_unchecked();
}
c10::IntArrayRef sizes() const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
return c10::asIntArrayRefUnchecked(sizes_);
}
int64_t stride(int64_t dim) const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
dim = c10::maybe_wrap_dim(dim, this->dim());
return strides_.at(static_cast<size_t>(dim)).as_int_unchecked();
}
c10::IntArrayRef strides() const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
return c10::asIntArrayRefUnchecked(strides_);
}
int64_t storage_offset() const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
return storage_offset_.as_int_unchecked();
}
int64_t numel() const {
TORCH_INTERNAL_ASSERT(!has_symbolic_sizes_strides_);
return numel_.as_int_unchecked();
}
c10::SymInt sym_size(int64_t dim) const {
dim = c10::maybe_wrap_dim(dim, this->dim());
return sizes_.at(static_cast<size_t>(dim));
}
c10::SymIntArrayRef sym_sizes() const {
return sizes_;
}
c10::SymInt sym_stride(int64_t dim) const {
dim = c10::maybe_wrap_dim(dim, this->dim());
return strides_.at(static_cast<size_t>(dim));
}
c10::SymIntArrayRef sym_strides() const {
return strides_;
}
c10::SymInt sym_storage_offset() const {
return storage_offset_;
}
c10::SymInt sym_numel() const {
return numel_;
}
TensorGeometry transpose(int64_t dim0, int64_t dim1) {
TensorGeometry r = *this; // copy
TORCH_CHECK(
dim0 < dim(),
"transpose: dim0=",
dim0,
" out of range (dim=",
dim(),
")")
TORCH_CHECK(
dim1 < dim(),
"transpose: dim1=",
dim1,
" out of range (dim=",
dim(),
")")
std::swap(r.sizes_[dim0], r.sizes_[dim1]);
std::swap(r.strides_[dim0], r.strides_[dim1]);
return r;
}
std::vector<c10::SymInt>& mutable_sizes() {
return sizes_;
}
std::vector<c10::SymInt>& mutable_strides() {
return strides_;
}
c10::SymInt& mutable_storage_offset() {
return storage_offset_;
}
void recompute() {
// recalculate numel after a change
c10::SymInt numel = 1;
for (const auto& i : sizes_) {
numel = numel * i;
}
numel_ = std::move(numel);
has_symbolic_sizes_strides_ =
!c10::asIntArrayRefSlowOpt(sizes_).has_value();
}
private:
std::vector<c10::SymInt> sizes_;
std::vector<c10::SymInt> strides_;
c10::SymInt storage_offset_;
c10::SymInt numel_;
bool has_symbolic_sizes_strides_{false};
};
} // namespace at