mirror of
https://github.com/zebrajr/pytorch.git
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9bf7eb914d
Summary: In this PR, we are moving all functions away from `Variable::Impl`, in order to get rid of `Variable::Impl` (and the `data_` Tensor in it) in the next PR. Some of the functions (such as `set_requires_grad` / `requires_grad` / `grad`) will be living in `AutogradMeta` class, while others (such as `backward()` / `rebase_history()` / `grad_accumulator()` / `grad_fn()`) will be living in `Variable` class. This is the 2nd PR mentioned in https://github.com/pytorch/pytorch/issues/13638. Pull Request resolved: https://github.com/pytorch/pytorch/pull/15487 Differential Revision: D13553173 Pulled By: yf225 fbshipit-source-id: 691f9432d0cd0640af380c757f3e3a2f64f8851c
136 lines
3.4 KiB
C++
136 lines
3.4 KiB
C++
#include <c10/core/TensorImpl.h>
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#include <c10/core/Backend.h>
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#include <c10/core/WrapDimMinimal.h>
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#include <c10/util/Optional.h>
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C10_DEFINE_bool(
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caffe2_keep_on_shrink,
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true,
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"If set, keeps memory when a tensor is shrinking its size.");
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C10_DEFINE_int64(
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caffe2_max_keep_on_shrink_memory,
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LLONG_MAX,
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"The maximum memory in bytes to keep on shrink, if the difference between "
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"tensor sizes is bigger than this then tensor will be reset.");
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namespace c10 {
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at::Tensor& TensorImpl::grad() {
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if (autograd_meta()) {
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return autograd_meta()->grad();
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} else {
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AT_ERROR("grad is not implemented for Tensor");
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}
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}
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const at::Tensor& TensorImpl::grad() const {
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if (autograd_meta()) {
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return autograd_meta()->grad();
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} else {
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AT_ERROR("grad is not implemented for Tensor");
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}
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}
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TensorImpl::TensorImpl(TensorTypeId type_id, const caffe2::TypeMeta& data_type, Allocator *allocator, bool is_variable)
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: TensorImpl({}, type_id, data_type, is_variable) {
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// Variables, UndefinedTensors and SparseTensors don't have storages.
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if (!is_variable && type_id != UndefinedTensorId() && data_type.id() != caffe2::TypeIdentifier::uninitialized()
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&& type_id != SparseCPUTensorId() && type_id != SparseCUDATensorId()) {
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storage_ = Storage(data_type, 0, allocator, true);
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}
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}
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TensorImpl::TensorImpl(Storage&& storage, TensorTypeId type_id, bool is_variable)
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: TensorImpl(std::move(storage), type_id, storage.dtype(), is_variable) {}
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TensorImpl::TensorImpl(Storage&& storage, TensorTypeId type_id, const caffe2::TypeMeta& data_type, bool is_variable)
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: storage_(std::move(storage)),
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sizes_{0},
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storage_offset_(0),
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numel_(0),
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data_type_(data_type),
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type_id_(type_id),
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is_variable_(is_variable) {
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strides_.push_back(1);
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}
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IntList TensorImpl::sizes() const {
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return sizes_;
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}
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IntList TensorImpl::strides() const {
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return strides_;
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}
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bool TensorImpl::compute_contiguous() const {
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bool is_contiguous = true;
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if (is_empty())
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return is_contiguous;
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int64_t z = 1;
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for (int64_t d = dim() - 1; d >= 0; d--) {
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if (size(d) != 1) {
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if (stride(d) == z) {
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z *= size(d);
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} else {
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is_contiguous = false;
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break;
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}
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}
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}
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return is_contiguous;
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}
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void TensorImpl::release_resources() {
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if (storage_) {
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storage_ = {};
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}
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}
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int64_t TensorImpl::dim() const {
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return sizes_.size();
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}
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int64_t TensorImpl::size(int64_t d) const {
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d = at::maybe_wrap_dim(d, dim(), false);
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return sizes_[d];
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}
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int64_t TensorImpl::stride(int64_t d) const {
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d = at::maybe_wrap_dim(d, dim(), false);
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return strides_[d];
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}
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TensorImpl* TensorImpl::maybe_zero_dim(bool condition_when_zero_dim) {
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bool set_zero_dim = condition_when_zero_dim && this->sizes().size() == 1 && this->size(0) == 1;
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if (set_zero_dim) {
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resize_dim(0);
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}
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return this;
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}
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const Storage& TensorImpl::storage() const {
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return storage_;
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}
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static void deletePlacementDeleteContext(void* ptr) {
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delete static_cast<PlacementDeleteContext*>(ptr);
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}
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at::DataPtr PlacementDeleteContext::makeDataPtr(
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at::DataPtr&& data_ptr,
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PlacementDtor placement_dtor,
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size_t size,
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at::Device device) {
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auto* ptr = data_ptr.get();
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return {ptr,
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new PlacementDeleteContext(std::move(data_ptr), placement_dtor, size),
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&deletePlacementDeleteContext,
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device};
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}
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AutogradMetaInterface::~AutogradMetaInterface() {}
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} // namespace c10
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