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f531815526
Summary: Fixes https://github.com/pytorch/pytorch/issues/29161. I looked a bit at the code changes related to this and think I have all of the use cases of `DeprecatedTypeProperties` covered in the message, but suggestions from someone with more context on this would be very much appreciated :) Pull Request resolved: https://github.com/pytorch/pytorch/pull/30281 Differential Revision: D18830818 Pulled By: ezyang fbshipit-source-id: 1a7fcee15354ae09e6644577e7fa33bd26acfe20
109 lines
3.9 KiB
C++
109 lines
3.9 KiB
C++
#include <torch/csrc/autograd/saved_variable.h>
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#include <torch/csrc/autograd/edge.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/autograd/anomaly_mode.h>
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#include <ATen/Tensor.h>
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#include <cstdint>
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#include <list>
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#include <memory>
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#include <sstream>
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namespace torch { namespace autograd {
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SavedVariable::SavedVariable(const Variable& variable, bool is_output, bool is_inplace_view) {
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if (variable.defined()) {
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was_default_constructed_ = false;
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output_nr_ = variable.output_nr();
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requires_grad_ = variable.requires_grad();
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has_grad_fn_ = !variable.is_leaf();
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is_inplace_view_ = is_inplace_view;
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// These copies are all shared_ptr copies, so slightly more expensive.
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// Do them here instead of in the init list in case data is undefined.
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data_ = variable.tensor_data();
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if (variable.is_leaf()) {
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grad_accumulator_ = impl::grad_accumulator(variable);
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} else if (!is_output) {
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grad_fn_ = variable.grad_fn();
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} else if (is_inplace_view) {
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weak_grad_fn_ = variable.grad_fn();
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}
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version_counter_ = impl::version_counter(variable);
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saved_version_ = version_counter_.current_version();
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}
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}
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Variable SavedVariable::unpack(std::shared_ptr<Node> saved_for) const {
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if (!data_.defined()) {
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if (!was_default_constructed_) {
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throw std::runtime_error(ERR_BACKWARD_TWICE);
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}
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return Variable();
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}
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auto grad_fn = is_inplace_view_ ? weak_grad_fn_.lock() : grad_fn_;
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if (has_grad_fn_ && !grad_fn) {
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if (!saved_for) {
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// If saving the grad_fn would create a circular reference, then it must
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// be passed in to the unpack function.
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throw std::runtime_error("No grad_fn for non-leaf saved variable");
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}
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grad_fn = std::move(saved_for);
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}
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if (saved_version_ != version_counter_.current_version()) {
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std::stringstream message;
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message << "one of the variables needed for gradient computation has been "
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"modified by an inplace operation: [" << data_.toString() << " "
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<< data_.sizes() << "]";
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if (grad_fn) {
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message << ", which is output " << output_nr_
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<< " of " << grad_fn->name() << ",";
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}
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message << " is at version " << version_counter_.current_version()
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<< "; expected version " << saved_version_ << " instead.";
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if (!AnomalyMode::is_enabled()) {
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message << " Hint: enable anomaly detection to find the operation "
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"that failed to compute its gradient, with torch.autograd."
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"set_detect_anomaly(True).";
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}
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else {
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message << " Hint: the backtrace further above shows the operation "
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"that failed to compute its gradient. The variable in question "
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"was changed in there or anywhere later. Good luck!";
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}
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throw std::runtime_error(message.str());
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}
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// NB: saved views are unpacked as normal Variables (not views) even though
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// they still share the same storage. This works only because we never call
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// in-place functions on unpacked variables.
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Variable var;
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if (grad_fn) {
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var = make_variable(data_, Edge(std::move(grad_fn), output_nr_));
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} else {
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var = make_variable(data_, requires_grad_);
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}
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impl::set_version_counter(var, saved_version_);
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// If a Variable is a leaf (no grad_fn saved), and it requires_grad, then we
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// should have saved the grad accumulator. Even if the Variable no longer
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// alive, the accumulator should be kept alive by the references in the
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// graph).
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if (requires_grad_ && !var.grad_fn() && grad_accumulator_.expired())
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throw std::logic_error("No grad accumulator for a saved leaf!");
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impl::set_grad_accumulator(var, grad_accumulator_);
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return var;
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}
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const char* ERR_BACKWARD_TWICE =
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"Trying to backward through the graph a second time, but the buffers have "
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"already been freed. Specify retain_graph=True when calling backward "
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"the first time.";
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}} // namespace torch::autograd
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