saved_variable.cpp

#include <torch/csrc/autograd/saved_variable.h>

#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/autograd/anomaly_mode.h>

#include <ATen/Tensor.h>

#include <cstdint>
#include <list>
#include <memory>
#include <sstream>

namespace torch { namespace autograd {

SavedVariable::SavedVariable(const Variable& variable, bool is_output, bool is_inplace_view) {
  if (variable.defined()) {
    was_default_constructed_ = false;
    output_nr_ = variable.output_nr();
    requires_grad_ = variable.requires_grad();
    has_grad_fn_ = !variable.is_leaf();
    is_inplace_view_ = is_inplace_view;
    // These copies are all shared_ptr copies, so slightly more expensive.
    // Do them here instead of in the init list in case data is undefined.
    data_ = variable.tensor_data();
    if (variable.is_leaf()) {
      grad_accumulator_ = impl::grad_accumulator(variable);
    } else if (!is_output) {
      grad_fn_ = variable.grad_fn();
    } else if (is_inplace_view) {
      weak_grad_fn_ = variable.grad_fn();
    }
    version_counter_ = impl::version_counter(variable);
    saved_version_ = version_counter_.current_version();
  }
}

Variable SavedVariable::unpack(std::shared_ptr<Node> saved_for) const {
  if (!data_.defined()) {
    if (!was_default_constructed_) {
      throw std::runtime_error(ERR_BACKWARD_TWICE);
    }
    return Variable();
  }

  auto grad_fn = is_inplace_view_ ? weak_grad_fn_.lock() : grad_fn_;
  if (has_grad_fn_ && !grad_fn) {
    if (!saved_for) {
      // If saving the grad_fn would create a circular reference, then it must
      // be passed in to the unpack function.
      throw std::runtime_error("No grad_fn for non-leaf saved variable");
    }
    grad_fn = std::move(saved_for);
  }

  if (saved_version_ != version_counter_.current_version()) {
    std::stringstream message;
    message << "one of the variables needed for gradient computation has been "
        "modified by an inplace operation: [" << data_.toString() << " "
        << data_.sizes() << "]";
    if (grad_fn) {
        message << ", which is output " << output_nr_
            << " of " << grad_fn->name() << ",";
    }
    message << " is at version " << version_counter_.current_version()
        << "; expected version " << saved_version_ << " instead.";
    if (!AnomalyMode::is_enabled()) {
        message << " Hint: enable anomaly detection to find the operation "
            "that failed to compute its gradient, with torch.autograd."
            "set_detect_anomaly(True).";
    }
    else {
        message << " Hint: the backtrace further above shows the operation "
            "that failed to compute its gradient. The variable in question "
            "was changed in there or anywhere later. Good luck!";
    }
    throw std::runtime_error(message.str());
  }

  // NB: saved views are unpacked as normal Variables (not views) even though
  // they still share the same storage. This works only because we never call
  // in-place functions on unpacked variables.
  Variable var;
  if (grad_fn) {
    var = make_variable(data_, Edge(std::move(grad_fn), output_nr_));
  } else {
    var = make_variable(data_, requires_grad_);
  }
  impl::set_version_counter(var, saved_version_);

  // If a Variable is a leaf (no grad_fn saved), and it requires_grad, then we
  // should have saved the grad accumulator. Even if the Variable no longer
  // alive, the accumulator should be kept alive by the references in the
  // graph).
  if (requires_grad_ && !var.grad_fn() && grad_accumulator_.expired())
    throw std::logic_error("No grad accumulator for a saved leaf!");
  impl::set_grad_accumulator(var, grad_accumulator_);

  return var;
}

const char* ERR_BACKWARD_TWICE =
    "Trying to backward through the graph a second time, but the buffers have "
    "already been freed. Specify retain_graph=True when calling backward "
    "the first time.";

}} // namespace torch::autograd