pytorch/torch/csrc/autograd/functions/tensor.cpp

#include <torch/csrc/autograd/functions/tensor.h>

#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/functions/basic_ops.h>
#include <torch/csrc/autograd/functions/utils.h>
#include <torch/csrc/autograd/graph_task.h>
#include <torch/csrc/autograd/variable.h>

#include <ATen/ATen.h>
#include <c10/util/irange.h>

#include <cstddef>
#include <memory>
#include <stdexcept>
#include <utility>

namespace torch {
namespace autograd {

auto CopyBackwards::apply(variable_list&& grads) -> variable_list {
  check_input_variables("CopyBackwards", grads, 1, -1, true);
  auto grad = c10::MaybeOwned<at::Tensor>::borrowed(grads[0]);
  variable_list grad_inputs(2);
  if (grad->defined()) {
    if (task_should_compute_output(0)) {
      grad_inputs[0] = at::zeros_like(*grad, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
    }
    if (task_should_compute_output(1)) {
      // Handle R->C copies without raising a warning
      const auto src_type = src_options.dtype().toScalarType();
      if (!c10::isComplexType(src_type) && grad->is_complex()) {
        grad = c10::MaybeOwned<at::Tensor>::owned(at::real(grads[0]));
      }

      at::DeviceGuard device_guard(src_options.device());
      grad_inputs[1] = grad->to(src_options);
    }
  }
  return grad_inputs;
}

CopySlices::CopySlices(
    const Variable& base_var,
    at::TensorGeometry view_,
    std::function<at::Tensor(const at::Tensor&)> view_fn_,
    std::shared_ptr<Node> fn_)
    : Node(),
      base(base_var),
      view(std::move(view_)),
      view_fn(std::move(view_fn_)),
      fn(std::move(fn_)) {
  // Take the next_edges of fn as our own, except for index 0 which goes
  // to base instead of the view.
  add_input_metadata(base_var);
  const auto num_outputs = fn->num_outputs();
  next_edges_.reserve(num_outputs);
  add_next_edge(impl::gradient_edge(base_var));
  for (const auto i : c10::irange(1, num_outputs)) {
    add_next_edge(fn->next_edge(i));
  }
}

auto CopySlices::apply(variable_list&& inputs) -> variable_list {
  check_input_variables("CopySlices", inputs, 1, -1, true);
  auto& grad = inputs[0];
  if (!grad.defined()) {
    return variable_list(num_outputs());
  }

  // Acquire lock to here protect thread safety on fn
  // see Note [Thread Safety on Autograd Node]
  std::lock_guard<std::mutex> lock(mutex_);

  if (!fn) {
    throw std::runtime_error(ERR_BACKWARD_TWICE);
  }

  auto result =
      grad.new_empty_strided_symint(base.sym_sizes(), base.sym_strides());
  result.copy_(grad);

  at::Tensor grad_slice;
  if (view_fn) {
    grad_slice = view_fn(result);
  } else {
    auto offset = view.sym_storage_offset() - base.sym_storage_offset();
    grad_slice =
        result.as_strided_symint(view.sym_sizes(), view.sym_strides(), offset);
  }

  // See Note [View + Inplace update for view tensor] For more details on this
  // block Since the gradient edge for the 0th input is different between `this`
  // and `fn`, make sure that the one from `fn` has the same metadata in the
  // current GraphTask's exec_info as the one on `this`.
  const auto exec_info = get_current_graph_task_exec_info();
  if (exec_info && !exec_info->empty()) {
    const auto& fn_edge = fn->next_edge(0);
    const auto& this_edge = this->next_edge(0);
    TORCH_INTERNAL_ASSERT(fn_edge.is_valid() == this_edge.is_valid());
    if (fn_edge.is_valid()) {
      const auto fn_next_node = fn_edge.function.get();
      auto it = exec_info->find(fn_next_node);
      if (it == exec_info->end()) {
        // Node is not in the exec_info already
        if (task_should_compute_output(0)) {
          // And we need gradient for the corresponding output
          add_node_to_current_graph_task_exec_info(fn_next_node);
          // There is no need to remove this after execution because we are
          // guaranteed that this->next_edge(0) must be in the history of
          // fn->next_edge(0) (we cannot easily assert this as it might be far
          // away if there were many chained views). This means that, since
          // fn->next_edge(0) was not needed (no exec_info entry for it), we
          // know that nothing downstream of fn->next_edge(0) is needed either
          // (otherwise the whole path from that Node to this->next_edge(0)
          // would be needed as well). This means that no other Node will ever
          // look at fn->next_edge(0) metadata and thus there is no need to
          // clean them up.
        }
      } else {
        TORCH_INTERNAL_ASSERT(
            it->second.should_execute() == task_should_compute_output(0));
      }
    }
  }

  // Sanity check that the graph was never modified after the fact (it is
  // read-only!)
  TORCH_INTERNAL_ASSERT(num_outputs() == fn->num_outputs());
  for (const auto i : c10::irange(1, this->num_outputs())) {
    TORCH_INTERNAL_ASSERT(
        fn->next_edge(i).function.get() == this->next_edge(i).function.get());
  }

  // TODO: We clone grad_slice because we modify it below and "fn" might save
  // it for the backward of res. We might be able to avoid the clone() if
  // double-backprop is disabled.
  auto res = (*fn)({grad_slice.clone(at::MemoryFormat::Contiguous)});

  variable_list grad_inputs(num_outputs());
  for (const auto i : c10::irange(res.size())) {
    if (task_should_compute_output(i)) {
      AT_ASSERT(res[i].defined());
      if (i == 0) {
        grad_slice.copy_(res[i]);
        // NOLINTNEXTLINE(clang-analyzer-cplusplus.Move)
        grad_inputs[i] = std::move(result); // NOLINT(bugprone-use-after-move)
      } else {
        grad_inputs[i] = std::move(res[i]);
      }
    }
  }

  return grad_inputs;
}

void CopySlices::release_variables() {
  // Acquire lock to here protect thread safety on fn
  std::lock_guard<std::mutex> lock(mutex_);
  fn = nullptr;
}

} // namespace autograd
} // namespace torch