pytorch/torch/csrc/autograd/input_buffer.h

#pragma once

// The InputBuffer class accumulates a list of Variables for use by a
// function. It implements logic to avoid modifying the passed
// values in-place (adding an input twice will accumulate the result).
// This behaviour is needed and used only in backward graphs.

#include <vector>
#include <utility>
#include <memory>
#include <ATen/ATen.h>

#include <torch/csrc/autograd/variable.h>
#include <c10/util/Optional.h>
#include <c10/core/Stream.h>

namespace torch { namespace autograd {

struct InputBuffer {
  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
  explicit InputBuffer(size_t size)
    : buffer(size) {}
  InputBuffer(const InputBuffer& other) = delete;
  InputBuffer(InputBuffer&& other) = default;
  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
  explicit InputBuffer(variable_list&& inputs): buffer(std::move(inputs)) {};
  InputBuffer& operator=(InputBuffer&& other) = default;

  // Accumulates the variable at a specified index.
  // The optional CUDA streams determine which stream the accumulation
  // is run on and how the addition is synchronized.
  void add(size_t pos,
           Variable&& var,
           const c10::optional<c10::Stream>& opt_producer_stream,
           const c10::optional<c10::Stream>& opt_consumer_stream);

  at::Device device() const;

  Variable operator[](size_t pos) { return buffer[pos]; }

  // Returns the inputs as a list of variables. Destroys given InputBuffer.
  static std::vector<Variable> variables(InputBuffer&& g);

private:
  std::vector<Variable> buffer;
};

}}  // namespace torch::autograd