utils.h

#pragma once

#include <torch/csrc/WindowsTorchApiMacro.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/utils/variadic.h>

#include <ATen/ATen.h>

#include <functional>
#include <memory>
#include <vector>

namespace torch { namespace autograd {

using function_constructor = std::function<std::shared_ptr<Function>(edge_list&&)>;

TORCH_API variable_list wrap_outputs(const variable_list& inputs, tensor_list&& outputs,
                                     const function_constructor& ctr);

TORCH_API void check_input_variables(const char* name, const variable_list& inputs, int args, int required_args=-1);

struct ComputeRequiresGrad : IterArgs<ComputeRequiresGrad> {
  bool out = false;
  using IterArgs<ComputeRequiresGrad>::operator();
  void operator()(const at::Tensor& tensor) {
    const auto& var = static_cast<const Variable&>(tensor);
    if (var.defined() && var.requires_grad()) {
      out = true;
    }
  }
  bool short_circuit() {
    return out;
  }
};

template <typename... Args>
inline bool compute_requires_grad(Args&&... args) {
  if (!GradMode::is_enabled()) {
    return false;
  }
  return ComputeRequiresGrad().apply(std::forward<Args>(args)...).out;
}

inline void set_history(
    at::Tensor& variable,
    const std::shared_ptr<Function>& grad_fn) {
  if (grad_fn) {
    if (variable.defined()) {
      auto output_nr =
          grad_fn->add_input_metadata(variable);
      as_variable_ref(variable).set_gradient_edge({grad_fn, output_nr});
    } else {
      grad_fn->add_input_metadata(Function::undefined_input());
    }
  }
}

inline void set_history(
    std::vector<Variable>&& variables,
    const std::shared_ptr<Function>& grad_fn) {
  for (auto& variable : variables) {
    set_history(variable, grad_fn);
  }
}

inline void set_history(
    std::vector<Variable>& variables,
    const std::shared_ptr<Function>& grad_fn) {
  for (auto& variable : variables) {
    set_history(variable, grad_fn);
  }
}
}}