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#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&&)>;
/**
* Wraps the tensor outputs in variables and creates the grad_fn and sets the
* grad_fn if necessary.
*/
TORCH_API variable_list wrap_outputs(const variable_list& inputs, tensor_list&& outputs,
const function_constructor& ctr);
/// Checks that inputs contains exactly `args` items and that the first `required_args`
/// items are not nullptr. If not specified, `required_args` defaults to `args`.
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) {
AT_ASSERT(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);
}
}
}}
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