#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <torch/csrc/autograd/function.h>
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#include <torch/csrc/autograd/variable.h>
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#include <torch/csrc/utils/variadic.h>
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#include <ATen/ATen.h>
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#include <functional>
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#include <memory>
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#include <vector>
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namespace torch { namespace autograd {
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using function_constructor = std::function<std::shared_ptr<Node>(edge_list&&)>;
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/**
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* Wraps the tensor outputs in variables and creates the grad_fn and sets the
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* grad_fn if necessary.
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*/
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TORCH_API variable_list wrap_outputs(const variable_list& inputs, tensor_list&& outputs,
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const function_constructor& ctr);
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/// Checks that inputs contains exactly `args` items and that the first `required_args`
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/// items are not nullptr. If not specified, `required_args` defaults to `args`.
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TORCH_API void check_input_variables(const char* name, const variable_list& inputs, int args, int required_args=-1);
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struct ComputeRequiresGrad : IterArgs<ComputeRequiresGrad> {
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bool out = false;
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using IterArgs<ComputeRequiresGrad>::operator();
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void operator()(const at::Tensor& tensor) {
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const auto& var = static_cast<const Variable&>(tensor);
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if (var.defined() && var.requires_grad()) {
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out = true;
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}
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}
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bool short_circuit() {
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return out;
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}
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};
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template <typename... Args>
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inline bool compute_requires_grad(Args&&... args) {
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if (!GradMode::is_enabled()) {
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return false;
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}
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return ComputeRequiresGrad().apply(std::forward<Args>(args)...).out;
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}
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inline void set_history(
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at::Tensor& variable,
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const std::shared_ptr<Node>& grad_fn) {
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AT_ASSERT(grad_fn);
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if (variable.defined()) {
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auto output_nr =
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grad_fn->add_input_metadata(variable);
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as_variable_ref(variable).set_gradient_edge({grad_fn, output_nr});
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} else {
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grad_fn->add_input_metadata(Node::undefined_input());
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}
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}
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inline void set_history(
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std::vector<Variable>&& variables,
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const std::shared_ptr<Node>& grad_fn) {
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for (auto& variable : variables) {
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set_history(variable, grad_fn);
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}
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}
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inline void set_history(
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std::vector<Variable>& variables,
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const std::shared_ptr<Node>& grad_fn) {
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for (auto& variable : variables) {
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set_history(variable, grad_fn);
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}
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}
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}}
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