#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <ATen/ATen.h>
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#include <cstdint>
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#include <memory>
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namespace torch { namespace autograd {
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struct Variable;
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struct Node;
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TORCH_API extern const char* ERR_BACKWARD_TWICE;
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/// A snapshot of a variable at a certain version. A `SavedVariable` stores
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/// enough information to reconstruct a variable from a certain point in time.
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class TORCH_API SavedVariable {
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public:
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SavedVariable() = default;
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SavedVariable(const Variable& variable, bool is_output, bool is_inplace_view=false);
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SavedVariable(SavedVariable&&) = default;
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SavedVariable& operator=(SavedVariable&&) = default;
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/// Reconstructs the saved variable. Pass `saved_for` as the gradient
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/// function if constructing the `SavedVariable` with it would have caused a
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/// circular reference.
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Variable unpack(std::shared_ptr<Node> saved_for = nullptr) const;
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void reset_data() {
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return data_.reset();
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}
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void reset_grad_function() {
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grad_fn_.reset();
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}
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private:
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at::Tensor data_;
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// The gradient function associated with this node. If has_grad_fn
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// is false, then this is a leaf node. Note that the grad_fn is not saved if
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// it would create a circular reference. In that case, the grad_fn must be
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// passed in to the unpack function when reconstructing the Variable.
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std::shared_ptr<Node> grad_fn_;
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// Weak version of grad_fn_ that prevents leaks in rebase_history() for
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// inplace views.
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std::weak_ptr<Node> weak_grad_fn_;
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std::weak_ptr<Node> grad_accumulator_;
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c10::VariableVersion version_counter_;
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uint32_t saved_version_ = 0;
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uint32_t output_nr_ = 0;
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bool was_default_constructed_ = true;
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bool requires_grad_ = false;
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bool has_grad_fn_ = false;
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bool is_inplace_view_ = false;
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};
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}} // namespace torch::autograd
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