#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 <memory>
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#include <string>
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#include <vector>
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namespace torch { namespace autograd {
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struct TORCH_API Error : public Node {
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Error(std::string msg, edge_list&& next_edges)
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: Node(std::move(next_edges))
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, msg(std::move(msg)) {}
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Error(std::string msg)
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: msg(std::move(msg)) {}
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variable_list apply(variable_list&& inputs) override;
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std::string msg;
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};
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// We print grad_fn names in tensor printing. For functions with backward
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// NYI, grad_fn=<Error> will be printed if we use Error, which is confusing. So
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// special case with a new NotImplemented function here.
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struct TORCH_API NotImplemented : public Error {
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NotImplemented(const std::string& forward_fn, edge_list&& next_edges)
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: Error("derivative for " + forward_fn + " is not implemented",
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std::move(next_edges)) {}
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NotImplemented(const std::string& forward_fn)
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: Error("derivative for " + forward_fn + " is not implemented") {}
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};
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// Identity in forward, Error in backward. Used to implement @once_differentiable
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struct TORCH_API DelayedError : public Node {
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DelayedError(std::string msg, int num_inputs)
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: msg(std::move(msg)) {
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for (int i = 0; i < num_inputs; i++)
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add_input_metadata(Node::undefined_input());
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}
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variable_list apply(variable_list&& inputs) override;
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std::string msg;
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};
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struct TORCH_API GraphRoot : public Node {
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GraphRoot(edge_list functions, variable_list inputs)
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: Node(std::move(functions)),
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outputs(std::move(inputs)) {}
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variable_list apply(variable_list&& inputs) override {
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return outputs;
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}
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variable_list outputs;
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};
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}}
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