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ea12fc8a9f
Summary: This reverts #147804 due to internal revert. --- This diff reverts D70262395 Reviewed By: RossMcKenzie Differential Revision: D70318024 @diff-train-skip-merge Pull Request resolved: https://github.com/pytorch/pytorch/pull/148164 Approved by: https://github.com/xmfan
109 lines
3.1 KiB
C++
109 lines
3.1 KiB
C++
#pragma once
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#include <c10/util/irange.h>
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#include <torch/csrc/Export.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::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)), msg(std::move(msg)) {}
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Error(std::string msg) : msg(std::move(msg)) {}
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variable_list apply(variable_list&& inputs) override;
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void compiled_args(CompiledNodeArgs& args) override;
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variable_list apply_with_saved(
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const variable_list& inputs,
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SwapSavedVariables& saved) 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(
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"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
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// @once_differentiable
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struct TORCH_API DelayedError : public Node {
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DelayedError(std::string msg, int64_t num_inputs) : msg(std::move(msg)) {
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for ([[maybe_unused]] const auto _ [[maybe_unused]] :
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c10::irange(num_inputs)) {
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add_input_metadata(Node::undefined_input());
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}
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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 UndefinedGrad : public Node {
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UndefinedGrad() {
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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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};
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struct TORCH_API UndefinedGradBackward : public Node {
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UndefinedGradBackward(edge_list&& next_edges) : Node(std::move(next_edges)) {}
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UndefinedGradBackward() = default;
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variable_list apply(variable_list&& inputs) override;
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void compiled_args(CompiledNodeArgs& args) override {}
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variable_list apply_with_saved(
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const variable_list& inputs,
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SwapSavedVariables& saved) override {
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return apply(variable_list(inputs));
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}
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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)), outputs(std::move(inputs)) {
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// Ensures calls to stream() on a GraphRoot instance reflect current
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// stream(s) on devices of root grad tensors at the time the instance is
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// constructed.
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for (const auto& t : outputs) {
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add_input_metadata(t);
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}
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}
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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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void compiled_args(CompiledNodeArgs& args) override;
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variable_list apply_with_saved(
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const variable_list& inputs,
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SwapSavedVariables& saved) override;
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variable_list outputs;
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};
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struct TORCH_API Identity : public Node {
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variable_list apply(variable_list&& inputs) override;
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};
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} // namespace torch::autograd
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