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3deb4791c3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/12034 We need ATen and Caffe2 to line up, and the rule is that if you have any private/protected members, you should declare it as a class. Class we go. (There are some other obvious candidates for this treatment, but I've kept this patch just to Tensor) Reviewed By: gchanan, mingzhe09088 Differential Revision: D10024467 fbshipit-source-id: 17cfe2741ba9c3f56cb87d6f5d1afd3c61a8e4fe
60 lines
1.5 KiB
C++
60 lines
1.5 KiB
C++
#pragma once
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#include <memory>
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#include <vector>
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#include "ATen/core/optional.h"
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#include "torch/csrc/WindowsTorchApiMacro.h"
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namespace at {
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class Tensor;
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}
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namespace torch { namespace jit {
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// The interpreter run Graphs with Tensor inputs and Tensor outputs
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// a separate component in the autograd handles unwrapping and wrapping
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// variable objects for use in the interpreter.
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struct Node;
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struct GraphExecutor;
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struct CodeImpl;
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struct InterpreterStateImpl;
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struct Graph;
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struct Node;
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struct IValue;
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using Stack = std::vector<IValue>;
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struct TORCH_API Code {
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Code()
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: pImpl(nullptr) {}
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Code(std::shared_ptr<Graph>& graph);
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~Code();
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const std::vector<GraphExecutor*>& grad_executors();
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explicit operator bool() const {
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return pImpl != nullptr;
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}
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private:
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std::shared_ptr<CodeImpl> pImpl;
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friend struct InterpreterStateImpl;
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friend std::ostream & operator<<(std::ostream & out, const Code & code);
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};
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struct InterpreterState {
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InterpreterState(const Code & code);
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// advance the interpreter state by running one stage. Returning the
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// outputs for that stage, suspending the computation.
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// Call this function again continues computation where it left off.
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void runOneStage(Stack & stack);
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~InterpreterState();
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// create a copy of InterpreterState with its current state
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// used when retain_graph=True so that stages can be re-run
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InterpreterState clone() const;
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private:
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InterpreterState(InterpreterStateImpl * pImpl);
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std::shared_ptr<InterpreterStateImpl> pImpl;
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};
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}}
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