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Summary: The premise of this approach is that a small subset of neural networks are well represented by a data flow graph. The README contains more information. The name is subject to change, but I thought it was a cute reference to fire. suo let me know if you'd prefer this in a different spot. Since it lowers a JIT'd module directly I assumed the JIT folder would be appropriate. There is no exposed Python interface yet (but is mocked up in `test_accelerant.py`) Pull Request resolved: https://github.com/pytorch/pytorch/pull/42753 Reviewed By: zou3519 Differential Revision: D23043771 Pulled By: bwasti fbshipit-source-id: 5353731e3aae31c08b5b49820815da98113eb551
29 lines
715 B
C++
29 lines
715 B
C++
#pragma once
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#include <ATen/core/interned_strings.h>
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#include <ATen/core/ivalue.h>
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#include <torch/csrc/jit/api/module.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/passes/constant_propagation.h>
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#include <torch/csrc/jit/passes/inliner.h>
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namespace torch {
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namespace jit {
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class TORCH_API StaticRuntime {
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public:
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StaticRuntime(std::shared_ptr<torch::jit::Graph> g) : graph_(std::move(g)) {}
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StaticRuntime(
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const torch::jit::Module& m,
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std::shared_ptr<torch::jit::Graph> g);
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std::vector<at::Tensor> run(const std::vector<at::Tensor>& inps) const;
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private:
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std::shared_ptr<torch::jit::Graph> graph_;
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torch::jit::Module module_;
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};
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} // namespace jit
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} // namespace torch
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