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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
46 lines
1.2 KiB
C++
46 lines
1.2 KiB
C++
#include <torch/csrc/jit/runtime/static/impl.h>
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namespace torch {
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namespace jit {
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StaticRuntime::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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: graph_(std::move(g)), module_(m.deepcopy()) {
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Inline(*graph_);
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ConstantPropagation(graph_);
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for (auto n : graph_->nodes()) {
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if (n->kind() == c10::Symbol::fromQualString("prim::GetAttr")) {
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throw std::runtime_error("Cannot accelerate unfrozen graphs");
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}
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}
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}
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std::vector<at::Tensor> StaticRuntime::run(
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const std::vector<at::Tensor>& inps) const {
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std::vector<torch::jit::IValue> stack;
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if (graph_->inputs().at(0)->type()->is_module()) {
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stack.emplace_back(module_._ivalue());
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}
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for (const auto& inp : inps) {
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stack.emplace_back(inp);
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}
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torch::jit::Code code(graph_, "");
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torch::jit::InterpreterState interp(code);
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interp.run(stack);
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std::vector<at::Tensor> out;
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for (const auto& v : stack) {
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if (v.isTuple()) {
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auto t = v.toTuple();
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for (const auto& el : t->elements()) {
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out.emplace_back(el.toTensor());
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}
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} else {
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out.emplace_back(v.toTensor());
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}
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}
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return out;
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}
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} // namespace jit
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} // namespace torch
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