mirror of
https://github.com/zebrajr/pytorch.git
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70bed6a55a
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52258 Removes deprecated preprocess method from the backend interface. Preprocessing logic should be now registered along with the backend interface (i.e. PyTorchBackendInterface) via the BackendPreprocessFunction. Also refactored internal dependencies. ghstack-source-id: 121704837 Test Plan: Validates all related tests pass: buck test mode/dev //caffe2/test/cpp/jit:jit -- --exact 'caffe2/test/cpp/jit:jit - BackendTest.ToBackend' python test/test_jit.py TestBackends ===== Glow buck test mode/dev //glow/fb/torch_glow/tests:TorchGlowBackendTests buck test mode/dev //glow/fb/torch_glow/tests:torch_glow_backend_tests Reviewed By: jackm321 Differential Revision: D26443479 fbshipit-source-id: afdc51ae619ced293d10c7a6a12f3530e4c4e53c
85 lines
2.6 KiB
C++
85 lines
2.6 KiB
C++
#include <torch/csrc/jit/backends/backend.h>
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namespace torch {
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namespace custom_backend {
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// This custom JIT backend is intended to do the minimal amount of work
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// necessary to test that the JIT backend registration endpoints and
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// code generation are working correctly. It is not intended to
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// produce numerically correct results.
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class CustomBackend : public torch::jit::PyTorchBackendInterface {
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public:
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// Constructor.
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explicit CustomBackend() {}
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virtual ~CustomBackend() = default;
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c10::impl::GenericDict compile(
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c10::IValue processed,
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c10::impl::GenericDict method_compile_spec) override {
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auto spec =
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c10::impl::toTypedDict<std::string, at::IValue>(method_compile_spec);
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// Return the same string as a value for every key in method_compile_spec.
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auto handles = c10::Dict<std::string, std::string>();
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for (auto it = spec.begin(), end = spec.end(); it != end; ++it) {
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handles.insert(it->key(), it->key());
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}
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return c10::impl::toGenericDict(handles);
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}
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c10::impl::GenericList execute(
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c10::IValue handle,
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c10::impl::GenericList inputs) override {
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TORCH_INTERNAL_ASSERT(handle.isString());
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TORCH_INTERNAL_ASSERT(inputs.size() > 0);
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c10::List<at::Tensor> output_list;
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// Implement simple accumulator and negative accumulator (?) ops. Return one
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// or both of them depending on the handle to make sure multiple outputs are
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// handled.
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c10::IValue value = inputs[0];
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at::Tensor accum = value.toTensor();
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accum = accum.clone();
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at::Tensor sub_accum = value.toTensor();
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sub_accum = sub_accum.clone();
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for (size_t i = 1, e = inputs.size(); i < e; ++i) {
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value = inputs[i];
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accum.add_(value.toTensor(), 1.0);
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sub_accum.sub_(value.toTensor(), 1.0);
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}
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if (handle.toStringRef() == "accum") {
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output_list.emplace_back(accum);
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} else if (handle.toStringRef() == "sub_accum") {
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output_list.emplace_back(sub_accum);
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} else if (handle.toStringRef() == "forward") {
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output_list.emplace_back(accum);
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output_list.emplace_back(sub_accum);
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}
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return c10::impl::toList(output_list);
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}
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};
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c10::IValue preprocess(
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const torch::jit::Module& mod,
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const c10::Dict<c10::IValue, c10::IValue>& method_compile_spec) {
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return mod._ivalue();
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}
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// clang-format off
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# if defined(_WIN32)
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# if defined(custom_ops_EXPORTS)
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# define CUSTOM_BACKEND_API __declspec(dllexport)
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# else
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# define CUSTOM_BACKEND_API __declspec(dllimport)
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# endif
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# else
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# define CUSTOM_BACKEND_API
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# endif
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// clang-format on
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CUSTOM_BACKEND_API std::string getBackendName();
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} // namespace custom_backend
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} // namespace torch
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