mirror of
https://github.com/zebrajr/pytorch.git
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f235c65a2b
Summary: This is a re-land off https://github.com/pytorch/pytorch/pull/51797 with fix for spurious libcuda dependency Fix limits the scope of `no-as-needed` linker flag to just `jitbackend_test` Pull Request resolved: https://github.com/pytorch/pytorch/pull/52340 Reviewed By: agolynski, iseeyuan Differential Revision: D26476168 Pulled By: malfet fbshipit-source-id: f909428af82182b3bffd020ca18cca7a9b5846b6
82 lines
2.4 KiB
C++
82 lines
2.4 KiB
C++
#include <torch/csrc/jit/backends/backend.h>
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namespace torch {
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namespace jit {
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// This test 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 TestBackend : public PyTorchBackendInterface {
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public:
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// Constructor.
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explicit TestBackend() {}
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virtual ~TestBackend() = default;
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c10::IValue preprocess(
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c10::IValue mod,
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c10::impl::GenericDict method_compile_spec) override {
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return mod;
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}
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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 (const auto& it : spec) {
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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 Module& mod,
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const c10::Dict<IValue, IValue>& method_compile_spec) {
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return mod._ivalue();
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}
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namespace {
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static auto cls = torch::jit::backend<TestBackend>("test_backend", preprocess);
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}
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} // namespace jit
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} // namespace torch
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