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b2520ab3dc
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52603 This PR introduced a backend with minimum compilation capability to the to_<backend> flow. The targets are: - Demonstrate the end-to-end flow with adding a backend -> compilation -> runtime - How the backend compilation errors be surfaced to the user, with the original model's source code information. (C++ only in this PR. Python APIs will be demonstrated in a following PR.) Changes: - Compilation 1. A backend with minimum compilation features, "backend_with_compiler_demo" is added. 2. The compilation happens AOT in the ```pre_process``` function registered to this backend. 3. Compiled results are stored in a string blob for each method. They are serialized to the lowered module with ```__get_state__``` function. 4. Error message with model source code is thrown, for features not handled by the backend compiler. - Runtime 1. The compiled blob is loaded in ```__set_state__``` method. 2. The ```compile``` function of the backend pass through the AOT compiled blob. (TODO: parsing the blob to the format that the backend can understand can happen here.) 3. The ```execute``` function of the backend executes the specified method (handle). Test Plan: - ```BackendTest.TestCompiler```: the C++ end-to-end demonstration on a supported model. After compilation and running, the lowered model produces the same result as the original torchscript model. - ```BackendTest.TestCompilerNotSupport```: Demonstrate the error message from the AOT compilation for a feature not supported from the input module. The error message looks like: ``` "The node of aten::mul is not supported in this compiler. Source code: File "<string>", line 3 def forward(self, x, h): return x * h ~~~~~ <--- HERE ``` Reviewed By: raziel Differential Revision: D26593968 Pulled By: iseeyuan fbshipit-source-id: 8f264f60a0470e9f07e36fdeccbf17da6c1d7cd7
76 lines
2.3 KiB
C++
76 lines
2.3 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::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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namespace {
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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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static auto cls = torch::jit::backend<TestBackend>("test_backend", preprocess);
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} // namespace
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} // namespace jit
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} // namespace torch
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