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9ce9803abe
Summary: RFC: https://github.com/pytorch/rfcs/pull/40 This PR (re)introduces python codegen for unboxing wrappers. Given an entry of `native_functions.yaml` the codegen should be able to generate the corresponding C++ code to convert ivalues from the stack to their proper types. To trigger the codegen, run ``` tools/jit/gen_unboxing.py -d cg/torch/share/ATen ``` Merged changes on CI test. In https://github.com/pytorch/pytorch/issues/71782 I added an e2e test for static dispatch + codegen unboxing. The test exports a mobile model of mobilenetv2, load and run it on a new binary for lite interpreter: `test/mobile/custom_build/lite_predictor.cpp`. ## Lite predictor build specifics 1. Codegen: `gen.py` generates `RegisterCPU.cpp` and `RegisterSchema.cpp`. Now with this PR, once `static_dispatch` mode is enabled, `gen.py` will not generate `TORCH_LIBRARY` API calls in those cpp files, hence avoids interaction with the dispatcher. Once `USE_LIGHTWEIGHT_DISPATCH` is turned on, `cmake/Codegen.cmake` calls `gen_unboxing.py` which generates `UnboxingFunctions.h`, `UnboxingFunctions_[0-4].cpp` and `RegisterCodegenUnboxedKernels_[0-4].cpp`. 2. Build: `USE_LIGHTWEIGHT_DISPATCH` adds generated sources into `all_cpu_cpp` in `aten/src/ATen/CMakeLists.txt`. All other files remain unchanged. In reality all the `Operators_[0-4].cpp` are not necessary but we can rely on linker to strip them off. ## Current CI job test coverage update Created a new CI job `linux-xenial-py3-clang5-mobile-lightweight-dispatch-build` that enables the following build options: * `USE_LIGHTWEIGHT_DISPATCH=1` * `BUILD_LITE_INTERPRETER=1` * `STATIC_DISPATCH_BACKEND=CPU` This job triggers `test/mobile/lightweight_dispatch/build.sh` and builds `libtorch`. Then the script runs C++ tests written in `test_lightweight_dispatch.cpp` and `test_codegen_unboxing.cpp`. Recent commits added tests to cover as many C++ argument type as possible: in `build.sh` we installed PyTorch Python API so that we can export test models in `tests_setup.py`. Then we run C++ test binary to run these models on lightweight dispatch enabled runtime. Pull Request resolved: https://github.com/pytorch/pytorch/pull/69881 Reviewed By: iseeyuan Differential Revision: D33692299 Pulled By: larryliu0820 fbshipit-source-id: 211e59f2364100703359b4a3d2ab48ca5155a023 (cherry picked from commit 58e1c9a25e3d1b5b656282cf3ac2f548d98d530b)
19 lines
477 B
C++
19 lines
477 B
C++
#include <gtest/gtest.h>
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std::string add_negative_flag(const std::string& flag) {
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std::string filter = ::testing::GTEST_FLAG(filter);
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if (filter.find('-') == std::string::npos) {
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filter.push_back('-');
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} else {
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filter.push_back(':');
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}
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filter += flag;
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return filter;
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}
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int main(int argc, char* argv[]) {
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::testing::InitGoogleTest(&argc, argv);
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::testing::GTEST_FLAG(filter) = add_negative_flag("*_CUDA:*_MultiCUDA");
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return RUN_ALL_TESTS();
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}
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