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468a9d448e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/37681 By passing by value, we can std::move, and avoid unnecessarily copying args that are part of any std::function/lambda state (e.g. in the jit interpreter, there is a std::vector<> stack passed in the InterpreterContinuation) This makes the api also consistent with e.g. folly and best practices. Added a minor at::launch() benchmark to test/cpp/, the difference is mostly noticeable when copying the std::function<> internal args is non-trivial. Benchmarks pre/post (min over ~5 runs) NoData: 5.81 us -> 5.63 us (-3.2%) WithData(0): 6.67 us -> 5.88 us (-11.8%) WithData(4): 6.98 us -> 6.51 us (-6.7%) WithData(256): 9.44 us -> 7.89 (-16.5%) ghstack-source-id: 103322321 Test Plan: - perf: buck run mode/opt caffe2/test/cpp/api:parallel_benchmark pre/post - correctness buck test mode/dev-nosan caffe2/test/... Reviewed By: dzhulgakov Differential Revision: D21355148 fbshipit-source-id: 3567e730845106f1991091e4a892d093e00571c3 |
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| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| functional.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| parallel_benchmark.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.cpp | ||
| support.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.cpp | ||
C++ Frontend Tests
In this folder live the tests for PyTorch's C++ Frontend. They use the GoogleTest test framework.
CUDA Tests
To make a test runnable only on platforms with CUDA, you should suffix your
test with _CUDA, e.g.
TEST(MyTestSuite, MyTestCase_CUDA) { }
To make it runnable only on platforms with at least two CUDA machines, suffix
it with _MultiCUDA instead of _CUDA, e.g.
TEST(MyTestSuite, MyTestCase_MultiCUDA) { }
There is logic in main.cpp that detects the availability and number of CUDA
devices and supplies the appropriate negative filters to GoogleTest.
Integration Tests
Integration tests use the MNIST dataset. You must download it by running the following command from the PyTorch root folder:
$ python tools/download_mnist.py -d test/cpp/api/mnist
The required paths will be referenced as test/cpp/api/mnist/... in the test
code, so you must run the integration tests from the PyTorch root folder.