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https://github.com/zebrajr/pytorch.git
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713e706618
Summary: There are still a few work to be done: - Move logging and unify AT_WARN with LOG(ERROR). - A few header files are still being plumbed through, need cleaning. - caffe2::EnforceNotMet aliasing is not done yet. - need to unify the macros. See c10/util/Exception.h This is mainly a codemod and not causing functional changes. If you find your job failing and trace back to this diff, usually it can be fixed by the following approaches: (1) add //caffe2/c10:c10 to your dependency (or transitive dependency). (2) change objects such as at::Error, at::Optional to the c10 namespace. (3) change functions to the c10 namespace. Especially, caffe2::MakeString is not overridden by the unified c10::str function. Nothing else changes. Please kindly consider not reverting this diff - it involves multiple rounds of rebasing and the fix is usually simple. Contact jiayq@ or AI Platform Dev for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/12354 Reviewed By: orionr Differential Revision: D10238910 Pulled By: Yangqing fbshipit-source-id: 7794d5bf2797ab0ca6ebaccaa2f7ebbd50ff8f32 |
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| .. | ||
| any.cpp | ||
| CMakeLists.txt | ||
| cursor.cpp | ||
| expanding-array.cpp | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modules.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| optim.cpp | ||
| ordered-dict.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.h | ||
| tensor_cuda.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.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.