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https://github.com/zebrajr/pytorch.git
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d8dab6ffa8
Summary: ezyang on the template hack smessmer on SFINAE of the `TensorOptions(Device)` goldsborough on the C++ API test changes zdevito on the `jit` codegen changes Pull Request resolved: https://github.com/pytorch/pytorch/pull/13146 Reviewed By: ezyang Differential Revision: D12823809 Pulled By: SsnL fbshipit-source-id: 98d65c401c98fda1c6fa358e4538f86c6495abdc |
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|---|---|---|
| .. | ||
| any.cpp | ||
| CMakeLists.txt | ||
| cursor.cpp | ||
| dataloader.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.