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9601deb1b3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/65147 I think they trigger an MSVC bug per https://github.com/pytorch/pytorch/issues/48763 ghstack-source-id: 138247203 Test Plan: breakpointed https://www.internalfb.com/intern/sandcastle/job/9007199738584981/ and sush'ed into the host and ran `buck build arvr/mode/win/opt //xplat/caffe2:autograd_libtorch_test_ovrsource` in `/cygdrive/d/ovrsource-null-hg` Reviewed By: soulitzer Differential Revision: D30992685 fbshipit-source-id: 06c6fb2c18d55490f89fc91ee5b7a4c5a7faf1c6 |
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|---|---|---|
| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| fft.cpp | ||
| functional.cpp | ||
| grad_mode.cpp | ||
| imethod.cpp | ||
| inference_mode.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| meta_tensor.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| moduledict.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| namespace.cpp | ||
| nn_utils.cpp | ||
| operations.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| parallel_benchmark.cpp | ||
| parameterdict.cpp | ||
| parameterlist.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| special.cpp | ||
| static.cpp | ||
| support.cpp | ||
| support.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_flatten.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.cpp | ||
| transformer.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.