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Nikita Shulga 74c44ba9d6 Revert D33850228: [pytorch][PR] Implement Tanh Gelu Approximation Test Plan: revert-hammer Differential Revision: D33850228 (`23d03025dc`) Original commit changeset: 3cc33fb298e4 Original Phabricator Diff: D33850228 (`23d03025dc`) fbshipit-source-id: 9436e7df73c2b2e2011f321674f24973316d3692 (cherry picked from commit c9efb582233fec9abfe86bb85d2b496047bf62a7)		2022-01-31 17:44:19 +00:00
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any.cpp
autograd.cpp
CMakeLists.txt
dataloader.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
dispatch.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
enum.cpp
expanding-array.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
fft.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
functional.cpp	Revert D33850228: [pytorch][PR] Implement Tanh Gelu Approximation	2022-01-31 17:44:19 +00:00
grad_mode.cpp
imethod.cpp
inference_mode.cpp
init.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
init_baseline.h
init_baseline.py
integration.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
jit.cpp
memory.cpp
meta_tensor.cpp
misc.cpp
module.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
moduledict.cpp
modulelist.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
modules.cpp	Revert D33850228: [pytorch][PR] Implement Tanh Gelu Approximation	2022-01-31 17:44:19 +00:00
namespace.cpp
nn_utils.cpp	use irange for loops 10 (#69394 )	2021-12-09 09:49:34 -08:00
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

README.md

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.