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History

Peter Bell da7863f46b Add one dimensional FFTs to torch.fft namespace (#43011 ) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43011 Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D23751850 Pulled By: mruberry fbshipit-source-id: 8dc5fec75102d8809eeb85a3d347ba1b5de45b33		2020-09-19 23:32:22 -07:00
..
any.cpp
autograd.cpp	Don't materialize output grads (#41821 )	2020-08-11 04:27:07 -07:00
CMakeLists.txt	C++ API TransformerEncoderLayer (#42633 )	2020-08-07 11:49:42 -07:00
dataloader.cpp
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp	Add one dimensional FFTs to torch.fft namespace (#43011 )	2020-09-19 23:32:22 -07:00
functional.cpp
init.cpp
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp
memory.cpp
misc.cpp	Throw error if `torch.set_deterministic(True)` is called with nondeterministic CuBLAS config (#41377 )	2020-08-05 12:42:24 -07:00
module.cpp
modulelist.cpp
modules.cpp	Implemented torch::nn::Unflatten in libtorch (#42613 )	2020-08-14 15:32:13 -07:00
namespace.cpp
nn_utils.cpp	[WIP] Fix cpp grad accessor API (#40887 )	2020-07-16 09:11:12 -07:00
operations.cpp	[Codemod][GleanFbcode] Remove dead includes in caffe2/test (#43953 )	2020-09-01 21:48:28 -07:00
optim.cpp	[WIP] Fix cpp grad accessor API (#40887 )	2020-07-16 09:11:12 -07:00
optim_baseline.h
optim_baseline.py
ordered_dict.cpp
parallel.cpp
parallel_benchmark.cpp
parameterdict.cpp
parameterlist.cpp	Impl for ParameterList (#41259 )	2020-07-12 20:50:31 -07:00
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
transformer.cpp	C++ APIs Transformer NN Module Top Layer (#44333 )	2020-09-11 08:25:27 -07:00

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.