pytorch/test/cpp/api
Mike Ruberry 013e6a3d9d Revert D24698027: Fix auto exponent issue for torch.pow
Test Plan: revert-hammer

Differential Revision:
D24698027 (8ef7ccd669)

Original commit changeset: f23fdb65c925

fbshipit-source-id: 9a67a2c6310c9e4fdefbb421a8cd4fa41595bc9a
2020-11-15 03:58:44 -08:00
..
any.cpp
autograd.cpp Revert D24698027: Fix auto exponent issue for torch.pow 2020-11-15 03:58:44 -08:00
CMakeLists.txt C++ API TransformerEncoderLayer (#42633) 2020-08-07 11:49:42 -07:00
dataloader.cpp
dispatch.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#39023) 2020-05-27 14:07:26 -07:00
enum.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
expanding-array.cpp
fft.cpp Add one dimensional FFTs to torch.fft namespace (#43011) 2020-09-19 23:32:22 -07:00
functional.cpp [c++] Distance-agnostic triplet margin loss (#45377) 2020-09-30 12:37:35 -07:00
init_baseline.h
init_baseline.py
init.cpp [Codemod][GleanFbcode] Remove dead includes in caffe2/test (#39023) 2020-05-27 14:07:26 -07:00
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 [pytorch] Route default warning sync to LOG(WARNING) - second try (#36984) 2020-04-23 01:08:00 -07:00
modulelist.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
modules.cpp Fix return-type-is-always-copy warning (#47279) 2020-11-03 08:53:24 -08:00
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_baseline.h Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
optim_baseline.py Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
optim.cpp [WIP] Fix cpp grad accessor API (#40887) 2020-07-16 09:11:12 -07:00
ordered_dict.cpp
parallel_benchmark.cpp [aten] Pass std::function<> to thread_pool by value, instead of const ref. (#37681) 2020-05-05 08:41:38 -07:00
parallel.cpp [PyTorch] Modify data_parallel to work with small tensors (#37704) 2020-05-04 11:06:42 -07:00
parameterdict.cpp Python/C++ API Parity: Add impl and tests for ParameterDict (#40654) 2020-06-29 08:50:44 -07:00
parameterlist.cpp Impl for ParameterList (#41259) 2020-07-12 20:50:31 -07:00
README.md
rnn.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
sequential.cpp [C++ API] RNN / GRU / LSTM layer refactoring (#34322) 2020-03-15 17:48:29 -07:00
serialize.cpp Add AdamW to C++ frontend (#40009) 2020-06-18 15:28:12 -07:00
static.cpp
support.cpp
support.h Changes warnings generated in cpp to show point of Python origination (#36052) 2020-04-25 21:18:58 -07:00
tensor_cuda.cpp
tensor_indexing.cpp [pytorch] Route default warning sync to LOG(WARNING) - second try (#36984) 2020-04-23 01:08:00 -07:00
tensor_options_cuda.cpp
tensor_options.cpp
tensor.cpp Change to.dtype_layout to c10-full (#41169) 2020-07-10 16:04:34 -07:00
torch_include.cpp
transformer.cpp C++ APIs Transformer NN Module Top Layer (#44333) 2020-09-11 08:25:27 -07:00

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.