pytorch/test/cpp/api
Edward Yang 8eee8460f8 codegen: Resolve overload ambiguities created by defaulted arguments (#49348)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49348

This is a redux of #45666 post refactor, based off of
d534f7d4c5
Credit goes to peterbell10 for the implementation.

Fixes #43945.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Test Plan: Imported from OSS

Reviewed By: smessmer

Differential Revision: D25594004

Pulled By: ezyang

fbshipit-source-id: c8eb876bb3348308d6dc8ba7bf091a2a3389450f
2021-01-04 11:59:16 -08:00
..
any.cpp
autograd.cpp Fix auto exponent issue for torch.pow (#49809) 2020-12-29 17:02:56 -08:00
CMakeLists.txt Implement C++ ModuleDict (#47707) 2020-11-19 08:07:51 -08:00
dataloader.cpp
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp Remove deprecated spectral ops from torch namespace (#48594) 2020-12-05 04:12:32 -08:00
functional.cpp Add PixelUnshuffle (#49334) 2020-12-22 20:14:55 -08:00
init.cpp
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp
memory.cpp
misc.cpp codegen: Resolve overload ambiguities created by defaulted arguments (#49348) 2021-01-04 11:59:16 -08:00
module.cpp
moduledict.cpp Implement C++ ModuleDict (#47707) 2020-11-19 08:07:51 -08:00
modulelist.cpp
modules.cpp Add PixelUnshuffle (#49334) 2020-12-22 20:14:55 -08:00
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 Adding support for CuDNN-based LSTM with projections (#47725) 2020-12-16 11:27:02 -08:00
sequential.cpp
serialize.cpp
static.cpp
support.cpp
support.h
tensor.cpp
tensor_cuda.cpp
tensor_indexing.cpp Making ops c10-full: list of optional tensors (#49138) 2021-01-04 05:04:02 -08:00
tensor_options.cpp [PyTorch] Narrow Device to 2 bytes by narrowing DeviceType and DeviceIndex (#47023) 2020-11-18 19:39:40 -08:00
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.