mirror of https://github.com/saymrwulf/pytorch.git synced 2026-05-15 21:00:47 +00:00

History

shibo19 a5e1d38025 add check for torch_arg (#108397 ) Fixes https://github.com/pytorch/pytorch/issues/108219 add check for torch_arg marco, as for inchannel/outchannel/groups, it should be greater than 0. Pull Request resolved: https://github.com/pytorch/pytorch/pull/108397 Approved by: https://github.com/mikaylagawarecki		2023-09-08 23:18:27 +00:00
..
any.cpp
autograd.cpp
CMakeLists.txt
dataloader.cpp
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp
functional.cpp	add input check at the beginning for C++ API `interpolate` (#108506 )	2023-09-05 17:56:17 +00:00
grad_mode.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	add check for torch_arg (#108397 )	2023-09-08 23:18:27 +00:00
namespace.cpp
nested.cpp
nn_utils.cpp
operations.cpp
optim.cpp	[Reland] Elimates c10::guts::to_string (#108748 )	2023-09-07 13:35:17 +00:00
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	[Reland] Elimates c10::guts::to_string (#108748 )	2023-09-07 13:35:17 +00:00
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.