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History

cyy 1aad5c98b4 [structural binding][5/N] Replace std::tie with structural binding (#120142 ) This PR follows https://github.com/pytorch/pytorch/pull/119774, it is a continued work to clean up std::tie. Pull Request resolved: https://github.com/pytorch/pytorch/pull/120142 Approved by: https://github.com/albanD		2024-02-21 22:32:55 +00:00
..
any.cpp
autograd.cpp	[structural binding][5/N] Replace std::tie with structural binding (#120142 )	2024-02-21 22:32:55 +00:00
CMakeLists.txt	Rename singleton int to nested int (#119661 )	2024-02-16 19:21:17 +00:00
dataloader.cpp
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp
functional.cpp	Add torch check for dtype within bilinear (#118900 )	2024-02-03 00:02:00 +00:00
grad_mode.cpp
inference_mode.cpp
init_baseline.h
init_baseline.py
init.cpp
integration.cpp
jit.cpp
memory.cpp
meta_tensor.cpp
misc.cpp
module.cpp
moduledict.cpp
modulelist.cpp
modules.cpp	[CI] Update clang-format (#116002 )	2023-12-18 14:58:46 +00:00
namespace.cpp
nested_int.cpp	Rename singleton int to nested int (#119661 )	2024-02-16 19:21:17 +00:00
nested.cpp
nn_utils.cpp
operations.cpp
optim_baseline.h
optim_baseline.py
optim.cpp
ordered_dict.cpp
parallel_benchmark.cpp
parallel.cpp
parameterdict.cpp
parameterlist.cpp
README.md
rnn.cpp	Fixed crash when calling pad_packed_tensor when packed with cuda tensors and ensure_sorted=false due to indexing with tensors on different devices (#115028 )	2023-12-07 18:09:18 +00:00
sequential.cpp
serialize.cpp
special.cpp
static.cpp
support.cpp
support.h
tensor_cuda.cpp
tensor_flatten.cpp
tensor_indexing.cpp
tensor_options_cuda.cpp
tensor_options.cpp
tensor.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.