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mikey dagitses 322e4b4c8a set -Wsuggest-override for builds (#89852 ) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/pytorch/pytorch/pull/89852). * __->__ #89852 * #89851 set -Wsuggest-override for builds Summary: This was flagged by a Meta internal build. Test Plan: Rely on CI. Pull Request resolved: https://github.com/pytorch/pytorch/pull/89852 Approved by: https://github.com/malfet		2022-12-19 22:08:47 +00:00
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any.cpp
autograd.cpp
CMakeLists.txt
dataloader.cpp	set -Wsuggest-override for builds (#89852 )	2022-12-19 22:08:47 +00:00
dispatch.cpp
enum.cpp
expanding-array.cpp
fft.cpp
functional.cpp
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
namespace.cpp
nested.cpp
nn_utils.cpp	Changing the use from ASSERT_EQ to ASSERT_FLOAT_EQ on nn_utils test. (#83693 )	2022-11-15 04:10:52 +00:00
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
sequential.cpp
serialize.cpp	Error on ZeroTensor serialization (#88803 )	2022-11-11 08:51:29 +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.