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

History

Scott Wolchok 4c9eb57914 [PyTorch] Narrow Device to 2 bytes by narrowing DeviceType and DeviceIndex (#47023 ) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/47023 DeviceType pretty clearly only needs 1 byte. DeviceIndex only needs 1 byte given that machines don't have anywhere near 255 GPUs in them as far as I know. ghstack-source-id: 116901430 Test Plan: Existing tests, added assertion to catch if my assumption about DeviceIndex is incorrect Reviewed By: dzhulgakov Differential Revision: D24605460 fbshipit-source-id: 7c9a89027fcf8eebd623b7cdbf6302162c981cd2		2020-11-18 19:39:40 -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
dataloader.cpp
dispatch.cpp
enum.cpp
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.cpp
init_baseline.h
init_baseline.py
integration.cpp
jit.cpp
memory.cpp
misc.cpp
module.cpp
modulelist.cpp
modules.cpp	Fix return-type-is-always-copy warning (#47279 )	2020-11-03 08:53:24 -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
sequential.cpp
serialize.cpp
static.cpp
support.cpp
support.h
tensor.cpp
tensor_cuda.cpp
tensor_indexing.cpp
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++ APIs Transformer NN Module Top Layer (#44333 )	2020-09-11 08:25:27 -07:00

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.