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

History

Paweł Piskorski 2d9b2bcfba Extend TensorImpl with BackendMeta (#97429 ) BackendMeta offers a binary interface for the backend to attach arbitrary data to TensorImpl. TensorImpl has exactly one "slot" for backend metadata, however backend is free to compose any structure that is opaque to the framework beyond iheriting standard BackendMeta base. Change-Id: I670fcdd16dd1c2b00f7eaa1cbc5b5dfea59a6221 Fixes #ISSUE_NUMBER Pull Request resolved: https://github.com/pytorch/pytorch/pull/97429 Approved by: https://github.com/ezyang		2023-04-04 23:47:03 +00:00
..
any.cpp
autograd.cpp	Revert "[BE][autograd Function] Raise an error if input is returned as-is and saved for forward or backward in setup_context (#97212 )"	2023-03-30 22:03:07 +00:00
CMakeLists.txt
dataloader.cpp
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
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
special.cpp
static.cpp
support.cpp
support.h
tensor.cpp	Extend TensorImpl with BackendMeta (#97429 )	2023-04-04 23:47:03 +00:00
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.