pytorch/test/cpp/api
Edward Yang a5bcde97af Revert D17427577: C++ API parity: at::Tensor::version
Test Plan: revert-hammer

Differential Revision:
D17427577

Original commit changeset: e9b3e76ca44d

fbshipit-source-id: a5bbae208ba33a31f90ab5c9b199f232de0c6d1b
2019-09-20 11:19:43 -07:00
..
any.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
autograd.cpp Improve handling of mixed-type tensor operations (#22273) 2019-09-05 18:26:09 -07:00
CMakeLists.txt Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
dataloader.cpp add sorting policy to ChunkDataset (#23053) 2019-07-29 12:34:02 -07:00
expanding-array.cpp Rewrite C++ API tests in gtest (#11953) 2018-09-21 21:28:16 -07:00
functional.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
init_baseline.h Kaiming Initialization (#14718) 2019-02-15 14:58:22 -08:00
init_baseline.py Kaiming Initialization (#14718) 2019-02-15 14:58:22 -08:00
init.cpp Fix torch::nn::init::orthogonal_ with CNNs (#18915) 2019-04-09 10:39:15 -07:00
integration.cpp Move isnan to C++ (#15722) 2019-01-08 10:42:33 -08:00
jit.cpp Add Pickler C++ API (#23241) 2019-08-12 14:43:31 -07:00
memory.cpp Hide c10::optional and nullopt in torch namespace (#12927) 2018-10-26 00:08:04 -07:00
misc.cpp C++ API parity: at::Tensor::grad 2019-09-18 09:20:38 -07:00
module.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
modulelist.cpp Adding ModuleList to modules.h (#25346) 2019-08-29 10:49:22 -07:00
modules.cpp Distance module (#26424) 2019-09-20 07:28:49 -07:00
optim_baseline.h Use torch:: instead of at:: in all C++ APIs (#13523) 2018-11-06 14:32:25 -08:00
optim_baseline.py Use torch:: instead of at:: in all C++ APIs (#13523) 2018-11-06 14:32:25 -08:00
optim.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
ordered_dict.cpp C++ unregister_module function for Module (#26088) 2019-09-12 18:38:57 -07:00
parallel.cpp C++ API parity: at::Tensor::grad 2019-09-18 09:20:38 -07:00
README.md Rewrite C++ API tests in gtest (#11953) 2018-09-21 21:28:16 -07:00
rnn.cpp Make options.name_ private, and change all callsites to use options.name() (#26419) 2019-09-19 14:48:22 -07:00
sequential.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
serialize.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
static.cpp Re-organize C++ API torch::nn folder structure (#26262) 2019-09-17 10:07:29 -07:00
support.h Add TORCH_WARN_ONCE, and use it in Tensor.data<T>() (#25207) 2019-08-27 21:42:44 -07:00
tensor_cuda.cpp Deprecate tensor.data<T>(), and codemod tensor.data<T>() to tensor.data_ptr<T>() (#24886) 2019-08-21 20:11:24 -07:00
tensor_options_cuda.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
tensor_options.cpp Revert D15920763: Move TensorOptions to ATen/core 2019-08-13 12:07:18 -07:00
tensor.cpp Revert D17427577: C++ API parity: at::Tensor::version 2019-09-20 11:19:43 -07:00
torch_include.cpp Add get/set_num_interop_threads into torch.h include (#20659) 2019-05-20 00:34:59 -07:00

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.