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Rewrite C++ API tests in gtest (#11953)

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Summary:
This PR is a large codemod to rewrite all C++ API tests with GoogleTest (gtest) instead of Catch.

You can largely trust me to have correctly code-modded the tests, so it's not required to review every of the 2000+ changed lines. However, additional things I changed were:

1. Moved the cmake parts for these tests into their own `CMakeLists.txt` under `test/cpp/api` and calling `add_subdirectory` from `torch/CMakeLists.txt`
2. Fixing DataParallel tests which weren't being compiled because `USE_CUDA` wasn't correctly being set at all.
3. Updated README

ezyang ebetica
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11953

Differential Revision: D9998883

Pulled By: goldsborough

fbshipit-source-id: affe3f320b0ca63e7e0019926a59076bb943db80
This commit is contained in:
Peter Goldsborough
2018-09-21 21:12:37 -07:00
committed by Facebook Github Bot
parent d0db23e95a
commit 825181ea9d
27 changed files with 2154 additions and 2182 deletions
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37
test/cpp/api/README.md
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# C++ API Tests
# C++ Frontend Tests
In this folder live the tests for PyTorch's C++ API (formerly known as
autogradpp). They use the [Catch2](https://github.com/catchorg/Catch2) test
framework.
In this folder live the tests for PyTorch's C++ Frontend. They use the
[GoogleTest](https://github.com/google/googletest) test framework.
## CUDA Tests
The way we handle CUDA tests is by separating them into a separate `TEST_CASE`
(e.g. we have `optim` and `optim_cuda` test cases in `optim.cpp`), and giving
them the `[cuda]` tag. Then, inside `main.cpp` we detect at runtime whether
CUDA is available. If not, we disable these CUDA tests by appending `~[cuda]`
to the test specifications. The `~` disables the tag.
To make a test runnable only on platforms with CUDA, you should suffix your
test with `_CUDA`, e.g.
One annoying aspect is that Catch only allows filtering on test cases and not
sections. Ideally, one could have a section like `LSTM` inside the `RNN` test
case, and give this section a `[cuda]` tag to only run it when CUDA is
available. Instead, we have to create a whole separate `RNN_cuda` test case and
put all these CUDA sections in there.
```cpp
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.
```cpp
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:
```shell
```sh
$ 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.