fef9a66d08
Summary:
This PR is the final step to making `torch::` the only namespace users of the C++ API ever see. Basically, I did:
``` cpp
namespace torch {
using namespace at;
}
```
And then changed `torch::` to `at::` almost everywhere. This worked surprisingly well out of the box. So users can now write `torch::relu` and `torch::log_softmax` and `torch::conv2d` instead of having to know when to use `at::` and when `torch::`. This is happy!
Another thing I did was to have `using Dtype = at::ScalarType`, which will be the eventual name anyway.
ebetica ezyang apaszke zdevito
Closes https://github.com/pytorch/pytorch/pull/8911
Reviewed By: ezyang
Differential Revision: D8668230
Pulled By: goldsborough
fbshipit-source-id: a72ccb70fca763c396c4b0997d3c4767c8cf4fd3
C++ API Tests
In this folder live the tests for PyTorch's C++ API (formerly known as autogradpp). They use the Catch2 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.
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.
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