mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
8756ec989e
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24342 Right now the two APIs that provided in autograd package only have python bindings and we could not call them either in C++ API or in TorchScript. This PR make these two APIs available purely in C++ (with preserving semantics) and can be used in C++ API and TorchScript Differential Revision: D16923271 fbshipit-source-id: 049d6fbd94cd71ecc08b2716f74d52ac061f861e |
||
|---|---|---|
| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| expanding-array.cpp | ||
| init.cpp | ||
| init_baseline.h | ||
| init_baseline.py | ||
| integration.cpp | ||
| jit.cpp | ||
| memory.cpp | ||
| misc.cpp | ||
| module.cpp | ||
| modulelist.cpp | ||
| modules.cpp | ||
| optim.cpp | ||
| optim_baseline.h | ||
| optim_baseline.py | ||
| ordered_dict.cpp | ||
| parallel.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| static.cpp | ||
| support.h | ||
| tensor.cpp | ||
| tensor_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.cpp | ||
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.