mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
c7a7c2b62f
Summary: Enable Gelu bf16/fp32 in CPU path using Mkldnn implementation. User doesn't need to_mkldnn() explicitly. New Gelu fp32 performs better than original one. Add Gelu backward for https://github.com/pytorch/pytorch/pull/53615. Pull Request resolved: https://github.com/pytorch/pytorch/pull/58525 Reviewed By: ejguan Differential Revision: D29940369 Pulled By: ezyang fbshipit-source-id: df9598262ec50e5d7f6e96490562aa1b116948bf |
||
|---|---|---|
| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| fft.cpp | ||
| functional.cpp | ||
| grad_mode.cpp | ||
| imethod.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 | ||
| 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 | ||
| tensor_cuda.cpp | ||
| tensor_flatten.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options.cpp | ||
| tensor_options_cuda.cpp | ||
| torch_include.cpp | ||
| transformer.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.