mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
516f3198d6
See this doc: https://docs.google.com/document/d/1KiRdnoj6B4cI3yl017hTbCqcOGO1gWIpUf20sldipHM/edit# Two issues (1) regarding hooks in general and (2) regarding retains grad hooks are fixed, Python hooks, which rely on a different mechanism are not discussed here: - Hooks in cpp in general - (fixed) new hooks to registered to a newer version of the tensor no longer get applied to grad_fn associated with older version of the tensor when the first hook was ever registered - (unchanged) hooks registered to the older version of the tensor remain active on - Retains grad hooks - (fixed) now get moved to the latest grad_fn. NB: To the user, retains_grad is not considered hooks or expected to behave like hooks (which we consider properties of the grad_fn) vs retains_gradness which is a property of the tensor. - (not in this PR) Python hooks - (will fix) same issue as hooks in cpp where new hooks are being applied to grad_fn associated with the older version of the tensor Pull Request resolved: https://github.com/pytorch/pytorch/pull/79996 Approved by: https://github.com/albanD |
||
|---|---|---|
| .. | ||
| 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_baseline.h | ||
| init_baseline.py | ||
| init.cpp | ||
| 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_baseline.h | ||
| optim_baseline.py | ||
| optim.cpp | ||
| ordered_dict.cpp | ||
| parallel_benchmark.cpp | ||
| parallel.cpp | ||
| parameterdict.cpp | ||
| parameterlist.cpp | ||
| README.md | ||
| rnn.cpp | ||
| sequential.cpp | ||
| serialize.cpp | ||
| special.cpp | ||
| static.cpp | ||
| support.cpp | ||
| support.h | ||
| tensor_cuda.cpp | ||
| tensor_flatten.cpp | ||
| tensor_indexing.cpp | ||
| tensor_options_cuda.cpp | ||
| tensor_options.cpp | ||
| tensor.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.