mirror of
https://github.com/saymrwulf/pytorch.git
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f3aca45a16
Fixes https://github.com/pytorch/pytorch/issues/96887 We error out in BOTH the case when graph is created and when it is not created. Still bc-breaking, but not as severe because we are limiting to the case where someone uses setup_context. This makes setup_context and non-setup_context versions diverge in their behavior - With the non-setup_context version, saved variables are assumed to have the grad_fn of the inputs. - But now with the setup_context version, we produce an error for this case. Pull Request resolved: https://github.com/pytorch/pytorch/pull/97212 Approved by: https://github.com/zou3519 |
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| .. | ||
| any.cpp | ||
| autograd.cpp | ||
| CMakeLists.txt | ||
| dataloader.cpp | ||
| dispatch.cpp | ||
| enum.cpp | ||
| expanding-array.cpp | ||
| fft.cpp | ||
| functional.cpp | ||
| grad_mode.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 | ||
| nested.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.