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3fe4718d16
Summary: This PR adds a `padding_idx` parameter to `nn.EmbeddingBag` and `nn.functional.embedding_bag`. As with `nn.Embedding`'s `padding_idx` argument, if an embedding's index is equal to `padding_idx` it is ignored, so it is not included in the reduction. This PR does not add support for `padding_idx` for quantized or ONNX `EmbeddingBag` for opset10/11 (opset9 is supported). In these cases, an error is thrown if `padding_idx` is provided. Fixes https://github.com/pytorch/pytorch/issues/3194 Pull Request resolved: https://github.com/pytorch/pytorch/pull/49237 Reviewed By: walterddr, VitalyFedyunin Differential Revision: D26948258 Pulled By: jbschlosser fbshipit-source-id: 3ca672f7e768941f3261ab405fc7597c97ce3dfc |
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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 | ||
| 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.