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c2f8f5076c
Summary: Couple questions: 1) I used the log1p implementation in #8969 as a guide especially for testing. I'm not sure what the ```skipIfROCM``` annotation is for, so unsure if i need it for my test. 2) I implemented the branching logic in the narrow function itself; is this the right place to do so? I noticed that there a number of places where sparse-specific logic is handled with just an if statement in this file. Or should I implement a separate dispatch in native_functions.yml as in the log1p? And of course, happy to make any any other updates/changes that I may have missed as well. This is my first PR to the project. Pull Request resolved: https://github.com/pytorch/pytorch/pull/11342 Differential Revision: D9978430 Pulled By: weiyangfb fbshipit-source-id: e73dc20302ab58925afb19e609e31f4a38c634ad |
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| autograd.rst | ||
| bottleneck.rst | ||
| checkpoint.rst | ||
| conf.py | ||
| cpp_extension.rst | ||
| cuda.rst | ||
| cudnn_deterministic.rst | ||
| data.rst | ||
| distributed_deprecated.rst | ||
| distributed.rst | ||
| distributions.rst | ||
| dlpack.rst | ||
| ffi.rst | ||
| index.rst | ||
| jit.rst | ||
| legacy.rst | ||
| model_zoo.rst | ||
| multiprocessing.rst | ||
| nn.rst | ||
| onnx.rst | ||
| optim.rst | ||
| sparse.rst | ||
| storage.rst | ||
| tensor_attributes.rst | ||
| tensors.rst | ||
| torch.rst | ||