Add torch.sparse overview section #85265
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cpuhrsch
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docs/source/sparse.rst
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| is in turn also backed and powered by sparse storage formats and kernels. | ||
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| Also note that, for now, the user doesn't have a choice of the output layout. For example, | ||
| adding a sparse Tensor to a regular strided Tensor results in a sparse Tensor. |
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| adding a sparse Tensor to a regular strided Tensor results in a sparse Tensor. | |
| adding a sparse Tensor to a regular strided Tensor results in a strided Tensor. |
cpuhrsch
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/easycla As part of the transition to the PyTorch Foundation, this project now requires contributions be covered under the new CLA. See #85559 for additional details. This comment will trigger a new check of this PR. If you are already covered, you will simply see a new "EasyCLA" check that passes. If you are not covered, a bot will leave a new comment with a link to sign. |
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docs/source/sparse.rst
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| Unary functions | ||
| --------------- | ||
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| All zero-preserving unary functions support sparse |
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All zero-preserving unary functions listed below support sparse...
or possibly
All zero-preserving unary functions will support sparse...
Some zero-preserving unary functions, such as reLU, are not in fact supported, but the goal is to make sure they all are, so we might want the wording here to reflect that any missing zero-preserving unary function support is a reportable bug.
I also do not think this list is completely correct: torch.angle does not appear to support COO. Also all layouts support torch.expm1 which is missing.
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I'll update the wording to say that all zero preserving unary functions can be supported and encourage the reader to open an issue if they find one that's missing. I think it's fair to say that they're pretty easily supported, so it's ok to encourage people to report it. What do you think?
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That sounds great. It is easy to add them so we should encourage people to point out missing ones.
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Hey @cpuhrsch. |
cpuhrsch
added
release notes: sparse
The goal of this section is to provide a general overview of how PyTorch handles sparsity for readers who are already familiar with sparse matrices and their operators. Pull Request resolved: pytorch#85265 Approved by: https://github.com/jisaacso
The goal of this section is to provide a general overview of how PyTorch handles sparsity for readers who are already familiar with sparse matrices and their operators. Pull Request resolved: #85265 Approved by: https://github.com/jisaacso
The goal of this section is to provide a general overview of how PyTorch handles sparsity for readers who are already familiar with sparse matrices and their operators.