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Summary: This PR is a refactor of semi-structured sparsity support. **deprecation**: Before `torch.sparse.to_sparse_semi_structured` had a kwarg param `transposed=False`, which has been removed. This kwarg was unused and now thros a deprecation warning. Namely, I've taken the subclassing implementation that xFormers has created and brought it over to PyTorch, as part of our plan to upstream runtime 2:4 sparsity. I've also copied over all the op support that Daniel implemenented that did not depend on the fast sparsification routines, into `_sparse_semi_structured_ops.py` With this subclass, all of our internal tests pass, as well as those in xFormers. The main change is that we now define a base subclass, `SparseSemiStructuredTensor` that is inherited from for each of the specific backends. We also now can arbitrarily override the sparse dispatch table with `_load_dispatch_table()`, idea being this is still general enough where users don't need to modify pytorch source code to get their model working. This also adds in padding support and stores alg_id and fuse_transpose as flags on the tensor, instead of hardcoding them. There still remains two components in xFormers that will need to be ported over eventually: - the autograd functions (`Sparsify24`, `Sparsify24_like`) - fast sparsification routines that they rely on Test Plan: Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/117302 Approved by: https://github.com/alexsamardzic, https://github.com/HDCharles |
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| .. | ||
| __init__.py | ||
| _semi_structured_conversions.py | ||
| _semi_structured_ops.py | ||
| _triton_ops_meta.py | ||
| _triton_ops.py | ||
| semi_structured.py | ||