[sparse][semi-structured] Fix RuntimeError when passing in non-contiguous input to SparseSemiStructured linear #114593
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Pull Request resolved: #105595 Approved by: https://github.com/jcaip
Summary: Currently, PyTorch incorrectly calculates the size of the returned matrix when we pass a non-contiguous batched (>2d) input to the semi-structured sparse subclass. This is most common in MLP layers, where we have 2 linear layers back to back. This will lead to an error like the following: ``` RuntimeError: shape '[20, 64, 64, 3072]' is invalid for input of size 62914560 ``` Where the size of the sparse matmul result is off because we infer the output shape with the wrong tensor shape. This happens because of a bug where we did not update the subclass tensor shape when doing transpose. For semi-structured sparsity, transposing is a no-op where we just set the boolean flag, but we forgot to also update the tensor shape. Note that this error goes away in inference mode, since we avoid decomposing the aten.linear op and handle shape folding ourselves, which changes the execution path. An alternative way to fix this issue is to set TORCH_FLATTEN_LINEAR_3D=True, which will also fix this error. Test Plan: ``` python test/test_sparse_semi_structured.py -k test_mlp ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: #110420 Approved by: https://github.com/alexsamardzic, https://github.com/cpuhrsch
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/114593
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Summary:
This PR also brings in changes from #105595, which are needed for the changes in #110420
Currently, PyTorch incorrectly calculates the size of the returned matrix when we pass a non-contiguous batched (>2d) input to the semi-structured sparse subclass.
This is most common in MLP layers, where we have 2 linear layers back to back.
This will lead to an error like the following:
Where the size of the sparse matmul result is off because we infer the output shape with the wrong tensor shape.
This happens because of a bug where we did not update the subclass tensor shape when doing transpose.
For semi-structured sparsity, transposing is a no-op where we just set the boolean flag, but we forgot to also update the tensor shape.
Note that this error goes away in inference mode, since we avoid decomposing the aten.linear op and handle shape folding ourselves, which changes the execution path.
An alternative way to fix this issue is to set
TORCH_FLATTEN_LINEAR_3D=True, which will also fix this error.
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: #110420 Approved by: https://github.com/alexsamardzic, https://github.com/cpuhrsch