-
Notifications
You must be signed in to change notification settings - Fork 26.3k
Open
Labels
enhancementNot as big of a feature, but technically not a bug. Should be easy to fixNot as big of a feature, but technically not a bug. Should be easy to fixmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmultriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
Description
In order to move lobpcg into torch.linalg we should:
- Update the docs: When is this function preferable over
eigvalsoreigvalsh. Some estimations on the size of the input matrix and its rank would be very helpful to make them more usable (needs benchmarks). - Format the docs for them to be in line with the rest of torch.linalg
- Implement the
backwardsfor non-symmetric matrices (would fix torch.lobpcg always breaks for autograd #38948) - Address the performance concerns from [Feature Pitch] Fast extremal eigensolvers #58828
- Review the API and perhaps split it into two functions, one with a simpler API and a simpler name and a full one (needs some proposals for how to do this).
cc @jianyuh @nikitaved @pearu @mruberry @heitorschueroff @walterddr @IvanYashchuk @xwang233 @lezcano
Metadata
Metadata
Assignees
Labels
enhancementNot as big of a feature, but technically not a bug. Should be easy to fixNot as big of a feature, but technically not a bug. Should be easy to fixmodule: linear algebraIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmulIssues related to specialized linear algebra operations in PyTorch; includes matrix multiply matmultriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module