pymc-devs / pymc-devs/pytensor
Consider `mlx-addons` for MLX linalg dispatches
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- Dominant language
- Python
- Stars
- 644
- Forks
- 208
- Avg merge
- 2d 14h
- Merged PRs (30d)
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Description
Description
I found this project: https://github.com/guillaume-osmo/mlx-addons
It looks vibe coded, but it has MLX GPU kernels for cholesky, QR, random SVD, LU, triangular solve, and others. Also far be it from me to judge vibe coding these days :) Would be a big speedup for many models, especially GP/KF/MvN stuff. It's MIT licensed so we could "take inspiration" or try to work with them directly.
Tagging the dev @guillaume-osmo/@thegodone in case you're interested in collaborating on this.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the linked mlx-addons project and its GPU kernels for Cholesky, QR, random SVD, LU, and triangular solve, then inspect PyTensor's existing linalg dispatch paths. Done would require a defined integration or collaboration plan with measurable benefits for the affected models; no repository files or tests are named in the issue.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100