Lightning-AI / Lightning-AI/lightning-thunder
Support PyTorch custom ops when they have meta functions (also autograd)
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- Dominant language
- Python
- Stars
- 1.5k
- Forks
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Description
PyTorch allows to register meta functions with custom ops
With that information, we should be able to apply the fallback mechanism that @kiya00 developed for PyTorch operations in thunder.
https://pytorch.org/docs/stable/library.html
There also is support for registering backward rules, which might also be of interest.
cc @apaz-cli
Contributor guide
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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 with the PyTorch custom operator documentation linked in the issue, then inspect Thunder's existing PyTorch fallback mechanism developed by @kiya00. Define how registered meta functions should enable that fallback and determine whether registered backward rules are in scope. Done means custom ops with meta functions work through the fallback path, with coverage for the supported behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- compilers, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100