NVIDIA / NVIDIA/cuEquivariance
Finer granularity of control over triangular multiplicative update
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 433
- Forks
- 42
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
It is a great pleasure to use cuequivariance_torch.triangle_multiplicative_update, but the all-encompassing API is a bit problmatic for us. For instance, it is easy to just set layernorm's weight and bias to 1s and 0s respectively, but not that easy if we would just want to do a rms norm instead of layernorm.
Could you guys consider providing finer control?
Best,
Rui
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 at the cuequivariance_torch.triangle_multiplicative_update entry point and inspect how its layernorm behavior is currently exposed. Clarify the desired finer-grained normalization controls, including RMS normalization, then identify or add focused tests showing that the resulting API supports those choices.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- api, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100