[QST] Is there a two_pass_precision mode for brute force knn?
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Description
My project uses RAFT brute force knn and I noticed a drop in precision when I upgraded to the latest RAFT. I moved to cuvs but still see cosine similarity that's off by more than 1e-04 when compared to the dot product cpu calculation. Is this a bad use case for cuvs?
I had this issue before when I was using cuML and two_pass_precission fixed it, unfortunately it also suffered from a different correctness bug https://github.com/rapidsai/cuml/issues/5569. Would appreciate any suggestions.
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Research direction
Start by reproducing the cuVS brute-force KNN cosine-similarity result and comparing it with the CPU dot-product calculation. Check whether cuVS provides an equivalent to cuML's two_pass_precision mode, and review the linked cuML issue 5569. Done means identifying the supported precision behavior or documenting the limitation and recommended approach.
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Assessment
- Domain
- machine-learning, search
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100