Measure when MLIR is faster or slower to update the heuristic
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- Dominant language
- C++
- Stars
- 333
- Forks
- 150
- Avg merge
- 4d 19h
- Merged PRs (30d)
- 54
Description
Gather mxr/pythons files from all the models including for NCHW/NHWC/fp16/bf16/fp32 and rewrite_dot/on/off. Measure the performance for each file with and without MLIR on all the different hardware. Make a list for when MLIR is slower and use that list to update the heuristic for when we pick MLIR.
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
Begin by locating the mxr/pythons files for the models and the heuristic that chooses MLIR. Benchmark each file with and without MLIR across NCHW, NHWC, fp16, bf16, fp32, rewrite_dot on/off, and the available hardware. Done means documenting slower cases and updating the heuristic based on those measurements.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- compilers, machine-learning, performance
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100