feat: add MLIR attention optimization example (OpenEvolve) #196
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
New example under examples/attention_optimization/ with docs, configs, evaluator, MLIR IRs, scripts, and tests. Evolves transformation parameters to optimize attention kernels; supports IR-sim and real execution. Aims for 15–32% speedup; usage documented in README. TODO: * integration with mlir-opt for automated profiling to complete the validation loop * GPU support
|
|
wangcheng0825
pushed a commit
to wangcheng0825/openevolve
that referenced
this pull request
Sep 15, 2025
…kevolve_openevolve feat: add MLIR attention optimization example (OpenEvolve)
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
New example under examples/attention_optimization/ with docs, configs, evaluator, MLIR IRs, scripts, and tests. Evolves transformation parameters to optimize attention kernels; supports IR-sim and real execution. Aims for 15–32% speedup; usage documented in README.
TODO: