sglang-diffusion-ako4all-kernel

To automate the AKO4ALL optimization loop for an SGLang diffusion kernel, use this Skill to bootstrap a custom harness, run baseline microbenchmarks, execute iterative tuning, and validate end-to-end denoise results.

Establishing a reproducible AKO4ALL workflow requires a clean AKO4ALL setup, a mirrored kernel structure across input, reference, solution, and bench directories, and iterative logging in ITERATIONS.md to guide convergence.

End-to-end denoise validation works by running model-level checks after iterative kernel tuning to ensure the optimized SGLang diffusion kernel maintains correct output quality while achieving performance improvements.

Yes, you can use this harness to benchmark an existing SGLang diffusion kernel by bootstrapping a custom test environment that mirrors the real kernel flow and running baseline microbenchmarks against it.

The best way to maintain repo hygiene during AKO4ALL kernel optimization is to refresh the sibling AKO4ALL repository to ensure clean baselines and synchronize with upstream changes for reproducible results.

You need to log iterations in ITERATIONS.md during diffusion kernel tuning because it provides a structured history of changes and benchmark results, which guides rapid convergence towards optimal performance.