sglang-diffusion-ako4all-kernel

To optimize SGLang diffusion kernels with AKO4ALL, you bootstrap an AKO4ALL harness, run reproducible microbenchmarks, and use NCU-guided iteration to port the best kernel back into sglang with end-to-end denoise validation.

Kernel validation in sglang requires reproducible microbench setups and end-to-end denoise checks to ensure performance improvements transfer to production inference without regressions.

Yes, a clean AKO4ALL checkout on the upstream main branch is required to ensure reproducible microbenchmarks and proper repository hygiene during kernel optimization.

You set up custom microbenchmarks by bootstrapping the AKO4ALL harness for your diffusion kernel, enabling reproducible benchmarking to measure performance changes accurately during NCU-guided iterations.

Yes, AKO4ALL supports cloning or refreshing sibling repository layouts, allowing automatic repo hygiene and synchronization when porting optimized kernels back into sglang.

The best way to run NCU-guided iteration is combining AKO4ALL microbench setups with end-to-end denoise validation, iterating until the best kernel ports back into sglang with model-level checks.