sglang-diffusion-ako4all-kernel

Optimize SGLang diffusion kernels with AKO4ALL by setting up a dedicated harness, running baseline microbenchmarks, integrating NCUs, and validating end-to-end denoise results across model families. This structured workflow ensures iterative tuning and production-grade validation before porting updates back to SGLang.

The AKO4ALL workflow for diffusion kernel tuning involves preparing a clean repository harness, establishing baseline microbenchmarks, performing iterative experiments with NCUs, and validating end-to-end denoise results. It maintains repo hygiene and synchronizes optimized kernels back into SGLang for production checks.

Set up a microbenchmark harness for SGLang diffusion kernels by cloning or updating the AKO4ALL repository in your base project, ensuring a clean worktree, then running the AKO4ALL kernel workflow to establish baselines and drive iterative optimization.

Yes, AKO4ALL validates end-to-end denoise results across model families to ensure real-world applicability of optimized SGLang diffusion kernels. This validation confirms that kernel tuning maintains correctness and performance across different diffusion model architectures.

Integrate NCUs into diffusion kernel optimization by using them within the AKO4ALL harness to collect performance metrics during microbenchmarks. These NCU insights drive iterative experimentation, ensuring optimized SGLang kernels meet production-grade performance checks before porting.

AKO4ALL for SGLang kernel development requires strict repo hygiene, a dedicated harness setup, and iterative baseline establishment. Limitations include the need for continuous upstream synchronization and the requirement to port validated kernels back to SGLang manually to maintain production-grade checks.