sglang-diffusion-ako4all-kernel

You can automate SGLang diffusion kernel tuning by running a workflow that handles preflight checks, harness bootstrap, baseline capture, iterative optimization, and model-level validation. This creates a reproducible optimization pipeline across GPU and CPU environments.

AKO4ALL kernel tuning is an iterative optimization process for diffusion kernels. It validates model performance by capturing baseline metrics, running microbenchmarks, and executing end-to-end model-level denoise validation to ensure tuning improvements transfer effectively back to the model.

Yes, the workflow supports porting your best kernel changes back into SGLang. After benchmarking and iterative optimization in the AKO4ALL harness, it verifies the optimized kernels on real models within your SGLang project to ensure performance gains.

Yes, git is required. The workflow relies on git to maintain a clean AKO4ALL repository state during preflight hygiene and repository synchronization, ensuring the harness bootstrap correctly mirrors the real SGLang kernel workloads.

The best way to establish a reproducible baseline is to use a dedicated tuning workflow that performs preflight hygiene and repository synchronization. This ensures a clean AKO4ALL environment before capturing baseline metrics for iterative optimization.

Your optimization lacks model-level validation because it skips end-to-end denoise checks. A complete workflow must transfer ported kernel changes back into SGLang and verify them on real models to guarantee that microbench improvements translate to actual performance gains.