sglang-diffusion-ako4all-kernel

name	sglang-diffusion-ako4all-kernel
description	Use when optimizing an existing SGLang diffusion kernel with AKO4ALL, including AKO4ALL repo hygiene, custom microbench setup, ncu-guided iteration, and end-to-end denoise validation. Also use when a sibling AKO4ALL repo must be cloned or refreshed before starting kernel tuning work.

SGLang Diffusion AKO4ALL Kernel

Use this skill to run the full AKO4ALL-based optimization loop for an existing SGLang diffusion kernel. It is the default implementation path once the benchmark/profile skill has already shown that a hotspot is real and not covered by an existing fast path. This workflow bootstraps a custom AKO harness, benchmarks and profiles the kernel, iterates with ncu, ports the best version back to sglang, then validates with targeted tests and model-level denoise runs.

This skill assumes a sibling repo layout like:

<base-dir>/
├── sglang/
└── AKO4ALL/

If AKO4ALL/ is missing under the current base directory, clone it first.

Use This Skill When

• tuning an existing diffusion Triton, CUDA JIT, CuTeDSL, or runtime-integrated kernel in sglang
• sglang-diffusion-benchmark-profile has already ruled out an existing in-repo fast path or overlap family
• creating a custom AKO4ALL harness for a real diffusion kernel instead of using the default benchmark tasks
• validating that a kernel-level win transfers to Qwen, FLUX, Wan, Hunyuan, MOVA, or other diffusion denoise latency
• preparing PR artifacts such as microbench tables, ncu before/after data, and proof image outputs

Do not start here when the bottleneck has not been proven yet. First use ../sglang-diffusion-benchmark-profile/SKILL.md to:

• measure the real denoise regression
• collect the perf dump baseline
• capture one representative torch.profiler trace
• rule out existing mainline fast paths

If a future specialized optimization skill matches the kernel family better than AKO4ALL, hand off there instead. The diagnosis contract stays the same.

Mandatory AKO4ALL Preflight

Before any AKO work:

1. Run scripts/ensure_ako4all_clean.sh [base-dir].
2. If <base-dir>/AKO4ALL does not exist, the script clones it.
3. Do not continue unless AKO4ALL is:
   • on the upstream default branch, usually main
   • fully clean with no tracked or untracked local changes
   • exactly synced to upstream/<default-branch>
4. If the script reports local commits, divergence, or a dirty worktree, stop and clean or re-clone the repo before continuing.

The script creates an upstream remote automatically when missing. By default it uses the existing origin URL, or AKO4ALL_URL if you need to override the clone source.

Workflow

1. Scope the Kernel

• Identify the exact kernel entry point and runtime call sites in sglang.
• Record the target shapes, dtypes, model families, and whether the kernel is on a hot path.
• Reuse existing unit tests and benchmark entry points when they already exist.

2. Bootstrap the AKO Harness

Inside the clean AKO4ALL repo:

• read TASK.md and HINTS.md
• create a custom harness instead of relying on the stock benchmark tasks
• mirror the real SGLang kernel into:
  • input/reference.py
  • input/<kernel>.py
  • solution/<kernel>.py
  • bench/bench_<kernel>.py
• keep a short context note in context/ when the kernel has model-specific shape assumptions or perf conclusions

The custom benchmark should:

• cover representative diffusion shapes
• check correctness against the reference kernel
• report aggregate runtime plus per-shape results when useful

3. Establish the Baseline

• run the AKO custom microbench before changing the kernel
• capture one representative ncu baseline on the hottest meaningful shape
• note whether the bottleneck looks like registers, occupancy, instruction count, launch config, or memory latency

4. Iterate in AKO4ALL

• change one idea at a time
• rerun the microbench after every change
• update ITERATIONS.md with hypothesis, result, and next step
• prefer simple, explainable wins over clever rewrites that do not transfer

After 3 consecutive no-improvement or regression iterations:

• rerun ncu
• re-read ITERATIONS.md
• change direction instead of continuing blind sweeps

5. Port the Best Version Back to SGLang

• apply the best candidate to the real sglang kernel file
• run import or syntax checks and targeted tests first
• keep the AKO solution/ version aligned with the main-tree version you actually want to keep

6. Validate on Real Models

• use the benchmark/profile skill for denoise perf dumps and before/after comparison
• prefer exact local snapshot validation when testing local edits on a GPU box
• run targeted kernel tests first
• run model-level denoise benchmarks with perf dumps
• compare baseline vs optimized runs with compare_perf.py
• if the PR needs proof that generation still works, save one real model output image

7. Prepare PR Artifacts

At minimum, keep:

• one microbench table
• one denoise-stage table
• one end-to-end table
• one ncu before/after pair on the most representative kernel shape
• one generated image when the kernel affects production inference

See references/ako-loop.md for the checklist and common stop rules.

Operating Rules

• Treat AKO4ALL repo hygiene as a gate, not a suggestion.
• Prefer exact local snapshot validation over hand-wavy “remote tree is close enough”.
• Keep model-level validation honest: if microbench improves but denoise does not, do not keep the AKO-only variant in the main code path.
• When writing conclusions, explain the win in terms of measurable causes such as lower registers per thread, higher occupancy, fewer executed instructions, or better scheduler eligibility.