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sglang-diffusion-benchmark-profile

Testing & Quality
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Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/FutureMLS-Lab/OSCAR/blob/HEAD/sglang-research/python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/SKILL.md

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SGLang Diffusion Benchmark and Profile

Use this skill when measuring denoise performance, finding the slow op, checking whether an existing fast path can solve it, or verifying that a hotspot is real before any kernel work in sglang.multimodal_gen.

This skill is diagnosis-first. It owns:

  • checked-in denoise benchmark presets
  • perf dump collection and before/after comparison
  • torch.profiler trace capture and quick hotspot ranking
  • mapping hot kernels back to known fast paths and fusion families
  • handing confirmed kernel work to a specialized optimization skill such as ../sglang-diffusion-ako4all-kernel/SKILL.md

This skill does not own low-level kernel authoring or standalone Nsight workflows.

Preflight

Before running any benchmark, profiler, or kernel-validation command:

  • use scripts/diffusion_skill_env.py to derive the repo root from sglang.__file__
  • verify the repo is writable
  • export HF_TOKEN before using gated Hugging Face models such as black-forest-labs/FLUX.*
  • export FLASHINFER_DISABLE_VERSION_CHECK=1
  • choose idle GPU(s) before starting perf work

Main Reference

  • benchmark-and-profile.md — canonical denoise benchmark, perf dump, and torch.profiler workflow; uses the checked-in nightly-aligned presets, including LTX-2 two-stage
  • existing-fast-paths.md — map bottlenecks to existing fused kernels, packed QKV paths, fused QK norm + RoPE, and distributed overlap patterns before proposing new code
  • scripts/diffusion_skill_env.py — preflight helper: repo root discovery via sglang.__file__, write-access probe, benchmark/profile output directories, idle GPU selection
  • scripts/bench_diffusion_denoise.py — end-to-end denoise benchmark preset runner via sglang generate; use --list-models to inspect preset order, then save perf dumps by label and compare them with compare_perf.py

Opportunity Discovery Rule

Before calling a diffusion hotspot "new", first classify it with existing-fast-paths.md.

Always rule out these existing families first:

  • merged Z-Image residual-form modulation
  • fused diffusion QK norm + RoPE
  • NVFP4 / Nunchaku packed QKV
  • Nunchaku fused GELU MLP
  • Ulysses / USP attention overlap
  • turbo-layer async all-to-all overlap
  • torch.compile compute / communication reorder
  • dual-stream diffusion execution