| name | sglang-diffusion-benchmark-profile |
| description | Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang. |
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
