Sglang Diffusion Benchmark Profile

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