# Vllm Omni Diffusion Benchmark Profile > Benchmark, profile, and tune vLLM-Omni diffusion models, especially Wan/Qwen/Helios style pipelines on GPU or NPU. Use this when measuring denoise latency, collecting torch or torch_npu profiler traces, reading ASCEND_PROFILER_OUTPUT artifacts, comparing before/after performance, or diagnosing bottlenecks such as SP communication, VAE convs, data transforms, offload overlap, and RoPE overhead. Use when this capability is needed. - Skill: `tomevault-io/vllm-omni-diffusion-benchmark-profile` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/vllm-omni-diffusion-benchmark-profile` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/vllm-omni-diffusion-benchmark-profile/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/vllm-omni-diffusion-benchmark-profile --- # vLLM-Omni Diffusion Benchmark and Profile Use this skill when the task is to benchmark, profile, compare, or optimize a vLLM-Omni diffusion model. Primary target: - **Denoise latency / DiT hot path** Secondary targets: - End-to-end latency - VAE encode/decode cost - Communication overhead - Data transform / copy overhead - Offload overlap quality Correctness rule: - Do not accept a perf improvement if output quality or generation correctness regresses. ## When To Use Use this skill for: - `image_to_video.py`, `text_to_video.py`, `text_to_image.py`, image edit, or other standalone diffusion examples - Diffusion serving runs started with `vllm serve ... --profiler-config ...` - GPU `torch.profiler` analysis - NPU `torch_npu.profiler` / `ASCEND_PROFILER_OUTPUT` analysis - Before/after perf comparison for PRs Do not use this skill for: - Autoregressive-only LLM profiling - Audio/tokenizer-only perf work unless the model actually uses the diffusion stack ## Core Workflow 1. Reproduce with a fixed command, fixed seed, fixed resolution, fixed frame count, and fixed steps. 2. Record an end-to-end baseline first. 3. For profiling, reduce `num_inference_steps` to `2` unless the task specifically needs long-run behavior. 4. Keep the same shape and parallel config across before/after runs. 5. Profile one thing at a time: - end-to-end wall clock - pipeline stage timing - operator/top kernel view - communication view - call-stack attribution 6. After each change, re-run: - correctness check - same benchmark command - same profiler setup ## Key Metric Hierarchy 1. **Denoise / DiT latency**: main optimization target 2. **End-to-end latency**: sanity check and user-facing metric 3. **Communication wait**: critical for SP/Ulysses on multi-device runs 4. **Data transform/copy cost**: often hidden inside `ViewCopy`, `TransData`, `Transpose`, `InplaceCopy` 5. **VAE encode/decode cost**: especially important for video models ## Benchmarking Rules - Always compare with the same: - model - prompt / image input - seed - height / width - num_frames - num_inference_steps - guidance scale - parallel config - For profiling, prefer `2` steps. - For end-to-end reporting, use the real target steps. - If using NPU and the model is close to memory limits, note whether: - `--enable-layerwise-offload` - `--vae-patch-parallel-size` - `--vae-use-tiling` are enabled, because these materially change the bottleneck mix. ## vLLM-Omni Profiling Entry Points ### Standalone diffusion examples Use `--profiler-dir` to enable torch/torch_npu profiler output. Common examples: - `examples/offline_inference/image_to_video/image_to_video.py` - `examples/offline_inference/text_to_video/text_to_video.py` - `examples/offline_inference/text_to_image/text_to_image.py` Typical NPU Wan2.2 I2V profile command: ```bash cd /root/vllm-workspace/vllm-omni/examples/offline_inference/image_to_video python image_to_video.py \ --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \ --image cherry_blossom.jpg \ --prompt "Cherry blossoms swaying gently in the breeze, petals falling, smooth motion" \ --negative-prompt "" \ --height 512 \ --width 768 \ --num-frames 49 \ --guidance-scale 4.0 \ --num-inference-steps 2 \ --flow-shift 12.0 \ --fps 16 \ --output i2v_output.mp4 \ --enable-layerwise-offload \ --ulysses-degree 8 \ --vae-patch-parallel-size 8 \ --vae-use-tiling \ --profiler-dir ./wan22_step2_profile ``` ### Serving mode Use: ```bash vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers \ --omni \ --port 8091 \ --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile"}' ``` Then control profiling with `/start_profile` and `/stop_profile`. ## Analysis Workflow ### GPU - Read `.json.gz` trace in Perfetto or `chrome://tracing` - If available, use `key_averages()` tables or exported tables - Attribute time to: - attention kernels - GEMMs - norm / activation - copies / layout transforms ### NPU NPU profiling uses `torch_npu.profiler.tensorboard_trace_handler(...)` and must be parsed offline. Core files after `analyse()`: - `step_trace_time.csv` - `op_statistic.csv` - `operator_details.csv` - `communication.json` - `kernel_details.csv` - `trace_view.json` Use: ```python from torch_npu.profiler.profiler import analyse analyse("./wan22_step2_profile/diffusion_worker_0") ``` Or adapt: - `/root/vllm-workspace/analyze_profiling.py` - `examples/offline_inference/image_to_video/analyse.py` For NPU artifact interpretation, read: - `references/npu-workflow.md` For common bottlenecks and likely fixes, read: - `references/known-hotspots.md` ## Fast Diagnostic Questions When reading a profile, answer these in order: 1. Is the bottleneck mainly **DiT**, **VAE**, or **communication**? 2. Is the time in **real compute**, or mostly **wait/synchronization**? 3. Are the expensive ops on the hot path repeated per step / per block / per CFG branch? 4. Is the model paying for work that should be skipped? - unnecessary SP on cross-attn - full-shape work before local shard - serial VAE encode/decode instead of parallel path 5. Is the backend spending time on memory movement instead of math? ## Before/After Comparison Always summarize changes using the same fields: - End-to-end latency - Stage time - Compute vs Communication(Not Overlapped) - Top 10 operators by time - Top communication ops - Most important call-stack attribution changes For NPU, prefer comparing: - `step_trace_time.csv` - `op_statistic.csv` - `communication.json` - `operator_details.csv` ## Diffusion Perf Tuning Principles - Prefer removing unnecessary work over micro-optimizing already-necessary work. - Sequence-parallel strategy matters more than optimizing a single `all_to_all_*` wrapper. - For video models, VAE can be a first-order bottleneck, not just DiT. - On NPU, strided writes and layout transforms can dominate hot paths. - Offload only helps if copy is actually overlapped with compute. - Small hot-path functions called thousands of times can outweigh large isolated kernels. ## Deliverables A good perf investigation should usually produce: 1. The exact benchmark command used 2. The exact profiling command used 3. A short table of before/after metrics 4. A root-cause statement tied to code paths 5. A concrete next optimization candidate ## Final Check Before Claiming Improvement - Same inputs and same settings as baseline - No correctness regression - Improvement reproduced at least once - Improvement explained by profiler evidence, not just wall-clock noise --- > Source: [hsliuustc0106/vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills) — distributed by [TomeVault](https://tomevault.io).