159 lines
3.7 KiB
Plaintext
159 lines
3.7 KiB
Plaintext
---
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title: "Ring SP Benchmark: Wan2.2-TI2V-5B (u1r2 vs Baseline)"
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metatags:
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description: "Review Ring-SP benchmark results for Wan2.2-TI2V-5B-Diffusers in SGLang Diffusion."
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---
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This page reports Ring-SP performance for `Wan2.2-TI2V-5B-Diffusers` using:
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- Parallel config: `sp=2, ulysses=1, ring=2` (short: `u1r2`)
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- Baseline config: `sp=1, ulysses=1, ring=1` (short: `u1r1`)
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## Benchmark Setup
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- Model: `Wan2.2-TI2V-5B-Diffusers`
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- GPU: `48G RTX40 series * 2`
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## Online Serving
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### Ring SP (`u1r2`)
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```bash
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sglang serve \
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--model-type diffusion \
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--model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
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--num-gpus 2 --sp-degree 2 --ulysses-degree 1 --ring-degree 2 \
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--port 8898
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```
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### Baseline (`u1r1`)
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```bash
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sglang serve \
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--model-type diffusion \
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--model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
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--num-gpus 1 --sp-degree 1 --ulysses-degree 1 --ring-degree 1 \
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--port 8898
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```
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## Benchmarks
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### Benchmark Disclaimer
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These benchmarks are provided for reference under one specific setup and command configuration. Actual performance may vary with model settings, runtime environment, and request patterns.
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### Stage Time Breakdown
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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</colgroup>
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<thead>
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<tr>
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<th>Stage / Metric</th>
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<th><code>u1r2</code> (s)</th>
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<th><code>u1r1</code> baseline (s)</th>
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<th>Speedup</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>InputValidation</td>
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<td>0.1060</td>
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<td>0.1029</td>
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<td>0.97x</td>
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</tr>
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<tr>
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<td>TextEncoding</td>
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<td>1.3965</td>
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<td>2.2261</td>
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<td>1.59x</td>
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</tr>
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<tr>
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<td>LatentPreparation</td>
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<td>0.0002</td>
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<td>0.0002</td>
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<td>1.00x</td>
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</tr>
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<tr>
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<td>TimestepPreparation</td>
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<td>0.0003</td>
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<td>0.0004</td>
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<td>1.33x</td>
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</tr>
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<tr>
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<td>Denoising</td>
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<td>52.6358</td>
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<td>71.6785</td>
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<td>1.36x</td>
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</tr>
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<tr>
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<td>Decoding</td>
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<td>7.6708</td>
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<td>13.4314</td>
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<td>1.75x</td>
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</tr>
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<tr>
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<td><strong>Total</strong></td>
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<td><strong>63.74</strong></td>
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<td><strong>90.63</strong></td>
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<td><strong>1.42x</strong></td>
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</tr>
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</tbody>
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</table>
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### Memory Usage
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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<col style={{width: "25%"}} />
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</colgroup>
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<thead>
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<tr>
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<th>Memory Metric</th>
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<th><code>u1r2</code> (GB)</th>
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<th><code>u1r1</code> baseline (GB)</th>
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<th>Delta</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Peak GPU Memory</td>
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<td>20.07</td>
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<td>27.40</td>
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<td>-7.33</td>
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</tr>
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<tr>
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<td>Peak Allocated</td>
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<td>13.35</td>
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<td>20.40</td>
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<td>-7.05</td>
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</tr>
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<tr>
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<td>Memory Overhead</td>
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<td>6.72</td>
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<td>7.00</td>
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<td>-0.28</td>
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</tr>
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<tr>
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<td>Overhead Ratio</td>
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<td>33.5%</td>
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<td>25.6%</td>
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<td>+7.9pp</td>
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</tr>
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</tbody>
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</table>
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## Summary
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- End-to-end latency improves from `90.63s` to `63.74s` (`1.42x`).
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- Main gains come from `Denoising` (`1.36x`) and `Decoding` (`1.75x`).
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- Absolute memory usage drops noticeably on Ring-SP (`Peak GPU Memory -7.33GB`, `Peak Allocated -7.05GB`).
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- Overhead ratio rises (`+7.9pp`), so future tuning can focus on reducing communication/runtime overhead while preserving the latency gain.
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