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[diffusion] doc: update docs architecture (#27767)

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---
title: "Ring SP Benchmark: Wan2.2-TI2V-5B (u1r2 vs Baseline)"
title: "Sequence Parallelism"
tag: "preserve"
metatags:
description: "Review Ring-SP benchmark results for Wan2.2-TI2V-5B-Diffusers in SGLang Diffusion."
description: "Configure sequence parallelism, Ulysses, and ring-based sequence splitting for SGLang Diffusion workloads."
---
This page reports Ring-SP performance for `Wan2.2-TI2V-5B-Diffusers` using:
Sequence parallelism splits long image or video latent sequences across GPUs. In SGLang Diffusion, the public controls are:
- Parallel config: `sp=2, ulysses=1, ring=2` (short: `u1r2`)
- Baseline config: `sp=1, ulysses=1, ring=1` (short: `u1r1`)
- `--sp-degree`: total sequence parallel degree
- `--ulysses-degree`: Ulysses parallel degree
- `--ring-degree`: ring parallel degree
## Benchmark Setup
The degrees must satisfy:
- Model: `Wan2.2-TI2V-5B-Diffusers`
- GPU: `48G RTX40 series * 2`
```text
sp_degree = ulysses_degree * ring_degree
```
## Online Serving
Use SP when sequence length or video shape makes the DiT forward pass the bottleneck and the model supports sequence sharding. For latency-oriented multi-GPU Qwen/Wan deployments, also compare against CFG parallelism and FSDP; SP is not automatically the best multi-GPU setting for every model.
### Ring SP (`u1r2`)
## Recommended Commands
### Two-GPU Sequence Parallelism
This example uses two GPUs with `sp=2`, `ulysses=1`, and `ring=2`.
```bash
sglang serve \
--model-type diffusion \
--model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
--num-gpus 2 --sp-degree 2 --ulysses-degree 1 --ring-degree 2 \
--model-path Wan-AI/Wan2.2-TI2V-5B-Diffusers \
--num-gpus 2 \
--sp-degree 2 \
--ulysses-degree 1 \
--ring-degree 2 \
--port 8898
```
### Baseline (`u1r1`)
### Single-GPU Baseline
Use an explicit single-GPU baseline before attributing a gain to sequence parallelism.
```bash
sglang serve \
--model-type diffusion \
--model-path /model/HuggingFace/Wan-AI/Wan2.2-TI2V-5B-Diffusers \
--num-gpus 1 --sp-degree 1 --ulysses-degree 1 --ring-degree 1 \
--model-path Wan-AI/Wan2.2-TI2V-5B-Diffusers \
--num-gpus 1 \
--sp-degree 1 \
--ulysses-degree 1 \
--ring-degree 1 \
--port 8898
```
## Benchmarks
## Choosing The Degrees
### Benchmark Disclaimer
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<colgroup>
<col style={{width: "28%"}} />
<col style={{width: "32%"}} />
<col style={{width: "40%"}} />
</colgroup>
<thead>
<tr style={{borderBottom: "2px solid #d55816"}}>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Setting</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Typical use</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td style={{padding: "9px 12px"}}><code>--sp-degree 1</code></td>
<td style={{padding: "9px 12px"}}>Single-GPU or no sequence splitting</td>
<td style={{padding: "9px 12px"}}>Use this as the baseline.</td>
</tr>
<tr>
<td style={{padding: "9px 12px"}}><code>--ulysses-degree N</code></td>
<td style={{padding: "9px 12px"}}>Ulysses-only sequence parallelism</td>
<td style={{padding: "9px 12px"}}>When ring parallelism is not needed, keep <code>--ring-degree 1</code>.</td>
</tr>
<tr>
<td style={{padding: "9px 12px"}}><code>--ring-degree N</code></td>
<td style={{padding: "9px 12px"}}>Ring-based sequence splitting over long sequences</td>
<td style={{padding: "9px 12px"}}>Keep <code>--sp-degree</code> equal to <code>ulysses_degree * ring_degree</code>.</td>
</tr>
</tbody>
</table>
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.
## Benchmarking Guidance
When benchmarking SP, compare the same model, precision, resolution, frame count, step count, scheduler settings, prompt type, and output path. Report both stage latency and peak GPU memory; SP can reduce per-GPU memory while adding communication overhead.
Useful metrics:
- End-to-end latency
- Denoising stage latency
- Decoding stage latency
- Peak GPU memory and peak allocated memory
- Communication or runtime overhead when available
## Reference Benchmark
The following numbers are a reference measurement for one setup. They are not a general promise for all Wan2.2 deployments.
- Model: `Wan-AI/Wan2.2-TI2V-5B-Diffusers`
- Hardware: two 48 GB RTX 40-series GPUs for sequence parallelism, one 48 GB RTX 40-series GPU for baseline
- Sequence parallel config: `sp=2, ulysses=1, ring=2` (`u1r2`)
- Baseline config: `sp=1, ulysses=1, ring=1` (`u1r1`)
### Stage Time Breakdown
@@ -150,9 +212,4 @@ These benchmarks are provided for reference under one specific setup and command
</tbody>
</table>
## Summary
- End-to-end latency improves from `90.63s` to `63.74s` (`1.42x`).
- Main gains come from `Denoising` (`1.36x`) and `Decoding` (`1.75x`).
- Absolute memory usage drops noticeably on Ring-SP (`Peak GPU Memory -7.33GB`, `Peak Allocated -7.05GB`).
- Overhead ratio rises (`+7.9pp`), so future tuning can focus on reducing communication/runtime overhead while preserving the latency gain.
In this setup, end-to-end latency improved from `90.63s` to `63.74s` (`1.42x`) and peak GPU memory dropped by `7.33GB`. The overhead ratio increased, so future tuning should still check communication and runtime overhead on the target hardware.