[FEAT] support multi-stage deployment (#2396)

Signed-off-by: ZhengWG <zwg0606@gmail.com>
Signed-off-by: Zheng Wengang <zwg0606@gmail.com>
Signed-off-by: Peiqi Yin <60515999+yinpeiqi@users.noreply.github.com>
Signed-off-by: yinpe <11810305@mail.sustech.edu.cn>
Signed-off-by: yinpeiqi <yinpeiqi809@gmail.com>
Co-authored-by: Peiqi Yin <60515999+yinpeiqi@users.noreply.github.com>
Co-authored-by: yinpe <11810305@mail.sustech.edu.cn>
Co-authored-by: yinpeiqi <yinpeiqi809@gmail.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>
Co-authored-by: Chenguang Zheng <645327136@qq.com>

docs/user_guide/diffusion/multi_replica_serving.md

@@ -0,0 +1,80 @@
+# Diffusion Multi-Replica Serving
+
+Diffusion stages can be replicated in online serving by setting `runtime.num_replicas` on a `stage_type: diffusion` stage. Each replica owns an independent diffusion engine process and receives requests through round-robin routing with request affinity.
+
+## When to Use
+
+Use multi-replica serving when one diffusion process does not saturate the available GPUs, or when you want higher request throughput for many independent image/video generation requests. It improves concurrency throughput; it does not make a single request faster.
+
+## Configuration
+
+A replica consumes the devices required by that diffusion stage. For single-device diffusion pipelines, set `runtime.devices` to one device per replica:
+
+```yaml
+stages:
+  - stage_id: 0
+    stage_type: diffusion
+    runtime:
+      devices: "0,1,2,3"
+      num_replicas: 4
+    engine_args:
+      model: Qwen/Qwen-Image
+      parallel_config:
+        world_size: 1
+```
+
+For multi-device diffusion pipelines, `parallel_config.world_size` is treated as the number of devices per replica. For example, four 2-GPU replicas need eight devices:
+
+```yaml
+stages:
+  - stage_id: 0
+    stage_type: diffusion
+    runtime:
+      devices: "0,1,2,3,4,5,6,7"
+      num_replicas: 4
+    engine_args:
+      model: Wan-AI/Wan2.2-T2V-A14B-Diffusers
+      parallel_config:
+        world_size: 2
+```
+
+The runtime splits the device list into contiguous chunks per replica. With the second example, replica 0 uses `0,1`, replica 1 uses `2,3`, replica 2 uses `4,5`, and replica 3 uses `6,7`.
+
+## Serving
+
+Start the server with the stage config:
+
+```bash
+vllm serve Qwen/Qwen-Image \
+  --omni \
+  --port 8099 \
+  --stage-configs-path /path/to/diffusion_multi_replica.yaml
+```
+
+Then send normal OpenAI-compatible diffusion chat requests. The orchestrator routes new requests across replicas and keeps updates/abort calls on the same replica for each request.
+
+## Benchmarking
+
+Benchmark each replica count by changing `runtime.num_replicas` and `runtime.devices`, restarting the server, then running:
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --base-url http://localhost:8099 \
+  --model Qwen/Qwen-Image \
+  --task t2i \
+  --dataset random \
+  --num-prompts 8 \
+  --request-rate inf \
+  --num-inference-steps 2 \
+  --width 256 --height 256 \
+  --output-file diffusion_replica_4.json
+```
+
+For a quick scaling sweep, test `num_replicas` values `1`, `2`, `3`, and `4` with the same prompt count and generation parameters. Compare throughput and end-to-end latency from the JSON outputs.
+
+## Notes and Limitations
+
+- Multi-replica serving is currently supported for diffusion stages. Single-stage mode still rejects non-diffusion replicas.
+- `runtime.devices` must provide exactly `num_replicas * devices_per_replica` devices.
+- Request-level latency may stay similar or slightly increase because each request still runs on one replica.
+- Throughput improves only when enough concurrent requests are issued and each replica has enough GPU memory.