bench_serving support PD Disaggregation (#11542)

docs/advanced_features/pd_disaggregation.md

@@ -17,6 +17,10 @@ For the design details, please refer to [link](https://docs.google.com/document/
 
 Currently, we support Mooncake and NIXL as the transfer engine.
 
+## Profiling in PD Disaggregation Mode
+
+When you need to profile prefill or decode workers in PD disaggregation mode, please refer to the [Profile In PD Disaggregation Mode](https://docs.sglang.ai/developer_guide/benchmark_and_profiling.html#profile-in-pd-disaggregation-mode) section in the Benchmark and Profiling guide. Due to torch profiler limitations, prefill and decode workers must be profiled separately using dedicated command-line options.
+
 ## Router Integration
 
 For deploying PD disaggregation at scale with load balancing and fault tolerance, SGLang provides a router. The router can distribute requests between prefill and decode instances using various routing policies. For detailed information on setting up routing with PD disaggregation, including configuration options and deployment patterns, see the [SGLang Router documentation](router.md#mode-3-prefill-decode-disaggregation).

docs/developer_guide/benchmark_and_profiling.md

@@ -47,6 +47,48 @@ Please make sure that the `SGLANG_TORCH_PROFILER_DIR` should be set at both serv
 
 For more details, please refer to [Bench Serving Guide](./bench_serving.md).
 
+### Profile In PD Disaggregation Mode
+
+When profiling in PD disaggregation mode, prefill and decode workers **must be profiled separately** due to torch profiler limitations. The `bench_serving` command provides dedicated options for this:
+
+#### Profile Prefill Workers
+
+```bash
+# set trace path
+export SGLANG_TORCH_PROFILER_DIR=/root/sglang/profile_log
+
+# start prefill and decode servers (see PD disaggregation docs for setup)
+python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode prefill
+python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode decode --port 30001 --base-gpu-id 1
+
+# start router
+python -m sglang_router.launch_router --pd-disaggregation --prefill http://127.0.0.1:30000 --decode http://127.0.0.1:30001 --host 0.0.0.0 --port 8000
+
+# send profiling request targeting prefill workers
+python -m sglang.bench_serving --backend sglang --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 10 --sharegpt-output-len 100 --profile --pd-separated --profile-prefill-url http://127.0.0.1:30000
+```
+
+#### Profile Decode Workers
+
+```bash
+# send profiling request targeting decode workers
+python -m sglang.bench_serving --backend sglang --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 10 --sharegpt-output-len 100 --profile --pd-separated --profile-decode-url http://127.0.0.1:30001
+```
+
+#### Important Notes
+
+- `--profile-prefill-url` and `--profile-decode-url` are **mutually exclusive** - you cannot profile both at the same time
+- Both options support multiple worker URLs for multi-instance setups:
+  ```bash
+  # Profile multiple prefill workers
+  python -m sglang.bench_serving --backend sglang --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 10 --profile --pd-separated --profile-prefill-url http://127.0.0.1:30000 http://127.0.0.1:30002
+
+  # Profile multiple decode workers
+  python -m sglang.bench_serving --backend sglang --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 10 --profile --pd-separated --profile-decode-url http://127.0.0.1:30001 http://127.0.0.1:30003
+  ```
+- Make sure `SGLANG_TORCH_PROFILER_DIR` is set on all worker nodes before starting the servers
+- For more details on setting up PD disaggregation, see [PD Disaggregation Guide](../advanced_features/pd_disaggregation.md)
+
 ### Profile a server with `sglang.bench_offline_throughput`
 ```bash
 export SGLANG_TORCH_PROFILER_DIR=/root/sglang/profile_log