[PD]feat(bench): add --fake-prefill flag for decode-only stress testing (#22973)

docs/developer_guide/bench_serving.md

@@ -341,6 +341,41 @@ python3 -m sglang.bench_serving \
   --random-output-len 256
 ```
 
+10) Fake decode stress testing (PD disaggregation, decode-only):
+
+When benchmarking pure decode performance in a PD disaggregation setup, you can bypass the prefill node entirely by using `--fake-prefill`. This requires the decode server to be started with `--disaggregation-transfer-backend fake`:
+
+```bash
+# Step 1: Start a decode-only server with fake transfer backend
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-3.1-8B-Instruct \
+  --disaggregation-mode decode \
+  --disaggregation-transfer-backend fake \
+  --port 30001
+
+# Step 2: Run bench_serving with --fake-prefill
+python3 -m sglang.bench_serving \
+  --backend sglang \
+  --host 127.0.0.1 --port 30001 \
+  --model meta-llama/Llama-3.1-8B-Instruct \
+  --dataset-name random \
+  --num-prompts 500 \
+  --random-input-len 1024 --random-output-len 256 \
+  --fake-prefill
+```
+
+Similarly, `bench_one_batch_server` also supports `--fake-prefill`:
+
+```bash
+python3 -m sglang.bench_one_batch_server \
+  --base-url http://127.0.0.1:30001 \
+  --model-path meta-llama/Llama-3.1-8B-Instruct \
+  --batch-size 32 --input-len 1024 --output-len 256 \
+  --fake-prefill
+```
+
+The `--fake-prefill` flag automatically injects special sentinel values into each request, telling the decode server to skip real KV transfer and generate fake KV data locally.
+
 ### Troubleshooting
 
 - All requests failed: verify `--backend`, server URL/port, `--model`, and authentication. Check warmup errors printed by the script.

python/sglang/bench_serving.py

@@ -44,6 +44,7 @@ from sglang.benchmark.utils import (
     remove_prefix,
     set_ulimit,
 )
+from sglang.srt.disaggregation.utils import FAKE_BOOTSTRAP_HOST
 from sglang.srt.utils.network import NetworkAddress
 
 _ROUTING_KEY_HEADER = "X-SMG-Routing-Key"
@@ -1709,6 +1710,11 @@ def run_benchmark(args_: argparse.Namespace):
     if args.extra_request_body:
         extra_request_body = json.loads(args.extra_request_body)
 
+    # Inject bootstrap fields for fake decode benchmarking
+    if getattr(args, "fake_prefill", False):
+        extra_request_body["bootstrap_host"] = FAKE_BOOTSTRAP_HOST
+        extra_request_body["bootstrap_room"] = 0
+
     if args.tokenize_prompt:
         assert (
             args.backend == "sglang"
@@ -2339,6 +2345,14 @@ if __name__ == "__main__":
         ],
         help="Underlying workload for the mooncake dataset.",
     )
+    parser.add_argument(
+        "--fake-prefill",
+        action="store_true",
+        default=False,
+        help="Enable fake prefill mode for decode-only benchmarking. "
+        "Use with a decode server running --disaggregation-transfer-backend fake "
+        "to benchmark pure decode performance without a real prefill node.",
+    )
     parser.add_argument(
         "--tag", type=str, default=None, help="The tag to be dumped to output."
     )

python/sglang/test/bench_one_batch_server_internal.py

@@ -19,6 +19,7 @@ from transformers import AutoProcessor, PreTrainedTokenizer
 from sglang.benchmark.datasets import get_dataset
 from sglang.benchmark.utils import get_processor, get_tokenizer
 from sglang.profiler import run_profile
+from sglang.srt.disaggregation.utils import FAKE_BOOTSTRAP_HOST
 from sglang.srt.entrypoints.http_server import launch_server
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import is_blackwell, kill_process_tree
@@ -113,6 +114,7 @@ class BenchArgs:
     seed: int = 42
     cache_hit_rate: float = 0.0
     backend: str = "sglang"
+    fake_prefill: bool = False
     server_args_for_metrics: Optional[List[str]] = None
 
     @staticmethod
@@ -242,6 +244,14 @@ class BenchArgs:
             choices=["sglang", "vllm"],
             help="Backend server type (sglang or vllm).",
         )
+        parser.add_argument(
+            "--fake-prefill",
+            action="store_true",
+            default=BenchArgs.fake_prefill,
+            help="Enable fake prefill mode for decode-only benchmarking. "
+            "Use with a decode server running --disaggregation-transfer-backend fake "
+            "to benchmark pure decode performance without a real prefill node.",
+        )
         parser.add_argument(
             "--server-args-for-metrics",
             type=str,
@@ -426,6 +436,7 @@ def run_one_case(
     gsp_system_prompt_len: int = BenchArgs.gsp_system_prompt_len,
     gsp_question_len: int = BenchArgs.gsp_question_len,
     gsp_output_len: int = BenchArgs.gsp_output_len,
+    fake_prefill: bool = False,
 ):
     if backend == "vllm":
         # You need to have export VLLM_SERVER_DEV_MODE=1 in your environment to use this endpoint.
@@ -520,6 +531,9 @@ def run_one_case(
         payload["input_ids"] = input_ids
         if image_data is not None:
             payload["image_data"] = image_data
+        if fake_prefill:
+            payload["bootstrap_host"] = FAKE_BOOTSTRAP_HOST
+            payload["bootstrap_room"] = 0
         gen_url = url + "/generate"
 
     # Warm up cache if cache_hit_rate > 0.0
@@ -864,6 +878,7 @@ def run_benchmark_internal(
                 parallel_batch=bench_args.parallel_batch,
                 backend=bench_args.backend,
                 model_name=model_name,
+                fake_prefill=bench_args.fake_prefill,
                 **gsp_kwargs,
             )
         print("=" * 8 + " Warmup End   " + "=" * 8 + "\n")
@@ -900,6 +915,7 @@ def run_benchmark_internal(
                     cache_hit_rate=bench_args.cache_hit_rate,
                     backend=bench_args.backend,
                     model_name=model_name,
+                    fake_prefill=bench_args.fake_prefill,
                     **gsp_kwargs,
                 )
             )
@@ -945,6 +961,7 @@ def run_benchmark_internal(
                             profile_output_dir=bench_args.profile_output_dir,
                             backend=bench_args.backend,
                             model_name=model_name,
+                            fake_prefill=bench_args.fake_prefill,
                             **gsp_kwargs,
                         )
                     )