Fix Mooncake serving benchmark trace rows (#37201)

python/sglang/benchmark/serving.py

@@ -1340,7 +1340,7 @@ async def benchmark(
     base_url: str,
     model_id: str,
     tokenizer: PreTrainedTokenizerBase,
-    input_requests: List[DatasetRow],
+    input_requests: List[Union[DatasetRow, Dict[str, Any]]],
     request_rate: float,
     max_concurrency: Optional[int],
     disable_tqdm: bool,
@@ -1364,8 +1364,14 @@ async def benchmark(
     else:
         raise ValueError(f"Unknown backend: {backend}")
 
+    is_mooncake = args.dataset_name == "mooncake"
     # Multi-turn iff prompt[0] is a valid per-round payload. Single-shot
     # OpenAI messages (List[Dict]) is excluded since its first element is a dict.
+    if is_mooncake:
+        # Mooncake dataset rows are raw trace dictionaries. They are converted
+        # into DatasetRow objects by get_mooncake_request_over_time below.
+        is_multi_turn = False
+    else:
         first_prompt = input_requests[0].prompt
         is_multi_turn = (
             isinstance(first_prompt, list)
@@ -1389,7 +1395,7 @@ async def benchmark(
     print(f"Starting warmup with {warmup_requests} sequences...")
 
     # Handle the data structure difference for the warmup request
-    if args.dataset_name == "mooncake":
+    if is_mooncake:
         # For mooncake, input_requests is a list of dicts.
         # We need to build a temporary DatasetRow for the warmup phase.
         warmup_record = input_requests[0]
@@ -1493,7 +1499,7 @@ async def benchmark(
     tasks: List[asyncio.Task] = []
     pbar_total = len(input_requests)
     if (
-        backend == "sglang" and args.dataset_name == "mooncake"
+        backend == "sglang" and is_mooncake
     ):  # Assuming mooncake is mainly for sglang or similar backends
         print("Using time-based Mooncake request scheduler, ignoring --request-rate.")
         request_generator = get_mooncake_request_over_time(
@@ -1517,7 +1523,9 @@ async def benchmark(
         lora_probs = None
 
     pbar = None if disable_tqdm else tqdm(total=pbar_total)
+    benchmark_requests: List[DatasetRow] = []
     async for request in request_generator:
+        benchmark_requests.append(request)
         if lora_names is not None and len(lora_names) != 0:
             if lora_request_distribution == "uniform":
                 lora_name = random.choice(lora_names)
@@ -1603,7 +1611,7 @@ async def benchmark(
     # Compute metrics and print results
     benchmark_duration = time.perf_counter() - benchmark_start_time
     metrics, output_lens = calculate_metrics(
-        input_requests=None if is_multi_turn else input_requests,
+        input_requests=None if is_multi_turn else benchmark_requests,
         outputs=outputs,
         dur_s=benchmark_duration,
         tokenizer=tokenizer,