[Bench] feat: mooncake trace integration (#9839)

Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>
Signed-off-by: Teng Ma <sima.mt@alibaba-inc.com>
Co-authored-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

docs/developer_guide/bench_serving.md

@@ -305,6 +305,21 @@ python3 -m sglang.bench_serving \
   --disable-ignore-eos
 ```
 
+9) Evaluating large-scale KVCache sharing with mooncake trace (sglang only):
+
+```bash
+python3 -m sglang.bench_serving \
+  --backend sglang \
+  --host 127.0.0.1 --port 30000 \
+  --model mode-name \
+  --dataset-name mooncake \
+  --mooncake-slowdown-factor 1.0 \
+  --mooncake-num-rounds 1000 \
+  --mooncake-workload conversation|mooncake|agent|synthetic
+  --use-trace-timestamps true \
+  --random-output-len 256
+```
+
 ### Troubleshooting
 
 - All requests failed: verify `--backend`, server URL/port, `--model`, and authentication. Check warmup errors printed by the script.

python/sglang/bench_serving.py

@@ -75,6 +75,7 @@ class RequestFuncInput:
     lora_name: str
     image_data: Optional[List[str]]
     extra_request_body: Dict[str, Any]
+    timestamp: Optional[float] = None
 
 
 @dataclass
@@ -696,6 +697,22 @@ def get_dataset(args, tokenizer):
             apply_chat_template=args.apply_chat_template,
             random_sample=True,
         )
+    elif args.dataset_name == "mooncake":
+        # For mooncake, we don't generate the prompts here.
+        # We just load the raw trace data. The async generator will handle the rest.
+        if not args.dataset_path:
+            local_path = os.path.join("/tmp",  args.mooncake_workload + "_trace.jsonl")
+        else:
+            local_path = args.dataset_path
+
+        if not os.path.exists(local_path):
+            download_and_cache_file(MOONCAKE_DATASET_URL[args.mooncake_workload], local_path)
+
+        with open(local_path, "r") as f:
+            all_requests_data = [json.loads(line) for line in f if line.strip()]
+
+        # Limit the number of requests based on --num-prompts
+        input_requests = all_requests_data[: args.num_prompts]
     else:
         raise ValueError(f"Unknown dataset: {args.dataset_name}")
     return input_requests
@@ -750,6 +767,12 @@ class BenchmarkMetrics:
 
 
 SHAREGPT_URL = "https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json"
+MOONCAKE_DATASET_URL = {
+    "mooncake": "https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/arxiv-trace/mooncake_trace.jsonl",
+    "conversation": "https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/traces/conversation_trace.jsonl",
+    "synthetic": "https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/traces/synthetic_trace.jsonl",
+    "toolagent": "https://raw.githubusercontent.com/kvcache-ai/Mooncake/main/FAST25-release/traces/toolagent_trace.jsonl",
+}
 
 
 def download_and_cache_file(url: str, filename: Optional[str] = None):
@@ -808,6 +831,80 @@ class DatasetRow:
     prompt_len: int
     output_len: int
     image_data: Optional[List[str]] = None
+    timestamp: Optional[float] = None
+
+
+async def get_mooncake_request_over_time(
+    input_requests: List[Dict],
+    tokenizer: PreTrainedTokenizerBase,
+    slowdown_factor: float,
+    num_rounds: int,
+) -> AsyncGenerator[DatasetRow, None]:
+    """
+    An async generator that yields requests based on the timestamps in the Mooncake trace file,
+    with support for multi-round sessions.
+    """
+    if not input_requests:
+        return
+
+    input_requests.sort(key=lambda r: r["timestamp"])
+
+    start_time = time.perf_counter()
+    trace_start_time_ms = input_requests[0]["timestamp"]
+
+    for record in input_requests:
+        # Calculate when this entire session should start
+        relative_arrival_time_s = (record["timestamp"] - trace_start_time_ms) / 1000.0
+        target_arrival_time_s = relative_arrival_time_s * slowdown_factor
+
+        current_elapsed_time_s = time.perf_counter() - start_time
+        sleep_duration_s = target_arrival_time_s - current_elapsed_time_s
+        if sleep_duration_s > 0:
+            await asyncio.sleep(sleep_duration_s)
+
+        # Once the session starts, generate all rounds for it as a burst
+        # This simulates a user engaging in a multi-turn conversation
+
+        # Base user query constructed from hash_ids
+        user_query_base = ""
+        hash_ids = record.get("hash_ids", [])
+        for hash_id in hash_ids:
+            user_query_base += f"{hash_id}" + " ".join(
+                ["hi"] * 128
+            )  # Shorter for multi-round
+        user_query_base += "Tell me a story based on this context."
+
+        output_len_per_round = record.get("output_length", 256)
+        chat_history = []
+
+        for i in range(num_rounds):
+            # Add user query for the current round
+            chat_history.append(
+                {"role": "user", "content": f"Round {i+1}: {user_query_base}"}
+            )
+
+            # Form the full prompt from history
+            try:
+                full_prompt_text = tokenizer.apply_chat_template(
+                    chat_history, tokenize=False, add_generation_prompt=True
+                )
+            except Exception:
+                full_prompt_text = "\n".join(
+                    [f"{msg['role']}: {msg['content']}" for msg in chat_history]
+                )
+
+            prompt_len = len(tokenizer.encode(full_prompt_text))
+
+            yield DatasetRow(
+                prompt=full_prompt_text,
+                prompt_len=prompt_len,
+                output_len=output_len_per_round,
+            )
+
+            # Add a placeholder assistant response for the next round's context
+            # We use a placeholder because we don't know the real response
+            placeholder_response = " ".join(["story"] * output_len_per_round)
+            chat_history.append({"role": "assistant", "content": placeholder_response})
 
 
 def sample_mmmu_requests(
@@ -1359,19 +1456,41 @@ def sample_generated_shared_prefix_requests(
 async def get_request(
     input_requests: List[DatasetRow],
     request_rate: float,
+    use_trace_timestamps: bool = False,
+    slowdown_factor: float = 1.0,
 ) -> AsyncGenerator[DatasetRow, None]:
-    input_requests = iter(input_requests)
-    for request in input_requests:
-        yield request
+    if use_trace_timestamps:
+        print(
+            f"Using trace timestamps for request generation with slowdown factor {slowdown_factor}."
+        )
+        # Sort requests by timestamp for correct replay
+        input_requests.sort(key=lambda r: r.timestamp)
 
-        if request_rate == float("inf"):
-            # If the request rate is infinity, then we don't need to wait.
-            continue
+        start_time = time.perf_counter()
+        trace_start_time_ms = input_requests[0].timestamp if input_requests else 0
+
+        for request in input_requests:
+            trace_time_s = (request.timestamp - trace_start_time_ms) / 1000.0
+            target_arrival_time = start_time + (trace_time_s * slowdown_factor)
+
+            sleep_duration = target_arrival_time - time.perf_counter()
+            if sleep_duration > 0:
+                await asyncio.sleep(sleep_duration)
+
+            yield request
+    else:
+        input_requests_iter = iter(input_requests)
+        for request in input_requests_iter:
+            yield request
+
+            if request_rate == float("inf"):
+                # If the request rate is infinity, then we don't need to wait.
+                continue
 
-        # Sample the request interval from the exponential distribution.
-        interval = np.random.exponential(1.0 / request_rate)
-        # The next request will be sent after the interval.
-        await asyncio.sleep(interval)
+            # Sample the request interval from the exponential distribution.
+            interval = np.random.exponential(1.0 / request_rate)
+            # The next request will be sent after the interval.
+            await asyncio.sleep(interval)
 
 
 def calculate_metrics(
@@ -1397,7 +1516,7 @@ def calculate_metrics(
                 tokenizer.encode(outputs[i].generated_text, add_special_tokens=False)
             )
             retokenized_output_lens.append(retokenized_output_len)
-            total_input += input_requests[i].prompt_len
+            total_input += outputs[i].prompt_len
             if output_len > 1:
                 tpots.append((outputs[i].latency - outputs[i].ttft) / (output_len - 1))
             itls += outputs[i].itl
@@ -1469,6 +1588,9 @@ async def benchmark(
     pd_separated: bool = False,
     flush_cache: bool = False,
     warmup_requests: int = 1,
+    use_trace_timestamps: bool = False,
+    mooncake_slowdown_factor=1.0,
+    mooncake_num_rounds=1,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -1488,8 +1610,32 @@ async def benchmark(
     # Warmup
     print(f"Starting warmup with {warmup_requests} sequences...")
 
-    # Use the first request for all warmup iterations
-    test_request = input_requests[0]
+    # Handle the data structure difference for the warmup request
+    if args.dataset_name == "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]
+
+        # Build prompt from hash_ids, just like in the async generator
+        hash_ids = warmup_record.get("hash_ids", [])
+        prompt_text = ""
+        for hash_id in hash_ids:
+            prompt_text += f"{hash_id}" + " ".join(["hi"] * 512)
+        prompt_text += "Can you tell me a detailed story in 1000 words?"
+
+        output_len = warmup_record.get("output_length", 32)
+        prompt_len = len(tokenizer.encode(prompt_text))
+
+        # Create a temporary DatasetRow object for warmup
+        test_request = DatasetRow(
+            prompt=prompt_text,
+            prompt_len=prompt_len,
+            output_len=output_len,
+            image_data=None,  # Mooncake doesn't have image data
+        )
+    else:
+        # For all other datasets, input_requests is a list of DatasetRow objects
+        test_request = input_requests[0]
 
     if lora_names is not None and len(lora_names) != 0:
         lora_name = lora_names[0]
@@ -1543,12 +1689,26 @@ async def benchmark(
         if profile_output.success:
             print("Profiler started")
 
-    pbar = None if disable_tqdm else tqdm(total=len(input_requests))
-
     # Run all requests
     benchmark_start_time = time.perf_counter()
     tasks: List[asyncio.Task] = []
-    async for request in get_request(input_requests, request_rate):
+    pbar_total = len(input_requests)
+    if (
+        backend == "sglang" and args.dataset_name == "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(
+            input_requests, tokenizer, mooncake_slowdown_factor, mooncake_num_rounds
+        )
+        print(
+            f"Starting Mooncake trace replay. Sessions: {len(input_requests)}, Rounds per session: {mooncake_num_rounds}. Slowdown factor: {mooncake_slowdown_factor}"
+        )
+        pbar_total *= args.mooncake_num_rounds
+    else:
+        request_generator = get_request(input_requests, request_rate)
+
+    pbar = None if disable_tqdm else tqdm(total=pbar_total)
+    async for request in request_generator:
         if lora_names is not None and len(lora_names) != 0:
             idx = random.randint(0, len(lora_names) - 1)
             lora_name = lora_names[idx]
@@ -1564,6 +1724,7 @@ async def benchmark(
             lora_name=lora_name,
             image_data=request.image_data,
             extra_request_body=extra_request_body,
+            timestamp=request.timestamp,
         )
 
         tasks.append(
@@ -1609,7 +1770,11 @@ async def benchmark(
 
     print("\n{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
     print("{:<40} {:<10}".format("Backend:", backend))
-    print("{:<40} {:<10}".format("Traffic request rate:", request_rate))
+    print(
+        "{:<40} {:<10}".format(
+            "Traffic request rate:", "trace" if use_trace_timestamps else request_rate
+        )
+    )
     print(
         "{:<40} {:<10}".format(
             "Max request concurrency:",
@@ -1678,7 +1843,7 @@ async def benchmark(
             # Arguments
             "backend": args.backend,
             "dataset_name": args.dataset_name,
-            "request_rate": request_rate,
+            "request_rate": "trace" if use_trace_timestamps else request_rate,
             "max_concurrency": max_concurrency,
             "sharegpt_output_len": args.sharegpt_output_len,
             "random_input_len": args.random_input_len,
@@ -1731,7 +1896,9 @@ async def benchmark(
         elif args.dataset_name.startswith("random"):
             output_file_name = f"{args.backend}_{now}_{args.num_prompts}_{args.random_input_len}_{args.random_output_len}.jsonl"
         else:
-            output_file_name = f"{args.backend}_{now}_{args.num_prompts}_sharegpt.jsonl"
+            output_file_name = (
+                f"{args.backend}_{now}_{args.num_prompts}_{args.dataset_name}.jsonl"
+            )
 
     result_details = {
         "input_lens": [output.prompt_len for output in outputs],
@@ -1786,6 +1953,17 @@ def run_benchmark(args_: argparse.Namespace):
     if not hasattr(args, "tokenize_prompt"):
         args.tokenize_prompt = False
 
+    if not hasattr(args, "use_trace_timestamps"):
+        args.use_trace_timestamps = False
+    if not hasattr(args, "mooncake_slowdown_factor"):
+        args.mooncake_slowdown_factor = 1.0
+
+    if not hasattr(args, "mooncake_slowdown_factor"):
+        args.mooncake_slowdown_factor = 1.0
+
+    if not hasattr(args, "mooncake_num_rounds"):
+        args.mooncake_num_rounds = 1
+
     print(f"benchmark_args={args}")
 
     # Set global environments
@@ -1919,6 +2097,9 @@ def run_benchmark(args_: argparse.Namespace):
             pd_separated=args.pd_separated,
             flush_cache=args.flush_cache,
             warmup_requests=args.warmup_requests,
+            use_trace_timestamps=args.use_trace_timestamps,
+            mooncake_slowdown_factor=args.mooncake_slowdown_factor,
+            mooncake_num_rounds=args.mooncake_num_rounds,
         )
     )
 
@@ -1975,6 +2156,7 @@ if __name__ == "__main__":
             "generated-shared-prefix",
             "mmmu",
             "random-image",
+            "mooncake",
         ],
         help="Name of the dataset to benchmark on.",
     )
@@ -2051,6 +2233,11 @@ if __name__ == "__main__":
         help="Number of requests per second. If this is inf, then all the requests are sent at time 0. "
         "Otherwise, we use Poisson process to synthesize the request arrival times. Default is inf.",
     )
+    parser.add_argument(
+        "--use-trace-timestamps",
+        action="store_true",
+        help="Use timestamps from the trace file for request scheduling. Only valid for 'mooncake' dataset.",
+    )
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -2174,5 +2361,33 @@ if __name__ == "__main__":
         default=256,
         help="Target length in tokens for outputs in generated-shared-prefix dataset",
     )
+    mooncake_group = parser.add_argument_group("mooncake dataset arguments")
+    mooncake_group.add_argument(
+        "--mooncake-slowdown-factor",
+        type=float,
+        default=1.0,
+        help="Slowdown factor for replaying the mooncake trace. "
+        "A value of 2.0 means the replay is twice as slow. "
+        "NOTE: --request-rate is IGNORED in mooncake mode.",
+    )
+    mooncake_group.add_argument(
+        "--mooncake-num-rounds",
+        type=int,
+        default=1,
+        help="Number of conversation rounds for each session in the mooncake dataset. "
+        "A value > 1 will enable true multi-turn session benchmarking.",
+    )
+    mooncake_group.add_argument(
+        "--mooncake-workload",
+        type=str,
+        default="conversation",
+        choices=[
+            "mooncake",
+            "conversation",
+            "synthetic",
+            "toolagent",
+        ],
+        help="Underlying workload for the mooncake dataset.",
+    )
     args = parser.parse_args()
     run_benchmark(args)