Rename sglang.bench_latency to sglang.bench_one_batch (#2118)

python/sglang/bench_one_batch_server.py

@@ -0,0 +1,191 @@
+"""
+Benchmark the latency of running a single batch with a server.
+
+This script launches a server and uses the HTTP interface.
+It accepts server arguments (the same as launch_server.py) and benchmark arguments (e.g., batch size, input lengths).
+
+Usage:
+python3 -m sglang.bench_server_latency --model meta-llama/Meta-Llama-3.1-8B --batch-size 1 16 64 --input-len 1024 --output-len 8
+
+python3 -m sglang.bench_server_latency --model None --base-url http://localhost:30000 --batch-size 16 --input-len 1024 --output-len 8
+"""
+
+import argparse
+import dataclasses
+import itertools
+import json
+import multiprocessing
+import time
+from typing import Tuple
+
+import numpy as np
+import requests
+
+from sglang.srt.server import launch_server
+from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import kill_child_process
+
+
+@dataclasses.dataclass
+class BenchArgs:
+    run_name: str = "default"
+    batch_size: Tuple[int] = (1,)
+    input_len: Tuple[int] = (1024,)
+    output_len: Tuple[int] = (16,)
+    result_filename: str = "result.jsonl"
+    base_url: str = ""
+    skip_warmup: bool = False
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser):
+        parser.add_argument("--run-name", type=str, default=BenchArgs.run_name)
+        parser.add_argument(
+            "--batch-size", type=int, nargs="+", default=BenchArgs.batch_size
+        )
+        parser.add_argument(
+            "--input-len", type=int, nargs="+", default=BenchArgs.input_len
+        )
+        parser.add_argument(
+            "--output-len", type=int, nargs="+", default=BenchArgs.output_len
+        )
+        parser.add_argument(
+            "--result-filename", type=str, default=BenchArgs.result_filename
+        )
+        parser.add_argument("--base-url", type=str, default=BenchArgs.base_url)
+        parser.add_argument("--skip-warmup", action="store_true")
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace):
+        # use the default value's type to case the args into correct types.
+        attrs = [(attr.name, type(attr.default)) for attr in dataclasses.fields(cls)]
+        return cls(
+            **{attr: attr_type(getattr(args, attr)) for attr, attr_type in attrs}
+        )
+
+
+def launch_server_internal(server_args):
+    try:
+        launch_server(server_args)
+    except Exception as e:
+        raise e
+    finally:
+        kill_child_process()
+
+
+def launch_server_process(server_args: ServerArgs):
+    proc = multiprocessing.Process(target=launch_server_internal, args=(server_args,))
+    proc.start()
+    base_url = f"http://{server_args.host}:{server_args.port}"
+    timeout = 600
+
+    start_time = time.time()
+    while time.time() - start_time < timeout:
+        try:
+            headers = {
+                "Content-Type": "application/json; charset=utf-8",
+            }
+            response = requests.get(f"{base_url}/v1/models", headers=headers)
+            if response.status_code == 200:
+                return proc, base_url
+        except requests.RequestException:
+            pass
+        time.sleep(10)
+    raise TimeoutError("Server failed to start within the timeout period.")
+
+
+def run_one_case(
+    url: str,
+    batch_size: int,
+    input_len: int,
+    output_len: int,
+    run_name: str,
+    result_filename: str,
+):
+    input_ids = [
+        [int(x) for x in np.random.randint(0, high=16384, size=(input_len,))]
+        for _ in range(batch_size)
+    ]
+
+    tic = time.time()
+    response = requests.post(
+        url + "/generate",
+        json={
+            "input_ids": input_ids,
+            "sampling_params": {
+                "temperature": 0,
+                "max_new_tokens": output_len,
+                "ignore_eos": True,
+            },
+        },
+    )
+    latency = time.time() - tic
+
+    _ = response.json()
+    output_throughput = batch_size * output_len / latency
+    overall_throughput = batch_size * (input_len + output_len) / latency
+
+    print(f"batch size: {batch_size}")
+    print(f"latency: {latency:.2f} s")
+    print(f"output throughput: {output_throughput:.2f} token/s")
+    print(f"(input + output) throughput: {overall_throughput:.2f} token/s")
+
+    if result_filename:
+        with open(result_filename, "a") as fout:
+            res = {
+                "run_name": run_name,
+                "batch_size": batch_size,
+                "input_len": input_len,
+                "output_len": output_len,
+                "latency": round(latency, 4),
+                "output_throughput": round(output_throughput, 2),
+                "overall_throughput": round(overall_throughput, 2),
+            }
+            fout.write(json.dumps(res) + "\n")
+
+
+def run_benchmark(server_args: ServerArgs, bench_args: BenchArgs):
+    if bench_args.base_url:
+        proc, base_url = None, bench_args.base_url
+    else:
+        proc, base_url = launch_server_process(server_args)
+
+    # warmup
+    if not bench_args.skip_warmup:
+        run_one_case(
+            base_url,
+            batch_size=16,
+            input_len=1024,
+            output_len=16,
+            run_name="",
+            result_filename="",
+        )
+
+    # benchmark
+    try:
+        for bs, il, ol in itertools.product(
+            bench_args.batch_size, bench_args.input_len, bench_args.output_len
+        ):
+            run_one_case(
+                base_url,
+                bs,
+                il,
+                ol,
+                bench_args.run_name,
+                bench_args.result_filename,
+            )
+    finally:
+        if proc:
+            kill_child_process(proc.pid, include_self=True)
+
+    print(f"\nResults are saved to {bench_args.result_filename}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    BenchArgs.add_cli_args(parser)
+    args = parser.parse_args()
+    server_args = ServerArgs.from_cli_args(args)
+    bench_args = BenchArgs.from_cli_args(args)
+
+    run_benchmark(server_args, bench_args)