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from __future__ import annotations
import os
import json
import atexit
import argparse
import subprocess
from pathlib import Path
from typing import Literal
def set_power_limit(power_limit: int, gpu_ids: list[int]) -> None:
for gpu_id in gpu_ids:
subprocess.check_call([
"docker", "exec", "nvml",
"nvidia-smi", "-i", str(gpu_id), "-pm", "1",
])
subprocess.check_call([
"docker", "exec", "nvml",
"nvidia-smi", "-i", str(gpu_id), "-pl", str(power_limit),
])
def start_server(
backend: Literal["vllm", "tgi"],
server_image: str,
port: int,
model: str,
huggingface_token: str,
gpu_ids: list[int],
max_num_seqs: int,
log_level: str,
result_root: str,
benchmark_name: str,
) -> str:
gpu_str = ",".join(str(gpu_id) for gpu_id in gpu_ids)
gpu_str = f'"device={gpu_str}"'
hf_cache_path = "/data/leaderboard/hfcache"
models_dir = f"{os.getcwd()}/models"
revision_filename = f"{model}/revision.txt"
revision_path = f"{models_dir}/{revision_filename}"
container_name = f"leaderboard-{backend}-{''.join(str(gpu_id) for gpu_id in gpu_ids)}"
assert Path(hf_cache_path).exists(), f"Hugging Face cache not found: {hf_cache_path}"
assert Path(revision_path).exists(), f"Revision file not found: {revision_path}"
if backend == "vllm":
server_cmd = [
"docker", "run",
"--gpus", gpu_str,
"--ipc", "host",
"--name", container_name,
"-e", f"HF_TOKEN={huggingface_token}",
"-e", f"LOG_LEVEL={log_level}",
"-e", f"RESULT_FILE_PREFIX=/results/{benchmark_name}",
"-p", f"{port}:8000",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{result_root}:/results",
server_image,
"--model", model,
"--revision", open(revision_path).read().strip(),
"--tensor-parallel-size", str(len(gpu_ids)),
"--gpu-memory-utilization", "0.95",
"--trust-remote-code",
"--enable-chunked-prefill", "False",
"--max-model-len", "4096",
"--disable-frontend-multiprocessing",
"--max-num-seqs", str(max_num_seqs),
]
elif backend == "tgi":
server_cmd = [
"docker", "run",
"--gpus", gpu_str,
"--ipc", "host",
"--name", container_name,
"-e", f"HUGGING_FACE_HUB_TOKEN={huggingface_token}",
"-e", f"LOG_LEVEL={log_level}",
"-p", f"{port}:80",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{models_dir}:/models",
server_image,
"--model-id", model,
"--revision", open(revision_path).read().strip(),
"--huggingface-hub-cache", "/root/.cache/huggingface/hub",
"--num-shard", str(len(gpu_ids)),
"--cuda-memory-fraction", "0.95",
"--max-concurrent-requests", "512",
"--max-stop-sequences", "7",
"--trust-remote-code",
]
else:
raise ValueError(f"Unknown backend: {backend}")
print("Server:", " ".join(server_cmd))
subprocess.Popen(server_cmd)
return container_name
def start_client(
backend: Literal["vllm", "tgi"],
port: int,
model: str,
dataset: str,
request_rate: str,
gpu_ids: list[int],
benchmark_name: str,
power_limit: int,
max_num_seqs: int,
data_dup_factor: int,
) -> subprocess.Popen:
client_cmd = [
"python", "scripts/benchmark_client.py",
"--backend", backend,
"--port", str(port),
"--model", model,
"--dataset", dataset,
"--request-rate", request_rate,
"--benchmark-name", benchmark_name,
"--power-limit", str(power_limit),
"--max-num-seqs", str(max_num_seqs),
"--data-dup-factor", str(data_dup_factor),
]
print("Client:", " ".join(client_cmd))
return subprocess.Popen(
client_cmd,
env=os.environ | {"CUDA_VISIBLE_DEVICES": ",".join(str(gpu_id) for gpu_id in gpu_ids)},
)
def terminate_server(container_name: str) -> None:
subprocess.run(["docker", "kill", "-s", "INT", container_name])
subprocess.run(["timeout", "30", "docker", "wait", container_name])
subprocess.run(["docker", "rm", "-f", container_name])
def run_evalplus_eval(dataset: str, benchmark_name: str) -> None:
benchmark_path = Path(benchmark_name)
results_dir = benchmark_path.parent.absolute()
benchmark_filename = f"{benchmark_path.name}+results+evalplus.jsonl"
assert results_dir.exists(), f"Results directory not found: {results_dir}"
assert (results_dir / benchmark_filename).exists(), f"Benchmark file not found: {results_dir / benchmark_filename}"
evalplus_cmd = [
"docker", "run",
"-v", f"{results_dir}:/app",
"ganler/evalplus:v0.2.0",
"--dataset", dataset,
"--samples", benchmark_filename,
]
print("EvalPlus:", " ".join(evalplus_cmd))
output = subprocess.check_output(evalplus_cmd).decode("utf-8")
print(output)
key = ""
results = {}
for line in output.split("\n"):
if "Base" in line:
key = line.strip()
if "pass@1" in line:
results[key] = float(line.split(" ")[1][:-1])
with open(f"{benchmark_name}+results+evalplus_acc.json", "w", encoding="utf-8") as f:
json.dump(results, f, indent=2)
def main(args: argparse.Namespace) -> None:
if args.model.startswith("models/"):
args.model = args.model[len("models/"):]
if args.model.endswith("/"):
args.model = args.model[:-1]
results_dir = Path(args.result_root) / args.model
results_dir.mkdir(parents=True, exist_ok=True)
benchmark_name = f"{args.backend}+rate{args.request_rate}+pl{args.power_limit}+maxbs{args.max_num_seqs}+gpus{''.join(str(i) for i in args.gpu_ids)}"
if args.mode == "codegen":
results_dir.mkdir(parents=True, exist_ok=True)
port = 8000 + args.gpu_ids[0]
server_handle = start_server(
args.backend,
args.server_image,
port,
args.model,
args.huggingface_token,
args.gpu_ids,
args.max_num_seqs,
args.log_level,
str(results_dir.absolute()),
benchmark_name,
)
kill_fn = lambda: terminate_server(server_handle)
atexit.register(kill_fn)
set_power_limit(args.power_limit, args.gpu_ids)
client_handle = start_client(
args.backend,
port,
args.model,
args.dataset,
args.request_rate,
args.gpu_ids,
str(results_dir / benchmark_name),
args.power_limit,
args.max_num_seqs,
args.data_dup_factor,
)
try:
exit_code = client_handle.wait(timeout=2 * 3600)
except subprocess.TimeoutExpired:
client_handle.terminate()
raise RuntimeError("Benchmark client timed out after two hours")
if exit_code != 0:
raise RuntimeError(f"Benchmark client exited with code {exit_code}")
terminate_server(server_handle)
atexit.unregister(kill_fn)
elif args.mode == "eval":
run_evalplus_eval(args.dataset, str(results_dir / benchmark_name))
else:
raise ValueError(f"Unknown mode: {args.mode}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--backend", required=True, choices=["vllm", "tgi"], help="Server to benchmark.")
parser.add_argument("--server-image", required=True, help="Docker image to use for the server.")
parser.add_argument("--model", required=True, help="Model to benchmark, e.g., meta-llama/Llama-2-7b-chat-hf.")
parser.add_argument("--dataset", required=True, choices=["humaneval", "mbpp"], help="EvalPlus dataset to use.")
parser.add_argument("--request-rate", required=True, help="Poisson process rate for request arrival times.")
parser.add_argument("--max-num-seqs", required=True, help="vLLM --max-num-seqs to use.")
parser.add_argument("--power-limit", type=int, required=True, help="GPU power limit in Watts.")
parser.add_argument("--result-root", default="results", help="Root directory to save results.")
parser.add_argument("--huggingface-token", required=True, help="Hugging Face API token.")
parser.add_argument("--gpu-ids", nargs="+", type=int, required=True, help="GPU IDs to use for the server.")
parser.add_argument("--log-level", default="INFO", help="Logging level for the server.")
parser.add_argument("--mode", required=True, choices=["codegen", "eval"], help="Mode to run the script in.")
parser.add_argument("--data-dup-factor", type=int, default=1, help="How many times to repeat the dataset to generate more requests.")
main(parser.parse_args())
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