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from __future__ import annotations
import os
import json
import shlex
import atexit
import argparse
import subprocess
from pathlib import Path
import time
from typing import Literal
import requests
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,
nnodes: int,
node_id: int,
head_node_address: str,
log_level: str,
preemption_mode: 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"
tokconf_filename = f"{model}/tokenizer_config.json"
tokconf_path = f"{models_dir}/{tokconf_filename}"
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)}"
dcgm_sock_path = "/var/run/nvidia-dcgm.sock"
assert Path(hf_cache_path).exists(), f"Hugging Face cache not found: {hf_cache_path}"
assert Path(tokconf_path).exists(), f"Tokenizer config not found: {tokconf_path}"
assert Path(revision_path).exists(), f"Revision file not found: {revision_path}"
if backend == "vllm":
extra_docker_args = []
if "google/gemma-2-" in model:
extra_docker_args = ["-e", "VLLM_ATTENTION_BACKEND=FLASHINFER"]
# Single node benchmark, not much to worry about.
if nnodes == 1:
server_cmd = [
"docker", "run",
"--gpus", gpu_str,
"--ipc", "host",
"--net", "host",
"--name", container_name,
"-e", f"HF_TOKEN={huggingface_token}",
"-e", f"LOG_LEVEL={log_level}",
"-e", f"RESULT_FILE_PREFIX=/results/{benchmark_name}",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{result_root}:/results",
*extra_docker_args,
server_image,
"--port", str(port),
"--model", model,
"--revision", open(revision_path).read().strip(),
"--chat-template", json.load(open(tokconf_path))["chat_template"],
"--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),
"--preemption-mode", preemption_mode,
"--swap-space", "4" if preemption_mode == "recompute" else "100",
]
# Multi-node benchmark, need to distinguish Ray head and worker nodes.
# Currently NCCL environment variables are tailored for AWS environments. Tested for p4d.24xlarge and p5.48xlarge.
else:
instance_type = requests.get("http://169.254.169.254/latest/meta-data/instance-type").text
if instance_type == "p4d.24xlarge":
nccl_envs = ["-e", "NCCL_SOCKET_IFNAME=ens32", "-e", "NCCL_DEBUG=Info", "-e", "FI_PROVIDER=efa", "-e", "FI_EFA_USE_DEVICE_RDMA=1"]
elif instance_type == "p5.48xlarge":
nccl_envs = ["-e", "NCCL_SOCKET_IFNAME=enp71s0", "-e", "NCCL_DEBUG=Info", "-e", "FI_PROVIDER=efa", "-e", "FI_EFA_USE_DEVICE_RDMA=1"]
else:
nccl_envs = ["-e", "NCCL_DEBUG=Info"]
print("WARNING: AWS instance type is neither p4d.24xlarge or p5.48xlarge. This script has not been tested on other instances.")
# Script is running on the head node.
if node_id == 0:
time.sleep(3) # Wait for the worker nodes to start.
cmd = " ".join([
"ray", "start", "--head", "--port=6379", "&&",
"sleep", "5", "&&",
"python3", "-m", "vllm.entrypoints.openai.api_server",
"--model", model,
"--revision", open(revision_path).read().strip(),
"--chat-template", shlex.quote(json.load(open(tokconf_path))["chat_template"]),
"--tensor-parallel-size", str(len(gpu_ids)),
"--pipeline-parallel-size", str(nnodes),
"--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),
"--preemption-mode", preemption_mode,
"--swap-space", "4" if preemption_mode == "recompute" else "100",
])
server_cmd = [
"docker", "run",
"--gpus", gpu_str,
"--ipc", "host",
"--net", "host",
"--name", container_name,
"--privileged",
"--device", "/dev/infiniband",
"--entrypoint", "/bin/bash",
*nccl_envs,
"-e", f"HF_TOKEN={huggingface_token}",
"-e", f"LOG_LEVEL={log_level}",
"-e", f"RESULT_FILE_PREFIX=/results/{benchmark_name}",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{result_root}:/results",
server_image,
"-c", cmd,
]
else:
cmd = " ".join(["ray", "start", "--block", f"--address={head_node_address}:6379"])
server_cmd = [
"docker", "run",
"--gpus", gpu_str,
"--ipc", "host",
"--net", "host",
"--name", container_name,
"--privileged",
"--device", "/dev/infiniband",
"--entrypoint", "/bin/bash",
*nccl_envs,
"-e", f"HF_TOKEN={huggingface_token}",
"-e", f"LOG_LEVEL={log_level}",
"-e", f"RESULT_FILE_PREFIX=/results/{benchmark_name}",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{result_root}:/results",
server_image,
"-c", cmd,
]
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}",
"-e", f"RESULT_FILE_PREFIX=/results/{benchmark_name}",
"-p", f"{port}:80",
"-v", f"{hf_cache_path}:/root/.cache/huggingface",
"-v", f"{models_dir}:/models",
"-v", f"{result_root}:/results",
server_image,
"--model-id", model,
"--revision", open(revision_path).read().strip(),
"--huggingface-hub-cache", "/root/.cache/huggingface/hub",
"--tokenizer-config-path", f"/models/{tokconf_filename}",
"--cuda-memory-fraction", "0.95",
"--num-shard", str(len(gpu_ids)),
"--max-concurrent-requests", "512",
"--trust-remote-code",
"--enable-chunked-prefill", "false",
]
else:
raise ValueError(f"Unknown backend: {backend}")
if Path(dcgm_sock_path).exists():
# Right after docker run.
server_cmd.insert(2, f"{dcgm_sock_path}:{dcgm_sock_path}")
server_cmd.insert(2, "-v")
print("Server:", " ".join(server_cmd))
subprocess.Popen(server_cmd)
return container_name
def start_client(
backend: Literal["vllm", "tgi"],
port: int,
model: str,
sharegpt_path: str,
request_rate: str,
gpu_ids: list[int],
benchmark_name: str,
power_limit: int,
nnodes: 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,
"--sharegpt-path", sharegpt_path,
"--request-rate", request_rate,
"--benchmark-name", benchmark_name,
"--power-limit", str(power_limit),
"--nnodes", str(nnodes),
"--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 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)}"
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.nnodes,
args.node_id,
args.head_node_address,
args.log_level,
args.preemption_mode,
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)
if args.node_id == 0:
client_handle = start_client(
args.backend,
port,
args.model,
args.sharegpt_path,
args.request_rate,
args.gpu_ids,
str(results_dir / benchmark_name),
args.power_limit,
args.nnodes,
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}")
else:
# If this ever executes, it means that it's a multi-node benchmark.
# We want to wait until the server is terminated.
time.sleep(500)
while True:
try:
requests.get(f"http://{args.head_node_address}:{port}/health")
time.sleep(3)
except requests.exceptions.ConnectionError:
time.sleep(5)
break
terminate_server(server_handle)
atexit.unregister(kill_fn)
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("--sharegpt-path", required=True, help="Path to the ShareGPT dataset to feed to the server.")
parser.add_argument("--request-rate", required=True, help="Poisson process rate for request arrival times.")
parser.add_argument("--max-num-seqs", type=int, default=256, help="Maximum number of sequences to run in each vLLM iteration.")
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("--nnodes", type=int, default=1, help="Number of nodes in the cluster.")
parser.add_argument("--node-id", type=int, default=0, help="ID of the node the script was launched on.")
parser.add_argument("--head-node-address", help="Address of the Ray head node.")
parser.add_argument("--log-level", default="INFO", help="Logging level for the server.")
parser.add_argument("--data-dup-factor", type=int, default=1, help="How many times to repeat the ShareGPT dataset to generate more requests.")
parser.add_argument("--preemption-mode", default="recompute", help="vLLM engine preemption mode.")
main(parser.parse_args())
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