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"""
Usage:
# if you want to run benchmarks directly
# mtbench:20 means only run 20 samples in the dataset
python bench_eagle3.py \
--model meta-llama/Llama-3.1-8B-Instruct \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/sglang-EAGLE3-LLaMA3.1-Instruct-8B \
--port 30000 \
--config-list 1,0,0,0 1,3,1,4 \
--benchmark-list mtbench:20 \
--dtype bfloat16
or if you want run sglang alone.
# launch sglang
python3 -m sglang.launch_server \
--model meta-llama/Llama-3.1-8B-Instruct \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path lmsys/sglang-EAGLE3-LLaMA3.1-Instruct-8B \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--mem-fraction-static 0.75 \
--cuda-graph-max-bs 1 \
--tp 1 \
--trust-remote-code \
--host 0.0.0.0 \
--port 30000 \
--dtype bfloat16
# then run benchmarks
python bench_eagle3.py \
--model-path meta-llama/Llama-3.1-8B-Instruct \
--port 30000 \
--config-list 1,0,0,0 \
--benchmark-list mtbench:80 \
--dtype bfloat16 \
--skip-launch-server
"""
import argparse
import json
import os
import time
from dataclasses import asdict
from typing import List
import requests
from benchmarker import BENCHMARKS
from sglang.srt.server_args import ServerArgs
from sglang.test.test_utils import kill_process_tree, popen_launch_server
from sglang.utils import wait_for_server
def parse_args():
parser = argparse.ArgumentParser()
sglang_group = parser.add_argument_group("sglang")
ServerArgs.add_cli_args(sglang_group)
# make the follow args a group
benchmark_group = parser.add_argument_group("benchmark")
benchmark_group.add_argument(
"--skip-launch-server", action="store_true", default=False
)
benchmark_group.add_argument("--timeout-for-server-launch", type=int, default=600)
benchmark_group.add_argument("--num-prompts", type=int, default=80)
benchmark_group.add_argument("--output-dir", type=str, default="./results")
benchmark_group.add_argument(
"--config-list", type=str, nargs="+", default=["1,0,0,0", "1,3,1,4"]
)
benchmark_group.add_argument(
"--name",
type=str,
default=None,
help="name of this benchmark run, if provided, will be added to the output file name",
)
benchmark_group.add_argument(
"--benchmark-list",
type=str,
nargs="+",
default=[
"mtbench:80",
"gsm8k:200",
"humaneval:200",
"math500:200",
"ceval:200",
],
help=f"The list of benchmarks to run. The format is <benchmark-name>:<num-prompts>:<subset>,<subset>. We support the following benchmarks: {', '.join(BENCHMARKS.benchmarks.keys())}",
)
benchmark_group.add_argument(
"--enable-multi-turn-conversation",
action="store_true",
default=False,
)
return parser.parse_args()
def launch_sglang_server(
server_args: ServerArgs,
base_url: str,
batch_size: int,
steps: int,
topk: int,
num_draft_tokens: int,
timeout: int,
):
"""
This function launches the SGLang server with the given server arguments.
"""
sglang_args: List[str] = []
if steps > 0:
sglang_args.extend(
[
"--speculative-algorithm",
"EAGLE3",
"--speculative-num-steps",
str(steps),
"--speculative-eagle-topk",
str(topk),
"--speculative-num-draft-tokens",
str(num_draft_tokens),
"--speculative-draft-model-path",
server_args.speculative_draft_model_path,
]
)
sglang_args.extend(
[
"--cuda-graph-max-bs",
str(batch_size),
"--mem-fraction-static",
str(server_args.mem_fraction_static),
"--tp-size",
str(server_args.tp_size),
"--max-running-requests",
str(batch_size),
]
)
if server_args.trust_remote_code:
sglang_args.extend(["--trust-remote-code"])
if server_args.disable_radix_cache:
sglang_args.extend(["--disable-radix-cache"])
if server_args.ep_size:
sglang_args.extend(["--ep-size", str(server_args.ep_size)])
if server_args.attention_backend:
sglang_args.extend(["--attention-backend", server_args.attention_backend])
if server_args.quantization:
sglang_args.extend(["--quantization", server_args.quantization])
if server_args.dtype:
sglang_args.extend(["--dtype", server_args.dtype])
process = popen_launch_server(
server_args.model_path,
base_url,
timeout=timeout,
other_args=sglang_args,
env={
"SGLANG_RECORD_STEP_TIME": "1",
"SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN": "1",
**os.environ,
},
)
return process
def send_flush_cache_request(base_url: str):
requests.post(base_url + "/flush_cache")
def main():
args = parse_args()
server_args: ServerArgs = ServerArgs.from_cli_args(args)
configs = [tuple(map(int, config.split(","))) for config in args.config_list]
# split the arg into list of (bench_name, num_prompts)
benchmark_list = []
for item in args.benchmark_list:
splits = item.split(":")
if len(splits) == 1:
bench_name = splits[0]
num_prompts = None
subset = None
elif len(splits) == 2:
bench_name, num_prompts = splits
subset = None
elif len(splits) == 3:
bench_name, num_prompts, subset = splits
subset = subset.split(",")
else:
raise ValueError(f"Invalid benchmark list format: {item}")
benchmark_list.append((bench_name, num_prompts, subset))
assert len(benchmark_list) != 0, "the number of benchmark list is 0"
base_url = f"http://localhost:{args.port}"
results = {}
results["model"] = server_args.speculative_draft_model_path
def run_benchmarks(batch_size: int, steps: int, topk: int, num_draft_tokens: int):
for benchmark_name, num_prompts, subset in benchmark_list:
print(
f"Running benchmark {benchmark_name} with {num_prompts} prompts, batch size {batch_size}, steps {steps}, topk {topk}, num_draft_tokens {num_draft_tokens}, subset {subset}"
)
benchmarkder_cls = BENCHMARKS.get(benchmark_name)
num_prompts = int(num_prompts) if num_prompts is not None else None
if subset is None:
benchmarker = benchmarkder_cls(num_samples=num_prompts)
else:
benchmarker = benchmarkder_cls(num_samples=num_prompts, subset=subset)
metrics_list = benchmarker.run(
host=args.host, port=args.port, batch_size=batch_size
)
send_flush_cache_request(base_url)
if benchmark_name not in results:
results[benchmark_name] = []
results[benchmark_name].append(
dict(
batch_size=batch_size,
steps=steps,
topk=topk,
num_draft_tokens=num_draft_tokens,
metrics=[asdict(metric) for metric in metrics_list],
num_samples=num_prompts,
)
)
if args.skip_launch_server:
batch_size = configs[0][0] if len(configs) > 0 else 8
run_benchmarks(batch_size, None, None, None)
else:
# we itearate over each config from args
for batch_size, steps, topk, num_draft_tokens in configs:
process = launch_sglang_server(
server_args,
base_url,
batch_size,
steps,
topk,
num_draft_tokens,
args.timeout_for_server_launch,
)
wait_for_server(base_url)
run_benchmarks(batch_size, steps, topk, num_draft_tokens)
kill_process_tree(process.pid)
process.wait()
os.makedirs(args.output_dir, exist_ok=True)
timestamp = time.strftime("%Y%m%d_%H%M%S")
result_file = os.path.join(
args.output_dir,
f"{args.name + '_' if args.name else ''}results_{timestamp}.jsonl",
)
with open(result_file, "w") as f:
json.dump(results, f, indent=4)
print(f"Results saved to {result_file}")
if __name__ == "__main__":
main()
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