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Fix dependency & crash issues (#539)

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This commit is contained in:
Ying Sheng
2024-06-12 21:23:19 -07:00
committed by GitHub
parent 94aead9e8d
commit 1374334d38
4 changed files with 5 additions and 296 deletions
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6
benchmark/latency_throughput/bench_throughput.py
View File

@@ -290,9 +290,9 @@ if __name__ == "__main__":
parser.add_argument( parser.add_argument(
"--dataset", type=str, help="Path to the dataset." "--dataset", type=str, help="Path to the dataset."
) )
parser.add_argument("--input-len", type=str, default=2048) parser.add_argument("--input-len", type=int, default=2048)
parser.add_argument("--output-len", type=str, default=256) parser.add_argument("--output-len", type=int, default=256)
parser.add_argument("--range-ratio", type=float, default=0.5) parser.add_argument("--range-ratio", type=float, default=1.0)
parser.add_argument( parser.add_argument(
"--tokenizer", type=str, "--tokenizer", type=str,
default="NousResearch/Meta-Llama-3-8B", default="NousResearch/Meta-Llama-3-8B",

291
benchmark/latency_throughput/synthetic_benchmark.py
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@@ -1,291 +0,0 @@
"""Benchmark online serving throughput.
On the server side, run one of the following commands:
(SRT backend)
python -m sglang.launch_server \
--model <your_model> --tp <num_gpus> \
--port 30000 --enable-flashinfer --disable-radix-cache
(vLLM backend)
python -m vllm.entrypoints.api_server \
--model <your_model> --tensor <num_gpus> --swap-space 16 \
--disable-log-requests --port 30000
(TGI backend)
./launch_hf_server.sh <your_model>
On the client side, run:
python benchmarks/benchmark_serving.py \
--backend <backend> \
--tokenizer <your_model> \
--num-prompt <num_prompts> \
--request-rate <request_rate>
--input-len <input_len> \
--output-len <output_len> \
--port 30000
"""
import argparse
import asyncio
import json
import random
import time
from typing import AsyncGenerator, List, Tuple
import aiohttp
import numpy as np
from tqdm.asyncio import tqdm_asyncio
from transformers import PreTrainedTokenizerBase
from sglang.srt.hf_transformers_utils import get_tokenizer
def sample_requests(
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
input_len: int,
output_len: int,
) -> List[Tuple[str, int, int]]:
prompt = "Hello " * input_len
prompt_token_ids = list(tokenizer(prompt).input_ids)
requests = []
for i in range(num_requests):
requests.append((prompt, len(prompt_token_ids), output_len))
return requests
async def get_request(
input_requests: List[Tuple[str, int, int]],
request_rate: float,
) -> AsyncGenerator[Tuple[str, int, int], None]:
input_requests = iter(input_requests)
for request in input_requests:
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)
async def send_request(
backend: str,
api_url: str,
prompt: str,
prompt_len: int,
output_len: int,
best_of: int,
use_beam_search: bool,
) -> None:
headers = {"User-Agent": "Benchmark Client"}
if backend == "vllm":
pload = {
"prompt": prompt,
"n": 1,
"best_of": best_of,
"use_beam_search": use_beam_search,
"temperature": 0.0 if use_beam_search else 1.0,
"top_p": 1.0,
"max_tokens": output_len,
"ignore_eos": True,
"stream": False,
}
elif backend == "tgi":
assert not use_beam_search
params = {
"best_of": best_of,
"max_new_tokens": output_len,
"do_sample": True,
}
pload = {
"inputs": prompt,
"parameters": params,
}
elif backend == "srt":
assert not use_beam_search
params = {
"ignore_eos": True,
"max_new_tokens": output_len,
}
pload = {
"text": prompt,
"sampling_params": params,
}
elif backend == "lightllm":
assert not use_beam_search
params = {
"ignore_eos": True,
"max_new_tokens": output_len,
}
pload = {
"inputs": prompt,
"parameters": params,
}
else:
raise ValueError(f"Unknown backend: {backend}")
request_start_time = time.perf_counter()
first_token_latency = None
timeout = aiohttp.ClientTimeout(total=3 * 3600)
async with aiohttp.ClientSession(timeout=timeout) as session:
while True:
async with session.post(api_url, headers=headers, json=pload) as response:
chunks = []
async for chunk, _ in response.content.iter_chunks():
if first_token_latency is None:
first_token_latency = time.perf_counter() - request_start_time
chunks.append(chunk)
output = b"".join(chunks).decode("utf-8")
output = json.loads(output)
# Re-send the request if it failed.
if "error" not in output:
break
request_latency = time.perf_counter() - request_start_time
return (prompt_len, output_len, request_latency, first_token_latency)
async def benchmark(
backend: str,
api_url: str,
input_requests: List[Tuple[str, int, int]],
best_of: int,
use_beam_search: bool,
request_rate: float,
) -> None:
tasks: List[asyncio.Task] = []
async for request in get_request(input_requests, request_rate):
prompt, prompt_len, output_len = request
task = asyncio.create_task(
send_request(
backend,
api_url,
prompt,
prompt_len,
output_len,
best_of,
use_beam_search,
)
)
tasks.append(task)
request_latency = await tqdm_asyncio.gather(*tasks)
return request_latency
def main(args: argparse.Namespace):
print(args)
random.seed(args.seed)
np.random.seed(args.seed)
api_url = f"http://{args.host}:{args.port}/generate"
tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
input_requests = sample_requests(args.num_prompts, tokenizer, args.input_len, args.output_len)
benchmark_start_time = time.perf_counter()
# (prompt len, output len, latency, first_token_latency)
request_latency = asyncio.run(
benchmark(
args.backend,
api_url,
input_requests,
args.best_of,
args.use_beam_search,
args.request_rate,
)
)
benchmark_end_time = time.perf_counter()
benchmark_time = benchmark_end_time - benchmark_start_time
print(f"Total time: {benchmark_time:.2f} s")
# Compute the perf statistics.
throughput = np.sum([output_len for _, output_len, _, _ in request_latency]) / benchmark_time
print(f"Throughput: {throughput} token/s")
avg_per_token_latency = np.mean(
[
latency / (prompt_len + output_len)
for prompt_len, output_len, latency, _ in request_latency
]
)
print(f"Average latency per token: {avg_per_token_latency:.2f} s")
avg_per_output_token_latency = np.mean(
[(latency - first_token_latency) / output_len for _, output_len, latency, first_token_latency in request_latency]
)
print(f"Average TPOT: {avg_per_output_token_latency * 1000:.0f} ms")
avg_first_token_latency = np.mean(
[first_token_latency for _, _, _, first_token_latency in request_latency]
)
print(f"Average TTFT: {avg_first_token_latency:.2f} s")
stats = {"num_prompts": args.num_prompts, "input_len": args.input_len, "output_len": args.output_len,
"total_time (s)": benchmark_time, "throughput (token/s)": throughput, "avg_per_token_latency (s)": avg_per_token_latency,
"TPOT (ms)": avg_per_output_token_latency, "TTFT (s)": avg_first_token_latency}
with open(args.output_file, "a") as f:
f.write(json.dumps(stats) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Benchmark the online serving throughput."
)
parser.add_argument(
"--backend",
type=str,
default="vllm",
choices=["vllm", "tgi", "srt", "lightllm"],
)
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=8000)
parser.add_argument(
"--tokenizer", type=str, required=True, help="Name or path of the tokenizer."
)
parser.add_argument(
"--best-of",
type=int,
default=1,
help="Generates `best_of` sequences per prompt and " "returns the best one.",
)
parser.add_argument("--use-beam-search", action="store_true")
parser.add_argument(
"--num-prompts", type=int, default=1000, help="Number of prompts to process."
)
parser.add_argument(
"--request-rate",
type=float,
default=float("inf"),
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.",
)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument(
"--trust-remote-code",
action="store_true",
help="trust remote code from huggingface",
)
parser.add_argument(
"--input-len",
type=int,
default=512,
help="Number of input tokens"
)
parser.add_argument(
"--output-len",
type=int,
default=128,
help="Number of output tokens"
)
parser.add_argument(
"--output-file",
type=str,
default="perf_stats.jsonl",
help="output file path for performance statistics"
)
args = parser.parse_args()
main(args)

2
python/pyproject.toml
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@@ -21,7 +21,7 @@ dependencies = [
[project.optional-dependencies] [project.optional-dependencies]
srt = ["aiohttp", "fastapi", "psutil", "rpyc", "torch", "uvloop", "uvicorn", srt = ["aiohttp", "fastapi", "psutil", "rpyc", "torch", "uvloop", "uvicorn",
"zmq", "vllm==0.4.3", "interegular", "pydantic", "pillow", "packaging", "huggingface_hub", "hf_transfer", "outlines>=0.0.41"] "zmq", "vllm==0.5.0", "interegular", "pydantic", "pillow", "packaging", "huggingface_hub", "hf_transfer", "outlines>=0.0.41"]
openai = ["openai>=1.0", "tiktoken"] openai = ["openai>=1.0", "tiktoken"]
anthropic = ["anthropic>=0.20.0"] anthropic = ["anthropic>=0.20.0"]
litellm = ["litellm>=1.0.0"] litellm = ["litellm>=1.0.0"]

2
python/sglang/global_config.py
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@@ -27,7 +27,7 @@ class GlobalConfig:
# Request dependency time due to network delay # Request dependency time due to network delay
self.request_dependency_delay = 0.02 self.request_dependency_delay = 0.02
self.wait_for_new_request_delay = 0.0004 self.wait_for_new_request_delay = 0.0006
# New generation token ratio estimation # New generation token ratio estimation
self.base_new_token_ratio = 0.4 self.base_new_token_ratio = 0.4