22085081bb
Co-authored-by: Ying Sheng <sqy1415@gmail.com> Co-authored-by: Liangsheng Yin <hnyls2002@gmail.com> Co-authored-by: Zhiqiang Xie <xiezhq@stanford.edu> Co-authored-by: parasol-aser <3848358+parasol-aser@users.noreply.github.com> Co-authored-by: LiviaSun <33578456+ChuyueSun@users.noreply.github.com> Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
import argparse
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import asyncio
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from concurrent.futures import ThreadPoolExecutor
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from functools import partial
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import json
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import time
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from tqdm import tqdm
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import numpy as np
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from sglang.test.test_utils import add_common_other_args_and_parse, call_generate_lightllm, call_generate_vllm, call_generate_srt_raw
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from sglang.utils import read_jsonl, dump_state_text
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def json_decode(document, generate):
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s = "Please extract the information of a city from the following wikipedia page.\n"
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s += "Page begin.\n" + document + "Page end.\n"
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s += "Here is the name, country, and symbol of the city in JSON format.\n"
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s += '{\n'
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s += ' "name": "'
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s += generate(s, max_tokens=8, stop='"') + '",\n'
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s += ' "country": "'
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s += generate(s, max_tokens=8, stop='"') + '",\n'
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s += ' "air port code": "'
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s += generate(s, max_tokens=8, stop='"') + '",\n'
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s += ' "top 3 landmarks": "'
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s += generate(s, max_tokens=24, stop='"') + '",\n'
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s += '}\n'
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return s
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def main(args):
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lines = read_jsonl(args.data_path)
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arguments = []
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for i in range(len(lines[:args.num_questions])):
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arguments.append({
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"document": lines[i]["document"],
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})
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states = [None] * len(arguments)
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# Select backend
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if args.backend == "lightllm":
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url = f"{args.host}:{args.port}/generate"
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generate = partial(call_generate_lightllm, url=url, temperature=0)
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elif args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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generate = partial(call_generate_vllm, url=url, temperature=0)
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elif args.backend == "srt-raw":
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url = f"{args.host}:{args.port}/generate"
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generate = partial(call_generate_srt_raw, url=url, temperature=0)
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elif args.backend == "guidance":
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from guidance import models, gen
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model = models.LlamaCpp("/home/ubuntu/model_weights/CodeLlama-7b-instruct-hf.gguf", n_gpu_layers=-1, n_ctx=11000)
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def generate(prompt, max_tokens, stop):
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out = model + prompt + gen(name="answer",
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max_tokens=max_tokens, temperature=0, stop=stop)
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return out["answer"]
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# warmup
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generate("Hello!", max_tokens=8, stop=None)
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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# Run requests
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def get_one_answer(i):
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states[i] = json_decode(generate=generate, **arguments[i])
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tic = time.time()
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if args.parallel == 1:
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for i in tqdm(range(len(arguments))):
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get_one_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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executor.map(get_one_answer, list(range(len(arguments))))
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latency = time.time() - tic
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# Compute accuracy
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print(f"Latency: {latency:.3f}")
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# Write results
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dump_state_text(f"tmp_output_{args.backend}.txt", states)
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with open(args.result_file, "a") as fout:
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value = {
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"task": "long_json_decode",
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"backend": args.backend,
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"num_gpus": 1,
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"latency": round(latency, 3),
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"num_requests": args.num_questions,
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"other": {
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"num_questions": args.num_questions,
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"parallel": args.parallel,
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}
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-path", type=str, default="questions.jsonl")
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parser.add_argument("--num-questions", type=int, default=100)
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args = add_common_other_args_and_parse(parser)
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main(args)
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