112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
import argparse
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import json
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import os
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import time
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import uuid
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from concurrent.futures import ThreadPoolExecutor
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from fastchat.model import get_conversation_template
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from tqdm import tqdm
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from sglang.test.test_utils import add_common_other_args_and_parse, get_call_generate
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def load_questions(filename):
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questions = []
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with open(filename, "r") as fin:
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for line in fin:
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obj = json.loads(line)
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questions.append(obj)
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return questions
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def write_answers(filename, model_id, questions, answers):
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with open(os.path.expanduser(filename), "w") as fout:
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for i in range(len(answers)):
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ans_json = {
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"question_id": questions[i]["question_id"],
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"answer_id": uuid.uuid4().hex,
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"model_id": model_id,
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"choices": {
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"index": 0,
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"turns": [answers[i][0], answers[i][1]],
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},
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"tstamp": time.time(),
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}
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fout.write(json.dumps(ans_json) + "\n")
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def main(args):
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questions = load_questions(args.question_file)
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questions = (questions * 10)[: args.num_questions]
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max_tokens = 256
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model_id = "llama-2-chat"
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conv_main = get_conversation_template(model_id)
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# Select backend
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call_generate = get_call_generate(args)
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answers = [None] * len(questions)
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def get_answer(i):
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conv = conv_main.copy()
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cur_answers = []
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for j in range(2):
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q = questions[i]["turns"][j]
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conv.append_message(conv.roles[0], q)
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conv.append_message(conv.roles[1], None)
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prompt = conv.get_prompt()
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output = call_generate(prompt, temperature=0, max_tokens=max_tokens).strip()
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cur_answers.append(output)
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conv.update_last_message(output)
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answers[i] = cur_answers
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# Run requests
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tic = time.perf_counter()
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if args.parallel == 1:
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for i in tqdm(range(len(questions))):
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get_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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list(
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tqdm(
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executor.map(get_answer, list(range(len(questions)))),
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total=len(questions),
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)
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)
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latency = time.perf_counter() - tic
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print(f"#questions: {len(questions)}, Latency: {latency:.2f}")
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# Write results
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answer_file = args.answer_file or f"tmp_output_{args.backend}.txt"
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write_answers(answer_file, model_id, questions, answers)
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with open(args.result_file, "a") as fout:
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value = {
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"task": "mtbench",
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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("--question-file", type=str, default="question.jsonl")
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parser.add_argument("--answer-file", type=str, default=None)
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parser.add_argument("--num-questions", type=int, default=80)
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args = add_common_other_args_and_parse(parser)
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main(args)
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