147 lines
4.2 KiB
Python
147 lines
4.2 KiB
Python
import argparse
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import ast
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import asyncio
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import json
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import re
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import time
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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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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from sglang.utils import dump_state_text, read_jsonl
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INVALID = -9999999
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def get_one_example(lines, i, include_answer):
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ret = "Question: " + lines[i]["question"] + "\nAnswer:"
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if include_answer:
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ret += " " + lines[i]["answer"]
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return ret
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def get_few_shot_examples(lines, k):
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ret = ""
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for i in range(k):
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ret += get_one_example(lines, i, True) + "\n\n"
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return ret
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def get_answer_value(answer_str):
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answer_str = answer_str.replace(",", "")
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numbers = re.findall(r"\d+", answer_str)
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if len(numbers) < 1:
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return INVALID
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try:
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return ast.literal_eval(numbers[-1])
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except SyntaxError:
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return INVALID
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def main(args):
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lines = read_jsonl(args.data_path)
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# Construct prompts
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k = args.num_shot
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few_shot_examples = get_few_shot_examples(lines, k)
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questions = []
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labels = []
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for i in range(len(lines[: args.num_questions])):
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questions.append(get_one_example(lines, i, False))
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labels.append(get_answer_value(lines[i]["answer"]))
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assert all(l != INVALID for l in labels)
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states = [None] * len(labels)
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# Select backend
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call_generate = get_call_generate(args)
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# Run requests
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if args.backend != "lmql":
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# Use thread pool
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def get_one_answer(i):
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answer = call_generate(
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prompt=few_shot_examples + questions[i],
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# prompt="System: " + few_shot_examples + "<|separator|>\n\n" + questions[i],
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temperature=0,
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max_tokens=256,
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stop="Question",
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)
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states[i] = answer
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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(questions))):
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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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list(
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tqdm(
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executor.map(get_one_answer, list(range(len(questions)))),
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total=len(questions),
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)
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)
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else:
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# Use asyncio
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async def batched_call(batch_size):
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for i in range(0, len(questions), batch_size):
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tasks = []
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for q in questions[i : i + batch_size]:
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tasks.append(
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call_generate(
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few_shot_examples + q,
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temperature=0,
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max_tokens=256,
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stop="Question",
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)
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)
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rets = await asyncio.gather(*tasks)
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for j in range(len(rets)):
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states[i + j] = rets[j]
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tic = time.time()
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asyncio.run(batched_call(batch_size=args.parallel))
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latency = time.time() - tic
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preds = []
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for i in range(len(states)):
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preds.append(get_answer_value(states[i]))
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# Compute accuracy
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acc = np.mean(np.array(preds) == np.array(labels))
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invalid = np.mean(np.array(preds) == INVALID)
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print(f"Latency: {latency:.3f}")
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print(f"Invalid: {invalid:.3f}")
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print(f"Accuracy: {acc:.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": "gsm8k",
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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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"accuracy": round(acc, 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("--num-shot", type=int, default=5)
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parser.add_argument("--data-path", type=str, default="test.jsonl")
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parser.add_argument("--num-questions", type=int, default=200)
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args = add_common_other_args_and_parse(parser)
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main(args)
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