119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
import argparse
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import asyncio
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import json
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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_select
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from sglang.utils import download_and_cache_file, read_jsonl
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def get_one_example(lines, i, include_answer):
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ret = lines[i]["activity_label"] + ": " + lines[i]["ctx"] + " "
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if include_answer:
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ret += lines[i]["endings"][lines[i]["label"]]
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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 main(args):
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# Select backend
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call_select = get_call_select(args)
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# Read data
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url = "https://raw.githubusercontent.com/rowanz/hellaswag/master/data/hellaswag_val.jsonl"
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filename = download_and_cache_file(url)
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lines = list(read_jsonl(filename))
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# Construct prompts
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num_questions = args.num_questions
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num_shots = args.num_shots
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few_shot_examples = get_few_shot_examples(lines, num_shots)
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questions = []
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choices = []
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labels = []
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for i in range(len(lines[:num_questions])):
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questions.append(get_one_example(lines, i, False))
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choices.append(lines[i]["endings"])
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labels.append(lines[i]["label"])
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preds = [None] * len(labels)
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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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preds[i] = call_select(
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context=few_shot_examples + questions[i], choices=choices[i]
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)
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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, c in zip(
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questions[i : i + batch_size], choices[i : i + batch_size]
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):
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tasks.append(call_select(context=few_shot_examples + q, choices=c))
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rets = await asyncio.gather(*tasks)
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for j in range(len(rets)):
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preds[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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# Compute accuracy
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acc = np.mean(np.array(preds) == np.array(labels))
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print(f"Latency: {latency:.3f}")
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print(f"Accuracy: {acc:.3f}")
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# Write results
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with open(args.result_file, "a") as fout:
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value = {
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"task": "hellaswag",
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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-shots", type=int, default=20)
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parser.add_argument("--data-path", type=str, default="hellaswag_val.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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