[benchmark] Add benchmark scripts for ceval and boolq (#8946)
Co-authored-by: chenyuxing <2818499974@qq.com> Co-authored-by: hanqing <huang010706@126.com> Co-authored-by: Muggle <62579327+trawolf@users.noreply.github.com> Co-authored-by: ronnie_zheng <zl19940307@163.com>
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124
benchmark/boolq/bench_sglang.py
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124
benchmark/boolq/bench_sglang.py
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import argparse
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import json
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import time
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import numpy as np
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from sglang.api import set_default_backend
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from sglang.test.test_utils import (
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add_common_sglang_args_and_parse,
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select_sglang_backend,
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)
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from sglang.utils import read_jsonl
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def get_example(lines, i, answer):
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prompt = "Question: " + lines[i]["question"] + lines[i]["passage"] + "\nAnswer:"
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if answer:
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prompt += str(lines[i]["answer"])
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return prompt
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def few_shot_examples(lines, k):
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prompts = ""
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for i in range(k):
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prompts += get_example(lines, i, True) + "\n\n"
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return prompts
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def main(args):
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# Select backend
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set_default_backend(select_sglang_backend(args))
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# Read data
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train_data_path = args.train_data_path
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test_data_path = args.test_data_path
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lines_train = list(read_jsonl(train_data_path))
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lines_test = list(read_jsonl(test_data_path))
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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_shots = few_shot_examples(lines_train, num_shots)
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questions = []
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answer = []
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for i in range(len(lines_test[:num_questions])):
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questions.append(get_example(lines_test, i, False))
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answer.append(str(lines_test[i]["answer"]))
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arguments = [{"question": q} for q in questions]
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#####################################
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######### SGL Program Begin #########
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#####################################
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import sglang as sgl
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@sgl.function
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def few_shot_boolq(s, question):
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s += few_shots + question
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s += sgl.gen("answer", max_tokens=5, stop=["\n"])
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#####################################
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########## SGL Program End ##########
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#####################################
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# Run requests
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tic = time.perf_counter()
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states = few_shot_boolq.run_batch(
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arguments,
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temperature=0,
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num_threads=args.parallel,
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progress_bar=True,
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)
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latency = time.perf_counter() - tic
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preds = []
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for i in range(len(states)):
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preds.append(states[i]["answer"])
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# Compute accuracy
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acc = np.mean(np.array(preds) == np.array(answer))
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# Compute speed
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num_output_tokens = sum(
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s.get_meta_info("answer")["completion_tokens"] for s in states
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)
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output_throughput = num_output_tokens / latency
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# Print results
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print(f"Accuracy: {acc:.3f}")
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print(f"Latency: {latency:.3f} s")
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print(f"Output throughput: {output_throughput:.3f} token/s")
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# Results
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with open(args.result_file, "a") as fout:
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value = {
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"task": "boolq",
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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=5)
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parser.add_argument(
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"--train-data-path", type=str, default="./boolq/data/train-00000-of-00001.json"
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)
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parser.add_argument(
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"--test-data-path",
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type=str,
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default="./boolq/data/validation-00000-of-00001.json",
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)
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parser.add_argument("--num-questions", type=int, default=200)
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args = add_common_sglang_args_and_parse(parser)
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main(args)
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