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>
127 lines
3.9 KiB
Python
127 lines
3.9 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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USER_PREFIX = "[INST] "
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USER_SUFFIX = " [/INST]"
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ASSISTANT_PREFIX = ""
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ASSISTANT_SUFFIX = " </s><s>"
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def multi_document_qa(docs, question, generate):
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s = USER_PREFIX
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s += "Pleaes answer a question according to given documents.\n"
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s += "Question:" + question + "Documents begin.\n"
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s += "".join(docs)
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s += "\nDocuments end."
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s += ("\n\nBased on the above documents, please answer this question:\n" + question + "\nAnswer in three words or fewer.")
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s += USER_SUFFIX
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s += ASSISTANT_PREFIX
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answer = generate(s, max_tokens=16, stop=None)
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return answer
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def main(args):
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lines = read_jsonl(args.data_path)
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l = lines[0]
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arguments = []
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labels = []
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num_docs = 10
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if args.backend == "guidance":
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num_docs = 7 # due to OOM
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for i in range(len(l["questions"][:args.num_questions])):
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arguments.append({
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"docs": l["documents"][:num_docs],
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"question": l["questions"][i],
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})
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labels.append(l["answers"][i])
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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] = multi_document_qa(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(labels))):
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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(labels))))
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latency = time.time() - tic
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# Compute accuracy
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print(states)
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correct = 0
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for s, label in zip(states, labels):
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answer = s.lower()
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if all(x in answer for x in label.lower().split(" ")):
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correct += 1
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accuracy = correct / len(labels)
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print(f"Accuracy: {accuracy:.3f}")
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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": "multi_document_qa",
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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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"accuracy": accuracy,
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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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