sglang/benchmark/mmlu/bench_other.py

import argparse
import asyncio
import json
import os
import time
from concurrent.futures import ThreadPoolExecutor
from functools import partial

import numpy as np
import pandas as pd
import tiktoken
from tqdm import tqdm

from sglang.test.test_utils import (
    add_common_other_args_and_parse,
    call_generate_lightllm,
    call_generate_srt_raw,
    call_generate_vllm,
)

choices = ["A", "B", "C", "D"]

tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")


def format_subject(subject):
    l = subject.split("_")
    s = ""
    for entry in l:
        s += " " + entry
    return s


def format_example(df, idx, include_answer=True):
    prompt = df.iloc[idx, 0]
    k = df.shape[1] - 2
    for j in range(k):
        prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
    prompt += "\nAnswer:"
    if include_answer:
        prompt += " {}\n\n".format(df.iloc[idx, k + 1])
    return prompt


def gen_prompt(train_df, subject, k=-1):
    prompt = "The following are multiple choice questions (with answers) about{}.\n\n".format(
        format_subject(subject)
    )
    if k == -1:
        k = train_df.shape[0]
    for i in range(k):
        prompt += format_example(train_df, i)
    return prompt


model_initialized = None


def evaluate(args, subject, dev_df, test_df):
    prompts = []
    labels = []

    # Construct prompts
    k = args.ntrain
    train_prompt = gen_prompt(dev_df, subject, k)
    while len(tokenizer.encode(train_prompt)) > 1536:
        k -= 1
        train_prompt = gen_prompt(dev_df, subject, k)

    for i in range(test_df.shape[0]):
        prompt_end = format_example(test_df, i, include_answer=False)
        prompt = train_prompt + prompt_end
        prompts.append(prompt)

        label = test_df.iloc[i, test_df.shape[1] - 1]
        labels.append(label)

    preds = [None] * len(prompts)
    max_tokens = 1

    # Select backend
    global model_initialized

    if args.backend == "lightllm":
        url = f"{args.host}:{args.port}/generate"
        call_generate = partial(call_generate_lightllm, url=url, stop=None)
    elif args.backend == "vllm":
        url = f"{args.host}:{args.port}/generate"
        call_generate = partial(call_generate_vllm, url=url, stop=None)
    elif args.backend == "srt-raw":
        url = f"{args.host}:{args.port}/generate"
        call_generate = partial(call_generate_srt_raw, url=url, stop=None)
    elif args.backend == "guidance":
        from guidance import gen, models

        if model_initialized is None:
            model = models.LlamaCpp(
                "/home/ubuntu/model_weights/Llama-2-7b-chat.gguf",
                n_gpu_layers=-1,
                n_ctx=4096,
            )
            model_initialized = model
        else:
            model = model_initialized

        def call_generate(prompt, temperature, max_tokens):
            out = (
                model
                + prompt
                + gen(name="answer", max_tokens=max_tokens, temperature=0)
            )
            return out["answer"]

        # warmup
        call_generate("Hello,", temperature=1.0, max_tokens=8)

    elif args.backend == "lmql":
        import lmql

        model = lmql.model(
            "meta-llama/Llama-2-7b-chat-hf", endpoint=f"{args.host}:{args.port}"
        )

        @lmql.query(model=model)
        async def program(question):
            '''lmql
            """{question}[ANSWER]""" where len(TOKENS(ANSWER)) < 2
            return ANSWER
            '''

        async def call_generate(prompt, temperature, max_tokens):
            return await program(question=prompt, temperature=temperature)

    else:
        raise ValueError(f"Invalid backend: {args.backend}")

    # Run requests
    if args.backend != "lmql":
        # Use thread pool
        def get_one_answer(i):
            pred = call_generate(prompts[i], temperature=0, max_tokens=max_tokens)
            preds[i] = pred.strip()[0]

        tic = time.time()
        if args.parallel == 1:
            for i in range(len(prompts)):
                get_one_answer(i)
        else:
            with ThreadPoolExecutor(args.parallel) as executor:
                executor.map(get_one_answer, list(range(len(prompts))))
    else:
        # Use asyncio
        async def batched_call(batch_size):
            for i in range(0, len(prompts), batch_size):
                tasks = []
                for p in prompts[i : i + batch_size]:
                    tasks.append(call_generate(p, temperature=0, max_tokens=max_tokens))
                rets = await asyncio.gather(*tasks)
                for j in range(len(rets)):
                    preds[i + j] = rets[j].strip()[0]

        tic = time.time()
        asyncio.run(batched_call(batch_size=args.parallel))
    latency = time.time() - tic

    # Compute accuracy
    cors = [pred == label for pred, label in zip(preds, labels)]
    acc = np.mean(cors)
    cors = np.array(cors)

    print(
        "Average accuracy {:.3f}, latency {:.2f}, #q: {} - {}".format(
            acc, latency, len(prompts), subject
        )
    )

    return cors, acc, latency


def main(args):
    subjects = sorted(
        [
            f.split("_test.csv")[0]
            for f in os.listdir(os.path.join(args.data_dir, "test"))
            if "_test.csv" in f
        ]
    )

    all_cors = []
    all_latencies = []
    num_requests = 0

    for subject in tqdm(subjects[: args.nsub]):
        dev_df = pd.read_csv(
            os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
        )[: args.ntrain]
        test_df = pd.read_csv(
            os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
        )

        cors, acc, latency = evaluate(args, subject, dev_df, test_df)
        all_cors.append(cors)
        all_latencies.append(latency)
        num_requests += len(test_df)

    total_latency = np.sum(all_latencies)
    print("Total latency: {:.3f}".format(total_latency))

    weighted_acc = np.mean(np.concatenate(all_cors))
    print("Average accuracy: {:.3f}".format(weighted_acc))

    # Write results
    with open(args.result_file, "a") as fout:
        value = {
            "task": "mmlu",
            "backend": args.backend,
            "num_gpus": 1,
            "latency": round(total_latency, 3),
            "accuracy": round(weighted_acc, 3),
            "num_requests": num_requests,
            "other": {
                "nsub": args.nsub,
                "parallel": args.parallel,
            },
        }
        fout.write(json.dumps(value) + "\n")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--ntrain", type=int, default=5)
    parser.add_argument("--data_dir", type=str, default="data")
    parser.add_argument("--nsub", type=int, default=60)
    args = add_common_other_args_and_parse(parser)
    main(args)