benchmark_hunyuan_test2.py

#!/usr/bin/env python3
"""Minimal classify demo using token IDs as input.

This mirrors the docs example:
    llm = LLM(model="...", runner="pooling")
    (output,) = llm.classify("Hello, my name is")

but feeds DEFAULT_PROMPT_TOKEN_IDS via token_inputs instead of text.
"""

from vllm import LLM
from vllm.inputs import token_inputs
from transformers import AutoTokenizer

DEFAULT_PROMPT_TOKEN_IDS = [
    [127958, 58, 10172, 24575, 8437, 7489, 51, 60, 220, 57668, 102832, 80073, 75761, 102245, 39045, 57668, 105982, 103429, 88852, 9743, 34208, 2929, 3922, 101423, 83125, 110357, 107759, 82317, 101505, 101009, 1811, 15225, 61633, 3922, 101992, 80073, 120702, 17, 15, 17, 20, 8107, 15, 23, 9953, 17, 22, 9080, 3490, 2929, 5232, 86461, 102160, 36827, 31867, 19, 34208, 86461, 102160, 36827, 31867, 21, 107938, 105528, 198, 12, 11615, 101241, 5232, 111642, 198, 12, 11615, 104780, 101526, 105344, 5232, 100377, 104780, 198, 12, 11615, 106444, 101526, 105344, 5232, 101055, 106444, 271, 9743, 29411, 12, 52561, 229, 34972, 5232, 86461, 102160, 36827, 31867, 21, 101129, 91547, 6704, 238, 255, 28466, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 100824, 123798, 20, 24, 24, 24, 24186, 72718, 62, 59505, 112203, 32239, 198, 12, 73028, 96, 17161, 5232, 86461, 102160, 36827, 31867, 21, 101129, 91547, 6704, 238, 255, 28466, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 100824, 123798, 20, 24, 24, 24, 24186, 72718, 198, 20, 9953, 16, 24, 104223, 3922, 86461, 102160, 106518, 101612, 100733, 17039, 33671, 24186, 101016, 22656, 36827, 31867, 21, 101129, 102984, 91547, 1811, 101291, 57, 115718, 100532, 100880, 101612, 108616, 101016, 105164, 3922, 36827, 31867, 21, 10447, 37197, 32218, 112561, 117408, 35287, 42246, 111379, 100992, 103735, 1811, 107743, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 120928, 50991, 107758, 31867, 21, 1322, 102072, 36827, 31867, 21, 112848, 104946, 111379, 102735, 105541, 3922, 92672, 109034, 119927, 3922, 101739, 101911, 100532, 5486, 9080, 102874, 102581, 100518, 39135, 113577, 1811, 102317, 57237, 16, 13, 22, 24, 6358, 100547, 95337, 17, 13, 17, 7501, 10110, 36827, 31867, 21, 49372, 46281, 32943, 28037, 48915, 101026, 101058, 123721, 1811, 108895, 101035, 3922, 36827, 31867, 21, 101129, 100503, 72843, 17, 15, 4, 100477, 105516, 3922, 110349, 107371, 24946, 101971, 36, 100646, 105653, 3922, 123798, 20, 24, 24, 24, 24186, 72718, 6447, 198, 86461, 102160, 36827, 31867, 21, 5232, 116967, 109845, 101037, 10447, 94, 105, 72237, 27327, 76537, 198, 86461, 102160, 36827, 31867, 21, 113577, 105868, 58805, 94278, 238, 100667, 15592, 220, 22, 473, 220, 17, 21, 15, 107311, 3922, 23, 101021, 16, 21, 120312, 71600, 3922, 19, 20211, 102271, 102812, 103486, 1811, 103954, 9688, 19085, 10860, 55, 220, 20, 15, 21, 15, 120928, 50991, 3922, 16, 16, 20, 54, 100433, 118068, 71600, 3922, 101046, 16931, 1242, 220, 19, 100344, 1811, 105874, 17905, 3922, 107743, 16, 21, 108465, 17, 13, 20, 42, 220, 16, 21, 20, 11732, 109943, 53434, 108018, 3922, 101046, 38, 6354, 72501, 100344, 3922, 43292, 103897, 104196, 120822, 82317, 102698, 58322, 3922, 19, 15, 271, 9743, 91547, 58521, 29411, 482, 220, 58521, 31091, 5232, 964, 16, 21, 23, 198, 482, 220, 58521, 101241, 5232, 111642, 11, 76771, 239, 83301, 11, 47850, 233, 33748, 198, 482, 220, 58521, 105302, 5232, 109173, 271, 22452, 91547, 101143, 5232, 112203, 32239, 198, 482, 75677, 111, 55038, 101241, 5232, 104312, 11, 4996, 223, 98, 100563, 11, 220, 101766, 198, 482, 41766, 229, 81742, 33005, 5232, 100359, 198, 482, 75677, 111, 55038, 105344, 5232, 23, 198, 482, 61696, 225, 101028, 105344, 5232, 17, 271, 9743, 91547, 21082, 5232, 17, 15, 17, 20, 8107, 15, 20, 9953, 16, 24, 9080, 198, 482, 9085, 115, 251, 104944, 9039, 5232, 24, 24, 271, 15225, 67117, 83125, 110357, 107759, 5232, 127962, 127972, 127973, 127974, 127975, 127967],
    [127958, 58, 10172, 24575, 8437, 7489, 51, 60, 220, 57668, 102832, 80073, 75761, 102245, 39045, 57668, 105982, 103429, 88852, 9743, 34208, 2929, 3922, 101423, 83125, 110357, 107759, 82317, 101505, 101009, 1811, 15225, 61633, 3922, 101992, 80073, 120702, 17, 15, 17, 20, 8107, 15, 23, 9953, 17, 22, 9080, 3490, 2929, 5232, 86461, 102160, 36827, 31867, 19, 34208, 86461, 102160, 36827, 31867, 21, 107938, 105528, 198, 12, 11615, 101241, 5232, 111642, 198, 12, 11615, 104780, 101526, 105344, 5232, 100377, 104780, 198, 12, 11615, 106444, 101526, 105344, 5232, 101055, 106444, 271, 9743, 29411, 12, 52561, 229, 34972, 5232, 86461, 102160, 36827, 31867, 21, 101129, 91547, 6704, 238, 255, 28466, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 100824, 123798, 20, 24, 24, 24, 24186, 72718, 62, 59505, 112203, 32239, 198, 12, 73028, 96, 17161, 5232, 86461, 102160, 36827, 31867, 21, 101129, 91547, 6704, 238, 255, 28466, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 100824, 123798, 20, 24, 24, 24, 24186, 72718, 198, 20, 9953, 16, 24, 104223, 3922, 86461, 102160, 106518, 101612, 100733, 17039, 33671, 24186, 101016, 22656, 36827, 31867, 21, 101129, 102984, 91547, 1811, 101291, 57, 115718, 100532, 100880, 101612, 108616, 101016, 105164, 3922, 36827, 31867, 21, 10447, 37197, 32218, 112561, 117408, 35287, 42246, 111379, 100992, 103735, 1811, 107743, 100433, 118068, 5463, 55, 220, 20, 15, 21, 15, 120928, 50991, 107758, 31867, 21, 1322, 102072, 36827, 31867, 21, 112848, 104946, 111379, 102735, 105541, 3922, 92672, 109034, 119927, 3922, 101739, 101911, 100532, 5486, 9080, 102874, 102581, 100518, 39135, 113577, 1811, 102317, 57237, 16, 13, 22, 24, 6358, 100547, 95337, 17, 13, 17, 7501, 10110, 36827, 31867, 21, 49372, 46281, 32943, 28037, 48915, 101026, 101058, 123721, 1811, 108895, 101035, 3922, 36827, 31867, 21, 101129, 100503, 72843, 17, 15, 4, 100477, 105516, 3922, 110349, 107371, 24946, 101971, 36, 100646, 105653, 3922, 123798, 20, 24, 24, 24, 24186, 72718, 6447, 198, 86461, 102160, 36827, 31867, 21, 5232, 116967, 109845, 101037, 10447, 94, 105, 72237, 27327, 76537, 198, 86461, 102160, 36827, 31867, 21, 113577, 105868, 58805, 94278, 238, 100667, 15592, 220, 22, 473, 220, 17, 21, 15, 107311, 3922, 23, 101021, 16, 21, 120312, 71600, 3922, 19, 20211, 102271, 102812, 103486, 1811, 103954, 9688, 19085, 10860, 55, 220, 20, 15, 21, 15, 120928, 50991, 3922, 16, 16, 20, 54, 100433, 118068, 71600, 3922, 101046, 16931, 1242, 220, 19, 100344, 1811, 105874, 17905, 3922, 107743, 16, 21, 108465, 17, 13, 20, 42, 220, 16, 21, 20, 11732, 109943, 53434, 108018, 3922, 101046, 38, 6354, 72501, 100344, 3922, 43292, 103897, 104196, 120822, 82317, 102698, 58322, 3922, 19, 15, 271, 9743, 91547, 58521, 29411, 482, 220, 58521, 31091, 5232, 964, 16, 21, 23, 198, 482, 220, 58521, 101241, 5232, 111642, 11, 76771, 239, 83301, 11, 47850, 233, 33748, 198, 482, 220, 58521, 105302, 5232, 109173, 271, 22452, 91547, 101143, 5232, 112203, 32239, 198, 482, 75677, 111, 55038, 101241, 5232, 104312, 11, 4996, 223, 98, 100563, 11, 220, 101766, 198, 482, 41766, 229, 81742, 33005, 5232, 100359, 198, 482, 75677, 111, 55038, 105344, 5232, 23, 198, 482, 61696, 225, 101028, 105344, 5232, 17, 271, 9743, 91547, 21082, 5232, 17, 15, 17, 20, 8107, 15, 20, 9953, 16, 24, 9080, 198, 482, 9085, 115, 251, 104944, 9039, 5232, 24, 24, 271, 15225, 67117, 83125, 110357, 107759, 5232, 127962, 127972, 127973, 127974, 127975, 127967]
]

PROMPTS = "[GenRM-vCoT] 你是一个搜索排序专家，请你仔细阅读以下Doc和Query，给出文章满意度评分及具体原因。请注意，本次搜索时间是2025年08月27日。\n\nQuery：华硕天选4和华硕天选6性价比对比\n- Query领域：电子产品\n- Query时效需求等级：低时效\n- Query权威需求等级：弱权威\n\nDoc：\n- 标题：华硕天选6系列发布 搭载满功耗RTX 5060实际到手5999元起_手机新浪网\n- 正文：<pcut>华硕天选6系列发布 搭载满功耗RTX 5060实际到手5999元起\n5月19日晚，华硕旗下潮玩新次元游戏本天选6系列正式发布。作为Z世代青年的潮酷游戏装备，天选6 系列再一次印证了其出色的综合实力。搭载满功耗RTX 5060笔记本电脑GPU的天选6 Pro以及天选6皆拥有出色的性能释放，同时颜值出众，魔幻青、日蚀灰双色可选。薄至1.79cm轻约2.2kg（天选6），从内到外实现全面进阶。首发期间，天选6系列均享20%国家补贴，叠加晒单返E卡福利，到手5999元起！\n华硕天选6：超高选购价值 硬核能打\n华硕天选6可选全新AMD 锐龙 AI 7 H 260处理器，8核心16线程设计，4nm先进工艺打造。配备GeForce RTX 5060笔记本电脑GPU，115W满功耗设计，支持DLSS 4技术。屏幕上，搭载16英寸2.5K 165Hz电竞级面板，支持G-SYNC技术，无惧画面撕裂及拖影，40<pcut>\n\nDoc发布作者：\n - 作者名称：IT168\n - 作者领域：电子产品, 科技, 手机\n - 作者认证：未知\n\n Doc发布平台：新浪网\n - 平台领域：财经, 健康, 旅游\n - 备案类型：企业\n - 平台等级：8\n - 权威等级：2\n\nDoc发布时间：2025年05月19日\n - 距今天数：99\n\n请输出文章满意度评分："

MODELPATH = "/home/luopl/hunyuan_tx/test_2"


def test_prompt(llm):
    tokenizer = AutoTokenizer.from_pretrained(MODELPATH, trust_remote_code=True)
    input_ids = tokenizer(PROMPTS, return_tensors="pt", trust_remote_code=True)["input_ids"]

    outputs = llm.classify(token_inputs(input_ids[0], qfeat=[2, 0, 20]))

    for i, out in enumerate(outputs):
        probs = out.outputs.probs
        print(f"Request {i}, class probs = {probs}")


def test_tokenid(llm):
    for ids in DEFAULT_PROMPT_TOKEN_IDS:

        outputs = llm.classify(token_inputs(ids, qfeat=[2, 0, 20]))
        for i, out in enumerate(outputs):
            probs = out.outputs.probs
            print(f"Request {i}, class probs = {probs}")


if __name__ == "__main__":
    llm = LLM(model=MODELPATH, task="classify",
        trust_remote_code=True,
        enforce_eager=True,
        enable_chunked_prefill=False)

    test_prompt(llm)
    test_tokenid(llm)
    # print(input_ids)