deepseek r1的蒸馏模型使用xgrammar时输出异常

[distance92]

使用deepseek r1的蒸馏模型qwen2.5-7B时无输出，原版qwen模型有输出

[distance92]

from fastapi import FastAPI, Request
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import uvicorn
import torch
import xgrammar as xgr
import json
import datetime

# 设置设备参数
DEVICE = "cuda"  # 使用CUDA
DEVICE_ID = "0"  # CUDA设备ID，如果未设置则为空
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE  # 组合CUDA设备信息


# 清理GPU内存函数
def torch_gc():
    if torch.cuda.is_available():  # 检查是否可用CUDA
        with torch.cuda.device(CUDA_DEVICE):  # 指定CUDA设备
            torch.cuda.empty_cache()  # 清空CUDA缓存
            torch.cuda.ipc_collect()  # 收集CUDA内存碎片


# 创建FastAPI应用
app = FastAPI()

# 加载预训练的分词器和模型
model_name = "/root/model/DeepSeek-R1-Distill-Qwen-7B"
model = AutoModelForCausalLM.from_pretrained(
    model_name, torch_dtype='auto', device_map=DEVICE
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
full_vocab_size = config.vocab_size

# 编译语法
tokenizer_info = xgr.TokenizerInfo.from_huggingface(tokenizer, vocab_size=full_vocab_size)
grammar_compiler = xgr.GrammarCompiler(tokenizer_info)
compiled_grammar: xgr.CompiledGrammar = grammar_compiler.compile_builtin_json_grammar()


# 处理POST请求的端点
@app.post("/")
async def generate_json(request: Request):
    global model, tokenizer, compiled_grammar  # 声明全局变量
    json_post_raw = await request.json()  # 获取POST请求的JSON数据
    json_post = json.dumps(json_post_raw)  # 将JSON数据转换为字符串
    json_post_list = json.loads(json_post)  # 将字符串转换为Python对象
    prompt = json_post_list.get('prompt')  # 获取请求中的提示
    system_prompt = json_post_list.get('system')

    messages = [{"role": "system", "content": system_prompt},
                {"role": "user", "content": prompt}]

    input_ids = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    model_inputs = tokenizer([input_ids], return_tensors="pt").to(model.device)

    # 实例化LogitsProcessor并调用generate()
    xgr_logits_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)
    generated_ids = model.generate(
        **model_inputs, max_new_tokens=512, logits_processor=[xgr_logits_processor]
    )

    raw_response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    print("raw_response: " + repr(raw_response))

    # 后处理输出
    generated_ids = [
        output_ids[len(input_ids):]
        for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

    now = datetime.datetime.now()  # 获取当前时间
    time = now.strftime("%Y-%m-%d %H:%M:%S")  # 格式化时间为字符串

    # 构建响应JSON
    answer = {
        "response": response,
        "status": 200,
        "time": time
    }

    # 构建日志信息
    log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
    print(log)  # 打印日志
    torch_gc()  # 执行GPU内存清理
    return answer  # 返回响应


# 主函数入口
if __name__ == '__main__':
    uvicorn.run(app, host='172.26.123.154', port=6006, workers=1)  # 在指定端口和主机上启动应用

[Ubospica]

Hi @distance92 , thanks for reporting the error! Could you provide more details of your experiment, like the error message or the output before exception?