计算百川大模型的输出token_大模型获取token数

代码比较简单，记录一下免得以后要再copy一次。

首先需要在modeling_baichuan.py的BaichuanForCausalLM类中添加get_outputs函数

    def get_outputs(self, tokenizer, messages: List[dict], stream=False,
             generation_config: Optional[GenerationConfig]=None):
        generation_config = generation_config or self.generation_config
        input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
        outputs = self.generate(input_ids, generation_config=generation_config)
        return outputs

然后运行下面的代码计算，注意，因为我显卡空间不够，所以是半精度运行。

import os
import torch
import platform
from colorama import Fore, Style
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
import time
 
 
print("init model ...")
model = AutoModelForCausalLM.from_pretrained(
    "/home/Datasets/large_models/all_pt_model/baichuan2_13b_20240102",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
 
model.generation_config = GenerationConfig.from_pretrained(
    "/home/Datasets/large_models/all_pt_model/baichuan2_13b_20240102"
)
tokenizer = AutoTokenizer.from_pretrained(
    "/home/Datasets/large_models/all_pt_model/baichuan2_13b_20240102",
    use_fast=False,
    trust_remote_code=True
)
 
messages = []
outs = []
generate_tokens = []
query = ["怎么创建线程", "什么是信号量，给我创建信号量的示例", "发送邮件的示例", "如何查找设备", "你好，介绍一下自己，要一千字", "给我一个创建线程的代码示例", "详细介绍一下rt-thread", "rt-thread的历史发展", "创建设备的代码示例", ]
 
for q in query:
    messages = [{"role": "user", "content": q}]
    t1 = time.time()
    outputs = model.get_outputs(tokenizer, messages)
    t2 = time.time()
    outs.append(outputs)
    generate_tokens.append(len(outputs[0]) / (t2 - t1))
 
    print(generate_tokens)
 
 
print(sum(generate_tokens)/len(generate_tokens))