大模型推理性能测试工具使用心得

在大模型安全与隐私保护领域，推理性能测试是评估模型实际应用能力的重要环节。本文分享一套可复现的性能测试方案，帮助安全工程师更好地评估大模型推理效率。

测试环境准备

pip install torch transformers accelerate

核心测试脚本

import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def benchmark_model(model_name, prompt, num_runs=10):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.float16,
        device_map="auto"
    )
    
    # 预热
    _ = model.generate(tokenizer.encode(prompt, return_tensors="pt"), max_length=50)
    
    # 性能测试
    times = []
    for i in range(num_runs):
        start_time = time.time()
        output = model.generate(
            tokenizer.encode(prompt, return_tensors="pt"),
            max_length=100,
            num_beams=4,
            do_sample=True
        )
        end_time = time.time()
        times.append(end_time - start_time)
    
    avg_time = sum(times) / len(times)
    print(f"平均推理时间: {avg_time:.2f}秒")
    return avg_time

# 使用示例
benchmark_model("meta-llama/Llama-2-7b-chat-hf", "请解释什么是大模型安全")"