mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-22 23:23:55 +08:00
545 lines
21 KiB
Python
545 lines
21 KiB
Python
import asyncio
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import logging
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import os
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import statistics
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import time
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import concurrent.futures
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from typing import Dict, Optional
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import yaml
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import aiohttp
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from tabulate import tabulate
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from core.utils.llm import create_instance as create_llm_instance
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from config.settings import load_config
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# 设置全局日志级别为 WARNING，抑制 INFO 级别日志
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logging.basicConfig(level=logging.WARNING)
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description = "大语言模型性能测试"
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class LLMPerformanceTester:
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def __init__(self):
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self.config = load_config()
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# 使用更符合智能体场景的测试内容，包含系统提示词
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self.system_prompt = self._load_system_prompt()
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self.test_sentences = self.config.get("module_test", {}).get(
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"test_sentences",
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[
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"你好，我今天心情不太好，能安慰一下我吗？",
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"帮我查一下明天的天气如何？",
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"我想听一个有趣的故事，你能给我讲一个吗？",
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"现在几点了？今天是星期几？",
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"我想设置一个明天早上8点的闹钟提醒我开会",
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],
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)
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self.results = {}
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def _load_system_prompt(self) -> str:
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"""加载系统提示词"""
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try:
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prompt_file = os.path.join(
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os.path.dirname(os.path.dirname(__file__)), "agent-base-prompt.txt"
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)
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with open(prompt_file, "r", encoding="utf-8") as f:
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content = f.read()
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# 替换模板变量为测试值
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content = content.replace(
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"{{base_prompt}}", "你是小智，一个聪明可爱的AI助手"
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)
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content = content.replace(
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"{{emojiList}}", "😀,😃,😄,😁,😊,😍,🤔,😮,😱,😢,😭,😴,😵,🤗,🙄"
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)
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content = content.replace("{{current_time}}", "2024年8月17日 12:30:45")
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content = content.replace("{{today_date}}", "2024年8月17日")
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content = content.replace("{{today_weekday}}", "星期六")
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content = content.replace("{{lunar_date}}", "甲辰年七月十四")
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content = content.replace("{{local_address}}", "北京市")
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content = content.replace("{{weather_info}}", "今天晴，25-32℃")
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return content
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except Exception as e:
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print(f"无法加载系统提示词文件: {e}")
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return "你是小智，一个聪明可爱的AI助手。请用温暖友善的语气回复用户。"
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def _collect_response_sync(self, llm, messages, llm_name, sentence_start):
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"""同步收集响应数据的辅助方法"""
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chunks = []
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first_token_received = False
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first_token_time = None
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try:
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response_generator = llm.response("perf_test", messages)
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chunk_count = 0
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for chunk in response_generator:
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chunk_count += 1
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# 每处理一定数量的chunk就检查一下是否应该中断
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if chunk_count % 10 == 0:
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# 通过检查当前线程是否被标记为中断来提前退出
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import threading
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if (
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threading.current_thread().ident
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!= threading.main_thread().ident
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):
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# 如果不是主线程，检查是否应该停止
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pass
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# 检查chunk是否包含错误信息
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chunk_str = str(chunk)
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if (
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"异常" in chunk_str
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or "错误" in chunk_str
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or "502" in chunk_str.lower()
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):
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error_msg = chunk_str.lower()
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print(f"{llm_name} 响应包含错误信息: {error_msg}")
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# 抛出一个包含错误信息的异常
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raise Exception(chunk_str)
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if not first_token_received and chunk.strip() != "":
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first_token_time = time.time() - sentence_start
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first_token_received = True
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print(f"{llm_name} 首个 Token: {first_token_time:.3f}s")
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chunks.append(chunk)
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except Exception as e:
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# 更详细的错误信息
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error_msg = str(e).lower()
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print(f"{llm_name} 响应收集异常: {error_msg}")
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# 对于502错误或网络错误，直接抛出异常让上层处理
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if (
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"502" in error_msg
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or "bad gateway" in error_msg
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or "error code: 502" in error_msg
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or "异常" in str(e)
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or "错误" in str(e)
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):
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raise e
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# 对于其他错误，可以返回部分结果
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return chunks, first_token_time
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return chunks, first_token_time
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async def _check_ollama_service(self, base_url: str, model_name: str) -> bool:
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"""异步检查 Ollama 服务状态"""
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async with aiohttp.ClientSession() as session:
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try:
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async with session.get(f"{base_url}/api/version") as response:
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if response.status != 200:
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print(f"Ollama 服务未启动或无法访问: {base_url}")
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return False
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async with session.get(f"{base_url}/api/tags") as response:
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if response.status == 200:
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data = await response.json()
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models = data.get("models", [])
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if not any(model["name"] == model_name for model in models):
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print(
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f"Ollama 模型 {model_name} 未找到，请先使用 `ollama pull {model_name}` 下载"
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)
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return False
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else:
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print("无法获取 Ollama 模型列表")
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return False
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return True
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except Exception as e:
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print(f"无法连接到 Ollama 服务: {str(e)}")
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return False
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async def _test_single_sentence(
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self, llm_name: str, llm, sentence: str
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) -> Optional[Dict]:
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"""测试单个句子的性能"""
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try:
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print(f"{llm_name} 开始测试: {sentence[:20]}...")
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sentence_start = time.time()
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first_token_received = False
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first_token_time = None
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# 构建包含系统提示词的消息
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messages = [
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{"role": "system", "content": self.system_prompt},
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{"role": "user", "content": sentence},
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]
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# 使用asyncio.wait_for进行超时控制
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try:
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loop = asyncio.get_event_loop()
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# 创建响应收集任务
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future = executor.submit(
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self._collect_response_sync,
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llm,
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messages,
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llm_name,
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sentence_start,
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)
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# 使用asyncio.wait_for实现超时控制
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try:
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response_chunks, first_token_time = await asyncio.wait_for(
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asyncio.wrap_future(future), timeout=10.0
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)
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except asyncio.TimeoutError:
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print(f"{llm_name} 测试超时（10秒），跳过")
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# 强制取消future
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future.cancel()
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# 等待一小段时间确保线程池任务能够响应取消
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try:
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await asyncio.wait_for(
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asyncio.wrap_future(future), timeout=1.0
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)
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except (
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asyncio.TimeoutError,
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concurrent.futures.CancelledError,
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Exception,
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):
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# 忽略所有异常，确保程序继续执行
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pass
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return None
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except Exception as timeout_error:
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print(f"{llm_name} 处理异常: {timeout_error}")
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return None
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response_time = time.time() - sentence_start
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print(f"{llm_name} 完成响应: {response_time:.3f}s")
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return {
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"name": llm_name,
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"type": "llm",
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"first_token_time": first_token_time,
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"response_time": response_time,
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}
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except Exception as e:
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error_msg = str(e).lower()
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# 检查是否为502错误或网络错误
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if (
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"502" in error_msg
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or "bad gateway" in error_msg
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or "error code: 502" in error_msg
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):
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print(f"{llm_name} 遇到502错误，跳过测试")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "502网络错误",
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}
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print(f"{llm_name} 句子测试失败: {str(e)}")
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return None
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async def _test_llm(self, llm_name: str, config: Dict) -> Dict:
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"""异步测试单个 LLM 性能"""
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try:
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# 对于 Ollama，跳过 api_key 检查并进行特殊处理
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if llm_name == "Ollama":
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base_url = config.get("base_url", "http://localhost:11434")
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model_name = config.get("model_name")
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if not model_name:
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print("Ollama 未配置 model_name")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "网络错误",
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}
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if not await self._check_ollama_service(base_url, model_name):
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "网络错误",
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}
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else:
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if "api_key" in config and any(
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x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
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):
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print(f"跳过未配置的 LLM: {llm_name}")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "配置错误",
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}
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# 获取实际类型（兼容旧配置）
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module_type = config.get("type", llm_name)
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llm = create_llm_instance(module_type, config)
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# 统一使用 UTF-8 编码
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test_sentences = [
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s.encode("utf-8").decode("utf-8") for s in self.test_sentences
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]
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# 创建所有句子的测试任务
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sentence_tasks = []
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for sentence in test_sentences:
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sentence_tasks.append(
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self._test_single_sentence(llm_name, llm, sentence)
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)
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# 并发执行所有句子测试，并处理可能的异常
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sentence_results = await asyncio.gather(
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*sentence_tasks, return_exceptions=True
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)
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# 处理结果，过滤掉异常和None值
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valid_results = []
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for result in sentence_results:
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if isinstance(result, dict) and result is not None:
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valid_results.append(result)
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elif isinstance(result, Exception):
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error_msg = str(result).lower()
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if "502" in error_msg or "bad gateway" in error_msg:
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print(f"{llm_name} 遇到502错误，跳过该句子测试")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "502网络错误",
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}
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else:
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print(f"{llm_name} 句子测试异常: {result}")
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if not valid_results:
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print(f"{llm_name} 无有效数据，可能遇到网络问题或配置错误")
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "网络错误",
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}
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# 检查有效结果数量，如果太少则认为测试失败
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if len(valid_results) < len(test_sentences) * 0.3: # 至少要有30%的成功率
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print(
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f"{llm_name} 成功测试句子过少({len(valid_results)}/{len(test_sentences)})，可能网络不稳定或接口有问题"
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)
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": "网络错误",
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}
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first_token_times = [
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r["first_token_time"]
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for r in valid_results
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if r.get("first_token_time")
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]
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response_times = [r["response_time"] for r in valid_results]
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# 过滤异常数据（超出3个标准差的数据）
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if len(response_times) > 1:
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mean = statistics.mean(response_times)
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stdev = statistics.stdev(response_times)
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filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
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else:
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filtered_times = response_times
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return {
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"name": llm_name,
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"type": "llm",
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"avg_response": sum(response_times) / len(response_times),
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"avg_first_token": (
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sum(first_token_times) / len(first_token_times)
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if first_token_times
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else 0
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),
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"success_rate": f"{len(valid_results)}/{len(test_sentences)}",
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"errors": 0,
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}
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except Exception as e:
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error_msg = str(e).lower()
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if "502" in error_msg or "bad gateway" in error_msg:
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print(f"LLM {llm_name} 遇到502错误，跳过测试")
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else:
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print(f"LLM {llm_name} 测试失败: {str(e)}")
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error_type = "网络错误"
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if "timeout" in str(e).lower():
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error_type = "超时连接"
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return {
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"name": llm_name,
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"type": "llm",
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"errors": 1,
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"error_type": error_type,
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}
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def _print_results(self):
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"""打印测试结果"""
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print("\n" + "=" * 50)
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print("LLM 性能测试结果")
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print("=" * 50)
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if not self.results:
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print("没有可用的测试结果")
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return
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headers = ["模型名称", "平均响应时间(s)", "首Token时间(s)", "成功率", "状态"]
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table_data = []
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# 收集所有数据并分类
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valid_results = []
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error_results = []
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for name, data in self.results.items():
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if data["errors"] == 0:
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# 正常结果
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avg_response = f"{data['avg_response']:.3f}"
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avg_first_token = (
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f"{data['avg_first_token']:.3f}"
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if data["avg_first_token"] > 0
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else "-"
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)
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success_rate = data.get("success_rate", "N/A")
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status = "✅ 正常"
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# 保存用于排序的值
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first_token_value = (
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data["avg_first_token"]
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if data["avg_first_token"] > 0
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else float("inf")
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)
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valid_results.append(
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{
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"name": name,
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"avg_response": avg_response,
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"avg_first_token": avg_first_token,
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"success_rate": success_rate,
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"status": status,
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"sort_key": first_token_value,
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}
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)
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else:
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# 错误结果
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avg_response = "-"
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avg_first_token = "-"
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success_rate = "0/5"
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# 获取具体错误类型
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error_type = data.get("error_type", "网络错误")
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status = f"❌ {error_type}"
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error_results.append(
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[name, avg_response, avg_first_token, success_rate, status]
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)
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# 按首Token时间升序排序
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valid_results.sort(key=lambda x: x["sort_key"])
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# 将排序后的有效结果转换为表格数据
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for result in valid_results:
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table_data.append(
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[
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result["name"],
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result["avg_response"],
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result["avg_first_token"],
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result["success_rate"],
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result["status"],
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]
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)
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# 将错误结果添加到表格数据末尾
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table_data.extend(error_results)
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print(tabulate(table_data, headers=headers, tablefmt="grid"))
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print("\n测试说明:")
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print("- 测试内容：包含完整系统提示词的智能体对话场景")
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print("- 超时控制：单个请求最大等待时间为10秒")
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print("- 错误处理：自动跳过502错误和网络异常的模型")
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print("- 成功率：成功响应的句子数量/总测试句子数量")
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print("\n测试完成！")
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async def run(self):
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"""执行全量异步测试"""
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print("开始筛选可用 LLM 模块...")
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# 创建所有测试任务
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all_tasks = []
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|
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# LLM 测试任务
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if self.config.get("LLM") is not None:
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for llm_name, config in self.config.get("LLM", {}).items():
|
|
# 检查配置有效性
|
|
if llm_name == "CozeLLM":
|
|
if any(x in config.get("bot_id", "") for x in ["你的"]) or any(
|
|
x in config.get("user_id", "") for x in ["你的"]
|
|
):
|
|
print(f"LLM {llm_name} 未配置 bot_id/user_id，已跳过")
|
|
continue
|
|
elif "api_key" in config and any(
|
|
x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
|
|
):
|
|
print(f"LLM {llm_name} 未配置 api_key，已跳过")
|
|
continue
|
|
|
|
# 对于 Ollama，先检查服务状态
|
|
if llm_name == "Ollama":
|
|
base_url = config.get("base_url", "http://localhost:11434")
|
|
model_name = config.get("model_name")
|
|
if not model_name:
|
|
print("Ollama 未配置 model_name")
|
|
continue
|
|
|
|
if not await self._check_ollama_service(base_url, model_name):
|
|
continue
|
|
|
|
print(f"添加 LLM 测试任务: {llm_name}")
|
|
all_tasks.append(self._test_llm(llm_name, config))
|
|
|
|
print(f"\n找到 {len(all_tasks)} 个可用 LLM 模块")
|
|
print("\n开始并发测试所有模块...\n")
|
|
|
|
# 并发执行所有测试任务，但为每个任务设置独立超时
|
|
async def test_with_timeout(task, timeout=30):
|
|
"""为每个测试任务添加超时保护"""
|
|
try:
|
|
return await asyncio.wait_for(task, timeout=timeout)
|
|
except asyncio.TimeoutError:
|
|
print(f"测试任务超时（{timeout}秒），跳过")
|
|
return {
|
|
"name": "Unknown",
|
|
"type": "llm",
|
|
"errors": 1,
|
|
"error_type": "超时连接",
|
|
}
|
|
except Exception as e:
|
|
print(f"测试任务异常: {str(e)}")
|
|
return {
|
|
"name": "Unknown",
|
|
"type": "llm",
|
|
"errors": 1,
|
|
"error_type": "网络错误",
|
|
}
|
|
|
|
# 为每个任务包装超时保护
|
|
protected_tasks = [test_with_timeout(task) for task in all_tasks]
|
|
|
|
# 并发执行所有测试任务
|
|
all_results = await asyncio.gather(*protected_tasks, return_exceptions=True)
|
|
|
|
# 处理结果
|
|
for result in all_results:
|
|
if isinstance(result, dict):
|
|
if result.get("errors") == 0:
|
|
self.results[result["name"]] = result
|
|
else:
|
|
# 即使有错误也记录，用于显示失败状态
|
|
if result.get("name") != "Unknown":
|
|
self.results[result["name"]] = result
|
|
elif isinstance(result, Exception):
|
|
print(f"测试结果处理异常: {str(result)}")
|
|
|
|
# 打印结果
|
|
print("\n生成测试报告...")
|
|
self._print_results()
|
|
|
|
|
|
async def main():
|
|
tester = LLMPerformanceTester()
|
|
await tester.run()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|