|
|
import time
|
|
|
import asyncio
|
|
|
import logging
|
|
|
import statistics
|
|
|
import base64
|
|
|
from typing import Dict
|
|
|
from tabulate import tabulate
|
|
|
from config.settings import load_config
|
|
|
from core.utils.vllm import create_instance
|
|
|
|
|
|
# 设置全局日志级别为WARNING,抑制INFO级别日志
|
|
|
logging.basicConfig(level=logging.WARNING)
|
|
|
|
|
|
|
|
|
class AsyncVisionPerformanceTester:
|
|
|
def __init__(self):
|
|
|
self.config = load_config()
|
|
|
self.test_images = [
|
|
|
"../../docs/images/demo1.png",
|
|
|
"../../docs/images/demo2.png",
|
|
|
]
|
|
|
self.test_questions = [
|
|
|
"这张图片里有什么?",
|
|
|
"请详细描述这张图片的内容",
|
|
|
]
|
|
|
|
|
|
# 加载测试图片
|
|
|
self.results = {"vllm": {}}
|
|
|
|
|
|
async def _test_vllm(self, vllm_name: str, config: Dict) -> Dict:
|
|
|
"""异步测试单个视觉大模型性能"""
|
|
|
try:
|
|
|
# 检查API密钥配置
|
|
|
if "api_key" in config and any(
|
|
|
x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
|
|
|
):
|
|
|
print(f"⏭️ VLLM {vllm_name} 未配置api_key,已跳过")
|
|
|
return {"name": vllm_name, "type": "vllm", "errors": 1}
|
|
|
|
|
|
# 获取实际类型(兼容旧配置)
|
|
|
module_type = config.get("type", vllm_name)
|
|
|
vllm = create_instance(module_type, config)
|
|
|
|
|
|
print(f"🖼️ 测试 VLLM: {vllm_name}")
|
|
|
|
|
|
# 创建所有测试任务
|
|
|
test_tasks = []
|
|
|
for question in self.test_questions:
|
|
|
for image in self.test_images:
|
|
|
test_tasks.append(
|
|
|
self._test_single_vision(vllm_name, vllm, question, image)
|
|
|
)
|
|
|
|
|
|
# 并发执行所有测试
|
|
|
test_results = await asyncio.gather(*test_tasks)
|
|
|
|
|
|
# 处理结果
|
|
|
valid_results = [r for r in test_results if r is not None]
|
|
|
if not valid_results:
|
|
|
print(f"⚠️ {vllm_name} 无有效数据,可能配置错误")
|
|
|
return {"name": vllm_name, "type": "vllm", "errors": 1}
|
|
|
|
|
|
response_times = [r["response_time"] for r in valid_results]
|
|
|
|
|
|
# 过滤异常数据
|
|
|
mean = statistics.mean(response_times)
|
|
|
stdev = statistics.stdev(response_times) if len(response_times) > 1 else 0
|
|
|
filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
|
|
|
|
|
|
if len(filtered_times) < len(test_tasks) * 0.5:
|
|
|
print(f"⚠️ {vllm_name} 有效数据不足,可能网络不稳定")
|
|
|
return {"name": vllm_name, "type": "vllm", "errors": 1}
|
|
|
|
|
|
return {
|
|
|
"name": vllm_name,
|
|
|
"type": "vllm",
|
|
|
"avg_response": sum(response_times) / len(response_times),
|
|
|
"std_response": (
|
|
|
statistics.stdev(response_times) if len(response_times) > 1 else 0
|
|
|
),
|
|
|
"errors": 0,
|
|
|
}
|
|
|
|
|
|
except Exception as e:
|
|
|
print(f"⚠️ VLLM {vllm_name} 测试失败: {str(e)}")
|
|
|
return {"name": vllm_name, "type": "vllm", "errors": 1}
|
|
|
|
|
|
async def _test_single_vision(
|
|
|
self, vllm_name: str, vllm, question: str, image: str
|
|
|
) -> Dict:
|
|
|
"""测试单个视觉问题的性能"""
|
|
|
try:
|
|
|
print(f"📝 {vllm_name} 开始测试: {question[:20]}...")
|
|
|
start_time = time.time()
|
|
|
|
|
|
# 读取图片并转换为base64
|
|
|
with open(image, "rb") as image_file:
|
|
|
image_data = image_file.read()
|
|
|
image_base64 = base64.b64encode(image_data).decode("utf-8")
|
|
|
|
|
|
# 直接获取响应
|
|
|
response = vllm.response(question, image_base64)
|
|
|
response_time = time.time() - start_time
|
|
|
print(f"✓ {vllm_name} 完成响应: {response_time:.3f}s")
|
|
|
|
|
|
return {
|
|
|
"name": vllm_name,
|
|
|
"type": "vllm",
|
|
|
"response_time": response_time,
|
|
|
}
|
|
|
except Exception as e:
|
|
|
print(f"⚠️ {vllm_name} 测试失败: {str(e)}")
|
|
|
return None
|
|
|
|
|
|
def _print_results(self):
|
|
|
"""打印测试结果"""
|
|
|
vllm_table = []
|
|
|
for name, data in self.results["vllm"].items():
|
|
|
if data["errors"] == 0:
|
|
|
stability = data["std_response"] / data["avg_response"]
|
|
|
vllm_table.append(
|
|
|
[
|
|
|
name,
|
|
|
f"{data['avg_response']:.3f}秒",
|
|
|
f"{stability:.3f}",
|
|
|
]
|
|
|
)
|
|
|
|
|
|
if vllm_table:
|
|
|
print("\n视觉大模型性能排行:\n")
|
|
|
print(
|
|
|
tabulate(
|
|
|
vllm_table,
|
|
|
headers=["模型名称", "响应耗时", "稳定性"],
|
|
|
tablefmt="github",
|
|
|
colalign=("left", "right", "right"),
|
|
|
disable_numparse=True,
|
|
|
)
|
|
|
)
|
|
|
else:
|
|
|
print("\n⚠️ 没有可用的视觉大模型进行测试。")
|
|
|
|
|
|
async def run(self):
|
|
|
"""执行全量异步测试"""
|
|
|
print("🔍 开始筛选可用视觉大模型...")
|
|
|
|
|
|
if not self.test_images:
|
|
|
print(f"\n⚠️ {self.image_root} 路径下没有图片文件,无法进行测试")
|
|
|
return
|
|
|
|
|
|
# 创建所有测试任务
|
|
|
all_tasks = []
|
|
|
|
|
|
# VLLM测试任务
|
|
|
if self.config.get("VLLM") is not None:
|
|
|
for vllm_name, config in self.config.get("VLLM", {}).items():
|
|
|
if "api_key" in config and any(
|
|
|
x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]
|
|
|
):
|
|
|
print(f"⏭️ VLLM {vllm_name} 未配置api_key,已跳过")
|
|
|
continue
|
|
|
print(f"🖼️ 添加VLLM测试任务: {vllm_name}")
|
|
|
all_tasks.append(self._test_vllm(vllm_name, config))
|
|
|
|
|
|
print(f"\n✅ 找到 {len(all_tasks)} 个可用视觉大模型")
|
|
|
print(f"✅ 使用 {len(self.test_images)} 张测试图片")
|
|
|
print(f"✅ 使用 {len(self.test_questions)} 个测试问题")
|
|
|
print("\n⏳ 开始并发测试所有模型...\n")
|
|
|
|
|
|
# 并发执行所有测试任务
|
|
|
all_results = await asyncio.gather(*all_tasks, return_exceptions=True)
|
|
|
|
|
|
# 处理结果
|
|
|
for result in all_results:
|
|
|
if isinstance(result, dict) and result["errors"] == 0:
|
|
|
self.results["vllm"][result["name"]] = result
|
|
|
|
|
|
# 打印结果
|
|
|
print("\n📊 生成测试报告...")
|
|
|
self._print_results()
|
|
|
|
|
|
|
|
|
async def main():
|
|
|
tester = AsyncVisionPerformanceTester()
|
|
|
await tester.run()
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
asyncio.run(main())
|