116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
import argparse
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import json
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import os
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from openai import OpenAI
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="检查模型部署是否成功的工具",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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使用示例:
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python scripts/check_deployment_cn.py --base-url http://localhost:8000/v1 --apikey your-key --model autoglm-phone-9b
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python scripts/check_deployment_cn.py --base-url http://localhost:8000/v1 --apikey your-key --model autoglm-phone-9b --messages-file custom.json
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""",
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)
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parser.add_argument(
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"--base-url",
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type=str,
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required=True,
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help="API 服务的 base URL，例如: http://localhost:8000/v1",
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)
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parser.add_argument(
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"--apikey", type=str, default="EMPTY", help="API 密钥 (默认: EMPTY)"
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)
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parser.add_argument(
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"--model",
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type=str,
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required=True,
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help="要测试的模型名称，例如: autoglm-phone-9b",
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)
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parser.add_argument(
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"--messages-file",
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type=str,
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default="scripts/sample_messages.json",
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help="包含测试消息的 JSON 文件路径 (默认: scripts/sample_messages.json)",
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)
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parser.add_argument(
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"--max-tokens", type=int, default=3000, help="最大生成 token 数 (默认: 3000)"
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)
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parser.add_argument(
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"--temperature", type=float, default=0.0, help="采样温度 (默认: 0.0)"
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)
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parser.add_argument(
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"--top_p", type=float, default=0.85, help="nucleus sampling 参数 (默认: 0.85)"
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)
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parser.add_argument(
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"--frequency_penalty", type=float, default=0.2, help="频率惩罚参数 (默认: 0.2)"
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)
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args = parser.parse_args()
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# 读取测试消息
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if not os.path.exists(args.messages_file):
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print(f"错误: 消息文件 {args.messages_file} 不存在")
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exit(1)
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with open(args.messages_file) as f:
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messages = json.load(f)
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base_url = args.base_url
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api_key = args.apikey
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model = args.model
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print(f"开始测试模型推理...")
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print(f"Base URL: {base_url}")
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print(f"Model: {model}")
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print(f"Messages file: {args.messages_file}")
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print("=" * 80)
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try:
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client = OpenAI(
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base_url=base_url,
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api_key=api_key,
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)
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response = client.chat.completions.create(
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messages=messages,
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model=model,
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max_tokens=args.max_tokens,
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temperature=args.temperature,
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top_p=args.top_p,
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frequency_penalty=args.frequency_penalty,
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stream=False,
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)
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print("\n模型推理结果:")
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print("=" * 80)
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print(response.choices[0].message.content)
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print("=" * 80)
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if response.usage:
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print(f"\n统计信息:")
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print(f" - Prompt tokens: {response.usage.prompt_tokens}")
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print(f" - Completion tokens: {response.usage.completion_tokens}")
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print(f" - Total tokens: {response.usage.total_tokens}")
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print(f"\n请根据上述推理结果判断模型部署是否符合预期。")
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except Exception as e:
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print(f"\n调用 API 时发生错误:")
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print(f"错误类型: {type(e).__name__}")
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print(f"错误信息: {str(e)}")
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print(
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"\n提示: 请检查 base_url、api_key 和 model 参数是否正确，以及服务是否正在运行。"
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)
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exit(1)
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