163 lines
7.2 KiB
Python
163 lines
7.2 KiB
Python
import aiohttp
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import asyncio
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import time
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from tqdm import tqdm
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import random
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import sys
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# 配置
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# LLM_MODEL = "Qwen2-72B-Instruct"
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# LLM_ENDPOINT = "http://192.168.56.123:8822/v1"
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# LLM_MODEL = "deepseek-chat"
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# LLM_ENDPOINT = "https://api.deepseek.com/v1"
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LLM_MODEL = "qwen-max-2025-01-25"
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LLM_ENDPOINT = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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TEMPERATURE = 0.7 # 确保每次返回的都不同
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MAX_TOKENS = 8192
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# 问题列表
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questions = [
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"为什么鸟儿会唱歌？", "为什么我们有季节？", "为什么星星会闪烁？", "为什么我们会打哈欠？",
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"为什么太阳是热的？", "为什么猫会咕噜咕噜叫？", "为什么狗会吠？", "为什么鱼会游泳？",
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"为什么我们有指纹？", "为什么我们会打喷嚏？", "为什么我们有眉毛？", "为什么我们有头发？",
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"为什么我们有指甲？", "为什么我们有牙齿？", "为什么我们有骨头？", "为什么我们有肌肉？",
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"为什么我们有血液？", "为什么我们有心脏？", "为什么我们有肺？", "为什么我们有大脑？",
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"为什么我们有皮肤？", "为什么我们有耳朵？", "为什么我们有眼睛？", "为什么我们有鼻子？",
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"为什么我们有嘴巴？", "为什么我们有舌头？", "为什么我们有胃？", "为什么我们有肠子？",
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"为什么我们有肝脏？", "为什么我们有肾脏？", "为什么我们有膀胱？", "为什么我们有胰腺？",
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"为什么我们有脾脏？", "为什么我们有胆囊？", "为什么我们有甲状腺？", "为什么我们有肾上腺？",
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"为什么我们有垂体？", "为什么我们有下丘脑？", "为什么我们有胸腺？", "为什么我们有淋巴结？",
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"为什么我们有脊髓？", "为什么我们有神经？", "为什么我们有循环系统？", "为什么我们有呼吸系统？",
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"为什么我们有消化系统？", "为什么我们有免疫系统？"
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]
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def log_to_file(file, message):
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"""将消息追加写入指定的文件"""
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with open(file, 'a', encoding='utf-8') as f:
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f.write(message + '\n')
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async def fetch(session, url, file=None):
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start_time = time.time()
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question = random.choice(questions)
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json_payload = {
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"model": LLM_MODEL,
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"messages": [
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# {"role": "system", "content": "你的任务是学习和理解用户输入的文段，分析其中的实体关系，然后根据关系逻辑重新拟定你份合同，合同字数要在4000字以上。"},
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# {"role": "system", "content": "你的使命是翻译用户输入的文段。注意，一定要翻译完整。"},
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{"role": "system", "content": "你的任务是用专业学术语言，严谨的科学态度，全面的数据支持，完整详实地回答user的问题。"},
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{"role": "user", "content": question}
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],
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"temperature": TEMPERATURE,
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"max_tokens": MAX_TOKENS,
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"stream": False
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}
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headers = {
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"Content-Type": "application/json",
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# "Authorization": "Bearer sk-dba93353b0cc447ba55245e4f048c779" # deepseek
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"Authorization": "sk-8b498c0de2dc437aab8efa490d4021ba" # qwen
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}
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try:
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async with session.post(url, json=json_payload, headers=headers) as response:
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if response.status != 200:
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print(f"错误: 收到响应码 {response.status}")
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return 0, 0, 0
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response_json = await response.json()
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end_time = time.time()
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request_time = end_time - start_time
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completion_tokens = 0
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if 'usage' in response_json:
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usage = response_json['usage']
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completion_tokens = usage.get('completion_tokens', 0)
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prompt_tokens = usage.get('prompt_tokens', 0)
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else:
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print("警告: 响应中缺少 'usage' 字段。")
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answer = ""
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if 'choices' in response_json and len(response_json['choices']) > 0:
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answer = response_json['choices'][0]['message']['content']
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# completion_tokens = len(answer) / 1.5 # qwen
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# completion_tokens = len(answer) * 0.6 # deepseek
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else:
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print("警告: 响应中缺少 'choices' 字段或内容为空。")
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completion_tokens = 0
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# 将输入输出写入文件（保持原有日志格式不变）
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if file:
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log_to_file(file, f"Q: {question}\nA: {answer}\n")
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# 打印输入和输出的token数
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# print(f"输入token数: {input_tokens}, 输出token数: {output_tokens}")
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return prompt_tokens, completion_tokens, request_time
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except Exception as e:
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print(f"请求过程中发生异常: {e}")
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return 0, 0, 0
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async def bound_fetch(sem, session, url, pbar, file=None):
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async with sem:
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result = await fetch(session, url, file=file)
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pbar.update(1)
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return result
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async def run(load_url, max_concurrent_requests, total_requests, output_file):
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sem = asyncio.Semaphore(max_concurrent_requests)
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timeout = aiohttp.ClientTimeout(total=6000, connect=6000, sock_read=6000, sock_connect=6000)
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async with aiohttp.ClientSession(timeout=timeout) as session:
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tasks = []
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with tqdm(total=total_requests) as pbar:
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for _ in range(total_requests):
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task = asyncio.create_task(bound_fetch(sem, session, load_url, pbar, file=output_file))
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tasks.append(task)
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results = await asyncio.gather(*tasks)
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# 聚合token数和响应时间
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total_input_tokens = sum(result[0] for result in results)
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total_output_tokens = sum(result[1] for result in results)
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response_times = [result[2] for result in results]
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return total_input_tokens, total_output_tokens, response_times
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if __name__ == '__main__':
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if len(sys.argv) != 3:
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print("用法: python llm_test.py <C> <N>")
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sys.exit(1)
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try:
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C = int(sys.argv[1]) # 最大并发数
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N = int(sys.argv[2]) # 请求总数
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except ValueError:
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print("错误: C 和 N 必须是整数。")
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sys.exit(1)
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url = f'{LLM_ENDPOINT}/chat/completions'
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output_file = 'A800_Qwen2.5-72Bint8_bench_.txt'
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write('') # 清空文件内容
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start_time = time.time()
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total_input_tokens, total_output_tokens, response_times = asyncio.run(run(url, C, N, output_file))
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end_time = time.time()
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total_time = end_time - start_time
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avg_time_per_request = sum(response_times) / len(response_times) if response_times else 0
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tokens_per_second = (total_output_tokens) / total_time if total_time > 0 else 0
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final_output = (
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"最终表现:\n"
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f" 输入token数 : {total_input_tokens:.2f}\n"
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f" 输出token数 : {total_output_tokens:.2f}\n"
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f" 并发数 : {C}\n"
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f" 总请求数 : {N}\n"
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f" 总耗时 : {total_time:.2f} 秒\n"
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f" 平均耗时 : {avg_time_per_request:.2f} 秒\n"
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f" 吞吐(QPS) : {tokens_per_second:.2f} tokens/s"
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)
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print(final_output)
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log_to_file(output_file, final_output) # 将最终的表现也写入文件
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