代做安装预算的网站seo推广思路

0. 引言

这篇文章主要记录了B站AIGC科技官的"vLLM简介"视频截图。

1. vLLM简介

笔记 From Up主：

• KV Cache的大小与序列长度的大小是成正比的

2. vLLM启动日志解析

3. vLLM压力测试

我本机测试的示例代码，

import requests
import time# 接口配置（根据实际部署调整）
API_URL = "http://192.168.31.15:8000/v1/completions"
MODEL_NAME = "gpt-4o"  # 与启动命令的 --served-model-name 一致
HEADERS = {"Content-Type": "application/json", "Authorization": "Bearer sk-123456"}def test_token_rate(prompt: str, max_tokens: int = 512):"""测试单次请求的 Token 速率"""payload = {"model": "gpt-4o","prompt": prompt,"stream": True,  # 启用流式响应以统计 Token 延迟"max_tokens": max_tokens,"temperature": 0.7}start_time = time.perf_counter()first_token_received = Falsetoken_count = 0# 发送流式请求response = requests.post(API_URL, json=payload, headers=HEADERS, stream=True)for chunk in response.iter_lines():if chunk:chunk_str = chunk.decode("utf-8").strip()if chunk_str.startswith("data: "):# 统计首 Token 到达时间if not first_token_received:first_token_time = time.perf_counter()first_token_received = True# 累计生成 Token 数量token_count += 1end_time = time.perf_counter()return {"total_time": end_time - start_time,"first_token_latency": first_token_time - start_time if first_token_received else 0,"tokens_per_sec": token_count / (end_time - start_time)}# 测试执行
prompt = "假设你是唐朝诗人李白，请用七言绝句描述一次雪夜独钓的场景"
result = test_token_rate(prompt)
print(f"首 Token 延迟: {result['first_token_latency']:.2f}s")
print(f"Token 速率: {result['tokens_per_sec']:.2f} tokens/s")

我本机测试的示例结果，

首 Token 延迟: 0.36s
Token 速率: 39.10 tokens/s

我本机测试的示例代码，

import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed# 配置参数
API_URL = "http://192.168.31.15:8000/v1/completions"
MODEL_NAME = "gpt-4o"  # 与 vLLM 启动参数 --served-model-name 一致
CONCURRENCY = 10  # 并发请求数
MAX_TOKENS = 512  # 每个请求生成的最大 Token 数
TEST_PROMPT = "请用鲁迅的文学风格描写一次深夜咖啡馆的场景"
HEADERS = {"Content-Type": "application/json", "Authorization": "Bearer sk-123456"}def send_request(request_id: int):"""单个请求测试函数"""payload = {"model": "gpt-4o","prompt": TEST_PROMPT,"stream": True,"max_tokens": MAX_TOKENS,"temperature": 0.8}start_time = time.perf_counter()first_token_time = Nonetoken_count = 0try:response = requests.post(API_URL, json=payload, headers=HEADERS, stream=True)for chunk in response.iter_lines():if chunk:chunk_str = chunk.decode().strip()if chunk_str.startswith("data: "):if not first_token_time:first_token_time = time.perf_counter()token_count += 1except Exception as e:print(f"请求 {request_id} 失败: {str(e)}")return Noneend_time = time.perf_counter()return {"request_id": request_id,"total_time": end_time - start_time,"first_token_latency": first_token_time - start_time if first_token_time else 0,"tokens": token_count}def run_concurrent_test():"""执行并发测试"""results = []with ThreadPoolExecutor(max_workers=CONCURRENCY) as executor:futures = {executor.submit(send_request, i): i for i in range(CONCURRENCY)}for future in as_completed(futures):result = future.result()if result:results.append(result)# 统计结果total_tokens = sum(r["tokens"] for r in results)total_time = max(r["total_time"] for r in results)  # 取最长耗时作为总时间avg_first_latency = sum(r["first_token_latency"] for r in results) / len(results)print(f"\n测试报告: ")print(f"并发请求数: {CONCURRENCY}")print(f"总生成 Token 数: {total_tokens}")print(f"平均首 Token 延迟: {avg_first_latency:.2f}s")print(f"系统吞吐量: {total_tokens / total_time:.2f} tokens/s")if __name__ == "__main__":run_concurrent_test()

我本机测试的示例结果，

测试报告: 
并发请求数: 10
总生成 Token 数: 5130
平均首 Token 延迟: 0.39s
系统吞吐量: 355.00 tokens/s

4.vLLM分布式推理

未完待续！！！

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原视频链接：B站AIGC科技官 vLLM简介