sglang 部署Qwen2VL7B，大模型部署，速度测试，深度学习

sglang

项目github仓库：

https://github.com/sgl-project/sglang

项目说明书：

https://sgl-project.github.io/start/install.html

资讯：

https://github.com/sgl-project/sgl-learning-materials?tab=readme-ov-file#the-first-sglang-online-meetup

快得离谱：

[外链图片转存中...(img-E3n1Ivz9-1731913508383)]

图来源：https://lmsys.org/blog/2024-09-04-sglang-v0-3/

Docker使用：

bash 复制代码

docker run --gpus device=0 \
    --shm-size 32g \
    -p 30000:30000 \
    -v /root/xiedong/Qwen2-VL-7B-Instruct:/Qwen2-VL \
    --env "HF_TOKEN=abc-1234" \
    --ipc=host \
    -v /root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4:/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4 \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server --model-path /Qwen2-VL --host 0.0.0.0 --port 30000 --chat-template qwen2-vl --context-length 8192 --log-level-http warning

启动成功：

接口文档：

http://101.136.22.140:30000/docs

速度测试代码

bash 复制代码

import time
from openai import OpenAI

# 初始化OpenAI客户端
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:30000/v1')

# 定义图像路径
image_paths = [
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo256.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo512.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo768.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo1024.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo1280.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo2560.jpeg"
]

# 设置请求次数
num_requests = 10

# 存储每个图像的平均响应时间
average_speeds = {}

# 遍历每张图片
for image_path in image_paths:
    total_time = 0

    # 对每张图片执行 num_requests 次请求
    for _ in range(num_requests):
        start_time = time.time()

        # 发送请求并获取响应
        response = client.chat.completions.create(
            model="/Qwen2-VL",
            messages=[{
                'role': 'user',
                'content': [{
                    'type': 'text',
                    'text': 'Describe the image please',
                }, {
                    'type': 'image_url',
                    'image_url': {
                        'url': image_path,
                    },
                }],
            }],
            temperature=0.8,
            top_p=0.8
        )

        # 记录响应时间
        elapsed_time = time.time() - start_time
        total_time += elapsed_time

        # 打印当前请求的响应内容（可选）
        print(f"Response for {image_path}: {response.choices[0].message.content}")

    # 计算并记录该图像的平均响应时间
    average_speed = total_time / num_requests
    average_speeds[image_path] = average_speed

    print(f"Average speed for {image_path}: {average_speed} seconds")

# 输出所有图像的平均响应时间
for image_path, avg_speed in average_speeds.items():
    print(f"{image_path}: {avg_speed:.2f} seconds")

速度测试结果

sglang 测试结果：

Model	显存占用 (MiB)	分辨率	处理时间 (秒)
Qwen2-VL-7B-Instruct	70G	256 x 256	1.71
		512 x 512	1.52
		768 x 768	1.85
		1024 x 1024	2.05
		1280 x 1280	1.88
		2560 x 2560	3.26

纯transformer，不用加速框架，我之前的测了一张图的速度是：5.22 seconds，很慢。

附录-vllm速度测试

启动：

bash 复制代码

docker run --gpus device=0 \
    -v /root/xiedong/Qwen2-VL-7B-Instruct:/Qwen2-VL \
    -v /root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4:/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4 \
    -p 30000:8000 \
    --ipc=host \
    vllm/vllm-openai:latest \
    --model /Qwen2-VL --gpu_memory_utilization=0.9

代码：

bash 复制代码

import time
import base64
from openai import OpenAI

# 初始化OpenAI客户端
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:30000/v1')

# 定义图像路径
image_paths = [
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo256.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo512.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo768.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo1024.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo1280.jpeg",
    "/root/xiedong/Qwen2-VL-72B-Instruct-GPTQ-Int4/demo2560.jpeg"
]

# 设置请求次数
num_requests = 10

# 存储每个图像的平均响应时间
average_speeds = {}

# 将图片转换为 Base64 编码的函数
def image_to_base64(image_path):
    with open(image_path, "rb") as img_file:
        return base64.b64encode(img_file.read()).decode('utf-8')

# 遍历每张图片
for image_path in image_paths:
    total_time = 0

    # 将图片转换为 Base64 编码
    image_base64 = image_to_base64(image_path)

    # 对每张图片执行 num_requests 次请求
    for _ in range(num_requests):
        start_time = time.time()

        # 发送请求并获取响应
        response = client.chat.completions.create(
            model="/Qwen2-VL",
            messages=[{
                'role': 'user',
                'content': [{
                    'type': 'text',
                    'text': 'Describe the image please',
                }, {
                    'type': 'image_url',
                    'image_url': {
                        'url': f"data:image/jpeg;base64,{image_base64}",  # 使用Base64编码的图片
                    },
                }],
            }],
            temperature=0.8,
            top_p=0.8
        )

        # 记录响应时间
        elapsed_time = time.time() - start_time
        total_time += elapsed_time

        # 打印当前请求的响应内容（可选）
        print(f"Response for {image_path}: {response.choices[0].message.content}")

    # 计算并记录该图像的平均响应时间
    average_speed = total_time / num_requests
    average_speeds[image_path] = average_speed

    print(f"Average speed for {image_path}: {average_speed} seconds")

# 输出所有图像的平均响应时间
for image_path, avg_speed in average_speeds.items():
    print(f"{image_path}: {avg_speed:.2f} seconds")

速度：

Model	显存占用 (MiB)	分辨率	处理时间 (秒)
Qwen2-VL-72B-Instruct-GPTQ-Int4	70G	256 x 256	1.50
		512 x 512	1.59
		768 x 768	1.61
		1024 x 1024	1.67
		1280 x 1280	1.81
		2560 x 2560	1.97

https://www.dong-blog.fun/post/1856