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统计学
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抽样
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推断
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概率论
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计数
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</a>
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概率概念
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分布
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</span>
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</a>
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贝叶斯
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</span>
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</a>
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信息论
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</span>
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机器学习
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</span>
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<span class="md-nav__icon md-icon"></span>
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机器学习
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经典机器学习
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梯度机器学习
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</span>
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</a>
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深度学习
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</span>
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</a>
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强化学习
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</span>
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</a>
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分布式深度学习
|
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</span>
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|
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|
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|
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</a>
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计算语言学
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<span class="md-nav__icon md-icon"></span>
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计算语言学
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语言学基础
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</a>
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嵌入与序列模型
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</span>
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</a>
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Transformer 与语言模型
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</span>
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|
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</a>
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<span class="md-ellipsis">
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高级文本生成
|
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|
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|
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|
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</span>
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|
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|
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|
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</a>
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计算机视觉
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计算机视觉
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图像基础
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</span>
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</a>
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目标检测与分割
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多模态学习
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多模态学习
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多模态表征
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</span>
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</a>
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统一多模态架构
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</span>
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自主系统
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感知
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视觉-语言-动作模型
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自动驾驶
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</a>
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太空与极端机器人
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</span>
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图神经网络
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</a>
|
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</li>
|
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<li class="md-nav__item">
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<span class="md-ellipsis">
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GPU 架构与 CUDA
|
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|
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|
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</span>
|
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|
||
|
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|
||
</a>
|
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</li>
|
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Triton、TPU 与 Pallas
|
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</span>
|
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|
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|
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|
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</a>
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RISC-V 与嵌入式系统
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</span>
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Vulkan Compute 与跨平台 GPU
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AI 推理
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量化
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服务与批处理
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预填充 vs 解码：两个截然不同的阶段
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静态批处理（朴素方法）
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<a href="#_3" class="md-nav__link">
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连续批处理
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<span class="md-ellipsis">
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PagedAttention和vLLM
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调度策略
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</a>
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<a href="#_5" class="md-nav__link">
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分离式服务
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多模型和LoRA服务
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</a>
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受限和引导生成
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边缘推理
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</span>
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</a>
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ML 系统设计
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系统设计基础
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</a>
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云计算
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</a>
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<span class="md-ellipsis">
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大规模基础设施
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</span>
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|
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|
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</a>
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<span class="md-ellipsis">
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ML 系统设计
|
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|
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|
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</span>
|
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|
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|
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|
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</a>
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</li>
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<span class="md-ellipsis">
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|
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ML 设计案例
|
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|
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|
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|
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</span>
|
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|
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|
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|
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<li class="md-nav__item md-nav__item--nested">
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<span class="md-ellipsis">
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|
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应用 AI
|
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|
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|
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</span>
|
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<span class="md-nav__icon md-icon"></span>
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<span class="md-nav__icon md-icon"></span>
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应用 AI
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<span class="md-ellipsis">
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AI 金融
|
||
|
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|
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|
||
</span>
|
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|
||
|
||
|
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</a>
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</li>
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<a href="../../chapter%2019%3A%20applied%20AI/02.%20protein%20design/" class="md-nav__link">
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<span class="md-ellipsis">
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蛋白质设计
|
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|
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|
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|
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</span>
|
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|
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|
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|
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</a>
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</li>
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<span class="md-ellipsis">
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药物发现
|
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|
||
|
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|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
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|
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|
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|
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<li class="md-nav__item">
|
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<a href="../../chapter%2019%3A%20applied%20AI/04.%20agentic%20systems/" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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|
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智能体系统
|
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|
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|
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|
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</span>
|
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|
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|
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|
||
</a>
|
||
</li>
|
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|
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<li class="md-nav__item">
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<a href="../../chapter%2019%3A%20applied%20AI/05.%20healthcare/" class="md-nav__link">
|
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<span class="md-ellipsis">
|
||
|
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|
||
医疗健康
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
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</li>
|
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</ul>
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</nav>
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</li>
|
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<li class="md-nav__item md-nav__item--nested">
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<span class="md-ellipsis">
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|
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前沿 AI
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|
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</span>
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<span class="md-nav__icon md-icon"></span>
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<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_21_label" aria-expanded="false">
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<span class="md-nav__icon md-icon"></span>
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前沿 AI
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/01.%20quantum%20machine%20learning/" class="md-nav__link">
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<span class="md-ellipsis">
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量子机器学习
|
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|
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|
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|
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</span>
|
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|
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|
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|
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</a>
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<span class="md-ellipsis">
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神经形态计算
|
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</span>
|
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|
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</a>
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太空数据中心
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</span>
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</a>
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/04.%20decentralised%20AI/" class="md-nav__link">
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<span class="md-ellipsis">
|
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去中心化 AI
|
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|
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|
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|
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</span>
|
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|
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|
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|
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</a>
|
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</li>
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/05.%20brain%20machine%20interfaces/" class="md-nav__link">
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<span class="md-ellipsis">
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脑机接口
|
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|
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|
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|
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</span>
|
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|
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|
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|
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</a>
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</li>
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</ul>
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</nav>
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</nav>
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</div>
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</div>
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</div>
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<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
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<div class="md-sidebar__scrollwrap">
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<div class="md-sidebar__inner">
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<nav class="md-nav md-nav--secondary" aria-label="目录">
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目录
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
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<li class="md-nav__item">
|
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<a href="#vs" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
预填充 vs 解码：两个截然不同的阶段
|
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|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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<li class="md-nav__item">
|
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<a href="#_2" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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静态批处理（朴素方法）
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
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<li class="md-nav__item">
|
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<a href="#_3" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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连续批处理
|
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|
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</span>
|
||
</a>
|
||
|
||
</li>
|
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|
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<li class="md-nav__item">
|
||
<a href="#pagedattentionvllm" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
PagedAttention和vLLM
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
||
<li class="md-nav__item">
|
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<a href="#_4" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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调度策略
|
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|
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</span>
|
||
</a>
|
||
|
||
</li>
|
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|
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<li class="md-nav__item">
|
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<a href="#_5" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
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分离式服务
|
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|
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</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
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<a href="#lora" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
多模型和LoRA服务
|
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|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
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<a href="#_6" class="md-nav__link">
|
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<span class="md-ellipsis">
|
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|
||
受限和引导生成
|
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|
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</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_7" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
请求路由
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_8" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
推理指标
|
||
|
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</span>
|
||
</a>
|
||
|
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</li>
|
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|
||
<li class="md-nav__item">
|
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<a href="#colabnotebook" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
编程任务（使用CoLab或notebook）
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
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</nav>
|
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</div>
|
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</div>
|
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</div>
|
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|
||
|
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|
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<div class="md-content" data-md-component="content">
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<h1 id="_1">服务与批处理<a class="headerlink" href="#_1" title="Permanent link">¶</a></h1>
|
||
<p><em>向数千并发用户提供LLM服务需要的不只是加载模型和运行推理。本文涵盖预填充-解码分离、连续批处理、PagedAttention和vLLM、调度策略、分离式服务、多模型和LoRA服务，以及关键指标</em></p>
|
||
<ul>
|
||
<li>单个LLM推理请求很简单：输入token，生成输出token。但要向10,000个并发用户提供低延迟、高吞吐量的LLM服务，这是一个系统工程问题。朴素方法（一次处理一个请求）浪费了90%以上的GPU容量。智能批处理和调度可以在不增加硬件的情况下将吞吐量提高10-50倍。</li>
|
||
</ul>
|
||
<h2 id="vs">预填充 vs 解码：两个截然不同的阶段<a class="headerlink" href="#vs" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>LLM推理有两个不同的阶段，具有根本不同的计算特征：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>预填充</strong>（提示处理）：同时处理所有输入token。这是一个单次大规模矩阵乘法：<span class="arithmatex">\(O(\text{prompt\_length} \times d_{\text{model}}^2)\)</span>。提示可以并行处理（所有token都已知）。预填充是<strong>计算受限</strong>的：GPU的ALU是瓶颈。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>解码</strong>（token生成）：自回归地一次生成一个token。每个新token需要通过KV缓存关注所有先前的token。解码是<strong>内存带宽受限</strong>的：GPU大部分时间花在从内存加载模型权重和KV缓存上，而不是计算。每个解码步骤只产生一个token，但必须加载整个模型（70B FP16模型约140 GB）。</p>
|
||
</li>
|
||
<li>
|
||
<p>含义：</p>
|
||
</li>
|
||
</ul>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th></th>
|
||
<th>预填充</th>
|
||
<th>解码</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>处理的token</td>
|
||
<td>一次性全部（并行）</td>
|
||
<td>一次一个（顺序）</td>
|
||
</tr>
|
||
<tr>
|
||
<td>瓶颈</td>
|
||
<td>计算（FLOPS）</td>
|
||
<td>内存带宽</td>
|
||
</tr>
|
||
<tr>
|
||
<td>算术强度</td>
|
||
<td>高</td>
|
||
<td>非常低</td>
|
||
</tr>
|
||
<tr>
|
||
<td>GPU利用率</td>
|
||
<td>高（50-80%）</td>
|
||
<td>低（1-10%），无批处理时</td>
|
||
</tr>
|
||
<tr>
|
||
<td>延迟指标</td>
|
||
<td><strong>首token时间（TTFT）</strong></td>
|
||
<td><strong>每输出token时间（TPOT）</strong></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<ul>
|
||
<li>TTFT影响用户体验（多久直到响应开始流式传输）。TPOT决定感知的生成速度。用户可以容忍较高的TTFT（1-5秒），但期望快速的TPOT（对话应用每token 30-100毫秒）。</li>
|
||
</ul>
|
||
<h2 id="_2">静态批处理（朴素方法）<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>最简单的批处理：收集<span class="arithmatex">\(B\)</span>个请求，填充到相同长度，作为单个批次处理。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>问题1</strong>：请求有不同的提示长度，并生成不同数量的输出token。短请求提前完成，但必须等待批次中最长的请求完成后才能开始下一个批次。GPU在为剩余的一个长请求生成token时处于空闲状态。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>问题2</strong>：填充浪费计算。如果最长提示是2000个token，最短是50个，批次被填充到2000。GPU为短请求处理了1950个填充token——纯属浪费。</p>
|
||
</li>
|
||
</ul>
|
||
<p><img alt="静态批处理在等待最长请求时浪费GPU槽位；连续批处理立即填充释放的槽位" src="../../images/static_vs_continuous_batching.svg" /></p>
|
||
<h2 id="_3">连续批处理<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p><strong>连续批处理</strong>（也称为迭代级批处理）通过在单个解码步骤的粒度上操作来解决这两个问题，而不是整个请求。</p>
|
||
</li>
|
||
<li>
|
||
<p>在每个解码步骤：</p>
|
||
<ol>
|
||
<li>所有进行中的请求并行生成一个token（作为一个批次）。</li>
|
||
<li>完成的请求（生成EOS token）立即从批次中<strong>移除</strong>。</li>
|
||
<li>队列中的新请求立即<strong>插入</strong>到释放的槽位中。</li>
|
||
</ol>
|
||
</li>
|
||
<li>
|
||
<p>批次大小每步动态变化。GPU从不等候落后者，也没有浪费的填充（每个请求只使用它需要的槽位）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>影响</strong>：连续批处理通常比静态批处理提高吞吐量2-10倍，模型质量不变且延迟无明显增加。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="pagedattentionvllm">PagedAttention和vLLM<a class="headerlink" href="#pagedattentionvllm" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>KV缓存造成了一个内存管理噩梦。每个请求都有一个随着每个生成的token而增长的KV缓存。不同请求处于不同阶段（不同缓存大小）。为每个请求分配连续内存浪费空间（必须为最大可能长度分配，即使请求只生成几个token）。</li>
|
||
</ul>
|
||
<p><img alt="PagedAttention将虚拟KV缓存页映射到非连续的物理GPU内存，消除碎片并实现按需分配" src="../../images/paged_attention.svg" /></p>
|
||
<ul>
|
||
<li>
|
||
<p><strong>PagedAttention</strong>（Kwon等人，2023）将操作系统虚拟内存的概念（第13章）应用于KV缓存。缓存被划分为固定大小的<strong>页</strong>（token位置的块）。页按需分配，在物理GPU内存中可以是非连续的。</p>
|
||
</li>
|
||
<li>
|
||
<p>优势：</p>
|
||
<ul>
|
||
<li><strong>无碎片</strong>：页大小统一，因此请求之间没有浪费内存的"空洞"。</li>
|
||
<li><strong>惰性分配</strong>：仅在token实际生成时分配内存，而不是预分配最大长度。</li>
|
||
<li><strong>写时复制</strong>：共享共同前缀（例如系统提示）的请求共享相同的KV缓存页。仅当请求分叉时才复制页。</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p><strong>vLLM</strong>是基于PagedAttention构建的推理引擎。通过几乎消除KV缓存内存浪费，它实现了比静态分配服务（如没有分页注意力的HuggingFace text-generation-inference）高2-4倍的吞吐量。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_4">调度策略<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>当多个请求在等待且GPU只能处理有限批次时，<strong>调度</strong>决定服务哪些请求：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>先来先服务（FCFS）</strong>：按到达顺序处理请求。简单但不公平：一个提交10K-token生成的用户会阻塞所有后面的用户。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>最短作业优先（SJF）</strong>：处理最先完成的请求。最小化平均延迟，但惩罚长时间运行的请求（它们可能被饿死）。在实践中，估计输出长度未知，因此SJF使用启发式方法（提示长度、用户历史）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>抢占</strong>：如果高优先级请求到达，暂停低优先级的进行中请求（将其KV缓存交换到CPU内存或SSD），服务高优先级请求，然后恢复暂停的请求。vLLM支持此功能。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>基于优先级</strong>：为用户或请求类型分配优先级。实时交互查询比批处理作业获得更高优先级。结合抢占，这确保高优先级流量的延迟SLO。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Token预算</strong>：限制活跃批次中的总token数。这防止少量长请求独占GPU内存并饿死新请求。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_5">分离式服务<a class="headerlink" href="#_5" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>预填充和解码具有相反的计算特征。在同一GPU上运行两者意味着GPU在计算受限（预填充）和内存带宽受限（解码）之间交替，从未充分利用任一资源。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>分离式服务</strong>将它们分开：</p>
|
||
<ul>
|
||
<li><strong>预填充节点</strong>：为计算优化的GPU（高FLOPS，可能内存较少）。处理所有传入提示。</li>
|
||
<li><strong>解码节点</strong>：为内存带宽优化的GPU（大KV缓存容量，高内存带宽）。处理所有token生成。</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p>预填充节点计算初始KV缓存并通过NVLink或网络将其发送到解码节点。解码节点使用接收到的缓存生成token。</p>
|
||
</li>
|
||
<li>
|
||
<p>这是<strong>Mooncake</strong>（月之暗面）的架构，并正在被多个LLM服务团队探索。好处：每个GPU类型与其工作负载特征匹配，提高整体利用率。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="lora">多模型和LoRA服务<a class="headerlink" href="#lora" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>在生产中，你通常服务多个模型（不同层级的模型大小不同，不同任务的微调变体不同）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>模型复用</strong>：在同一GPU上加载多个模型，将请求路由到相应模型。GPU内存共享：一个40 GB GPU可能同时持有一个13B模型（26 GB）和一个7B模型（14 GB）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>LoRA服务</strong>：不是部署单独的微调模型，而是部署一个基础模型并带有多个<strong>LoRA适配器</strong>（第6章）。每个适配器增加<1%的参数。请求在推理时路由到相应的适配器。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>S-LoRA</strong>（Sheng等人，2023）：从一个基础模型服务数千个LoRA适配器。适配器存储在CPU上，按需分页到GPU内存。基础模型的KV缓存和权重被共享；只有小的LoRA矩阵因请求而异。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Punica</strong>（Chen等人，2023）：通过使用自定义CUDA内核在同一批次中为不同请求应用不同的LoRA矩阵，跨不同LoRA适配器对请求进行批处理。这避免了每个请求切换适配器的开销。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_6">受限和引导生成<a class="headerlink" href="#_6" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>许多应用需要LLM以特定格式产生输出：有效的JSON、SQL查询、特定语言的代码或遵循模式的响应。<strong>受限生成</strong>保证输出符合语法或模式。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>语法受限解码</strong>：在每个解码步骤，屏蔽会违反语法的token。如果到目前为止的输出是<code>{"name": "Alice", "age":</code>且语法要求接下来是整数，则屏蔽除数字外的所有token。LLM的概率分布在有效token上重新归一化。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Outlines</strong>（Willard & Louf，2023）：将JSON模式或正则表达式编译成有限状态机（FSM）。在每个解码步骤，FSM确定哪些token是有效的后续。无效token获得概率0。这保证了100%的模式合规，零重试。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>SGLang</strong>原生集成受限生成：你用Python指定输出结构，引擎高效处理token掩码和缓存。这与RadixAttention（前缀缓存）结合，使得结构化输出重用缓存的公共前缀。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>为什么重要</strong>：没有受限生成，你自由生成然后解析输出，失败时重试。对于复杂JSON模式，重试率通常为10-30%，浪费计算。受限生成完全消除了重试。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_7">请求路由<a class="headerlink" href="#_7" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>并非每个查询都需要最大的模型。<strong>请求路由</strong>根据估计的难度将查询定向到不同的模型：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>级联</strong>：先尝试小模型。如果小模型的置信度低于阈值（例如，top token的softmax概率<0.8），则升级到更大的模型。简单查询（80%+的流量）由小模型廉价服务；只有困难查询使用昂贵模型。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>学习型路由</strong>：训练一个轻量级分类器（或使用小模型的困惑度）来预测查询需要哪个模型层级。将"2+2等于多少？"路由到3B模型，将"解释量子纠缠的数学基础"路由到70B模型。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>影响</strong>：如果80%的查询可以由成本低10倍的模型处理，平均每查询成本下降约70%。这是多模型部署中影响最大的成本优化之一。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>设备端+云混合路由</strong>：<strong>Cactus</strong>（<a href="https://github.com/cactus-compute/cactus">github.com/cactus-compute/cactus</a>）在设备级别实现请求路由。它通过自定义ARM SIMD内核在设备端（手机、笔记本电脑、可穿戴设备）运行小模型，并在本地模型置信度低或查询超出设备能力时自动路由到云端模型。应用为两条路径使用OpenAI兼容API——路由是透明的。这是在基础设施级别的级联：第一层是免费的（设备端），第二层花钱（云API）。对于大多数查询简单的应用（助手问答、自动补全、转录），设备端处理覆盖70-90%的流量，边际成本为零。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_8">推理指标<a class="headerlink" href="#_8" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>正确的指标取决于用例：</li>
|
||
</ul>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>指标</th>
|
||
<th>测量内容</th>
|
||
<th>目标（对话式）</th>
|
||
<th>目标（批处理）</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><strong>TTFT</strong></td>
|
||
<td>首token时间</td>
|
||
<td><1 s</td>
|
||
<td>不太重要</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>TPOT</strong></td>
|
||
<td>每输出token时间</td>
|
||
<td><100 ms</td>
|
||
<td>不太重要</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>吞吐量</strong></td>
|
||
<td>token/秒（总计）</td>
|
||
<td>不太重要</td>
|
||
<td>最大化</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>p99延迟</strong></td>
|
||
<td>最差的1%请求</td>
|
||
<td><5 s</td>
|
||
<td><30 s</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>每token成本</strong></td>
|
||
<td>$/100万token</td>
|
||
<td>最小化</td>
|
||
<td>最小化</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>SLO合规率</strong></td>
|
||
<td>满足延迟目标的请求百分比</td>
|
||
<td>>99%</td>
|
||
<td>>95%</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<ul>
|
||
<li>
|
||
<p><strong>TTFT vs TPOT权衡</strong>：激进的批处理增加吞吐量（总token数/秒更多），但增加TPOT（每个token耗时更长，因为GPU处理更多请求）。调度策略必须平衡吞吐量（收入）与延迟（用户体验）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>每token成本</strong>是生产的最终指标。它结合了硬件成本（GPU租金）、吞吐量（token/秒）和利用率。运行在50% GPU利用率的系统比100%利用率的系统每token成本高2倍。这就是批处理、调度和PagedAttention如此重要的原因——它们提高了利用率。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="colabnotebook">编程任务（使用CoLab或notebook）<a class="headerlink" href="#colabnotebook" title="Permanent link">¶</a></h2>
|
||
<ol>
|
||
<li>
|
||
<p>模拟连续vs静态批处理并测量吞吐量差异。
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
|
||
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">import</span><span class="w"> </span><span class="nn">time</span>
|
||
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
|
||
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="k">def</span><span class="w"> </span><span class="nf">simulate_static_batching</span><span class="p">(</span><span class="n">requests</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
|
||
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="w"> </span><span class="sd">"""在固定批次中处理请求。等待所有完成。"""</span>
|
||
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a> <span class="n">total_tokens</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="n">total_time</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>
|
||
<a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">requests</span><span class="p">),</span> <span class="n">batch_size</span><span class="p">):</span>
|
||
<a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a> <span class="n">batch</span> <span class="o">=</span> <span class="n">requests</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||
<a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a> <span class="n">max_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="s1">'output_len'</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span>
|
||
<a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a> <span class="c1"># 批次中所有请求耗时等于最长请求</span>
|
||
<a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a> <span class="n">batch_time</span> <span class="o">=</span> <span class="n">max_len</span> <span class="o">*</span> <span class="mf">0.01</span> <span class="c1"># 每token 10ms</span>
|
||
<a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a> <span class="n">total_time</span> <span class="o">+=</span> <span class="n">batch_time</span>
|
||
<a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a> <span class="n">total_tokens</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="s1">'output_len'</span><span class="p">]</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span>
|
||
<a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a>
|
||
<a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a> <span class="k">return</span> <span class="n">total_tokens</span> <span class="o">/</span> <span class="n">total_time</span> <span class="c1"># token/秒</span>
|
||
<a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a>
|
||
<a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a><span class="k">def</span><span class="w"> </span><span class="nf">simulate_continuous_batching</span><span class="p">(</span><span class="n">requests</span><span class="p">,</span> <span class="n">max_batch</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
|
||
<a id="__codelineno-0-20" name="__codelineno-0-20" href="#__codelineno-0-20"></a><span class="w"> </span><span class="sd">"""使用连续批处理处理。移除完成请求，添加新请求。"""</span>
|
||
<a id="__codelineno-0-21" name="__codelineno-0-21" href="#__codelineno-0-21"></a> <span class="n">total_tokens</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-0-22" name="__codelineno-0-22" href="#__codelineno-0-22"></a> <span class="n">total_time</span> <span class="o">=</span> <span class="mi">0</span>
|
||
<a id="__codelineno-0-23" name="__codelineno-0-23" href="#__codelineno-0-23"></a> <span class="n">active</span> <span class="o">=</span> <span class="p">[]</span>
|
||
<a id="__codelineno-0-24" name="__codelineno-0-24" href="#__codelineno-0-24"></a> <span class="n">queue</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">requests</span><span class="p">)</span>
|
||
<a id="__codelineno-0-25" name="__codelineno-0-25" href="#__codelineno-0-25"></a>
|
||
<a id="__codelineno-0-26" name="__codelineno-0-26" href="#__codelineno-0-26"></a> <span class="k">while</span> <span class="n">active</span> <span class="ow">or</span> <span class="n">queue</span><span class="p">:</span>
|
||
<a id="__codelineno-0-27" name="__codelineno-0-27" href="#__codelineno-0-27"></a> <span class="c1"># 填充批次</span>
|
||
<a id="__codelineno-0-28" name="__codelineno-0-28" href="#__codelineno-0-28"></a> <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">active</span><span class="p">)</span> <span class="o"><</span> <span class="n">max_batch</span> <span class="ow">and</span> <span class="n">queue</span><span class="p">:</span>
|
||
<a id="__codelineno-0-29" name="__codelineno-0-29" href="#__codelineno-0-29"></a> <span class="n">active</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'remaining'</span><span class="p">:</span> <span class="n">queue</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="s1">'output_len'</span><span class="p">]})</span>
|
||
<a id="__codelineno-0-30" name="__codelineno-0-30" href="#__codelineno-0-30"></a>
|
||
<a id="__codelineno-0-31" name="__codelineno-0-31" href="#__codelineno-0-31"></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">active</span><span class="p">:</span>
|
||
<a id="__codelineno-0-32" name="__codelineno-0-32" href="#__codelineno-0-32"></a> <span class="k">break</span>
|
||
<a id="__codelineno-0-33" name="__codelineno-0-33" href="#__codelineno-0-33"></a>
|
||
<a id="__codelineno-0-34" name="__codelineno-0-34" href="#__codelineno-0-34"></a> <span class="c1"># 一个解码步骤：所有活跃请求生成1个token</span>
|
||
<a id="__codelineno-0-35" name="__codelineno-0-35" href="#__codelineno-0-35"></a> <span class="k">for</span> <span class="n">req</span> <span class="ow">in</span> <span class="n">active</span><span class="p">:</span>
|
||
<a id="__codelineno-0-36" name="__codelineno-0-36" href="#__codelineno-0-36"></a> <span class="n">req</span><span class="p">[</span><span class="s1">'remaining'</span><span class="p">]</span> <span class="o">-=</span> <span class="mi">1</span>
|
||
<a id="__codelineno-0-37" name="__codelineno-0-37" href="#__codelineno-0-37"></a> <span class="n">total_tokens</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">active</span><span class="p">)</span>
|
||
<a id="__codelineno-0-38" name="__codelineno-0-38" href="#__codelineno-0-38"></a> <span class="n">total_time</span> <span class="o">+=</span> <span class="mf">0.01</span> <span class="c1"># 每步10ms</span>
|
||
<a id="__codelineno-0-39" name="__codelineno-0-39" href="#__codelineno-0-39"></a>
|
||
<a id="__codelineno-0-40" name="__codelineno-0-40" href="#__codelineno-0-40"></a> <span class="c1"># 移除完成的请求</span>
|
||
<a id="__codelineno-0-41" name="__codelineno-0-41" href="#__codelineno-0-41"></a> <span class="n">active</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">active</span> <span class="k">if</span> <span class="n">r</span><span class="p">[</span><span class="s1">'remaining'</span><span class="p">]</span> <span class="o">></span> <span class="mi">0</span><span class="p">]</span>
|
||
<a id="__codelineno-0-42" name="__codelineno-0-42" href="#__codelineno-0-42"></a>
|
||
<a id="__codelineno-0-43" name="__codelineno-0-43" href="#__codelineno-0-43"></a> <span class="k">return</span> <span class="n">total_tokens</span> <span class="o">/</span> <span class="n">total_time</span>
|
||
<a id="__codelineno-0-44" name="__codelineno-0-44" href="#__codelineno-0-44"></a>
|
||
<a id="__codelineno-0-45" name="__codelineno-0-45" href="#__codelineno-0-45"></a><span class="c1"># 生成具有不同输出长度的请求</span>
|
||
<a id="__codelineno-0-46" name="__codelineno-0-46" href="#__codelineno-0-46"></a><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
|
||
<a id="__codelineno-0-47" name="__codelineno-0-47" href="#__codelineno-0-47"></a><span class="n">requests</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'output_len'</span><span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">500</span><span class="p">)}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)]</span>
|
||
<a id="__codelineno-0-48" name="__codelineno-0-48" href="#__codelineno-0-48"></a>
|
||
<a id="__codelineno-0-49" name="__codelineno-0-49" href="#__codelineno-0-49"></a><span class="n">static_tps</span> <span class="o">=</span> <span class="n">simulate_static_batching</span><span class="p">(</span><span class="n">requests</span><span class="p">)</span>
|
||
<a id="__codelineno-0-50" name="__codelineno-0-50" href="#__codelineno-0-50"></a><span class="n">continuous_tps</span> <span class="o">=</span> <span class="n">simulate_continuous_batching</span><span class="p">(</span><span class="n">requests</span><span class="p">)</span>
|
||
<a id="__codelineno-0-51" name="__codelineno-0-51" href="#__codelineno-0-51"></a>
|
||
<a id="__codelineno-0-52" name="__codelineno-0-52" href="#__codelineno-0-52"></a><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"静态批处理: </span><span class="si">{</span><span class="n">static_tps</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> tokens/s"</span><span class="p">)</span>
|
||
<a id="__codelineno-0-53" name="__codelineno-0-53" href="#__codelineno-0-53"></a><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"连续批处理: </span><span class="si">{</span><span class="n">continuous_tps</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> tokens/s"</span><span class="p">)</span>
|
||
<a id="__codelineno-0-54" name="__codelineno-0-54" href="#__codelineno-0-54"></a><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"加速比: </span><span class="si">{</span><span class="n">continuous_tps</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">static_tps</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">x"</span><span class="p">)</span>
|
||
</code></pre></div></p>
|
||
</li>
|
||
<li>
|
||
<p>计算PagedAttention的KV缓存内存节省。比较预分配（最坏情况）vs分页（实际使用）。
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="k">def</span><span class="w"> </span><span class="nf">paged_vs_preallocated</span><span class="p">(</span><span class="n">n_requests</span><span class="p">,</span> <span class="n">max_seq_len</span><span class="p">,</span> <span class="n">avg_seq_len</span><span class="p">,</span> <span class="n">page_size</span><span class="p">,</span> <span class="n">kv_per_token_bytes</span><span class="p">):</span>
|
||
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w"> </span><span class="sd">"""比较内存使用：预分配vs分页KV缓存。"""</span>
|
||
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a> <span class="c1"># 预分配：每个请求获得max_seq_len个槽位</span>
|
||
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a> <span class="n">preallocated_gb</span> <span class="o">=</span> <span class="n">n_requests</span> <span class="o">*</span> <span class="n">max_seq_len</span> <span class="o">*</span> <span class="n">kv_per_token_bytes</span> <span class="o">/</span> <span class="mf">1e9</span>
|
||
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>
|
||
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a> <span class="c1"># 分页：只分配使用的部分（按页粒度）</span>
|
||
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a> <span class="kn">import</span><span class="w"> </span><span class="nn">math</span>
|
||
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a> <span class="n">avg_pages</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">avg_seq_len</span> <span class="o">/</span> <span class="n">page_size</span><span class="p">)</span>
|
||
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a> <span class="n">paged_gb</span> <span class="o">=</span> <span class="n">n_requests</span> <span class="o">*</span> <span class="n">avg_pages</span> <span class="o">*</span> <span class="n">page_size</span> <span class="o">*</span> <span class="n">kv_per_token_bytes</span> <span class="o">/</span> <span class="mf">1e9</span>
|
||
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a>
|
||
<a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a> <span class="n">waste_preallocated</span> <span class="o">=</span> <span class="p">(</span><span class="n">max_seq_len</span> <span class="o">-</span> <span class="n">avg_seq_len</span><span class="p">)</span> <span class="o">/</span> <span class="n">max_seq_len</span>
|
||
<a id="__codelineno-1-12" name="__codelineno-1-12" href="#__codelineno-1-12"></a> <span class="n">waste_paged</span> <span class="o">=</span> <span class="p">(</span><span class="n">avg_pages</span> <span class="o">*</span> <span class="n">page_size</span> <span class="o">-</span> <span class="n">avg_seq_len</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">avg_pages</span> <span class="o">*</span> <span class="n">page_size</span><span class="p">)</span>
|
||
<a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a>
|
||
<a id="__codelineno-1-14" name="__codelineno-1-14" href="#__codelineno-1-14"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"请求数: </span><span class="si">{</span><span class="n">n_requests</span><span class="si">}</span><span class="s2">, 最大序列: </span><span class="si">{</span><span class="n">max_seq_len</span><span class="si">}</span><span class="s2">, 平均序列: </span><span class="si">{</span><span class="n">avg_seq_len</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" 预分配: </span><span class="si">{</span><span class="n">preallocated_gb</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> GB (浪费: </span><span class="si">{</span><span class="n">waste_preallocated</span><span class="si">:</span><span class="s2">.0%</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
|
||
<a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" 分页: </span><span class="si">{</span><span class="n">paged_gb</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> GB (浪费: </span><span class="si">{</span><span class="n">waste_paged</span><span class="si">:</span><span class="s2">.0%</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
|
||
<a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" 节省: </span><span class="si">{</span><span class="n">preallocated_gb</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">paged_gb</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> GB (</span><span class="si">{</span><span class="n">preallocated_gb</span><span class="o">/</span><span class="n">paged_gb</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">x)"</span><span class="p">)</span>
|
||
<a id="__codelineno-1-18" name="__codelineno-1-18" href="#__codelineno-1-18"></a> <span class="nb">print</span><span class="p">()</span>
|
||
<a id="__codelineno-1-19" name="__codelineno-1-19" href="#__codelineno-1-19"></a>
|
||
<a id="__codelineno-1-20" name="__codelineno-1-20" href="#__codelineno-1-20"></a><span class="c1"># Llama-70B：每层每token约1.3 KB，80层 = 每token约100 KB总计</span>
|
||
<a id="__codelineno-1-21" name="__codelineno-1-21" href="#__codelineno-1-21"></a><span class="n">kv_bytes</span> <span class="o">=</span> <span class="mi">100_000</span>
|
||
<a id="__codelineno-1-22" name="__codelineno-1-22" href="#__codelineno-1-22"></a>
|
||
<a id="__codelineno-1-23" name="__codelineno-1-23" href="#__codelineno-1-23"></a><span class="c1"># 场景1：短请求，大最大值</span>
|
||
<a id="__codelineno-1-24" name="__codelineno-1-24" href="#__codelineno-1-24"></a><span class="n">paged_vs_preallocated</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">max_seq_len</span><span class="o">=</span><span class="mi">4096</span><span class="p">,</span> <span class="n">avg_seq_len</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kv_per_token_bytes</span><span class="o">=</span><span class="n">kv_bytes</span><span class="p">)</span>
|
||
<a id="__codelineno-1-25" name="__codelineno-1-25" href="#__codelineno-1-25"></a>
|
||
<a id="__codelineno-1-26" name="__codelineno-1-26" href="#__codelineno-1-26"></a><span class="c1"># 场景2：不同长度</span>
|
||
<a id="__codelineno-1-27" name="__codelineno-1-27" href="#__codelineno-1-27"></a><span class="n">paged_vs_preallocated</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">max_seq_len</span><span class="o">=</span><span class="mi">8192</span><span class="p">,</span> <span class="n">avg_seq_len</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kv_per_token_bytes</span><span class="o">=</span><span class="n">kv_bytes</span><span class="p">)</span>
|
||
<a id="__codelineno-1-28" name="__codelineno-1-28" href="#__codelineno-1-28"></a>
|
||
<a id="__codelineno-1-29" name="__codelineno-1-29" href="#__codelineno-1-29"></a><span class="c1"># 场景3：长上下文</span>
|
||
<a id="__codelineno-1-30" name="__codelineno-1-30" href="#__codelineno-1-30"></a><span class="n">paged_vs_preallocated</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">max_seq_len</span><span class="o">=</span><span class="mi">131072</span><span class="p">,</span> <span class="n">avg_seq_len</span><span class="o">=</span><span class="mi">16000</span><span class="p">,</span> <span class="n">page_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kv_per_token_bytes</span><span class="o">=</span><span class="n">kv_bytes</span><span class="p">)</span>
|
||
</code></pre></div></p>
|
||
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