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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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GPU 架构与 CUDA
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Triton、TPU 与 Pallas
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移动GPU
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应用 AI
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</a>
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|
||
药物发现
|
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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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|
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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">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
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智能体系统
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
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</li>
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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">
|
||
|
||
|
||
医疗健康
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
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</a>
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</li>
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|
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|
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|
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<span class="md-ellipsis">
|
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|
||
|
||
前沿 AI
|
||
|
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|
||
|
||
</span>
|
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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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|
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/01.%20quantum%20machine%20learning/" class="md-nav__link">
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|
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|
||
<span class="md-ellipsis">
|
||
|
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|
||
量子机器学习
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
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</li>
|
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/02.%20neuromorphic%20computing/" class="md-nav__link">
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
神经形态计算
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
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|
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|
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|
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<span class="md-ellipsis">
|
||
|
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|
||
太空数据中心
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
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|
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<li class="md-nav__item">
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|
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<span class="md-ellipsis">
|
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去中心化 AI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
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|
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<a href="../../chapter%2020%3A%20bleeding%20edge%20AI/05.%20brain%20machine%20interfaces/" class="md-nav__link">
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|
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<span class="md-ellipsis">
|
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|
||
脑机接口
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
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</li>
|
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|
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|
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|
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|
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</ul>
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</nav>
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</li>
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|
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</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-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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<label class="md-nav__title" for="__toc">
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<span class="md-nav__icon md-icon"></span>
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目录
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</label>
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<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
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|
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<li class="md-nav__item">
|
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<a href="#_2" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
边缘约束
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_3" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
模型压缩流水线
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_4" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
设备端运行时
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_5" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
编译器栈
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
||
<li class="md-nav__item">
|
||
<a href="#_6" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
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硬件目标
|
||
|
||
</span>
|
||
</a>
|
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|
||
<nav class="md-nav" aria-label="硬件目标">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
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<a href="#gpu" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
移动GPU
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#npu" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
神经处理单元（NPU）
|
||
|
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</span>
|
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</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#llm" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
设备端LLM
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_7" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
联邦学习
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#_8" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
延迟优化
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#colabnotebook" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
编程任务（使用CoLab或notebook）
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<h1 id="_1">边缘推理<a class="headerlink" href="#_1" title="Permanent link">¶</a></h1>
|
||
<p><em>边缘推理在用户设备（手机、笔记本电脑、物联网传感器）上运行模型，无需将数据发送到云端。本文涵盖边缘限制、模型压缩流水线、设备端运行时、编译器栈、硬件目标（NPU、神经引擎）、设备端LLM、联邦学习和延迟优化</em></p>
|
||
<ul>
|
||
<li>
|
||
<p>云端推理需要网络连接，增加延迟（50-200毫秒往返），每次请求花费金钱，并将用户数据发送到第三方服务器。<strong>边缘推理</strong>消除了所有四个问题：模型本地运行，即时响应，每次推理零成本，且数据保持私密。</p>
|
||
</li>
|
||
<li>
|
||
<p>权衡：边缘设备的计算和内存比数据中心GPU小100-1000倍。使模型在这些约束下运行需要在每个层面进行积极优化。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Cactus</strong>（<a href="https://github.com/cactus-compute/cactus">github.com/cactus-compute/cactus</a>) 是一个专为移动和可穿戴设备构建的低延迟AI引擎。它在生产中展示了本文涵盖的许多技术：自定义ARM SIMD内核用于注意力和矩阵运算（第16章）、KV缓存量化（第17章文件01）、分块预填充、Apple和Qualcomm芯片上的NPU加速推理、零拷贝内存映射实现10倍更低的RAM使用，以及在设备端计算不足时的自动云回退。Cactus支持跨iOS、Android、macOS和嵌入式Linux的多模态推理（LLM、视觉、语音），并提供Swift、Kotlin、Python、Flutter、React Native和Rust的SDK。其基准测试显示，在M4 Pro上1.2B INT4模型解码达到100 tokens/s，在iPhone 17 Pro上达到48 tokens/s——这是优化边缘推理的具体示例。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_2">边缘约束<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>资源</th>
|
||
<th>云GPU（H100）</th>
|
||
<th>笔记本电脑（M4）</th>
|
||
<th>手机（Snapdragon 8 Gen 3）</th>
|
||
<th>IoT（ESP32）</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>内存</td>
|
||
<td>80 GB HBM3</td>
|
||
<td>16-36 GB 统一内存</td>
|
||
<td>8-12 GB LPDDR5</td>
|
||
<td>520 KB</td>
|
||
</tr>
|
||
<tr>
|
||
<td>计算</td>
|
||
<td>989 TFLOPS（FP8）</td>
|
||
<td>38 TOPS（神经引擎）</td>
|
||
<td>45 TOPS（NPU）</td>
|
||
<td>0.001 TOPS</td>
|
||
</tr>
|
||
<tr>
|
||
<td>功耗</td>
|
||
<td>700 W</td>
|
||
<td>15-30 W</td>
|
||
<td>5-10 W</td>
|
||
<td>0.1 W</td>
|
||
</tr>
|
||
<tr>
|
||
<td>存储</td>
|
||
<td>TB</td>
|
||
<td>256 GB-2 TB</td>
|
||
<td>128-512 GB</td>
|
||
<td>4 MB</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<ul>
|
||
<li>云GPU和手机NPU之间的计算差距约为20倍。GPU和微控制器之间的差距约为1,000,000倍。不同设备需要不同程度的压缩和不同的模型架构。</li>
|
||
</ul>
|
||
<h2 id="_3">模型压缩流水线<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>对于边缘部署，压缩不是单一技术——它是一个按顺序应用的互补技术<strong>流水线</strong>：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>完整模型（FP32，70B参数）
|
||
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> ↓ 知识蒸馏 → 更小模型（7B参数）
|
||
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> ↓ 结构化剪枝 → 移除冗余头/层（4B有效）
|
||
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a> ↓ 量化（INT4） → 4倍更小（2 GB）
|
||
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a> ↓ 编译器优化 → 融合内核，优化内存布局
|
||
<a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a> ↓ 运行时 → 设备端执行
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>每一步减少大小和延迟。顺序很重要：先蒸馏（减少架构），然后剪枝（移除结构），然后量化（降低精度），最后编译（为目标硬件优化）。在量化之后进行蒸馏会试图压缩已经损失质量的模型。</li>
|
||
</ul>
|
||
<h2 id="_4">设备端运行时<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p><strong>运行时</strong>加载模型、分配内存并在目标硬件上执行推理。每个平台有其偏好的运行时：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>ONNX Runtime</strong>：跨平台（Windows、Linux、macOS、iOS、Android）。支持CPU、GPU（CUDA、DirectML、CoreML、NNAPI）和许多加速器后端。最具可移植性的选项。模型从PyTorch/TensorFlow导出为ONNX格式。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>TensorFlow Lite（TFLite）</strong>：Google的边缘运行时。针对ARM CPU和Android NPU优化。二进制文件小巧（约1 MB）。支持INT8和float16。Android部署的标准。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Core ML</strong>：Apple的iOS/macOS运行时。根据模型特征自动使用神经引擎、GPU或CPU。模型使用<code>coremltools</code>从PyTorch/TensorFlow转换。与Apple硬件紧密集成（统一内存、神经引擎）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>ExecuTorch</strong>：Meta新推出的设备端PyTorch运行时。专为边缘部署设计，具有提前编译和操作级硬件加速器委派功能。PyTorch Mobile的继任者。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>TensorRT</strong>：NVIDIA的GPU推理优化运行时（第15章）。融合层、选择最优内核并自动量化。在NVIDIA GPU上比PyTorch eager模式快2-5倍。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>llama.cpp</strong>：用于LLM的单文件C++推理引擎。支持GGUF量化（Q4、Q5、Q8）、CPU（AVX/NEON）、Metal（Apple GPU）、CUDA和Vulkan。在消费级硬件上运行LLM的首选方案。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_5">编译器栈<a class="headerlink" href="#_5" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>在高级模型（PyTorch图）和硬件（NPU指令）之间是<strong>编译器栈</strong>，它为特定目标优化模型：</li>
|
||
</ul>
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>PyTorch模型
|
||
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a> ↓ 导出（torch.export、ONNX、TorchScript）
|
||
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>图IR（中间表示）
|
||
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a> ↓ 图优化
|
||
<a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a> - 常量折叠（编译时计算常量表达式）
|
||
<a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a> - 死代码消除（移除未使用的操作）
|
||
<a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a> - 算子融合（conv + bn + relu → 单个融合操作）
|
||
<a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a> - 布局转换（NCHW → NHWC用于ARM，通道最后）
|
||
<a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a> ↓ 降级
|
||
<a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a>硬件特定IR
|
||
<a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a> ↓ 后端优化
|
||
<a id="__codelineno-1-12" name="__codelineno-1-12" href="#__codelineno-1-12"></a> - 分块和循环排序（缓存友好的访问模式）
|
||
<a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a> - 向量化（SIMD，第16章）
|
||
<a id="__codelineno-1-14" name="__codelineno-1-14" href="#__codelineno-1-14"></a> - 内存规划（重用缓冲区以最小化峰值内存）
|
||
<a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a> - 内核选择（为每个操作选择最佳实现）
|
||
<a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a> ↓ 代码生成
|
||
<a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a>机器代码 / NPU指令
|
||
</code></pre></div>
|
||
<ul>
|
||
<li>
|
||
<p><strong>算子融合</strong>是影响最大的优化。一个Transformer块约有20个操作（矩阵乘、加法、层归一化、softmax等）。没有融合，每个操作将其输出写入内存，下一个操作再读回。有了融合，多个操作组合成一个内核，将数据保留在寄存器/缓存中。这可以使速度快2-5倍（第16章，屋顶模型）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>内存规划</strong>：编译器分析模型图以确定哪些张量的生命周期重叠，可以共享相同的内存缓冲区。一个有100个中间张量的模型可能只需要10张量的内存，因为大多数张量在其他张量创建之前就被消耗和释放了。这在内存有限的设备上至关重要。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_6">硬件目标<a class="headerlink" href="#_6" title="Permanent link">¶</a></h2>
|
||
<h3 id="gpu">移动GPU<a class="headerlink" href="#gpu" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p><strong>Qualcomm Adreno</strong>（Android）：支持OpenCL、Vulkan计算（第16章）和Qualcomm专有的SNPE（Snapdragon神经处理引擎）。Adreno GPU具有256-1024个ALU，支持FP16和INT8。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>ARM Mali</strong>（Android）：支持OpenCL和Vulkan。Mali GPU使用基于图块的架构（与桌面GPU不同），这影响最优内存访问模式。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Apple GPU</strong>（iOS/macOS）：通过Metal（Apple的GPU API）访问。统一内存架构意味着没有CPU↔GPU复制开销。Metal Performance Shaders（MPS）提供优化的ML原语。</p>
|
||
</li>
|
||
</ul>
|
||
<h3 id="npu">神经处理单元（NPU）<a class="headerlink" href="#npu" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li>
|
||
<p>NPU是专门为ML推理设计的固定功能加速器。它们在标准ML操作（矩阵乘、卷积、激活）上比GPU节能得多。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Apple神经引擎</strong>：16核，约38 TOPS（INT8）。通过Core ML访问。非常适合视觉模型和设备端扩散。不能运行任意代码——只支持Core ML支持的操作。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Qualcomm Hexagon NPU</strong>：集成到Snapdragon SoC中。支持INT8和INT4推理。通过SNPE或ONNX Runtime（带QNN后端）访问。为设备端功能如背景虚化、语音识别和实时翻译提供支持。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>Google Edge TPU</strong>：云端TPU的小型低功耗版本。4 TOPS，2W。用于Coral设备进行设备端推理。仅支持INT8量化的TFLite模型。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>委派模式</strong>：运行时在NPU（用于支持的操作）和CPU（用于不支持的操作）之间拆分模型图。最大化在NPU上运行的部分是性能和能效的关键。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="llm">设备端LLM<a class="headerlink" href="#llm" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>在手机和笔记本电脑上运行LLM已变得可行，得益于小模型和积极的量化：</li>
|
||
</ul>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>模型</th>
|
||
<th>参数</th>
|
||
<th>量化后大小</th>
|
||
<th>目标设备</th>
|
||
<th>性能</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Phi-3 Mini</td>
|
||
<td>3.8B</td>
|
||
<td>~2 GB（Q4）</td>
|
||
<td>手机/笔记本</td>
|
||
<td>iPhone 15上~15 tokens/s</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Gemma 2B</td>
|
||
<td>2B</td>
|
||
<td>~1.5 GB（Q4）</td>
|
||
<td>手机</td>
|
||
<td>Pixel 8上~20 tokens/s</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Llama 3.2 1B</td>
|
||
<td>1B</td>
|
||
<td>~700 MB（Q4）</td>
|
||
<td>手机</td>
|
||
<td>~30 tokens/s</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Llama 3.2 3B</td>
|
||
<td>3B</td>
|
||
<td>~2 GB（Q4）</td>
|
||
<td>手机/笔记本</td>
|
||
<td>~15 tokens/s</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Llama 3.1 8B</td>
|
||
<td>8B</td>
|
||
<td>~4.5 GB（Q4）</td>
|
||
<td>笔记本</td>
|
||
<td>M2上~20 tokens/s</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<ul>
|
||
<li>
|
||
<p><strong>挑战</strong>：</p>
|
||
<ul>
|
||
<li><strong>内存</strong>：3B Q4模型占2 GB，但长对话的KV缓存增加了显著额外内存。手机上的上下文长度通常限制在2-4K token。</li>
|
||
<li><strong>热节流</strong>：持续推理使手机发热。连续生成30秒后，SoC会降低时钟速度以防止过热，性能下降30-50%。</li>
|
||
<li><strong>电池</strong>：以15 tokens/s运行3B模型消耗约3-5W。30分钟的对话消耗典型手机电池约5%。偶尔使用可以接受，但始终在线应用存在问题。</li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<p><strong>llama.cpp</strong>是设备端LLM的标准。它在CPU（AVX2、NEON、I8MM）、Apple GPU（Metal）、NVIDIA GPU（CUDA）、AMD GPU（ROCm/Vulkan）甚至手机上（通过Android上的Termux）运行。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_7">联邦学习<a class="headerlink" href="#_7" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p><strong>联邦学习</strong>在许多设备上训练模型，无需集中数据。每个设备在其本地数据上训练，计算梯度更新，并将只有更新（而非数据）发送到聚合更新的中央服务器。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>算法</strong>（FedAvg）：</p>
|
||
<ol>
|
||
<li>服务器将当前模型发送给<span class="arithmatex">\(K\)</span>个选定设备。</li>
|
||
<li>每个设备在其本地数据上微调模型几步。</li>
|
||
<li>每个设备将其更新后的模型（或差异）发送回服务器。</li>
|
||
<li>服务器平均更新：<span class="arithmatex">\(W_{\text{new}} = \frac{1}{K} \sum_{k=1}^{K} W_k\)</span>。</li>
|
||
<li>重复。</li>
|
||
</ol>
|
||
</li>
|
||
<li>
|
||
<p><strong>隐私</strong>：原始数据从不离开设备。服务器只看到聚合的模型更新。<strong>差分隐私</strong>向更新添加噪声，使得无法从梯度中逆向推断单个数据点。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>通信效率</strong>：模型更新很大（与模型相同大小）。压缩技术减少了这一点：<strong>梯度量化</strong>（发送INT8梯度而不是FP32）、<strong>稀疏化</strong>（只发送最大的梯度）和<strong>梯度累积</strong>（做更多本地步骤，发送更少频率）。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>应用</strong>：Google的键盘预测（Gboard）、Apple的语音识别、健康监测（在敏感健康数据上训练而不集中数据）。</p>
|
||
</li>
|
||
</ul>
|
||
<h2 id="_8">延迟优化<a class="headerlink" href="#_8" title="Permanent link">¶</a></h2>
|
||
<ul>
|
||
<li>
|
||
<p>除了压缩，还有几种技术减少端到端推理延迟：</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>提前退出</strong>：在中间层添加分类头。如果模型在第6层（共24层）已经自信，则返回预测而不运行第7-24层。简单输入提前退出，困难输入使用完整模型。对于混合简单和困难输入的任务，平均延迟显著下降。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>模型分区</strong>：在NPU（对矩阵乘高效）、GPU（对不规则操作高效）和CPU（处理其他一切）之间拆分模型。编译器根据性能分析决定哪些操作去哪里。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>缓存</strong>：对于具有重复查询的应用（自动补全、代码补全），缓存最近的计算。如果用户输入"How do I"且模型最近生成了"How do I"的补全，可以重用缓存的KV缓存，完全跳过预填充阶段。</p>
|
||
</li>
|
||
<li>
|
||
<p><strong>推测性预取</strong>：预测用户下一步将做什么，在用户询问之前开始推理。聊天应用可能在用户阅读当前答案时开始生成可能后续问题的响应。</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>模拟模型压缩流水线。从float32模型开始，依次应用蒸馏（模拟）、剪枝和量化，并跟踪每一步的大小。
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="k">def</span><span class="w"> </span><span class="nf">compression_pipeline</span><span class="p">(</span><span class="n">original_params_M</span><span class="p">,</span> <span class="n">original_bits</span><span class="o">=</span><span class="mi">32</span><span class="p">):</span>
|
||
<a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a> <span class="n">size_mb</span> <span class="o">=</span> <span class="n">original_params_M</span> <span class="o">*</span> <span class="mf">1e6</span> <span class="o">*</span> <span class="n">original_bits</span> <span class="o">/</span> <span class="mi">8</span> <span class="o">/</span> <span class="mf">1e6</span>
|
||
<a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
|
||
<a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"原始: </span><span class="si">{</span><span class="n">original_params_M</span><span class="si">}</span><span class="s2">M 参数, </span><span class="si">{</span><span class="n">original_bits</span><span class="si">}</span><span class="s2">-位 → </span><span class="si">{</span><span class="n">size_mb</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> MB"</span><span class="p">)</span>
|
||
<a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a>
|
||
<a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a> <span class="c1"># 步骤1：知识蒸馏（减少参数）</span>
|
||
<a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a> <span class="n">distilled_params</span> <span class="o">=</span> <span class="n">original_params_M</span> <span class="o">*</span> <span class="mf">0.15</span> <span class="c1"># 70B → ~10B 等价</span>
|
||
<a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a> <span class="n">size_mb</span> <span class="o">=</span> <span class="n">distilled_params</span> <span class="o">*</span> <span class="mf">1e6</span> <span class="o">*</span> <span class="n">original_bits</span> <span class="o">/</span> <span class="mi">8</span> <span class="o">/</span> <span class="mf">1e6</span>
|
||
<a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"蒸馏后 (</span><span class="si">{</span><span class="n">distilled_params</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">M 参数): </span><span class="si">{</span><span class="n">size_mb</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> MB"</span><span class="p">)</span>
|
||
<a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a>
|
||
<a id="__codelineno-2-11" name="__codelineno-2-11" href="#__codelineno-2-11"></a> <span class="c1"># 步骤2：结构化剪枝（移除剩余30%）</span>
|
||
<a id="__codelineno-2-12" name="__codelineno-2-12" href="#__codelineno-2-12"></a> <span class="n">pruned_params</span> <span class="o">=</span> <span class="n">distilled_params</span> <span class="o">*</span> <span class="mf">0.7</span>
|
||
<a id="__codelineno-2-13" name="__codelineno-2-13" href="#__codelineno-2-13"></a> <span class="n">size_mb</span> <span class="o">=</span> <span class="n">pruned_params</span> <span class="o">*</span> <span class="mf">1e6</span> <span class="o">*</span> <span class="n">original_bits</span> <span class="o">/</span> <span class="mi">8</span> <span class="o">/</span> <span class="mf">1e6</span>
|
||
<a id="__codelineno-2-14" name="__codelineno-2-14" href="#__codelineno-2-14"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"剪枝后 (</span><span class="si">{</span><span class="n">pruned_params</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">M 参数): </span><span class="si">{</span><span class="n">size_mb</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> MB"</span><span class="p">)</span>
|
||
<a id="__codelineno-2-15" name="__codelineno-2-15" href="#__codelineno-2-15"></a>
|
||
<a id="__codelineno-2-16" name="__codelineno-2-16" href="#__codelineno-2-16"></a> <span class="c1"># 步骤3：INT4量化</span>
|
||
<a id="__codelineno-2-17" name="__codelineno-2-17" href="#__codelineno-2-17"></a> <span class="n">size_mb</span> <span class="o">=</span> <span class="n">pruned_params</span> <span class="o">*</span> <span class="mf">1e6</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">/</span> <span class="mi">8</span> <span class="o">/</span> <span class="mf">1e6</span>
|
||
<a id="__codelineno-2-18" name="__codelineno-2-18" href="#__codelineno-2-18"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"INT4量化后: </span><span class="si">{</span><span class="n">size_mb</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2"> MB"</span><span class="p">)</span>
|
||
<a id="__codelineno-2-19" name="__codelineno-2-19" href="#__codelineno-2-19"></a>
|
||
<a id="__codelineno-2-20" name="__codelineno-2-20" href="#__codelineno-2-20"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"总压缩比: </span><span class="si">{</span><span class="n">original_params_M</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mf">1e6</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">original_bits</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">8</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mf">1e6</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">size_mb</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">x"</span><span class="p">)</span>
|
||
<a id="__codelineno-2-21" name="__codelineno-2-21" href="#__codelineno-2-21"></a>
|
||
<a id="__codelineno-2-22" name="__codelineno-2-22" href="#__codelineno-2-22"></a><span class="nb">print</span><span class="p">(</span><span class="s2">"=== 从70B模型开始 ==="</span><span class="p">)</span>
|
||
<a id="__codelineno-2-23" name="__codelineno-2-23" href="#__codelineno-2-23"></a><span class="n">compression_pipeline</span><span class="p">(</span><span class="mi">70000</span><span class="p">)</span>
|
||
<a id="__codelineno-2-24" name="__codelineno-2-24" href="#__codelineno-2-24"></a>
|
||
<a id="__codelineno-2-25" name="__codelineno-2-25" href="#__codelineno-2-25"></a><span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">=== 从7B模型开始 ==="</span><span class="p">)</span>
|
||
<a id="__codelineno-2-26" name="__codelineno-2-26" href="#__codelineno-2-26"></a><span class="n">compression_pipeline</span><span class="p">(</span><span class="mi">7000</span><span class="p">)</span>
|
||
</code></pre></div></p>
|
||
</li>
|
||
<li>
|
||
<p>估计设备端推理延迟。给定模型的操作计数和硬件规格，计算是否满足延迟目标。
|
||
<div class="highlight"><pre><span></span><code><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="k">def</span><span class="w"> </span><span class="nf">estimate_latency</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">params_M</span><span class="p">,</span> <span class="n">bits</span><span class="p">,</span> <span class="n">compute_tops</span><span class="p">,</span> <span class="n">mem_bw_gbs</span><span class="p">,</span> <span class="n">seq_len</span><span class="o">=</span><span class="mi">256</span><span class="p">):</span>
|
||
<a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="w"> </span><span class="sd">"""估计内存带宽受限模型的token生成延迟。"""</span>
|
||
<a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a> <span class="c1"># 模型大小（字节）</span>
|
||
<a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a> <span class="n">model_bytes</span> <span class="o">=</span> <span class="n">params_M</span> <span class="o">*</span> <span class="mf">1e6</span> <span class="o">*</span> <span class="n">bits</span> <span class="o">/</span> <span class="mi">8</span>
|
||
<a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a>
|
||
<a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a> <span class="c1"># 解码是内存受限的：每token必须加载整个模型</span>
|
||
<a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a> <span class="n">time_per_token_ms</span> <span class="o">=</span> <span class="n">model_bytes</span> <span class="o">/</span> <span class="p">(</span><span class="n">mem_bw_gbs</span> <span class="o">*</span> <span class="mf">1e9</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1000</span>
|
||
<a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a>
|
||
<a id="__codelineno-3-9" name="__codelineno-3-9" href="#__codelineno-3-9"></a> <span class="c1"># 每秒token数</span>
|
||
<a id="__codelineno-3-10" name="__codelineno-3-10" href="#__codelineno-3-10"></a> <span class="n">tokens_per_sec</span> <span class="o">=</span> <span class="mi">1000</span> <span class="o">/</span> <span class="n">time_per_token_ms</span>
|
||
<a id="__codelineno-3-11" name="__codelineno-3-11" href="#__codelineno-3-11"></a>
|
||
<a id="__codelineno-3-12" name="__codelineno-3-12" href="#__codelineno-3-12"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">model_name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">params_M</span><span class="o">/</span><span class="mi">1000</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">B 参数 @ </span><span class="si">{</span><span class="n">bits</span><span class="si">}</span><span class="s2">-位 = </span><span class="si">{</span><span class="n">model_bytes</span><span class="o">/</span><span class="mf">1e9</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> GB"</span><span class="p">)</span>
|
||
<a id="__codelineno-3-13" name="__codelineno-3-13" href="#__codelineno-3-13"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" 内存带宽: </span><span class="si">{</span><span class="n">mem_bw_gbs</span><span class="si">}</span><span class="s2"> GB/s"</span><span class="p">)</span>
|
||
<a id="__codelineno-3-14" name="__codelineno-3-14" href="#__codelineno-3-14"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" 每token时间: </span><span class="si">{</span><span class="n">time_per_token_ms</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> ms"</span><span class="p">)</span>
|
||
<a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Tokens/秒: </span><span class="si">{</span><span class="n">tokens_per_sec</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
|
||
<a id="__codelineno-3-16" name="__codelineno-3-16" href="#__codelineno-3-16"></a> <span class="nb">print</span><span class="p">()</span>
|
||
<a id="__codelineno-3-17" name="__codelineno-3-17" href="#__codelineno-3-17"></a>
|
||
<a id="__codelineno-3-18" name="__codelineno-3-18" href="#__codelineno-3-18"></a><span class="c1"># Apple M2 Pro：200 GB/s 统一内存带宽</span>
|
||
<a id="__codelineno-3-19" name="__codelineno-3-19" href="#__codelineno-3-19"></a><span class="nb">print</span><span class="p">(</span><span class="s2">"=== Apple M2 Pro (200 GB/s) ==="</span><span class="p">)</span>
|
||
<a id="__codelineno-3-20" name="__codelineno-3-20" href="#__codelineno-3-20"></a><span class="n">estimate_latency</span><span class="p">(</span><span class="s2">"Llama-7B Q4"</span><span class="p">,</span> <span class="mi">7000</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">15.8</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
||
<a id="__codelineno-3-21" name="__codelineno-3-21" href="#__codelineno-3-21"></a><span class="n">estimate_latency</span><span class="p">(</span><span class="s2">"Llama-7B Q8"</span><span class="p">,</span> <span class="mi">7000</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mf">15.8</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
||
<a id="__codelineno-3-22" name="__codelineno-3-22" href="#__codelineno-3-22"></a><span class="n">estimate_latency</span><span class="p">(</span><span class="s2">"Llama-70B Q4"</span><span class="p">,</span> <span class="mi">70000</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mf">15.8</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
|
||
<a id="__codelineno-3-23" name="__codelineno-3-23" href="#__codelineno-3-23"></a>
|
||
<a id="__codelineno-3-24" name="__codelineno-3-24" href="#__codelineno-3-24"></a><span class="c1"># 手机（Snapdragon 8 Gen 3）：~50 GB/s LPDDR5</span>
|
||
<a id="__codelineno-3-25" name="__codelineno-3-25" href="#__codelineno-3-25"></a><span class="nb">print</span><span class="p">(</span><span class="s2">"=== Snapdragon 8 Gen 3 (50 GB/s) ==="</span><span class="p">)</span>
|
||
<a id="__codelineno-3-26" name="__codelineno-3-26" href="#__codelineno-3-26"></a><span class="n">estimate_latency</span><span class="p">(</span><span class="s2">"Phi-3 Mini Q4"</span><span class="p">,</span> <span class="mi">3800</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
|
||
<a id="__codelineno-3-27" name="__codelineno-3-27" href="#__codelineno-3-27"></a><span class="n">estimate_latency</span><span class="p">(</span><span class="s2">"Llama-3B Q4"</span><span class="p">,</span> <span class="mi">3000</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
|
||
</code></pre></div></p>
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