336 lines
20 KiB
HTML
336 lines
20 KiB
HTML
<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>机制：读取路径、记录结构与写入状态机 — Natural Memory v2</title>
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<meta name="description" content="从用户问题到证据前缀注入的八个环节，记录包含哪些字段，以及 active / superseded / quarantined / retracted 四个状态如何演化。">
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<meta name="color-scheme" content="dark">
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<meta name="theme-color" content="#0a0a0a">
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<script>document.documentElement.classList.add("js");</script>
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<meta property="og:type" content="website">
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<meta property="og:title" content="机制：读取路径、记录结构与写入状态机 — Natural Memory v2">
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<meta property="og:description" content="从用户问题到证据前缀注入的八个环节，记录包含哪些字段，以及 active / superseded / quarantined / retracted 四个状态如何演化。">
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<link rel="preload" href="assets/fonts/archivo-latin-wdth-normal.woff2" as="font" type="font/woff2" crossorigin>
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<link rel="stylesheet" href="assets/css/site.css?v=a9c21ef3a8">
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</head>
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<body data-page="mechanism" data-layout="docs">
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<a class="skip" href="#doc">跳到正文</a>
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<header class="site" id="site">
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<div class="topbar">
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<a class="brand" href="./" aria-label="Natural Memory v2 发布页">
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<svg class="brand__mark" viewBox="0 0 32 32" aria-hidden="true">
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<rect class="dim" x="2" y="2" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="12" y="2" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="22" y="2" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="2" y="12" width="7" height="7" rx="1.5"/>
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<rect class="on" x="12" y="12" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="22" y="12" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="2" y="22" width="7" height="7" rx="1.5"/>
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<rect class="dim" x="12" y="22" width="7" height="7" rx="1.5"/>
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<rect class="on" x="22" y="22" width="7" height="7" rx="1.5"/>
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</svg>
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<span class="brand__name">Natural Memory</span>
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<span class="pill pill--acc">v2</span>
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</a>
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<span class="crumb">
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<span class="crumb__n">02</span>
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<span class="crumb__t">一次读取与一次写入</span>
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</span>
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<span class="topbar__spacer"></span>
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<nav class="topnav" aria-label="快速导航">
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<a class="topnav__a" href="./">发布页</a> <a class="topnav__a is-on" href="./mechanism" aria-current="page">机制</a> <a class="topnav__a" href="./results">测量</a> <a class="topnav__a" href="./nm21">NM2.1</a> <a class="topnav__a" href="./ledger">台账</a> <a class="topnav__a" href="./limits">边界</a> <a class="topnav__a" href="./reproduce">复现</a>
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</nav>
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<button class="palbtn" id="palBtn" type="button" aria-label="检索全站内容（快捷键 /）">
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<span class="palbtn__t">检索</span><span class="kbd">/</span>
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</button>
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<span class="pill" id="serverChip" title="本站服务端运行时读数">
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<i class="dot"></i><span id="serverChipText">读取中</span>
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</span>
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</div>
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<div class="progress" id="progress"></div>
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</header>
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<div class="shell">
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<aside class="side">
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<nav class="side__in" aria-label="章节导航">
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<a class="side__back" href="./">← 返回发布页</a>
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<p class="side__grp">原理</p>
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<ul class="side__list">
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<li><a class="side__a" href="./division"><span class="side__n">01</span><span class="side__t">记忆与 KV 的分工</span></a></li>
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<li><a class="side__a is-on" href="./mechanism" aria-current="page"><span class="side__n">02</span><span class="side__t">一次读取与一次写入</span></a></li>
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</ul>
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<p class="side__grp">证据</p>
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<ul class="side__list">
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<li><a class="side__a" href="./results"><span class="side__n">03</span><span class="side__t">测量结果</span></a></li>
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<li><a class="side__a" href="./nm21"><span class="side__n">04</span><span class="side__t">NM2.1 · 之后做了什么</span></a></li>
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<li><a class="side__a" href="./examples"><span class="side__n">05</span><span class="side__t">实测样例</span></a></li>
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<li><a class="side__a" href="./ledger"><span class="side__n">06</span><span class="side__t">真实评测台账</span></a></li>
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</ul>
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<p class="side__grp">工程</p>
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<ul class="side__list">
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<li><a class="side__a" href="./cost"><span class="side__n">07</span><span class="side__t">代价账本</span></a></li>
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<li><a class="side__a" href="./capacity"><span class="side__n">08</span><span class="side__t">容量与存储</span></a></li>
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<li><a class="side__a" href="./limits"><span class="side__n">09</span><span class="side__t">我们不宣称的内容</span></a></li>
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<li><a class="side__a" href="./roadmap"><span class="side__n">10</span><span class="side__t">已解决的与下一步</span></a></li>
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<li><a class="side__a" href="./reproduce"><span class="side__n">11</span><span class="side__t">复现实验</span></a></li>
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</ul>
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</nav>
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</aside>
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<main class="doc" id="doc">
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<header class="doc__hd">
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<span class="kicker">02 / MECHANISM</span>
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<h1 class="doc__h">一次读取与一次写入</h1>
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<p class="doc__lede">从用户问题到证据前缀注入的八个环节，记录包含哪些字段，以及 active / superseded / quarantined / retracted 四个状态如何演化。</p>
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<p class="doc__figure">
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<span class="mega">1,048,576<small></small></span>
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<span class="doc__figure-n">地址空间设计容量</span>
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</p>
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</header>
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<p class="lede2">
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这一节把「<strong>一次读取</strong>」和「<strong>一次写入</strong>」拆开讲透：读取路径有哪八个环节、每一步的输入输出是什么、
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一条记录里到底存了什么、四个状态如何演化，以及我们为这些决定付出了什么代价。
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</p>
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<h2 class="h3" >一、读取路径：从提问到证据注入</h2>
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<div class="scrollx teaser--doc">
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<svg class="dgm" viewBox="0 0 1120 250" role="img"
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aria-label="读取路径示意：问题 → 隐状态地址 → LSH 粗索引 → 候选页 → 页内重排 → Top-K → 证据前缀 → 生成">
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<!-- 连接线（pathLength=1 让描边可以按 0→1 精确绘制） -->
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<g class="dgm__a">
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<path pathLength="1" d="M132 78 L156 78" /><path pathLength="1" d="M270 78 L294 78" /><path pathLength="1" d="M408 78 L432 78" />
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<path pathLength="1" d="M546 78 L570 78" /><path pathLength="1" d="M684 78 L708 78" /><path pathLength="1" d="M822 78 L846 78" />
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<path pathLength="1" d="M960 78 L984 78" />
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</g>
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<!-- 8 个环节 -->
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<g>
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<rect class="dgm__box" x="12" y="46" width="120" height="64" rx="8"/>
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<text class="dgm__n" x="24" y="66">01</text>
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<text class="dgm__l" x="24" y="86">用户问题</text>
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<text class="dgm__s" x="24" y="101">自然语言</text>
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</g>
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<g>
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<rect class="dgm__box" x="156" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="168" y="66">02</text>
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<text class="dgm__l" x="168" y="86">隐状态地址</text>
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<text class="dgm__s" x="168" y="101">Qwen hidden</text>
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</g>
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<g>
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<rect class="dgm__box" x="294" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="306" y="66">03</text>
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<text class="dgm__l" x="306" y="86">LSH 粗索引</text>
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<text class="dgm__s" x="306" y="101">730 桶</text>
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</g>
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<g>
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<rect class="dgm__box" x="432" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="444" y="66">04</text>
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<text class="dgm__l" x="444" y="86">候选页</text>
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<text class="dgm__s" x="444" y="101">≤4 页 · 32/页</text>
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</g>
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<g>
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<rect class="dgm__box" x="570" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="582" y="66">05</text>
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<text class="dgm__l" x="582" y="86">页内重排</text>
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<text class="dgm__s" x="582" y="101">text_retriever</text>
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</g>
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<g>
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<rect class="dgm__box dgm__box--acc" x="708" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="720" y="66">06</text>
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<text class="dgm__l" x="720" y="86">Top-K 记录</text>
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<text class="dgm__s" x="720" y="101">≤2 条</text>
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</g>
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<g>
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<rect class="dgm__box dgm__box--acc" x="846" y="46" width="114" height="64" rx="8"/>
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<text class="dgm__n" x="858" y="66">07</text>
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<text class="dgm__l" x="858" y="86">证据前缀</text>
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<text class="dgm__s" x="858" y="101">注入 Qwen</text>
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</g>
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<g>
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<rect class="dgm__box" x="984" y="46" width="124" height="64" rx="8"/>
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<text class="dgm__n" x="996" y="66">08</text>
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<text class="dgm__l" x="996" y="86">普通生成</text>
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<text class="dgm__s" x="996" y="101">回答</text>
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</g>
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<!-- 关键约束注释 -->
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<line class="dgm__rule" x1="12" y1="146" x2="1108" y2="146"/>
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<text class="dgm__note" x="12" y="172">无全量注意力</text>
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<text class="dgm__s" x="1108" y="172" text-anchor="end">BOUNDED READ</text>
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<text class="dgm__note" x="12" y="190">当前问题不与全部记忆记录做注意力，只与少量页面和记录交互</text>
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<text class="dgm__note" x="12" y="222">读取额外耗时 61.10 ms → 26.35 ms（批次 A → B） · 前缀命中率 96.97% · GPU cache 实际 17,990 / 上限 131,072 token</text>
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</svg>
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</div>
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<p class="small" style="max-width:78ch">
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点左侧任一环节查看它在做什么，也可以依次点一遍——注意整条链路上<strong>没有一步是「和全部记忆做注意力」</strong>，
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这是显存可控的根本原因。
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</p>
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<div class="flow">
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<div class="steps" id="steps" role="tablist" aria-label="记忆读取路径"></div>
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<div class="flow__detail card card--flat">
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<p class="flow__stage" id="flowIdx">STAGE 01 / 08</p>
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<h3 class="flow__title" id="flowTitle"></h3>
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<p class="flow__body" id="flowBody"></p>
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<p class="flow__io" id="flowIo"></p>
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<div class="flow__meta">
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<div><span>读取上限</span><b>2 records</b></div>
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<div><span>候选页上限</span><b>4 pages</b></div>
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<div><span>页容量</span><b>32 / page</b></div>
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<div><span>全量注意力</span><b>否</b></div>
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</div>
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</div>
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</div>
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<h2 class="h3" >每一步的输入、输出与约束</h2>
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<p class="small" style="max-width:78ch">
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括号里的数字是实测值，不是设计目标。
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</p>
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<div class="scrollx">
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<div class="gt gt--3">
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<div class="gt__h"><span>环节</span><span>输入 → 输出</span><span>约束或实测</span></div>
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<div class="gt__r"><span class="gt__k">01 用户问题</span><span class="gt__v">普通自然语言</span><span class="gt__why">不要求任何显式指令，不需要输入 <code>/remember</code> 之类的标记。</span></div>
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<div class="gt__r"><span class="gt__k">02 Qwen hidden state</span><span class="gt__v">问题 → 紧凑查询地址</span><span class="gt__why">地址由模型隐状态生成，不依赖外部 embedding 服务；语义地址编码用单条 batch 执行，避免编码阶段出现显存瞬时峰值。</span></div>
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<div class="gt__r"><span class="gt__k">03 LSH / 地址粗索引</span><span class="gt__v">查询地址 → 候选页</span><span class="gt__why">精确桶 + Hamming-1/2 探针；批次 A 的记忆状态里粗索引有 730 个桶、最近一次粗筛返回 24 个候选。</span></div>
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<div class="gt__r"><span class="gt__k">04 候选页</span><span class="gt__v">候选页 → 少量页面</span><span class="gt__why">页容量 32 条记录，默认最多读取 4 页；本次 active records 723 条落在 23 个页面上。</span></div>
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<div class="gt__r"><span class="gt__k">05 页内记录重排</span><span class="gt__v">页内记录 → 有序候选</span><span class="gt__why">记录级重排由独立的 text_retriever 完成（6 张量、1,573,377 参数）。它的 Top-1 命中率本身只有 23.20%，是当前的主要瓶颈之一。</span></div>
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<div class="gt__r"><span class="gt__k">06 Top-K 记录</span><span class="gt__v">有序候选 → 少量证据</span><span class="gt__why">正式测试中的读取上限为 2 条记录。目标记忆召回：批次 A 166/246，<strong>批次 B 228/246</strong>。</span></div>
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<div class="gt__r"><span class="gt__k">07 证据前缀注入</span><span class="gt__v">证据 → 模型内部前缀</span><span class="gt__why">命中的短证据被提升到 GPU 作为前缀；批次 B 有 96.97% 的题目实际注入了前缀，平均 410.34 token（批次 A 为 610.67）。</span></div>
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<div class="gt__r"><span class="gt__k">08 普通生成</span><span class="gt__v">前缀 + 问题 → 回答</span><span class="gt__why">记忆主体始终留在进程内存，只有热点记录进入有界 GPU cache。读取路径额外耗时：批次 A 61.10 ms → <strong>批次 B 26.35 ms</strong>。</span></div>
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</div>
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</div>
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<h2 class="h3" >二、一条记录里到底存了什么</h2>
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<p class="small" style="max-width:78ch">
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记录不是一段文本，而是一条带地址、带版本关系、带状态的结构化数据。下面左侧是全部字段，
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右侧是<strong>一条真实记录</strong>（逐字取自评测结果里的命中记录）。
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</p>
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<div class="grid g-2-1">
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<div>
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<div class="rec" id="recFields"></div>
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</div>
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<div>
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<div class="card card--acc" id="realRecord"></div>
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</div>
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</div>
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<h2 class="h3" >三、状态机：旧的答案不会凭空消失</h2>
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<p class="small" style="max-width:78ch">
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同一实体和属性出现新值时，<strong>旧值被标记为 superseded，而不是被覆盖或删除</strong>。
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这是「时间冲突」类问题能答对的前提：既要给出当前有效值，也要能说清旧值去哪了。
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</p>
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<div class="states" id="stateList"></div>
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<div class="callout callout--warn" >
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<p class="callout__t">写入路径的教训：检索救不回不存在的记录</p>
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<p class="callout__b">
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早期版本的退役授权完全依赖一个学习打分（阈值 ≥0.95），<strong>没有任何结构校验</strong>。
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结果是新写入把已有记录误判为旧值并退役：一次 20 条记录的测试里 active 只剩 12 条、retract 高达 16 次、目标记录只存活 8/16。
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修好之后：<strong>active 12/20 → 20/20，retract 16 → 0，目标记录存活 8/16 → 16/16，端到端 37.50% → 68.75%</strong>。
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这件事的结论很直白——<span class="serif-it">任何排序器都救不回一条已经不存在的记录。</span>
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</p>
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</div>
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<h2 class="h3" >四、四个设计决定与它们的代价</h2>
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<div class="grid g2" >
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<div class="card">
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<p class="eyebrow" >决定 1 · 两段式读取</p>
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<p class="small" ><strong>粗索引筛页 → 页内重排</strong>，而不是对全量记录做注意力。</p>
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<p class="tiny">好处：单次读取的候选规模被压到固定上限（≤4 页 / ≤2 条），成本与记忆总量解耦。
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代价：两段都可能出错，且错误会叠加——粗索引错了就再没有机会。</p>
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</div>
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<div class="card">
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<p class="eyebrow" >决定 2 · 用地址索引而非向量库</p>
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<p class="small" >语义地址 + LSH 桶放在模型包内，<strong>不引入外部检索服务</strong>。</p>
|
||
<p class="tiny">好处：部署时没有额外进程与网络依赖，读取耗时稳定（批次 B 26.35 ms）。
|
||
代价：召回质量受地址质量限制，零字面重叠的改写问法曾只有 18.40% 的 Top-1。</p>
|
||
</div>
|
||
<div class="card">
|
||
<p class="eyebrow" >决定 3 · 有界 GPU cache</p>
|
||
<p class="small" >热点记录进 GPU，上限 <strong>256 条 / 131,072 token</strong>，另有动态保留。</p>
|
||
<p class="tiny">好处：显存占用可预测，两个批次的实际用量分别是 15,885 / 17,990 token，fallback 与分配失败均为 0。
|
||
代价：cold page 会带来额外取数路径——不过本次两个批次都是 embedded / process-RAM 模式，cold page 为 0。</p>
|
||
</div>
|
||
<div class="card">
|
||
<p class="eyebrow" >决定 4 · 嵌入式 weight-shard 持久化</p>
|
||
<p class="small" >记忆快照写进模型包的 memory safetensors 切片，<strong>默认不用 SQLite、不用磁盘分页</strong>。</p>
|
||
<p class="tiny">好处：运行时依赖最少，重启只需加载权重切片、不回放历史聊天。
|
||
代价：模型包会随记忆增长变大，且<strong>固化更新需要重新保存权重切片</strong>——这是明确的工程负担，不是白拿的。</p>
|
||
</div>
|
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|
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|
||
<h2 class="h3" >五、这套机制仍然做不到的事</h2>
|
||
<div class="claims claims--loose" >
|
||
<div class="claim"><span class="claim__x">✕</span><span class="claim__t">任何自然语言表达都能稳定召回正确记忆</span><span class="claim__s">UNSUPPORTED</span></div>
|
||
<div class="claim"><span class="claim__x">✕</span><span class="claim__t">记忆写入永远不会出错</span><span class="claim__s">UNSUPPORTED</span></div>
|
||
<div class="claim"><span class="claim__x">✕</span><span class="claim__t">检索召回率等于回答正确率（批次 B 召回 92.68% / 回答 82.52%，差 10.16pp）</span><span class="claim__s">UNSUPPORTED</span></div>
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