{"id":75633,"date":"2026-02-12T19:17:51","date_gmt":"2026-02-12T11:17:51","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/75633.html"},"modified":"2026-02-12T19:17:51","modified_gmt":"2026-02-12T11:17:51","slug":"ai%e6%a0%b8%e5%bf%83%e7%9f%a5%e8%af%8694-%e5%a4%a7%e8%af%ad%e8%a8%80%e6%a8%a1%e5%9e%8b%e4%b9%8b-linear-attention-mechanism%ef%bc%88%e7%ae%80%e6%b4%81%e4%b8%94%e9%80%9a%e4%bf%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/75633.html","title":{"rendered":"AI\u6838\u5fc3\u77e5\u8bc694\u2014\u2014\u5927\u8bed\u8a00\u6a21\u578b\u4e4b Linear Attention Mechanism\uff08\u7b80\u6d01\u4e14\u901a\u4fd7\u6613\u61c2\u7248\uff09"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" alt=\"\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260212111747-698db6dbd9676.jpg\" \/><\/p>\n<p>\u7ebf\u6027\u6ce8\u610f\u529b\u673a\u5236 (Linear Attention Mechanism) \u662f\u4e3a\u4e86\u89e3\u51b3\u4f20\u7edf Transformer \u6a21\u578b\u201c\u8bb0\u6027\u8d8a\u597d&#xff0c;\u7b97\u5f97\u8d8a\u6162\u201d \u8fd9\u4e00\u81f4\u547d\u7f3a\u9677\u800c\u8bde\u751f\u7684\u4e00\u79cd\u4f18\u5316\u6280\u672f\u3002<\/p>\n<p>\u5b83\u7684\u6838\u5fc3\u76ee\u6807\u662f&#xff1a;\u628a\u5927\u6a21\u578b\u5904\u7406\u957f\u6587\u672c\u7684\u65f6\u95f4\u590d\u6742\u5ea6&#xff0c;\u4ece\u201c\u5e73\u65b9\u7ea7\u7206\u70b8\u201d (O(N^2)) \u964d\u4f4e\u5230\u201c\u7ebf\u6027\u589e\u957f\u201d (O(N))\u3002<\/p>\n<p>\u7b80\u5355\u6765\u8bf4&#xff0c;\u5b83\u662f\u8ba9 AI \u80fd\u591f\u4e00\u53e3\u6c14\u8bfb\u5b8c\u51e0\u5341\u4e07\u5b57\u7684\u5c0f\u8bf4&#xff0c;\u800c\u4e0d\u4f1a\u628a\u663e\u5361\u5185\u5b58\u6491\u7206\u7684\u5173\u952e\u6280\u672f\u4e4b\u4e00\u3002<\/p>\n<hr \/>\n<h4 style=\"background-color:transparent\">1.&#x1f422; \u80cc\u666f&#xff1a;\u4f20\u7edf\u6ce8\u610f\u529b\u7684\u201c\u5e73\u65b9\u74f6\u9888\u201d<\/h4>\n<p>\u8981\u7406\u89e3\u7ebf\u6027\u6ce8\u610f\u529b&#xff0c;\u5148\u5f97\u770b\u6807\u51c6\u6ce8\u610f\u529b (Standard Softmax Attention) \u7684\u75db\u70b9\u3002<\/p>\n<p>\u5728\u6807\u51c6\u7684 Transformer&#xff08;\u5982 GPT-4&#xff09;\u4e2d&#xff0c;\u8ba1\u7b97\u6ce8\u610f\u529b\u662f\u4e00\u4e2a\u5168\u5458\u793e\u4ea4\u7684\u8fc7\u7a0b&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u673a\u5236&#xff1a;\u5e8f\u5217\u91cc\u7684\u6bcf\u4e00\u4e2a\u5b57&#xff0c;\u90fd\u8981\u548c\u5e8f\u5217\u91cc\u5176\u4ed6\u6240\u6709\u7684\u5b57\u8fdb\u884c\u4e00\u6b21\u8ba1\u7b97&#xff08;\u63e1\u624b&#xff09;&#xff0c;\u770b\u770b\u5f7c\u6b64\u5173\u7cfb\u6709\u591a\u7d27\u5bc6\u3002<\/p>\n<\/li>\n<li>\n<p>\u4ee3\u4ef7&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u5982\u679c\u6587\u7ae0\u6709 100 \u4e2a\u5b57&#xff0c;\u8ba1\u7b97\u91cf\u662f 100 \\\\times 100 &#061; 10,000 \u6b21\u3002<\/p>\n<\/li>\n<li>\n<p>\u5982\u679c\u6587\u7ae0\u6709 1000 \u4e2a\u5b57&#xff0c;\u8ba1\u7b97\u91cf\u662f 1000 \\\\times 1000 &#061; 1,000,000 \u6b21\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u540e\u679c&#xff1a;\u968f\u7740\u6587\u7ae0\u957f\u5ea6 (N) \u53d8\u957f&#xff0c;\u8ba1\u7b97\u91cf\u548c\u663e\u5b58\u5360\u7528\u662f\u5e73\u65b9\u7ea7 (N^2) \u589e\u52a0\u7684\u3002\u8fd9\u5bfc\u81f4\u4f20\u7edf\u6a21\u578b\u5f88\u96be\u5904\u7406\u8d85\u957f\u4e0a\u4e0b\u6587&#xff08;\u6bd4\u5982 100k \u4ee5\u4e0a&#xff09;\u3002<\/p>\n<\/li>\n<\/ul>\n<hr \/>\n<h4 style=\"background-color:transparent\">2.\u26a1 \u539f\u7406&#xff1a;\u6570\u5b66\u4e0a\u7684\u201c\u5077\u61d2\u201d\u6280\u5de7<\/h4>\n<p>\u7ebf\u6027\u6ce8\u610f\u529b \u53d1\u73b0\u4e86\u4e00\u4e2a\u6570\u5b66\u4e0a\u7684\u201c\u6f0f\u6d1e\u201d&#xff08;\u6216\u8005\u8bf4\u7ed3\u5408\u5f8b\u7279\u6027&#xff09;&#xff0c;\u901a\u8fc7\u6539\u53d8\u8ba1\u7b97\u987a\u5e8f\u6765\u89c4\u907f\u90a3\u4e2a\u5de8\u5927\u7684\u77e9\u9635\u3002<\/p>\n<h5>A. \u6807\u51c6\u505a\u6cd5&#xff1a;\u5148\u76f8\u4e58&#xff0c;\u518d\u6c42\u548c<\/h5>\n<p>\u516c\u5f0f\u903b\u8f91\u662f&#xff1a;$$Attention(Q, K, V) &#061; \\\\text{Softmax}(Q \\\\times K^T) \\\\times $$<\/p>\n<li>\n<p>\u5148\u7b97 Q \\\\times K^T\u3002\u8fd9\u4f1a\u751f\u6210\u4e00\u4e2a\u5de8\u5927\u7684 N \\\\times N \u77e9\u9635&#xff08;\u6ce8\u610f\u529b\u5206\u6570\u56fe&#xff09;\u3002<\/p>\n<\/li>\n<li>\n<p>\u518d\u628a\u8fd9\u4e2a\u5de8\u5927\u77e9\u9635\u4e58\u4ee5 V\u3002<\/p>\n<\/li>\n<li>\n<p>\u74f6\u9888&#xff1a;\u90a3\u4e2a N \\\\times N \u7684\u4e2d\u95f4\u4ea7\u7269\u592a\u5927\u4e86\u3002<\/p>\n<\/li>\n<h5>B. \u7ebf\u6027\u505a\u6cd5&#xff1a;\u5148\u5408\u5e76&#xff0c;\u518d\u76f8\u4e58<\/h5>\n<p>\u516c\u5f0f\u903b\u8f91\u662f&#xff1a;$$Attention(Q, K, V) &#061; Q \\\\times (K^T \\\\times V$$<\/p>\n<p>\u6ce8&#xff1a;\u8fd9\u91cc\u9700\u8981\u7528\u6838\u51fd\u6570 \\\\phi(\\\\cdot) \u66ff\u6362\u6389\u975e\u7ebf\u6027\u7684 Softmax\u3002<\/p>\n<li>\n<p>\u5148\u7b97 K^T \\\\times V\u3002\u56e0\u4e3a K \u548c V \u7684\u7ef4\u5ea6&#xff08;d&#xff09;\u901a\u5e38\u5f88\u5c0f&#xff08;\u6bd4\u5982 64 \u6216 128&#xff09;&#xff0c;\u8fd9\u4e2a\u7ed3\u679c\u662f\u4e00\u4e2a\u5f88\u5c0f\u7684 d \\\\times d \u77e9\u9635&#xff0c;\u8ddf\u6587\u7ae0\u957f\u5ea6 N \u6ca1\u5173\u7cfb\u3002<\/p>\n<\/li>\n<li>\n<p>\u518d\u7528 Q \u53bb\u4e58\u4ee5\u8fd9\u4e2a\u5c0f\u77e9\u9635\u3002<\/p>\n<\/li>\n<li>\n<p>\u7ed3\u679c&#xff1a;\u4e0d\u7ba1\u6587\u7ae0\u6709\u591a\u957f&#xff0c;\u6211\u90fd\u4e0d\u9700\u8981\u751f\u6210\u90a3\u4e2a\u5de8\u5927\u7684\u6ce8\u610f\u529b\u56fe\u3002\u8ba1\u7b97\u91cf\u53d8\u6210\u4e86 N \\\\times d^2&#xff0c;\u8fd9\u53ea\u662f N \u7684\u7ebf\u6027\u500d\u6570\u3002<\/p>\n<\/li>\n<hr \/>\n<h4 style=\"background-color:transparent\">3.&#x1f3df;\ufe0f \u5f62\u8c61\u6bd4\u55bb&#xff1a;\u6d3e\u5bf9\u63e1\u624b vs. \u7559\u8a00\u7bb1<\/h4>\n<ul>\n<li>\n<p>\u6807\u51c6\u6ce8\u610f\u529b (O(N^2))&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u6d3e\u5bf9\u4e0a\u6709 1000 \u4e2a\u4eba\u3002\u6bcf\u4e2a\u4eba\u90fd\u5fc5\u987b\u548c\u5176\u4ed6 999 \u4e2a\u4eba\u4e00\u5bf9\u4e00\u63e1\u624b&#xff0c;\u5e76\u4ea4\u6362\u540d\u7247\u3002<\/p>\n<\/li>\n<li>\n<p>\u8017\u65f6\u6781\u957f&#xff0c;\u73b0\u573a\u4e71\u6210\u4e00\u9505\u7ca5\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u7ebf\u6027\u6ce8\u610f\u529b (O(N))&#xff1a;<\/p>\n<ul>\n<li>\n<p>\u6d3e\u5bf9\u4e0a\u6709 1000 \u4e2a\u4eba\u3002\u6bcf\u4e2a\u4eba\u8fdb\u95e8\u65f6&#xff0c;\u628a\u540d\u7247\u6254\u8fdb\u4e00\u4e2a\u516c\u5171\u7684\u201c\u7559\u8a00\u7bb1\u201d (K^T \\\\times V)\u3002<\/p>\n<\/li>\n<li>\n<p>\u6bcf\u4e2a\u4eba\u51fa\u95e8\u65f6&#xff0c;\u4ece\u7bb1\u5b50\u91cc\u62ff\u4e00\u4efd\u201c\u5927\u5bb6\u540d\u7247\u7684\u6c47\u603b\u6458\u8981\u201d\u5373\u53ef\u3002<\/p>\n<\/li>\n<li>\n<p>\u6bcf\u4e2a\u4eba\u53ea\u9700\u8981\u8ddf\u7bb1\u5b50\u4ea4\u4e92\u4e00\u6b21&#xff0c;\u901f\u5ea6\u6781\u5feb\u3002<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<hr \/>\n<h4 style=\"background-color:transparent\">4.\u2694\ufe0f \u4f18\u7f3a\u70b9\u6743\u8861<\/h4>\n<p>\u65e2\u7136\u7ebf\u6027\u6ce8\u610f\u529b\u8fd9\u4e48\u5feb&#xff0c;\u4e3a\u4ec0\u4e48 GPT-4 \u4e0d\u7acb\u523b\u5168\u90e8\u6362\u6210\u5b83&#xff1f;\u56e0\u4e3a\u5b83\u6709\u4ee3\u4ef7\u3002<\/p>\n<table>\n<tbody>\n<tr>\n<td>\u7279\u6027<\/td>\n<td>\u6807\u51c6\u6ce8\u610f\u529b (Softmax Attention)<\/td>\n<td>\u7ebf\u6027\u6ce8\u610f\u529b (Linear Attention)<\/td>\n<\/tr>\n<tr>\n<td>\u901f\u5ea6<\/td>\n<td>\u6162 (N^2)&#xff0c;\u957f\u6587\u5669\u68a6<\/td>\n<td>\u5feb (N)&#xff0c;\u957f\u6587\u65e0\u538b\u529b<\/td>\n<\/tr>\n<tr>\n<td>\u7cbe\u5ea6<\/td>\n<td>\u9ad8\u3002\u80fd\u7cbe\u51c6\u6355\u6349\u4efb\u610f\u4e24\u4e2a\u5b57\u4e4b\u95f4\u7684\u5fae\u5999\u5173\u7cfb\u3002<\/td>\n<td>\u7565\u4f4e\u3002\u56e0\u4e3a\u4f7f\u7528\u4e86\u6838\u51fd\u6570\u8fd1\u4f3c\u6216\u6539\u53d8\u4e86\u8ba1\u7b97\u903b\u8f91&#xff0c;\u4f1a\u6709\u4fe1\u606f\u538b\u7f29\u635f\u8017\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u53ec\u56de\u80fd\u529b<\/td>\n<td>\u5f3a\u3002\u80fd\u4ece 100 \u9875\u524d\u7684\u89d2\u843d\u91cc\u627e\u5230\u4e00\u4e2a\u540d\u5b57&#xff08;\u5927\u6d77\u635e\u9488&#xff09;\u3002<\/td>\n<td>\u5f31\u3002\u5bb9\u6613\u9057\u5fd8\u6781\u5176\u4e45\u8fdc\u6216\u7ec6\u5fae\u7684\u4fe1\u606f&#xff08;\u597d\u50cf\u8bb0\u5f97\u6709\u8fd9\u56de\u4e8b&#xff0c;\u4f46\u7ec6\u8282\u6a21\u7cca\u4e86&#xff09;\u3002<\/td>\n<\/tr>\n<tr>\n<td>\u4ee3\u8868\u6280\u672f<\/td>\n<td>Transformers (GPT, BERT)<\/td>\n<td>RWKV, Linear Transformer, Performer<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h4 style=\"background-color:transparent\">5.&#x1f52e; \u8fdb\u9636\u4e0e\u73b0\u72b6&#xff1a;RNN \u7684\u590d\u6d3b&#xff1f;<\/h4>\n<p>\u7ebf\u6027\u6ce8\u610f\u529b\u7684\u4e00\u4e2a\u795e\u5947\u526f\u4ea7\u54c1\u662f&#xff1a;\u5b83\u53ef\u4ee5\u50cf RNN&#xff08;\u5faa\u73af\u795e\u7ecf\u7f51\u7edc&#xff09;\u4e00\u6837\u8fd0\u884c\u3002<\/p>\n<ul>\n<li>\n<p>\u56e0\u4e3a\u5b83\u4e0d\u9700\u8981\u4e00\u6b21\u6027\u770b\u5168\u6240\u6709\u6587\u5b57&#xff0c;\u5b83\u53ef\u4ee5\u628a\u524d\u6587\u7684\u4fe1\u606f\u538b\u7f29\u6210\u4e00\u4e2a\u56fa\u5b9a\u7684\u72b6\u6001 (State)&#xff0c;\u7136\u540e\u8bfb\u4e00\u4e2a\u5b57&#xff0c;\u66f4\u65b0\u4e00\u4e0b\u72b6\u6001&#xff0c;\u518d\u8bfb\u4e00\u4e2a\u5b57\u3002<\/p>\n<\/li>\n<li>\n<p>\u8fd9\u610f\u5473\u7740&#xff1a;\u63a8\u7406\u65f6\u7684\u663e\u5b58\u5360\u7528\u662f\u6052\u5b9a\u7684&#xff01; \u4e0d\u7ba1\u4f60\u804a\u4e86 1 \u53e5\u8fd8\u662f 1 \u4e07\u53e5&#xff0c;\u5b83\u5360\u7528\u7684\u5185\u5b58\u4e00\u6837\u591a\u3002<\/p>\n<\/li>\n<\/ul>\n<p>\u76ee\u524d\u6700\u706b\u7684 Mamba (SSM \u72b6\u6001\u7a7a\u95f4\u6a21\u578b) \u548c RWKV&#xff0c;\u672c\u8d28\u4e0a\u90fd\u662f\u8fd9\u7c7b\u7ebf\u6027\u590d\u6742\u5ea6\u6a21\u578b\u7684\u6770\u51fa\u4ee3\u8868\u3002\u5b83\u4eec\u8bd5\u56fe\u5728\u201c\u4fdd\u6301\u7ebf\u6027\u901f\u5ea6\u201d\u7684\u540c\u65f6&#xff0c;\u628a\u201c\u7cbe\u5ea6\u201d\u63d0\u5347\u5230\u63a5\u8fd1\u6807\u51c6 Transformer \u7684\u6c34\u5e73\u3002<\/p>\n<h4 style=\"background-color:transparent\">\u603b\u7ed3<\/h4>\n<p>\u7ebf\u6027\u6ce8\u610f\u529b\u673a\u5236 \u662f AI \u4e3a\u4e86\u8ffd\u6c42\u201c\u65e0\u9650\u4e0a\u4e0b\u6587\u201d \u800c\u505a\u51fa\u7684\u6570\u5b66\u59a5\u534f\u4e0e\u521b\u65b0\u3002<\/p>\n<p>\u5b83\u6253\u7834\u4e86\u201c\u6587\u7ae0\u8d8a\u957f&#xff0c;\u667a\u5546\u8d8a\u6162\u201d\u7684\u9b54\u5492&#xff0c;\u662f\u672a\u6765 AI 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