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<h1><a href="https://nn.labml.ai/transformers/aft/index.html">免注意的变压器</a></h1>
<p>这是论文《<a href="https://papers.labml.ai/paper/2105.14103">免注意变压器》的 PyT</a> <a href="https://pytorch.org">orch</a> 实现。</p>
<p>本文用一种新的高效运算取代了<a href="https://nn.labml.ai/transformers/mha.html">自我注意力层</a>，该运算的内存复杂度为O（Td），其中T<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">d</span></span></span></span></span> 是序列长度，是嵌入的维数。</p>
<p>本文介绍了 AFT 以及 AFT-Local 和 AFT-conv。在这里，我们实现了AFT-Local，它关注自回归模型中的近距离令牌。</p>
<p><a href="https://app.labml.ai/run/6348e504c3a511eba9529daa283fb495"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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