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<h1><a href="https://nn.labml.ai/transformers/xl/index.html">变压器 XL</a></h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a> 中的 T <a href="https://papers.labml.ai/paper/1901.02860">ransformer-XL：超越固定长度上下文的细心语言模型</a>的实现。</p>
<p>Transformer 的注意力跨度有限，等于并行训练的序列的长度。所有这些位置都有固定的位置编码。Transformer XL 通过让每个位置关注预先计算的过去嵌入来增加这个注意力跨度。例如，如果上下文长度为<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" style="margin-right:0.01968em;">l</span></span></span></span></span>，它将保留上一批长<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" style="margin-right:0.01968em;">l</span></span></span></span></span>度的所有图层的嵌入，并将它们馈送到当前步骤。如果我们使用固定位置编码，这些预先计算的嵌入将具有与当前上下文相同的位置。它们引入了相对位置编码，其中位置编码是在注意力计算时引入的。</p>
<p>相对多头关注的带注释的实现正在进行中<a href="https://nn.labml.ai/transformers/xl/relative_mha.html"><code class="highlight"><span></span><span class="n">relative_mha</span><span class="o">.</span><span class="n">py</span></code>
</a>。</p>
<p><a href="https://nn.labml.ai/transformers/xl/experiment.html">以下是在 Tiny Shakespeare 数据集中训练变压器 XL 模型的训练代码</a>和笔记本。</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/xl/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a><a href="https://app.labml.ai/run/d3b6760c692e11ebb6a70242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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