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<h1><a href="https://nn.labml.ai/transformers/xl/index.html">Transformer XL</a></h1>
<p>This is an implementation of <a href="https://papers.labml.ai/paper/1901.02860">Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context</a> in <a href="https://pytorch.org">PyTorch</a>.</p>
<p>Transformer has a limited attention span, equal to the length of the sequence trained in parallel. All these positions have a fixed positional encoding. Transformer XL increases this attention span by letting each of the positions pay attention to precalculated past embeddings. For instance if the context length is <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>, it will keep the embeddings of all layers for previous batch of length <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> and feed them to current step. If we use fixed-positional encodings these pre-calculated embeddings will have the same positions as the current context. They introduce relative positional encoding, where the positional encodings are introduced at the attention calculation.</p>
<p>Annotated implementation of relative multi-headed attention is in <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>Here's <a href="https://nn.labml.ai/transformers/xl/experiment.html">the training code</a> and a notebook for training a transformer XL model on Tiny Shakespeare dataset.</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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