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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://arxiv.org/abs/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 $l$, it will keep the embeddings of
all layers for previous batch of length $l$ 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="relative_mha.html"><code>relative_mha.py</code></a>.</p>
<p>Here’s <a href="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/lab-ml/nn/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://web.lab-ml.com/run?uuid=d3b6760c692e11ebb6a70242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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