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<h1><a href="https://nn.labml.ai/transformers/compressive/index.html">Compressive Transformer</a></h1>
<p>This is an implementation of
<a href="https://arxiv.org/abs/1911.05507">Compressive Transformers for Long-Range Sequence Modelling</a>
in <a href="https://pytorch.org">PyTorch</a>.</p>
<p>This is an extension of <a href="https://nn.labml.ai/transformers/xl/index.html">Transformer XL</a> where past memories
are compressed to give a longer attention range.
That is, the furthest $n_{cm} c$ memories are compressed into
$n_{cm}$ memories, where $c$ is the compression rate.</p>
<h2>Compression operation</h2>
<p>The compression operation is defined as
$f_c: \mathbb{R}^{nc \times d} \rightarrow \mathbb{R}^{n \times d}$.
The paper introduces multiple choices for $f_c$ and we have only implemented
1D convolution which seems to give the best results.
Each layer has a separate compression operation $f_c^{(i)}$ where
$i$ is the layer number.</p>
<h2>Training compression operation</h2>
<p>Since training compression with BPTT requires maintaining
a very large computational graph (many time steps), the paper proposes
an <em>auto-encoding loss</em> and an <em>attention reconstruction loss</em>.
The auto-encoding loss decodes the original memories from the compressed memories
and calculates the loss.
Attention reconstruction loss computes the multi-headed attention results
on the compressed memory and on uncompressed memory and gets a mean squared error
between them.
We have implemented the latter here since it gives better results.</p>
<p>This implementation uses pre-layer normalization
while the paper uses post-layer normalization.
Pre-layer norm does the layer norm before FFN[../feedforward.html) and
self-attention, and the pass-through in the residual connection is not normalized.
This is supposed to be more stable in standard transformer setups.</p>
<p>Here are <a href="https://nn.labml.ai/transformers/compressive/experiment.html">the training code</a> and a notebook for training a compressive transformer
model on the Tiny Shakespeare dataset.</p>
<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/compressive/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/0d9b5338726c11ebb7c80242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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