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140 lines
6.1 KiB
HTML
140 lines
6.1 KiB
HTML
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<meta name="twitter:title" content="Feedback Transformer"/>
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<title>Feedback Transformer</title>
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<a href='#section-0'>#</a>
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<h1><a href="https://nn.labml.ai/transformers/feedback/index.html">Feedback Transformer</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
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<a href="https://arxiv.org/abs/2002.09402">Accessing Higher-level Representations in Sequential Transformers with Feedback Memory</a>.</p>
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<p>Normal transformers process tokens in parallel. Each transformer layer pays attention
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to the outputs of the previous layer.
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Feedback transformer pays attention to the output of all layers in previous steps.
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So this adds recurrence, and we need to process token-by-token.
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This slows down the training significantly (about 5X - 10X depending on the sequence length).
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However, when predicting Feedback Transformer is faster because you can predict the next token
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if you cache the memory vectors.</p>
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<p>In order to speed up the training the paper discusses starting with a short sequence length and
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gradually increasing it.
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They also discuss using a pretrained parallel transformer as the starting point.</p>
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<p>The original feedback transformer doesn’t keep the outputs of all layers.
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Instead it keeps weighted sum of the output of all layers.
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This reduces the memory used for caching during prediction.
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The first half of this file implements this.</p>
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<p>The updated feedback transformer shares weights used
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to calculate keys and values among the layers.
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We then calculate the keys and values for each step only once and keep
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them cached.
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The <a href="#shared_kv">second half</a> of this file implements this.
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We implemented a custom PyTorch function to improve performance.</p>
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<p>Here’s <a href="experiment.html">the training code</a> and a notebook for training a feedback transformer on Tiny Shakespeare dataset.</p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/feedback/experiment.ipynb">Colab Notebook</a></p>
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<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/feedback/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
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