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<h1><a href="https://nn.labml.ai/transformers/feedback/index.html">反馈变压器</a></h1>
<p>这是一篇论文《使用<a href="https://papers.labml.ai/paper/2002.09402">反馈存储器访问顺序变压器中的更高层次表示</a>》的 <a href="https://pytorch.org">PyTorch</a> 实现。</p>
<p>普通变压器并行处理令牌。每个变压器层都关注前一层的输出。反馈变压器注意前面步骤中所有层的输出。所以这增加了重复性，我们需要逐个令牌处理。这会大大减慢训练速度（根据序列长度约为5倍至10倍）。但是，预测反馈转换器时速度更快，因为如果缓存内存向量，则可以预测下一个标记。</p>
<p>为了加快训练速度，本文讨论了从短序列长度开始，然后逐渐增加序列长度。他们还讨论了使用预先训练的并联变压器作为起点。</p>
<p>原来的反馈变压器不会保留所有层的输出。相反，它保留所有图层输出的加权总和。这会减少预测期间用于缓存的内存。这个文件的前半部分实现了这个。</p>
<p>更新后的反馈转换器共享用于计算层间键和值的权重。然后，我们只计算每个步骤的键和值一次，并将其保持缓存。这个文件的后<a href="#shared_kv">半</a>部分实现了这个。我们实现了一个自定义 PyTorch 函数来提高性能。</p>
<p>这里是<a href="experiment.html">训练代码</a>和一本笔记本，用于在 Tiny Shakespeare 数据集中训练反馈转换器。</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/feedback/experiment.ipynb">Colab 笔记本</a></p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/transformers/feedback/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/d8eb9416530a11eb8fb50242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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