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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 <a href="https://pytorch.org">PyTorch 对</a>《使用<a href="https://papers.labml.ai/paper/2002.09402">反馈存储器访问序列变压器中的更高层次表示》一文的 PyT</a> orch 实现。</p>
<p>普通的变压器会并行处理代币。每个变压器层都注意前一层的输出。反馈变压器注意前面步骤中所有层的输出。因此，这会增加重复性，我们需要逐个代币进行处理。这会显著减慢训练速度（大约 5 到 10 倍，具体取决于序列长度）。但是，在预测反馈变换器时，速度更快，因为如果你缓存了内存向量，你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并逐渐增加序列长度的问题。他们还讨论了使用预训练的并行变压器作为起点。</p>
<p>原始反馈变压器不保留所有层的输出。相反，它保留所有图层输出的加权总和。这减少了预测期间用于缓存的内存。这个文件的前半部分实现了这一点。</p>
<p>更新后的反馈变压器在各层之间共享用于计算密钥和值的权重。然后，我们只计算每个步骤的键和值一次，并将其缓存。这个文件的<a href="#shared_kv">后半</a>部分实现了这一点。我们实现了一个自定义 PyTorch 函数来提高性能。</p>
<p>这是<a href="experiment.html">训练代码和一本</a>用于在 Tiny Shakespeare 数据集上训练反馈转换器的笔记本。</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></p>
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