用01序列逐步解析Transformer的五个关键组件
Introduction to Sequence Modeling with Transformers
- 逐个引入tokenization、embedding、masking等组件，观察每步能力变化
- 仅用01序列验证各组件作用，揭示其在模型中的实际功能
- 适合想搞懂Transformer底层机制的工程师阅读
理解Transformer架构及其工作原理对机器学习工程师至关重要。然而，即使具备扎实的机器学习或深度学习基础，真正掌握Transformer架构仍具挑战性。核心机制是注意力（attention），它支撑了Transformer的编码器-解码器结构。但若忽略注意力，仍有若干编程组件容易实现，却难以明确其整体作用：包括分词（tokenization）、嵌入（embedding）与反嵌入（un-embedding）、掩码（masking）、位置编码（positional encoding）和填充（padding）。本文聚焦于理解这些组件。为保持简洁，通过逐个添加组件并考察每一步后模型能做什么、不能做什么，进行渐进式分析。使用简单的0和1序列来研究每一步的运作机制。
原文摘要 · Abstract (English)
Understanding the transformer architecture and its workings is essential for machine learning (ML) engineers. However, truly understanding the transformer architecture can be demanding, even if you have a solid background in machine learning or deep learning. The main working horse is attention, which yields to the transformer encoder-decoder structure. However, putting attention aside leaves several programming components that are easy to implement but whose role for the whole is unclear. These components are 'tokenization', 'embedding' ('un-embedding'), 'masking', 'positional encoding', and 'padding'. The focus of this work is on understanding them. To keep things simple, the understanding is built incrementally by adding components one by one, and after each step investigating what is doable and what is undoable with the current model. Simple sequences of zeros (0) and ones (1) are used to study the workings of each step.
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