Understanding Transformer Attention Mechanisms: From Theory to PyTorch Implementation

The attention mechanism is the foundation of modern NLP. This article starts with the intuitive concept of "attention is all you need" and builds up to a complete PyTorch implementation of multi-head attention. Topics include: scaled dot-product attention, positional encodings with sinusoidal functions, masking for decoder self-attention, and the key/query/value projection matrices. We train a small transformer on a text generation task and visualize attention patterns. The code is available on GitHub and runs on a single GPU.