Understanding and Coding Self-Attention, Multi-Head Attention, Causal Attention, and Cross-Attention in LLMs

This article provides a comprehensive guide to understanding and implementing self-attention mechanisms used in transformer architectures and large language models (LLMs) like GPT-4 and Llama. It covers self-attention, multi-head attention, causal attention, and cross-attention, with hands-on coding examples in Python and PyTorch. The tutorial is designed for readers with basic knowledge of LLMs and attention mechanisms, focusing on building these components from scratch to deepen understanding. It includes code walkthroughs and explanations of how these mechanisms fit into the broader context of LLM development.