Lecture on Transformers and Attention Mechanism

Introduction

• Transformers are crucial in large language models and modern AI.
• Introduced in the 2017 paper "Attention is All You Need."
• Focus: Understanding the attention mechanism and its impact on data processing.

Recap and Context

• The goal is to predict the next word in a text sequence.
• Text is broken into tokens (often words).
• Each token is associated with a high-dimensional vector called an embedding.
• Directions in embedding space can represent semantic meanings.

Attention Mechanism

• Attention helps refine embeddings to include contextual meaning.
• Example: Word "mole" has different meanings based on context.
• Initial embeddings are context-free; attention adjusts embeddings based on context.

Detailed Explanation

Tokens and Embeddings

• Tokens are initially embedded with no contextual information.
• Embeddings encode both the word and its position in the text.

Attention Heads

• Single head of attention involves queries, keys, and values.
• Queries identify relevant context; keys match queries; values update embeddings.
• Compute dot products between keys and queries to measure relevance.
• Softmax function normalizes these scores.
• Attention pattern determines how words are updated.

Computations

• Use matrix vector products for efficient computation.
• Query (Q), Key (K), and Value (V) matrices are full of tunable parameters.
• Masking prevents future tokens from influencing past ones during training.
• Multi-headed attention involves running many attention processes in parallel.

Multi-Headed Attention

• Allows capturing various ways context can alter meaning.
• Each head has distinct query, key, and value matrices.
• GPT-3 uses 96 attention heads per block.

Parameter Count

• Large number of parameters are dedicated to attention in models like GPT-3.

Additional Concepts

Cross-Attention

• Cross-attention uses different datasets for keys and queries.

Practical Implementation

• Value matrices are often factored into two smaller matrices for efficiency.
• "Value down" and "Value up" help with mapping embeddings.

Transformer's Flow

• Data flows through multiple attention blocks and multilayer perceptrons.
• The aim is to capture higher-level and abstract meanings.

Conclusion

• Attention mechanisms are crucial for context understanding.
• The parallelizable nature of attention aids in efficient computation.
• Further learning resources by Andrej Karpathy, Chris Ola, and others.