Transformers Explained

Links:

• Notes and resources at ocdevel.com/mlg/33
• 3Blue1Brown videos: https://3blue1brown.com/
• Try a walking desk stay healthy & sharp while you learn & code
• Try Descript audio/video editing with AI power-tools

Background & Motivation

• RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware.
• Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability.

Core Architecture

• Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization.
• Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order.

Self-Attention Mechanism

• Q, K, V Explained:
  • Query (Q): The representation of the token seeking contextual info.
  • Key (K): The representation of tokens being compared against.
  • Value (V): The information to be aggregated based on the attention scores.
• Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces.
• Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly.

Masking

• Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation.
• Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions.

Feed-Forward Networks (MLPs)

• Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored.
• Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns.

Residual Connections & Normalization

• Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients.
• Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence.

Scalability & Efficiency Considerations

• Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs.
• Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention.

Training Paradigms & Emergent Properties

• Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm.
• Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked.

Interpretability & Knowledge Distribution

• Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers.
• Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.