MLG 033 Transformers

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Show notes: https://ocdevel.com/mlg/33

3Blue1Brown videos: https://3blue1brown.com/

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• 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.