Why it matters
Attention is what makes transformers work. Understanding its math is understanding the core computation of every LLM. Getting comfortable with the QKV pattern unlocks everything else.
The architecture
Given queries Q (n × d), keys K (m × d), values V (m × d_v). Similarity = Q K^T (n × m matrix of dot products). Convert to weights with softmax over each row. Output = weights × V.
Interpretation: each query attends to all keys, weighted by similarity, and reads a weighted combination of values.
How it works end to end
Self-attention: Q, K, V come from the same input. Each position attends to all positions (including itself).
Cross-attention: Q from one sequence, K and V from another. Enables encoder-decoder connection.
Masking: causal mask sets upper triangle of similarity to -infinity. Softmax makes those weights zero. Ensures autoregressive generation.