Self Attention vs Cross Attention by hand ✍️
Resize the matrices yourself 👉 https://byhand.ai/aMisxP

Two attention mechanisms, side by side. Both project X into queries; both compute attention via S = Kᵀ × Q and F = V × A. The only difference is the source of K and V.

Self attention uses X for everything. Q, K, and V all come from projecting X. Each X token attends to every other X token. The score matrix S is square — 128 × 128.

Cross attention uses X for queries and a second sequence E for keys and values. Each X token attends to every E token instead. The score matrix S is rectangular — 64 × 128.

Notice what's shared and what's not:

X is the same in both — same 36 × 128 input.

Q and K share the 16 dimension — that's what makes the dot product Kᵀ × Q valid in either case.

V dimensions are independent: self-attention uses 12, cross-attention uses 12. The choice doesn't depend on which mechanism you're using; it depends on what output dimension your downstream layer expects.

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