Scaled dot-product attention

$\operatorname{Attention}(Q,K,V) = \operatorname{Softmax}\!\left(\frac{QK^\top}{\sqrt{d_k}} + M\right)V$

Read it step by step, with $N$ tokens:

1. $QK^\top$ is an $N \times N$ table: how well each token's query matches each token's key. (In cross-attention the keys come from a different sequence of length $M$, and the table is $N \times M$.)
2. Divide by $\sqrt{d_k}$ so the scores stay in a range where softmax still responds to differences.
3. Add the mask $M$ to block positions a token must not see.
4. Softmax each row, so each token's weights add up to 1.
5. Multiply by $V$: each token gets a weighted blend of the value vectors.