152. Prefix-Tuning Attention

In prefix tuning we prepend learned key/value vectors to the attention's K and V (but not Q). The base model stays frozen; only the prefix tensors train.

Signature: def prefix_tuning_attention(Q: np.ndarray, K: np.ndarray, V: np.ndarray, K_prefix: np.ndarray, V_prefix: np.ndarray) -> np.ndarray

Shapes (single head):

• Q: (T_q, d_k)
• K, V: (T_kv, d_k)
• K_prefix, V_prefix: (P, d_k) — P learned prefix slots

Returns: scaled dot-product attention of Q against the augmented keys/values concat([K_prefix, K]) and concat([V_prefix, V]). Output shape (T_q, d_k).