Qkv Projections

Why split V from K instead of just reusing K as the value vector?

Each row of the post-softmax attention weight matrix corresponds to which side of the QK product?

Tying K and V to one projection, what is gained and what is risked?

Inside a T5 style encoder-decoder, which sub-layer reads the encoder outputs, and through which projection?

Walk through how a token's d_model embedding is split into per head pieces inside multi-head attention.

What does the dot product q . k actually measure inside an attention layer?