Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Figure 9.12 - Matching a query to the keys
The encoder’s hidden states are called "keys" (K), while the
decoder’s hidden state is called a "query" (Q).
"Wait a minute! I thought the encoder’s hidden states were called
"values" (V)."
You’re absolutely right. The encoder’s hidden states are used as both "keys" (K)
and "values" (V). Later on, we’ll apply affine transformations to the hidden states,
one for the "keys," another for the "values," so they will actually have different
values.
"Where do these names come from, anyway?"
Well, the general idea is that the encoder works like a key-value store, as if it were
some sort of database, and then the decoder queries it. The attention mechanism
looks the query up in its keys (the matching part) and returns its values. Honestly,
I don’t think this idea helps much, because the mechanism doesn’t return a single
original value, but rather a weighted average of all of them. But this naming
convention is used everywhere, so you need to know it.
"Why is 'the' a better match than 'zone' in this case?"
Fair enough. These are made-up values, and their sole purpose is to illustrate the
attention mechanism. If it helps, consider that sentences are more likely to start
with "the" than "zone," so the former is likely a better match to the special <start>
token.
"OK, I will play along."
Thanks! Even though we haven’t actually discussed how to match a given "query"
(Q) to the "keys" (K), we can update our diagram to include them.
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