I think that that is just a simplification in the implementation. The blue box note below the AttnDecoderRNN alludes to the fact that this approach is more limited than others.
What happens with the overly long attention weights is that they will get multiplied by the 0s in decoder_outputs.
Having the previous state and decoder input to predict weights seems not too uncommon to me - it sounds vaguely similar to what I have seen elsewhere. (I like to advocate Graves’ Handwriting RNN 14 for a gentle first contact with attention.)
I don’t think the computational complexity is the main limitation here.
Best regards
Thomas