Hi, I wan to do attention calculation. For my task, from priori , I can block lots of query-key pair. (like Figure 1 and Figure 2) In Figure 1,2 , I paint gray for blocked query-key pair.
Figure 1. example of query-key pair tensor
Figure 2. example of query-key pair square mask
mask for attention calculation as below.
square_mask= (-1*) square_mask square_mask= inf*square_mask attention_logit += square_mask attention_prob = nn.functional.softmax(attention_logit)
I think that , computing in this way, even only a fraction of query-key pair what I should be really interested in, the amount of memory required to perform the calculation is still the same as using whole query-key pair.
As shown below(Figure 3&4), if we extract only the parts we need from the entire matrix and proceed with the calculation, I think that it can be an efficient calculation in terms of memory. How can we do this effectively?
Is there a better way other than this one?
Figure 3. extracted query-key tensor
Figure 3. extracted query-key sqaure mask