Multi-Head Attention


i make a experiments:
in multi_attention the output is [batch_size,num_heads,queries,num_hiddens/num_heads]
i want to add a softmax to the output to check the weights of the output .
the vectof of first vector is [batch_size,0,queries,num_hiddens/num_heads] ,repeate the last dim num_heads, so we can have a vector [batch_size,num_heads,queries,num_hiddens]. then use the softmax weight to measure the weight of the heads.

For torch implementation, multi-head attention says it will train “ℎ independently learned linear projections.” However, the implementation for MultiHeadAttention has fixed number of parameters. Anyone can explain why is it? I think it should have ℎ times more parameters than single head attention.

The last dimension of queries is num_hidden, but the input size of w_q is query_size. What if num_hidden is not equal to query_size?