I got the following error with this code:
# START CODE HERE
# calculate self-attention using mha(~1 line)
attn_output = self.mha(x) # Self attention (batch_size, input_seq_len, embedding_dim)
TypeError: call() missing 1 required positional argument: ‘value’
What is the positional argument?
Hi yiwu,
Just go to the documentation of the MultiHeadAttention layer and pay attention to the example and how many arguments are passed 
Good luck
Rosa
Besides the call function has a ‘mask’ parameter that you need to use somewhere… well the place to use it is exactly in that layer. Check the parameters of the MultiHeadAttention to find where to use it.
Thank you for your reply.
It’s very helpful!!
I am also having a hard time with this exercise. Passing mask as an argument (attn_output = self.mha(x, mask)) gives me the following error
“InvalidArgumentError: cannot compute Einsum as input #1(zero-based) was expected to be a int64 tensor but is a float tensor [Op:Einsum]”
Reading the documentation I see that we need to pass two parameters to MultiHeadAttention. Number of heads I understand should be 3. One for each Q, V, and K. Then key dimension is the dimension of the K? Just passing integers into this function also gives an error. I am very confused now on how to use this function.
self.mha(…) requires four parameters.
The Q, V, and K parameters, and the mask.
All three of Q, V, and K are the ‘x’ variable, since this is self-attention.
Thank you @TMosh. This helped!