2
$\begingroup$

I was reading the paper Attention Is All You Need.

It seems like the last step of the encoder is a LayerNorm(relu(WX + B) + X), i.e. an add + normalization. This should result in a $n$ x $d^{model}$ matrix, where $n$ is the length of the input to the encoder.

How do we convert this $n$ x $d^{model}$ matrix into the keys $K$ and values $V$ that are fed into the decoder's encoder-decoder attention step?

Note that, if $h$ is the number of attention heads in the model, the dimensions of $K$ and $V$ should both be $n$ x $\frac{d^{model}}{h}$. For $h=8$, this means we need a $n$ x $\frac{d^{model}}{4}$ matrix.

Do we simply add an extra linear layer that learns a $d^{model}$ x $\frac{d^{model}}{4}$ weight matrix?

Or do we use the output of the final Add & Norm layer, and simply use the first $\frac{d^{model}}{4}$ columns of the matrix and discard the rest?

$\endgroup$
3
$\begingroup$

I have read the OpenNMT source code (https://github.com/OpenNMT/OpenNMT-py/blob/cd29c1dbfb35f4a2701ff52a1bf4e5bdcf02802e/onmt/modules/multi_headed_attn.py).

It seems like an extra linear layer learns the weights $W^{key}$ and $W^{value}$ (plus biases), so to get the output (keys and values), you multiply the output of the encoder's final add + norm layer by $W^{key}$ to get the keys, and by $W^{value}$ to get the values.

Additionally, these weights and biases seem to be independent across each of the decoding layer. So you feed the same encoder output (add + norm layer output), but multiply by different $W^{key}$ and $W^{value}$ matrices and add by different biases for each of the decoding layer, resulting in different keys and values for each layer

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.