0
$\begingroup$

I am hand-coding a transformer (https://arxiv.org/pdf/1706.03762.pdf) based primarily on the instructions I found at this blog: http://jalammar.github.io/illustrated-transformer/.

The first attention block takes matrix input of the shape [words, input dimension] and multiplies by the attention weight matrices of shape [input dimension, model dimension]. The model dimension is chosen to be less than the input dimension and is the dimension used as output in all subsequent steps.

There is a residual connection around the attention block and the input is meant to be added to the output of the attention block. However the output of the attention block is shape [words, model dimension] and the input is form [words, input dimension]. Should I interpolate the input down to the model dimension as is done in ResNet? Or maybe add another weight matrix to transform the input?

enter image description here

$\endgroup$
0
$\begingroup$

The input dimensionality is the embedding size, which is the same as the model dimensionality, as explained in section 3.4 of the article:

3.4 Embeddings and Softmax

Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $d_{model}$.

Therefore, the input dimensionality and the model dimensionality are the same, which makes them suitable for the residual connection.

$\endgroup$
2
  • $\begingroup$ Thank you for pointing this out. Indeed input dim = model dim. The concept I was missing was that the attention matrices can have a smaller dimension than the model dim. However, after they are concatenated they are multiplied by a matrix to have the original model dim again. $\endgroup$
    – mkohler
    Jan 26 at 22:26
  • $\begingroup$ I suggest to take a look to this answer for details on that matter. $\endgroup$
    – noe
    Jan 26 at 23:34

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.