The Transformer model implemented from scratch using PyTorch. The model uses weight sharing between the embedding layers and the pre-softmax linear layer. Training on the Multi30k machine translation task is shown.
Thank you for the excellent blog post and code.
Looking at the embedding scaling logic, it looks like you are first scaling the word embedding weights by 1/scale during initialisation and then you are scaling the word embeddings after lookup by scale. Is this intentional? Don't they cancel each other?
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