Why would evaluation be faster with more narrow layers? If there were fewer tokens it would definitely be faster, because transformers scale by tokens^2, but here "narrow" means number of channels, for presumably the same number of tokens.
If I remember correctly the fully connected layers after the attention block are [?, a*h] * [a*h, b*h] (for some scalars a,b and hidden size h), which means that transformers also scale with h^2. I don't know what fraction of the total FLOPs that section of the model takes for practical model sizes, but it would indicate that making the model narrower for the same number of params would reduce compute.
Oh yeah, I think you're right. If it's all fully-connected (and parts of a transformer are) then more thinner layers use fewer FLOPs than fewer thicker layers, for the same number of parameters. As long as the layers are wide enough to keep the GPU busy it'll run faster.
It is notable that they got only 2.5 extra BLEU while translating text to english, and 6.2 extra when translating text from english to another language.
Since the network will have seen far more english text than text of other languages, it suggests that performance on limited training data is more improved.
Actually no. Each layer requires output from previous one, which means sequentially computation. While wider layers can utilize GPU parallel computation better. This is kind of trade-off between less memory (less parameters) vs longer time.
The fact that layers themselves are narrower means that training and evaluation of the NN is also much faster.