transformer-pytorch VS attention-is-all-you-need-pytorch

In the original paper they mention they are only using the decoder part of the model. However, their description and implementations seem to be using the encoder part of the transformer not the encoder. For example, this implementation of the original transformer encoder layer matches what the one in the GPT implementation.

I implemented the Transformer model of Google Brain using Pytorch. It was specially written together in very detailed and easy explanatory comments. If you're a beginner who wants to implement Transformer, look at my code and try it! Detailed code can be found here. (https://github.com/hyunwoongko/transformer-pytorch)

The transformer model was invented to attend to context over the entire sequence length. Look at how the original authors used the Transformer for NMT in the original Vaswani et al publication. https://github.com/jadore801120/attention-is-all-you-need-py...

I did implement an "LLM" proof of concept from scratch in a course for my masters, pretty much doing a small implementation of a transformer from the Attention is all you Need paper (plus other resources). It was useless, but was a great experience to understand how it works. There are a few implementation like this out there, like this one: https://github.com/jadore801120/attention-is-all-you-need-pytorch (first google result). I think it is a fun exercise (the amount of fun depends on how much of a masochist you are :) ).

I'm curious as to why the second matrix multiplication is not followed by an activation unlike the first one. Is there any particular reason why a non-linearity would be trivial or even avoided in the second operation? For reference, variations of this can be witnessed in a number of different implementations, including BERT-pytorch and attention-is-all-you-need-pytorch.