sramshetty/mixture-of-depths

An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"

mixture-of-depths's Issues

compute about attention

It seems that in your code all the token in a sequence are put into the transformer (attention and ffn) block?

Qs on inference

Here, for auto regressive inference, the input seq length should be 1. It seems every token will be routed to attention and mlp since it will always be chosen as topk token weights. That's confusing.

Normalize topk_weight

Hi
First of all thanks for your implementation!
For the selected tokens you multiply the topk_weight by the output of the transformer block (here)

I think without any normalization, this multiplication can cause the model to give too much high value to the hidden_state and put nan after a rms_norm layer.

In this implementation they use softmax to normalize the topk_weight but they say also that this softmax break causality they mention also the auxiliary router.

I'm a bit confused about this normalization and this auxiliary router.

Thank you for your time!

Is the implementation wrong?

sorted_indices = torch.argsort(topk_indices)

should be

sorted_indices = torch.sort(topk_indices)

Right?