I'm going to train some Mamba variants from scratch on different tasks with both discrete and continuous input, but I'm not sure how the masking mechanism is implemented here (compared to transformer attention masking)

e.g. we want the model not to take into account some tokens, because depending on the number of padding tokens inference would give different results as I understand.