The reason why people don't always use abi3 is because not everything that can be done with the full API is even possible with the limited one, and some things that are possible carry a significant perf hit.
I think that's a reason, but I don't think it's the main one: the main one is that native builds don't generally default to abi3, so people (1) publish larger matrices than they actually need to, and (2) end up depending on non-abi3 constructs when abi3 ones are available.
(I don't know if this is the reason in Torch's case or not, but I know from experience that it's the reason for many other popular Python packages.)
Yes, you're right; I should have clarified my comment with, "people who know the difference to begin with", which is something one needs to learn first (and very few tutorials etc on Python native modules even mention the limited API).
