Whole net performance at comma, when we switch from BatchNorm to GroupNorm it adds 70ms to the training step time, and it's -70ms for no norm. We also wrote a custom AllNorm that's like 10% slower than BatchNorm (and I put several hours into trying to optimize it). Obviously not indicative of everyone's experience, but my point is BatchNorm is hyperoptimized and others, which are pretty much the same thing, aren't.
Thanks, that's certainly helpful anecdotal evidence.. yeah it seems like there should be an "AllNorm" implementation that covers all cases and is just fast. I was wondering because I'm currently looking at math_group_norm, which was ported from PyTorch/XLA and it results in a really weird decomposition that I'm astonished works at all. https://github.com/pytorch/pytorch/blob/master/aten/src/ATen...
I'm also wondering if the handcoded backward passes are actually "numerically correct", because e.g. epsilon doesn't appear in it at all. Someone worked out the gradients manually for BN here: https://web.archive.org/web/20180826123459/http://cthorey.gi...
You can clearly see epsilon appearing in the output. And of course there's the whole training vs. eval mode thing with BN which GN doesn't have.
