Patching Batch Norm

Created On: Jan 03, 2023 | Last Updated On: Jun 11, 2025

What’s happening?

Batch Norm requires in-place updates to running_mean and running_var of the same size as the input. Functorch does not support inplace update to a regular tensor that takes in a batched tensor (i.e. regular.add_(batched) is not allowed). So when vmapping over a batch of inputs to a single module, we end up with this error

How to fix

One of the best supported ways is to switch BatchNorm for GroupNorm. Options 1 and 2 support this

All of these options assume that you don’t need running stats. If you’re using a module this means that it’s assumed you won’t use batch norm in evaluation mode. If you have a use case that involves running batch norm with vmap in evaluation mode, please file an issue

Option 1: Change the BatchNorm

If you want to change for GroupNorm, anywhere that you have BatchNorm, replace it with:

BatchNorm2d(C, G, track_running_stats=False)

Here C is the same C as in the original BatchNorm. G is the number of groups to break C into. As such, C % G == 0 and as a fallback, you can set C == G, meaning each channel will be treated separately.

If you must use BatchNorm and you’ve built the module yourself, you can change the module to not use running stats. In other words, anywhere that there’s a BatchNorm module, set the track_running_stats flag to be False

BatchNorm2d(64, track_running_stats=False)

Option 2: torchvision parameter

Some torchvision models, like resnet and regnet, can take in a norm_layer parameter. These are often defaulted to be BatchNorm2d if they’ve been defaulted.

Instead you can set it to be GroupNorm.

import torchvision
from functools import partial
torchvision.models.resnet18(norm_layer=lambda c: GroupNorm(num_groups=g, c))

Here, once again, c % g == 0 so as a fallback, set g = c.

If you are attached to BatchNorm, be sure to use a version that doesn’t use running stats

import torchvision
from functools import partial
torchvision.models.resnet18(norm_layer=partial(BatchNorm2d, track_running_stats=False))

Option 3: functorch’s patching

functorch has added some functionality to allow for quick, in-place patching of the module to not use running stats. Changing the norm layer is more fragile, so we have not offered that. If you have a net where you want the BatchNorm to not use running stats, you can run replace_all_batch_norm_modules_ to update the module in-place to not use running stats

from torch.func import replace_all_batch_norm_modules_
replace_all_batch_norm_modules_(net)

Option 4: eval mode

When run under eval mode, the running_mean and running_var will not be updated. Therefore, vmap can support this mode

model.eval()
vmap(model)(x)
model.train()