Looks like we still miss that return at least in master. I am not sure whether some earlier changes were applied but got revert or not. Adding it in https://github.com/pytorch/pytorch/pull/42131 8

I was just starting out with DistributedDataParallel and was not sure whether it’s possible to switch modes, or one has to define the mode before using the wrapper or some other magic.

DDP’s train() and eval() should work as expected. Just please remember to wrap it with torch.no_grad() when running in eval mode.