  Recently, DeepNorm scales Transformers into extremely deep (i.e., 1000
layers) and reveals the promising potential of deep scaling. To stabilize the
training of deep models, DeepNorm (Wang et al., 2022) attempts to constrain the
model update to a constant value. Although applying such a constraint can
benefit the early stage of model training, it may lead to undertrained models
during the whole training procedure. In this paper, we propose BranchNorm,
which dynamically rescales the non-residual branch of Transformer in accordance
with the training period. BranchNorm not only theoretically stabilizes the
training with smooth gradient norms at the early stage, but also encourages
better convergence in the subsequent training stage. Experiment results on
multiple translation tasks demonstrate that BranchNorm achieves a better
trade-off between training stability and converge performance.
