2.3 Numerical Stability
The key stability challenge is to avoid numerical instability and divergence at large scale. We empirically observed in our experiments that the training instability gets severe and hard to deal with when we scale up model sizes, data, batch sizes, learning rate, etc. Vision Transformers particularly face training instability even at a lower parameter threshold. E.g., we find it challenging to train even ViT-H (with just 630M parameters) in mixed-precision mode without using strong data augmentation. We need to study the model properties and training recipes to make sure that the models train stably and converge.
3. Our Solutions
Figure 1 depicts our solutions to each of the challenges.



3.1 Addressing scaling challenges with data parallelism and model parallelism