How does layer normalization contribute to the stability and performance of deep Transformer networks?

Layer normalization contributes to the stability and performance of deep Transformer networks by normalizing the activations within each layer. This helps to address the problem of internal covariate shift, which is the change in the distribution of network activations as the parameters of the network change during training. Internal covariate shift can slow down training and make it difficult for the network to converge. Layer normalization normalizes the activations across the features for each sample in a batch. Specifically, for each layer, it calculates the mean and variance of the activations for each sample and then normalizes the activations by subtracting the mean and dividing by the standard deviation. This ensures that the activations have a consistent distribution across all layers, which helps to stabilize training and to prevent the gradients from vanishing or exploding. Additionally, layer normalization introduces two learnable parameters, a scale parameter and a shift parameter, that allow the network to learn the optimal scale and shift for the normalized activations. This allows the network to adapt to the specific characteristics of the data and to improve performance. By reducing internal covariate shift and allowing the network to learn the optimal scale and shift for the activations, layer normalization helps to stabilize training, accelerate convergence, and improve the generalization performance of deep Transformer networks. In essence, it makes the training process more robust and less sensitive to the initialization of the network and the choice of hyperparameters.