What is the role of Layer Normalization in GPT models?

Layer Normalization (LN) is a vital component in the structure of GPT models, especially when controlling gradient scales during training. Let’s explore how Layer Normalization operates within these models and why it’s critical.

Understanding Layer Normalization

Layer Normalization(LN) is a technique employed in deep learning models to standardize the inputs across the mini-batch for each layer. It calculates the mean and standard deviation of all feature dimensions for each sample independently, which are usually determined by the token embedding size. This method assists in stabilizing the learning process and reduces the required number of training steps.

Layer Normalization in GPT models

In GPT models, Layer Normalization is applied before the self-attention and feedforward blocks. This positioning of LN is key to managing the gradient scales, supporting the training process. The normalization process ensures that the feature values within each token possess a mean of 0 and a standard deviation of 1.

Why use Layer Normalization?

The normalization process aids in managing the scale of the gradients, which can substantially impact the training process. The gradients could become too large without normalization, leading to unstable training. By normalizing the gradients, the model can learn more effectively and efficiently.

Furthermore, Layer Normalization helps to preserve the relative order of the spikes or values of various tokens, even though it alters the magnitude of these spikes. This characteristic has been demonstrated to enhance the model’s training time and performance.

Layer Normalization in pre-LN and post-LN transformers

The positioning of Layer Normalization within the transformer architecture can lead to different versions of transformer models. The original post-LN transformer places Layer Normalization between the residual blocks, which can result in large expected gradients near the output layer. A learning rate warm-up stage is often utilized to handle these large gradients.

Conversely, the pre-LN transformer situates Layer Normalization inside the residual blocks. This positioning results in well-behaved gradients at initialization, eliminating the need for a warm-up stage. Furthermore, the pre-LN Transformer can be trained much faster than the post-LN Transformer using the same maximum learning rate.

Note: The GPT models use pre-LN, which applies Layer Normalization before self-attention and feed-forward blocks.

Conclusion

Layer Normalization plays a pivotal role in the structure of GPT models. It helps control the gradients’ scale, stabilizes the learning process, and boosts the model’s performance. Placing Layer Normalization within the transformer architecture can also lead to different versions of transformer models, each with advantages and trade-offs.