What methods are used to implement layer normalization in transformer architectures for stability

Question

Can you name the methods which are used to implement layer normalization in transformer architecture for stability?

Answer 1

The methods that are used to implement layer normalization in transformer architectures for stability are as follows: 

• Normalize Activations: Compute mean and variance across features, then scale and shift.
• Apply Learnable Parameters: Use learnable scale (γ\gammaγ) and shift (β\betaβ).

Here is the code snippet you can refer to:

The above is used to stabilize training dynamics, speed up convergence, and is applied after self-attention and feedforward sub-layers in Transformers.

Hence, by referring to the code above, you can implement layer normalization in transformer architectures for stability.