Unlocking Stable and Efficient Training for Long-Sequence Models: Layer-Wise Scaling
When working with transformer-based models, one of the primary challenges lies in training these models on long sequences, often exceeding 1,000 tokens. This can lead to exploding gradients, decreased stability, and a significant increase in training time. To mitigate these issues, researchers have introduced a technique called layer-wise scaling.
What is Layer-Wise Scaling?
Layer-wise scaling involves scaling the learnable parameters of the transformer model layer by layer. This approach allows the model to adapt to the increasing complexity of the input sequence, while maintaining stability and reducing the risk of exploding gradients.
How Does Layer-Wise Scaling Work?
In a standard transformer architecture, the learnable parameters are scaled uniformly across all layers. However, as the input sequence length increases, the number of tokens processed by each layer grows exponentia...
This post was originally shared as an AI/ML insight. Follow me for more expert content on artificial intelligence and machine learning.
Top comments (0)