Improving Transformer Models by Reordering their Sublayers – Ofir Press

The transformer layer is currently the primary component in natural language processing, playing a leading role in recent innovations such as BERT and GPT-3. Each transformer layer consists of a self-attention sublayer (s) followed by a feedforward sublayer (f), creating an interleaving pattern of self-attention and feedforward sublayers throughout a multilayer transformer model.

Improving Transformer Models by Reordering their Sublayers The transformer layer is currently the primary component in natural language processing, playing a leading role in recent innovations such as BERT and GPT-3. Each transformer layer consists of a self-attention sublayer (s) followed by a feedforward sublayer (f), creating an interleaving pattern of self-attention and feedforward sublayers throughout a multilayer transformer model. Is this interleaved pattern the best way to order these sublayers? In this post, I’ll explain how we recently found a better way to order these sublayers. Tha

related reading

• A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• transformer_attention.pdfarxiv.org
• Transformers from Scratche2eml.school
• The Annotated Transformernlp.seas.harvard.edu
• 1706.03762arxiv.org
• Transformer (deep learning) - Wikipediaen.wikipedia.org
• The Annotated Transformernlp.seas.harvard.edu
• Transformer Explainer: LLM Transformer Model Visually Explainedpoloclub.github.io
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• Everything About Transformerskrupadave.com
• Transformer (deep learning) - Wikipediaen.wikipedia.org