Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models

arXiv:2403.09635v1 Announce Type: cross
Abstract: In spite of their huge success, transformer models remain difficult to scale in depth. In this work, we develop a unified signal propagation theory and provide formulae that govern the moments of the forward and backward signal through the transformer model. Our framework can be used to understand and mitigate vanishing/exploding gradients, rank collapse, and instability associated with high attention scores. We also propose DeepScaleLM, an initialization and scaling scheme that conserves unit output/gradient moments …

abstract arxiv cs.ai cs.cl cs.cv cs.lg framework language language models moments propagation scale signal success theory through transformer transformer model transformer models transformers type work