MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway
  Dynamic Dense Connections
Authors: name: Da Xiao, name: Qingye Meng, name: Shengping Li, name: Xingyuan Yuan

We propose MUltiway Dynamic Dense (MUDD) connections, a simple yet effective
method to address the limitations of residual connections and enhance
cross-layer information flow in Transformers. Unlike existing dense connection
approaches with static and shared connection weights, MUDD generates connection
weights dynamically depending on hidden states at each sequence position and
for each decoupled input stream (the query, key, value or residual) of a
Transformer block. MUDD connections can be seamlessly integrated into any
Transformer architecture to create MUDDFormer. Extensive experiments show that
MUDDFormer significantly outperforms Transformers across various model
architectures and scales in language modeling, achieving the performance of
Transformers trained with 1.8X-2.4X compute. Notably, MUDDPythia-2.8B matches
Pythia-6.9B in pretraining ppl and downstream tasks and even rivals Pythia-12B
in five-shot settings, while adding only 0.23% parameters and 0.4% computation.
Code in JAX and PyTorch and pre-trained models are available at
https://github.com/Caiyun-AI/MUDDFormer .