  Scaling sequence length has become a critical demand in the era of large
language models. However, existing methods struggle with either computational
complexity or model expressivity, rendering the maximum sequence length
restricted. To address this issue, we introduce LongNet, a Transformer variant
that can scale sequence length to more than 1 billion tokens, without
sacrificing the performance on shorter sequences. Specifically, we propose
dilated attention, which expands the attentive field exponentially as the
distance grows. LongNet has significant advantages: 1) it has a linear
computation complexity and a logarithm dependency between any two tokens in a
sequence; 2) it can be served as a distributed trainer for extremely long
sequences; 3) its dilated attention is a drop-in replacement for standard
attention, which can be seamlessly integrated with the existing
Transformer-based optimization. Experiments results demonstrate that LongNet
yields strong performance on both long-sequence modeling and general language
tasks. Our work opens up new possibilities for modeling very long sequences,
e.g., treating a whole corpus or even the entire Internet as a sequence.
