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. In this work, 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 tokens; 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.

LongNet 是 Transformer 的一种变体，引入了扩张注意力（dilated attention）来扩展序列长度，使其能够处理超过 10 亿个标记的序列，同时不会牺牲短序列的性能表现。