LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
Yiran Ding Li Lyna Zhang Chengruidong Zhang Yuanyuan Xu Ning Shang Jiahang Xu Fan Yang Mao Yang
Large context window is a desirable feature in large language models (LLMs).
However, due to high fine-tuning costs, scarcity of long texts, and
catastrophic values introduced by new token positions, current extended context
windows are limited to around 128k tokens. This paper introduces LongRoPE that,
for the first time, extends the context window of pre-trained LLMs to an
impressive 2048k tokens, with up to only 1k fine-tuning steps at within 256k
training lengths, while maintaining performance at the original short context
window. This is achieved by three key innovations: (i) we identify and exploit
two forms of non-uniformities in positional interpolation through an efficient
search, providing a better initialization for fine-tuning and enabling an 8x
extension in non-fine-tuning scenarios; (ii) we introduce a progressive
extension strategy that first fine-tunes a 256k length LLM and then conducts a
second positional interpolation on the fine-tuned extended LLM to achieve a
2048k context window; (iii) we readjust LongRoPE on 8k length to recover the
short context window performance. Extensive experiments on LLaMA2 and Mistral
across various tasks demonstrate the effectiveness of our method. Models
extended via LongRoPE retain the original architecture with minor modifications
to the positional embedding, and can reuse most pre-existing optimizations.