Training-Free Long-Context Scaling of Large Language Models
Chenxin An Fei Huang Jun Zhang Shansan Gong Xipeng Qiu Chang Zhou Lingpeng Kong
The ability of Large Language Models (LLMs) to process and generate coherent
text is markedly weakened when the number of input tokens exceeds their
pretraining length. Given the expensive overhead of finetuning large-scale
models with longer sequences, we propose Dual Chunk Attention (DCA), which
enables Llama2 70B to support context windows of more than 100k tokens without
continual training. By decomposing the attention computation for long sequences
into chunk-based modules, DCA manages to effectively capture the relative
positional information of tokens within the same chunk (Intra-Chunk) and across
distinct chunks (Inter-Chunk), as well as integrates seamlessly with Flash
Attention. In addition to its impressive extrapolation capability, DCA achieves
performance on practical long-context tasks that is comparable to or even
better than that of finetuned models. When compared with proprietary models,
our training-free 70B model attains 94% of the performance of gpt-3.5-16k,
indicating it is a viable open-source alternative. All code and data used in
this work are released at \url{https://github.com/HKUNLP/ChunkLlama}.