DiJiang: Efficient Large Language Models through Compact Kernelization
In an effort to reduce the computational load of Transformers, research on
linear attention has gained significant momentum. However, the improvement
strategies for attention mechanisms typically necessitate extensive retraining,
which is impractical for large language models with a vast array of parameters.
In this paper, we present DiJiang, a novel Frequency Domain Kernelization
approach that enables the transformation of a pre-trained vanilla Transformer
into a linear complexity model with little training costs. By employing a
weighted Quasi-Monte Carlo method for sampling, the proposed approach
theoretically offers superior approximation efficiency. To further reduce the
training computational complexity, our kernelization is based on Discrete
Cosine Transform (DCT) operations. Extensive experiments demonstrate that the
proposed method achieves comparable performance to the original Transformer,
but with significantly reduced training costs and much faster inference speeds.
Our DiJiang-7B achieves comparable performance with LLaMA2-7B on various
benchmark while requires only about 1/50 training cost. Code is available at
https://github.com/YuchuanTian/DiJiang.
660a3f8ac50f8393c512058a
Hanting Chen
660a3f8ac50f8393c512058b
Zhicheng Liu
660a3f8ac50f8393c512058c
Xutao Wang
660a3f8ac50f8393c512058d
Yuchuan Tian
660a3f8ac50f8393c512058e
Yunhe Wang
DiJiang: Efficient Large Language Models through Compact Kernelization
In an effort to reduce the computational load of Transformers, research on
linear attention has gained significant momentum. However, the improvement
strategies for attention mechanisms typically necessitate extensive retraining,
which is impractical for large language models with a vast array of parameters.
In this paper, we present DiJiang, a novel Frequency Domain Kernelization
approach that enables the transformation of a pre-trained vanilla Transformer
into a linear complexity model with little training costs. By employing a
weighted Quasi-Monte Carlo method for sampling, the proposed approach
theoretically offers superior approximation efficiency. To further reduce the
training computational complexity, our kernelization is based on Discrete
Cosine Transform (DCT) operations. Extensive experiments demonstrate that the
proposed method achieves comparable performance to the original Transformer,
but with significantly reduced training costs and much faster inference speeds.
Our DiJiang-7B achieves comparable performance with LLaMA2-7B on various
benchmark while requires only about 1/50 training cost. Code is available at
https://github.com/YuchuanTian/DiJiang.
DiJiang, a Frequency Domain Kernelization approach, reduces the computational load of pre-trained Transformers with little additional training, achieving comparable performance to vanilla Transformers with significantly lower costs and faster inference.
linear attention Quasi-Monte Carlo method Discrete Cosine Transform (DCT) Frequency Domain Kernelization DiJiang LLaMA2-7B