Sparse Low-rank Adaptation of Pre-trained Language Models
Ning Ding Xingtai Lv Qiaosen Wang Yulin Chen Bowen Zhou Zhiyuan Liu Maosong Sun
Fine-tuning pre-trained large language models in a parameter-efficient manner
is widely studied for its effectiveness and efficiency. The popular method of
low-rank adaptation (LoRA) offers a notable approach, hypothesizing that the
adaptation process is intrinsically low-dimensional. Although LoRA has
demonstrated commendable performance, it is implemented with a fixed and
unalterable intrinsic rank that might not always be the ideal choice.
Recognizing the need for more flexible adaptation, we extend the methodology of
LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that
enables dynamic adjustments to the intrinsic rank during the adaptation
process. We achieve this through the incorporation of a gate unit optimized
with proximal gradient method in the training stage, controlling the
cardinality of rank under the sparsity of the gate. In the subsequent inference
stage, we eliminate the parameter blocks corresponding to the zeroed-out ranks,
to reduce each SoRA module back to a concise yet rank-optimal LoRA. Our
approach strengthens the representation power of LoRA by initializing it with a
higher rank, while efficiently taming a temporarily increased number of
parameters via updating in a sparse way. We further introduce a sparsifying
scheduler for SoRA, aiming to examine the impact of the number of non-zero
parameters on the model's memorization and generalization. Our experimental
results demonstrate that SoRA can outperform other baselines even with 70%
retained parameters and 70% training time.