  Gigantic pre-trained models have become central to natural language
processing (NLP), serving as the starting point for fine-tuning towards a range
of downstream tasks. However, two pain points persist for this paradigm: (a) as
the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the
fine-tuning process can be time-consuming and computationally expensive; (b)
the fine-tuned model has the same size as its starting point by default, which
is neither sensible due to its more specialized functionality, nor practical
since many fine-tuned models will be deployed in resource-constrained
environments. To address these pain points, we propose a framework for
resource- and parameter-efficient fine-tuning by leveraging the sparsity prior
in both weight updates and the final model weights. Our proposed framework,
dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two
key objectives: (i) parameter efficient fine-tuning - by enforcing
sparsity-aware low-rank updates on top of the pre-trained weights; and (ii)
resource-efficient inference - by encouraging a sparse weight structure towards
the final fine-tuned model. We leverage sparsity in these two directions by
exploiting both unstructured and structured sparse patterns in pre-trained
language models via a unified approach. Extensive experiments and in-depth
investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2)
on dozens of datasets, consistently demonstrate impressive
parameter-/inference-efficiency, while maintaining competitive downstream
performance. For instance, DSEE saves about 25% inference FLOPs while achieving
comparable performance, with 0.5% trainable parameters on BERT. Codes are
available in https://github.com/VITA-Group/DSEE.
