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Fine-tuning and inference with large Language Models (LM) are generally knownto be expensive. Parameter-efficient fine-tuning over pretrained LMs reducestraining memory by updating a small number of LM parameters but does notimprove inference efficiency. Structured pruning improves LM inferenceefficiency by removing consistent parameter blocks, yet often increasestraining memory and time. To improve both training and inference efficiency, weintroduce APT that adaptively prunes and tunes parameters for the LMs. At theearly stage of fine-tuning, APT dynamically adds salient tuning parameters forfast and accurate convergence while discarding unimportant parameters forefficiency. Compared to baselines, our experiments show that APT maintains upto 98% task performance when pruning RoBERTa and T5 models with 40% parametersleft while keeping 86.4% LLaMA models' performance with 70% parametersremained. Furthermore, APT speeds up LMs fine-tuning by up to 8x and reduceslarge LMs memory training footprint by up to 70%.

APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference