Early Transformers: A study on Efficient Training of Transformer Models through Early-Bird Lottery Tickets
<|reference_start|>Early Transformers: A study on Efficient Training of Transformer Models through Early-Bird Lottery Tickets: The training of Transformer models has revolutionized natural language processing and computer vision, but it remains a resource-intensive and time-consuming process. This paper investigates the applicability of the early-bird ticket hypothesis to optimize the training efficiency of Transformer models. We propose a methodology that combines iterative pruning, masked distance calculation, and selective retraining to identify early-bird tickets in various Transformer architectures, including ViT, Swin-T, GPT-2, and RoBERTa. Our experimental results demonstrate that early-bird tickets can be consistently found within the first few epochs of training or fine-tuning, enabling significant resource optimization without compromising performance. The pruned models obtained from early-bird tickets achieve comparable or even superior accuracy to their unpruned counterparts while substantially reducing memory usage. Furthermore, our comparative analysis highlights the generalizability of the early-bird ticket phenomenon across different Transformer models and tasks. This research contributes to the development of efficient training strategies for Transformer models, making them more accessible and resource-friendly. By leveraging early-bird tickets, practitioners can accelerate the progress of natural language processing and computer vision applications while reducing the computational burden associated with training Transformer models.<|reference_end|>
@article{cheekati2024early,
    title={Early Transformers: A study on Efficient Training of Transformer Models
  through Early-Bird Lottery Tickets},
    author={Shravan Cheekati},
    journal={arXiv preprint arXiv:2405.02353},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02353},
    primaryClass={cs.CL cs.LG}
}