LEMON: Lossless model expansion
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Keywords: model growth, efficient deep learning, continual learning
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TL;DR: We propose LEMON, a method that initializes large model with pretrained small model to save computational resources.
Abstract: Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the emergence of sophisticated reasoning capabilities in foundation models.
Such scaling generally requires training large models from scratch with random initialization, failing to leverage the knowledge acquired by their smaller counterparts, which are already resource-intensive to obtain.
To tackle this inefficiency, we present $\textbf{L}$ossl$\textbf{E}$ss $\textbf{MO}$del Expansio$\textbf{N}$ (LEMON), a recipe
to initialize scaled models using the weights of their smaller but pre-trained counterparts. This is followed by model training with an optimized learning rate scheduler tailored explicitly for the scaled models, substantially reducing the training time compared to training from scratch.
Notably, LEMON is versatile, ensuring compatibility with various network structures, including models like Vision Transformers and BERT.
Our empirical results demonstrate that LEMON reduces computational costs by 56.7\% for Vision Transformers and 33.2\% for BERT when compared to training from scratch.
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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 2181
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