Frustratingly Simple Memory Efficiency for Pre-trained Language Models via Dynamic Embedding Pruning

• URL: http://arxiv.org/abs/2309.08708v1
• Date: Fri, 15 Sep 2023 19:00:00 GMT
• Title: Frustratingly Simple Memory Efficiency for Pre-trained Language Models via Dynamic Embedding Pruning
• Authors: Miles Williams, Nikolaos Aletras
• Abstract summary: The memory footprint of pre-trained language models (PLMs) can hinder deployment in memory-constrained settings. We propose a simple yet effective approach that leverages this finding to minimize the memory footprint of the embedding matrix. We show that this approach provides substantial reductions in memory usage across a wide range of models and tasks.
• Score: 42.652021176354644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The extensive memory footprint of pre-trained language models (PLMs) can hinder deployment in memory-constrained settings, such as cloud environments or on-device. PLMs use embedding matrices to represent extensive vocabularies, forming a large proportion of the model parameters. While previous work towards parameter-efficient PLM development has considered pruning parameters within the transformer layers, pruning the embedding matrix as part of fine-tuning or inference has yet to be explored. We first demonstrate that a significant proportion of the vocabulary remains unused in these scenarios. We then propose a simple yet effective approach that leverages this finding to minimize the memory footprint of the embedding matrix. We show that this approach provides substantial reductions in memory usage across a wide range of models and tasks. Notably, our approach maintains equivalent downstream task performance while allowing a more efficient use of compute resources.

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