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arXiv · 2310.20077

Partial Tensorized Transformers for Natural Language Processing

Subhadra Vadlamannati·Ryan Solgi
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Abstract

The transformer architecture has revolutionized Natural Language Processing (NLP) and other machine-learning tasks, due to its unprecedented accuracy. However, their extensive memory and parameter requirements often hinder their practical applications. In this work, we study the effect of tensor-train decomposition to improve the accuracy and compress transformer vision-language neural networks, namely BERT and ViT. We focus both on embedding-layer compression and partial tensorization of neural networks (PTNN) through an algorithmic approach. Our novel PTNN approach significantly improves the accuracy of existing models by up to 5%, all without the need for post-training adjustments, breaking new ground in the field of tensor decomposition.

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Subhadra Vadlamannati, Ryan Solgi. 2023-10-30. Partial Tensorized Transformers for Natural Language Processing. https://arxiv.org/abs/2310.20077

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