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arXiv · 2401.16367

TQCompressor: improving tensor decomposition methods in neural networks via permutations

V. Abronin·A. Naumov·D. Mazur·D. Bystrov·K. Tsarova·Ar. Melnikov·I. Oseledets·S. Dolgov·R. Brasher·M. Perelshtein
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Abstract

We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage demands of pre-trained language models in NLP tasks and propose a permutation-based enhancement to Kronecker decomposition. This enhancement makes it possible to reduce loss in model expressivity which is usually associated with factorization. We demonstrate this method applied to the GPT-2$_{small}$. The result of the compression is TQCompressedGPT-2 model, featuring 81 mln. parameters compared to 124 mln. in the GPT-2$_{small}$. We make TQCompressedGPT-2 publicly available. We further enhance the performance of the TQCompressedGPT-2 through a training strategy involving multi-step knowledge distillation, using only a 3.1% of the OpenWebText. TQCompressedGPT-2 surpasses DistilGPT-2 and KnGPT-2 in comparative evaluations, marking an advancement in the efficient and effective deployment of models in resource-constrained environments.

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V. Abronin, A. Naumov, D. Mazur, D. Bystrov, K. Tsarova, Ar. Melnikov, I. Oseledets, S. Dolgov, R. Brasher, M. Perelshtein. 2024-01-29. TQCompressor: improving tensor decomposition methods in neural networks via permutations. https://arxiv.org/abs/2401.16367

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