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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- github.com/terra-quantum-public/tqcompressedgpt2OfficialIn paperpytorch★ 8
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.