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arXiv · 2108.06277

Towards Structured Dynamic Sparse Pre-Training of BERT

Anastasia Dietrich·Frithjof Gressmann·Douglas Orr·Ivan Chelombiev·Daniel Justus·Carlo Luschi
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Abstract

Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research. In this work, we develop and study a straightforward, dynamic always-sparse pre-training approach for BERT language modeling task, which leverages periodic compression steps based on magnitude pruning followed by random parameter re-allocation. This approach enables us to achieve Pareto improvements in terms of the number of floating-point operations (FLOPs) over statically sparse and dense models across a broad spectrum of network sizes. Furthermore, we demonstrate that training remains FLOP-efficient when using coarse-grained block sparsity, making it particularly promising for efficient execution on modern hardware accelerators.

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Anastasia Dietrich, Frithjof Gressmann, Douglas Orr, Ivan Chelombiev, Daniel Justus, Carlo Luschi. 2021-08-13. Towards Structured Dynamic Sparse Pre-Training of BERT. https://arxiv.org/abs/2108.06277

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