Rank and run-time aware compression of NLP Applications

• URL: http://arxiv.org/abs/2010.03193v1
• Date: Tue, 6 Oct 2020 16:03:15 GMT
• Title: Rank and run-time aware compression of NLP Applications
• Authors: Urmish Thakker, Jesse Beu, Dibakar Gope, Ganesh Dasika, Matthew Mattina
• Abstract summary: This paper proposes a new compression technique called Hybrid Matrix Factorization. It improves low-rank matrix factorization techniques by doubling the rank of the matrix. It can achieve more than 2.32x faster inference run-time than pruning and 16.77% better accuracy than LMF.
• Score: 12.965657113072325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while still having run-time constraints. As a result, there is a need for a compression technique that can achieve significant compression without negatively impacting inference run-time and task accuracy. This paper proposes a new compression technique called Hybrid Matrix Factorization that achieves this dual objective. HMF improves low-rank matrix factorization (LMF) techniques by doubling the rank of the matrix using an intelligent hybrid-structure leading to better accuracy than LMF. Further, by preserving dense matrices, it leads to faster inference run-time than pruning or structure matrix based compression technique. We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection, Language Modeling) and show that for similar accuracy values and compression factors, HMF can achieve more than 2.32x faster inference run-time than pruning and 16.77% better accuracy than LMF.

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