ProFormer: Towards On-Device LSH Projection Based Transformers

Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva


Abstract
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT. To surmount these challenges, we introduce ProFormer – a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices and preserve user privacy. We use LSH projection layer to dynamically generate word representations on-the-fly without embedding lookup tables leading to significant memory footprint reduction from O(V.d) to O(T), where V is the vocabulary size, d is the embedding dimension size and T is the dimension of the LSH projection representation.We also propose a local projection attention (LPA) layer, which uses self-attention to transform the input sequence of N LSH word projections into a sequence of N/K representations reducing the computations quadratically by O(Kˆ2). We evaluate ProFormer on multiple text classification tasks and observed improvements over prior state-of-the-art on-device approaches for short text classification and comparable performance for long text classification tasks. ProFormer is also competitive with other popular but highly resource-intensive approaches like BERT and even outperforms small-sized BERT variants with significant resource savings – reduces the embedding memory footprint from 92.16 MB to 1.7 KB and requires 16x less computation overhead, which is very impressive making it the fastest and smallest on-device model.
Anthology ID:
2021.eacl-main.246
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2823–2828
Language:
URL:
https://aclanthology.org/2021.eacl-main.246
DOI:
10.18653/v1/2021.eacl-main.246
Award:
 Honorable Mention for Best Short Paper
Bibkey:
Cite (ACL):
Chinnadhurai Sankar, Sujith Ravi, and Zornitsa Kozareva. 2021. ProFormer: Towards On-Device LSH Projection Based Transformers. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2823–2828, Online. Association for Computational Linguistics.
Cite (Informal):
ProFormer: Towards On-Device LSH Projection Based Transformers (Sankar et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.246.pdf
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