Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding

• URL: http://arxiv.org/abs/2206.15014v1
• Date: Thu, 30 Jun 2022 04:33:50 GMT
• Title: Compressing Pre-trained Transformers via Low-Bit NxM Sparsity for Natural Language Understanding
• Authors: Connor Holmes, Minjia Zhang, Yuxiong He, Bo Wu
• Abstract summary: Large pre-trained Transformer networks have demonstrated dramatic improvements in many natural language understanding tasks. New hardware supporting both NM semi-structured sparsity and low-precision integer computation is a promising solution to boost model serving efficiency. We propose a flexible compression framework NxMiFormer that performs simultaneous sparsification and quantization.
• Score: 20.75335227098455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, large pre-trained Transformer networks have demonstrated dramatic improvements in many natural language understanding tasks. However, the huge size of these models brings significant challenges to their fine-tuning and online deployment due to latency and cost constraints. New hardware supporting both N:M semi-structured sparsity and low-precision integer computation is a promising solution to boost DNN model serving efficiency. However, there have been very few studies that systematically investigate to what extent pre-trained Transformer networks benefit from the combination of these techniques, as well as how to best compress each component of the Transformer. We propose a flexible compression framework NxMiFormer that performs simultaneous sparsification and quantization using ADMM and STE-based QAT. Furthermore, we present and inexpensive, heuristic-driven search algorithm that identifies promising heterogeneous compression configurations that meet a compression ratio constraint. When evaluated across the GLUE suite of NLU benchmarks, our approach can achieve up to 93% compression of the encoders of a BERT model while retaining 98.2% of the original model accuracy and taking full advantage of the hardware's capabilities. Heterogeneous configurations found the by the search heuristic maintain 99.5% of the baseline accuracy while still compressing the model by 87.5%.

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