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arXiv · 2306.06629

GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model

Shicheng Tan·Weng Lam Tam·Yuanchun Wang·Wenwen Gong·Yang Yang·Hongyin Tang·Keqing He·Jiahao Liu·Jingang Wang·Shu Zhao·Peng Zhang·Jie Tang
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Abstract

Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world industrial-strength applications, which require the use of complex distillation methods on even larger-scale PLMs (over 10B), limited by memory on GPUs and the switching of methods. To overcome these challenges, we propose GKD, a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. With GKD, developers can build larger distillation models on memory-limited GPUs and easily switch and combine different distillation methods within a single framework. Experimental results show that GKD can support the distillation of at least 100B-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs.

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Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Yang Yang, Hongyin Tang, Keqing He, Jiahao Liu, Jingang Wang, Shu Zhao, Peng Zhang, Jie Tang. 2023-06-11. GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model. https://arxiv.org/abs/2306.06629

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