Easy and Efficient Transformer : Scalable Inference Solution For large NLP mode

• URL: http://arxiv.org/abs/2104.12470v1
• Date: Mon, 26 Apr 2021 11:00:56 GMT
• Title: Easy and Efficient Transformer : Scalable Inference Solution For large NLP mode
• Authors: Gongzheng li, Yadong Xi, Jingzhen Ding, Duan Wang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
• Abstract summary: This paper introduces a series of ultra-large-scale pre-training model optimization methods. An inference engine -- Easy and Efficient Transformer (EET) is proposed. EET achieves a 1.5-15x state-of-art speedup varying with context length.
• Score: 14.321889138798072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ultra-large-scale pre-training model can effectively improve the effect of a variety of tasks, and it also brings a heavy computational burden to inference. This paper introduces a series of ultra-large-scale pre-training model optimization methods that combine algorithm characteristics and GPU processor hardware characteristics, and on this basis, propose an inference engine -- Easy and Efficient Transformer (EET), Which has a significant performance improvement over the existing schemes. We firstly introduce a pre-padding decoding mechanism that improves token parallelism for generation tasks. Then we design high optimized kernels to remove sequence masks and achieve cost-free calculation for padding tokens, as well as support long sequence and long embedding sizes. Thirdly a user-friendly inference system with an easy service pipeline was introduced which greatly reduces the difficulty of engineering deployment with high throughput. Compared to Faster Transformer's implementation for GPT-2 on A100, EET achieves a 1.5-15x state-of-art speedup varying with context length.EET is available https://github.com/NetEase-FuXi/EET.

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