# ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs

**Authors:** Yujia Zhai, Chengquan Jiang, Leyuan Wang, Xiaoying Jia, Shang Zhang, Zizhong Chen, Xin Liu, Yibo Zhu  
**arXiv:** [2210.03052](https://arxiv.org/abs/2210.03052)  
**Date:** 2022-10-06  
**Summary page:** https://www.summarizepaper.com/en/paper/2210.03052v1/

## Key points

- ByteTransformer is a high-performance transformer model for variable-length inputs in NLP
- It addresses challenges of existing transformer models with large parameter space and computational overhead
- Proposes a zero padding algorithm to eliminate redundant computations on useless padded tokens
- Presents architectural-aware optimizations for the functioning modules of the transformer, particularly MHA algorithm
- Experimental results show ByteTransformer outperforms existing Transformer frameworks by up to 138%
- Paper is organized into sections: background information, systematic optimization approach, evaluation results, conclusion and future work
- ByteTransformer offers significant improvements in performance and efficiency for variable-length inputs in NLP tasks compared to existing Transformer frameworks.

## Summary

. The paper introduces ByteTransformer, a high-performance transformer model designed for variable-length inputs in Natural Language Processing (NLP). It addresses the challenges of existing transformer models that require a large parameter space and computational overhead when dealing with variable-length sequences by proposing a zero padding algorithm that eliminates redundant computations on useless padded tokens. Additionally, the paper presents architectural-aware optimizations for the functioning modules of the transformer, particularly the multi-head attention (MHA) algorithm. Experimental results demonstrate that ByteTransformer outperforms existing Transformer frameworks such as PyTorch JIT, TensorFlow XLA, Tencent TurboTransformer, and NVIDIA FasterTransformer by up to 138%. The rest of the paper is organized as follows: Section II provides background information on Transformer models and MHA, as well as related works on DL framework acceleration. Section III details the systematic optimization approach employed in ByteTransformer. Evaluation results are presented in Section IV. Finally, Section V concludes the paper and discusses future work. In summary, ByteTransformer offers significant improvements in performance and efficiency for variable-length inputs in NLP tasks compared to existing Transformer frameworks.