| 引用本文: | 何源宏,姜晶菲,许金伟.注意力机制量化剪枝优化方法.[J].国防科技大学学报,2024,46(1):113-120.[点击复制] |
| HE Yuanhong,JIANG Jingfei,XU Jinwei.Quantization and pruning optimization method for attention mechanism[J].Journal of National University of Defense Technology,2024,46(1):113-120[点击复制] |
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| 注意力机制量化剪枝优化方法 |
| 何源宏1,2,姜晶菲1,2,许金伟1,2 |
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(1. 国防科技大学 计算机学院, 湖南 长沙 410073;2. 国防科技大学 并行与分布计算全国重点实验室, 湖南 长沙 410073)
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| 摘要: |
| 面向基于注意力机制模型的巨大计算和访存开销问题,研究量化和剪枝协同优化的模型压缩技术,提出针对注意力机制中查询、键、值、概率共四个激活值矩阵的对称线性定点量化方法。同时,提出概率矩阵剪枝方法和渐进式剪枝策略,有效降低剪枝精度损失。在不同数据集上的实验结果表明,针对典型基于注意力机制模型BERT,在较低或者没有精度损失的情况下该优化方法可达到4位或8位定点量化、0.93~0.98的稀疏度,大幅度降低模型计算量,为加速量化稀疏模型的推理奠定良好的基础。 |
| 关键词: 自然语言处理 注意力机制 量化 剪枝 |
| DOI：10.11887/j.cn.202401012 |
| 投稿日期：2022-10-17 |
| 基金项目:重点实验室稳定支持重点资助项目(WDZC20215250103) |
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| Quantization and pruning optimization method for attention mechanism |
| HE Yuanhong1,2, JIANG Jingfei1,2, XU Jinwei1,2 |
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(1. College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China;2. National Key Laboratory of Paralle and Distributed Computing, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| To address the significant computation and memory overhead of models based on attention mechanism, model compression techniques, such as collaborative optimization of quantization and pruning, were studied. A symmetric linear fixed point quantization method was proposed for four activation matrices of query, key, value and probability in the attention mechanism. Meanwhile, a probability matrix pruning method and a progressive pruning strategy were proposed to effectively reduce the pruning accuracy loss. Experimental results on different datasets show that for the typical attention-based model BERT, this optimization method can achieve 4 bit or 8 bit fixed point quantization and 0.93~0.98 sparsity with little or no accuracy loss, which greatly reduces the model computation and lays a strong foundation for accelerating the inference of quantized sparse models. |
| Keywords: natural language processing attention mechanism quantization pruning |
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