Rete di Memoria Ultra-Scarsa

Ultra-Sparse Memory Network

November 19, 2024
Autori: Zihao Huang, Qiyang Min, Hongzhi Huang, Defa Zhu, Yutao Zeng, Ran Guo, Xun Zhou
cs.AI

Abstract

È ampiamente riconosciuto che le prestazioni dei modelli Transformer sono correlati in modo esponenziale al loro numero di parametri e complessità computazionale. Mentre approcci come Mixture of Experts (MoE) separano il conteggio dei parametri dalla complessità computazionale, affrontano comunque sfide nell'inferenza a causa dei costi elevati di accesso alla memoria. Questo lavoro introduce UltraMem, che incorpora uno strato di memoria ultra-sparsa su larga scala per affrontare tali limitazioni. Il nostro approccio riduce significativamente la latenza dell'inferenza pur mantenendo le prestazioni del modello. Esaminiamo anche le leggi di scalabilità di questa nuova architettura, dimostrando che non solo presenta proprietà di scalabilità favorevoli ma supera i modelli tradizionali. Nei nostri esperimenti, addestriamo reti con fino a 20 milioni di slot di memoria. I risultati mostrano che il nostro metodo raggiunge velocità di inferenza all'avanguardia e prestazioni del modello entro un determinato budget computazionale.
English
It is widely acknowledged that the performance of Transformer models is exponentially related to their number of parameters and computational complexity. While approaches like Mixture of Experts (MoE) decouple parameter count from computational complexity, they still face challenges in inference due to high memory access costs. This work introduces UltraMem, incorporating large-scale, ultra-sparse memory layer to address these limitations. Our approach significantly reduces inference latency while maintaining model performance. We also investigate the scaling laws of this new architecture, demonstrating that it not only exhibits favorable scaling properties but outperforms traditional models. In our experiments, we train networks with up to 20 million memory slots. The results show that our method achieves state-of-the-art inference speed and model performance within a given computational budget.

Summary

AI-Generated Summary

Paper Overview

The study introduces UltraMem, a novel architecture with large-scale, ultra-sparse memory layers to enhance computational efficiency and reduce inference latency while maintaining or improving model performance. UltraMem significantly outperforms the Mixture of Experts (MoE) model in terms of memory access costs during inference. Experimental results demonstrate state-of-the-art inference speed and model performance within a given computational budget. The research investigates the scaling laws of UltraMem, showing favorable scaling properties and addressing limitations of the Product Key Memory (PKM) concept.

Core Contribution

  • Introduction of UltraMem architecture with large-scale, ultra-sparse memory layers.
  • Development of the Tucker Decomposed Query-Key Retrieval (TDQKR) approach to improve memory retrieval efficiency.
  • Proposal of an approximated top-m algorithm for efficient rank-1 approximation.
  • Implementation of Implicit Value Expansion (IVE) and Multi-Core Scoring (MCS) techniques for enhanced performance.
  • Structural improvements over the Product Key Memory (PKM) concept.

Research Context

The study positions itself within the field of memory-augmented neural networks, focusing on enhancing computational efficiency and model performance through novel memory architectures and retrieval techniques.

Keywords

UltraMem, Memory-Augmented Neural Networks, Computational Efficiency, Inference Latency, Memory Retrieval, Tucker Decomposition, Top-m Algorithm, Implicit Value Expansion, Multi-Core Scoring, Product Key Memory, Neural Network Initialization

Background

The research background of this paper lies in the need for improved computational efficiency and reduced inference latency in neural network models. The study addresses limitations of existing memory architectures like Product Key Memory (PKM) and aims to enhance model performance through innovative memory structures and retrieval mechanisms.

Research Gap

  • Lack of efficient memory retrieval techniques for large-scale memory layers.
  • Need for improved computational efficiency without compromising model performance.
  • Limitations in existing memory architectures like Product Key Memory (PKM).

Technical Challenges

  • Efficient memory access and retrieval in large-scale memory layers.
  • Balancing computational efficiency with model performance.
  • Addressing limitations of existing memory architectures.

Prior Approaches

  • Product Key Memory (PKM) concept with its associated limitations.
  • Traditional memory retrieval methods in neural networks.
  • Challenges in scaling memory-augmented models efficiently.

Methodology

The research methodology of the paper involves the development and implementation of novel memory architectures and retrieval techniques to enhance computational efficiency and model performance in neural networks.

Theoretical Foundation

  • Utilization of Tucker Decomposition for memory retrieval efficiency.
  • Implementation of approximated top-m algorithm for rank-1 approximation.
  • Integration of Implicit Value Expansion (IVE) and Multi-Core Scoring (MCS) techniques.

Technical Architecture

  • UltraMem architecture with large-scale, ultra-sparse memory layers.
  • Structural improvements over the Product Key Memory (PKM) concept.
  • Fused Lookup-Reduce Operator for faster computations.

Implementation Details

  • Singular Value Decomposition (SVD) for rank-1 approximation.
  • Asynchronous Execution Strategy for concurrent processing.
  • Megatron support for training efficiency.

Innovation Points

  • Improved memory retrieval efficiency through Tucker Decomposition.
  • Introduction of approximated top-m algorithm for rank-1 approximation.
  • Utilization of Implicit Value Expansion (IVE) and Multi-Core Scoring (MCS) techniques.
  • Structural enhancements over existing memory architectures.
  • Efficient training through Megatron support and asynchronous execution.

Experimental Validation

The experimental validation in the literature involves rigorous testing and evaluation of the proposed UltraMem architecture and techniques to demonstrate their effectiveness in improving computational efficiency and model performance.

Setup

  • Training data from RedPajama and C4 datasets.
  • Evaluation across various benchmark datasets.
  • Specific configurations and parameters for different models.

Metrics

  • Performance metrics including accuracy, perplexity, and output standard deviation.
  • Evaluation of memory access, inference speed, and computational cost.
  • Comparative analysis with the Mixture of Experts (MoE) model.

Results

  • State-of-the-art inference speed and model performance achieved by UltraMem.
  • Improved training and validation losses through various techniques.
  • Impact of different configurations on model performance and computational cost.

Comparative Analysis

  • Comparison of UltraMem and MoE models in terms of memory access and inference processes.
  • Performance metrics showcasing UltraMem's advantages over MoE.
  • Scaling experiments to observe changes in model performance with respect to sparsity.
  • Ablation studies identifying significant improvements in model performance.

Impact and Implications

The study's impact and implications lie in the advancements made in memory-augmented neural networks, particularly in enhancing computational efficiency and model performance through novel memory architectures and retrieval techniques.

Key Findings

  • UltraMem outperforms traditional models in terms of memory access and inference speed.
  • Various techniques like IVE, TDQKR, and MCS significantly improve model performance.
  • Structural enhancements and initialization methods contribute to better training stability and performance.

Limitations

  • Increased computational cost with certain techniques like IVE.
  • Trade-offs between performance improvements and computational overhead.
  • Specific constraints in scaling the UltraMem architecture.

Future Directions

  • Further exploration of memory retrieval techniques in large-scale memory layers.
  • Optimization of computational efficiency without compromising model performance.
  • Investigation into more advanced initialization methods and structural enhancements.

Practical Significance

  • Real-world applications in improving computational efficiency of neural networks.
  • Potential for faster inference processes and reduced memory access costs.
  • Relevance in developing more efficient and high-performing AI systems.

Articoli in Evidenza

DeepSeek-R1: Incentivizzare la capacità di ragionamento nei LLM tramite Apprendimento per Rinforzo
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Zhen Zhang•Jan 22, 2025•3685

Rapporto Tecnico Qwen2.5
Qwen2.5 Technical Report

Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zihan Qiu•Dec 19, 2024•36311

MiniMax-01: Scalare i modelli di base con attenzione lampeggiante
MiniMax-01: Scaling Foundation Models with Lightning Attention

MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu, Cheng Zhu, Chunhao Zhang, Congchao Guo, Da Chen, Dong Li, Enwei Jiao, Gengxin Li, Guojun Zhang, Haohai Sun, Houze Dong, Jiadai Zhu, Jiaqi Zhuang, Jiayuan Song, Jin Zhu, Jingtao Han, Jingyang Li, Junbin Xie, Junhao Xu, Junjie Yan, Kaishun Zhang, Kecheng Xiao, Kexi Kang, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Zheng, Linbo Chai, Long Xing, Meizhi Ju, Mingyuan Chi, Mozhi Zhang, Peikai Huang, Pengcheng Niu, Pengfei Li, Pengyu Zhao, Qi Yang, Qidi Xu, Qiexiang Wang, Qin Wang, Qiuhui Li, Ruitao Leng, Shengmin Shi, Shuqi Yu, Sichen Li, Songquan Zhu, Tao Huang, Tianrun Liang, Weigao Sun, Weixuan Sun, Weiyu Cheng, Wenkai Li, Xiangjun Song, Xiao Su, Xiaodong Han, Xinjie Zhang, Xinzhu Hou, Xu Min, Xun Zou, Xuyang Shen, Yan Gong, Yingjie Zhu, Yipeng Zhou, Yiran Zhong, Yongyi Hu, Yuanxiang Fan, Yue Yu, Yufeng Yang, Yuhao Li, Yunan Huang, Yunji Li, Yunpeng Huang, Yunzhi Xu, Yuxin Mao, Zehan Li, Zekang Li, Zewei Tao, Zewen Ying, Zhaoyang Cong, Zhen Qin, Zhenhua Fan, Zhihang Yu, Zhuo Jiang, Zijia Wu•Jan 14, 2025•2836

PDF232November 22, 2024