Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work and Background


Analysis
3.1 Limitations about Existing ReLUficatio
3.2 dReLU


Are Neurons in Expert still Sparsely Activated?


dReLU Sparsification


Experiments Results
6.1 Downstream Tasks Performance
6.2 Sparsity of Sparsified Models


Practical Inference Speedup Evaluation
7.1 Experiments Setting
7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference
7.4 Deploy LLMs on mobile phones


Conclusion and References Related Work and Background Related Work and Background Related Work and Background Analysis
3.1 Limitations about Existing ReLUficatio
3.2 dReLU Analysis 3.1 Limitations about Existing ReLUficatio 3.1 Limitations about Existing ReLUficatio 3.2 dReLU 3.2 dReLU Are Neurons in Expert still Sparsely Activated? Are Neurons in Expert still Sparsely Activated? Are Neurons in Expert still Sparsely Activated? dReLU Sparsification dReLU Sparsification dReLU Sparsification Experiments Results
6.1 Downstream Tasks Performance
6.2 Sparsity of Sparsified Models Experiments Results 6.1 Downstream Tasks Performance 6.1 Downstream Tasks Performance 6.2 Sparsity of Sparsified Models 6.2 Sparsity of Sparsified Models Practical Inference Speedup Evaluation
7.1 Experiments Setting
7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference
7.4 Deploy LLMs on mobile phones Practical Inference Speedup Evaluation Practical Inference Speedup Evaluation 7.1 Experiments Setting 7.1 Experiments Setting 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.2 Pure CPU Inference and 7.3 Hybrid GPU-CPU Inference 7.4 Deploy LLMs on mobile phones 7.4 Deploy LLMs on mobile phones Conclusion and References Conclusion and References Conclusion and References A. Appendix / supplemental material A. Appendix / supplemental material B. Limitation B. Limitation C. Broader Impact C. Broader Impact 7 Practical Inference Speedup Evaluation In this section, we evaluate the practical acceleration in model generation achieved. During the SFT phase, we incorporate a predictor module for each FFN block. Notably, for the TurboSparseMixtral-47B, we train predictors for each expert. When an expert is routed, the neuron-level predictor identifies which neurons will be activated, enabling neuron-level sparse computation. We integrate our two models with PowerInfer, which is a state-of-the-art sparsely-activated framework to evaluate the actual generation speed. Authors:
(1) Yixin Song, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(2) Haotong Xie, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(3) Zhengyan Zhang, Department of Computer Science and Technology, Tsinghua University;
(4) Bo Wen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University;
(5) Li Ma, Shanghai Artificial Intelligence Laboratory;
(6) Zeyu Mi, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University Mi yzmizeyu@sjtu.edu.cn);
(7) Haibo Chen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University. Authors: Authors: (1) Yixin Song, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (2) Haotong Xie, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (3) Zhengyan Zhang, Department of Computer Science and Technology, Tsinghua University; (4) Bo Wen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University; (5) Li Ma, Shanghai Artificial Intelligence Laboratory; (6) Zeyu Mi, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University Mi yzmizeyu@sjtu.edu.cn); (7) Haibo Chen, Institute of Parallel and Distributed Systems (IPADS), Shanghai Jiao Tong University. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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TurboSparse: Elite Inference Speed via dReLU Sparsity

TurboSparse Inference Speedup: PowerInfer Integration for Real-Time LLM Decoding

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