SVDP: Training-Free Contextual Sparsity Predictors for Fast LLM Inference

Contextual sparsity is one of the approaches used to reduce computational complexity in the inference process of large language models (LLMs). Existing techniques for efficient LLM inference acceleration based on contextual sparsity with minimal accuracy degradation require training sparse pattern predictors. This paper presents a framework for accelerating the inference of ReGLU-based feed-forward networks (FFNs) within LLMs. The proposed framework provides a fast, training-free method for building sparse pattern predictors using truncation-aware singular value decomposition (SVD) of the gate projection matrix, along with a threshold calibration algorithm, and inference executors supporting conditional computation on CUDA and CANN devices. Experiments on three sparse LLMs with an average activation sparsity level of 90\% in the FFNs demonstrate up to a 1.8x end-to-end decoding time speedup with less than 1\% degradation in benchmark scores on tasks involving math and code generation. This work advances the deployment of LLMs on edge devices.