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arXiv · 2412.07174

Post-Training Statistical Calibration for Higher Activation Sparsity

Vui Seng Chua·Yujie Pan·Nilesh Jain
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Abstract

We present Statistical Calibrated Activation Pruning (SCAP), a post-training activation pruning framework that (1) generalizes sparsification by input activations of Fully-Connected layers for generic and flexible application across Transformers, and (2) features a simple Mode-Centering technique to pre-calibrate activation distributions for maximizing post-training sparsity. Our results demonstrate robust Pareto efficiency compared to prior methods, translating to a 1.5x additional LLM decoding speedup against CATS at iso model quality. SCAP effectiveness is empirically verified across a wide range of models, including recent Transformer Decoders, MoE, Mamba2, Encoding Transformer, and pre-quantized models, highlighting its practicality and scalability. The code is available at: https://github.com/IntelLabs/SCAP.

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Vui Seng Chua, Yujie Pan, Nilesh Jain. 2024-12-10. Post-Training Statistical Calibration for Higher Activation Sparsity. https://arxiv.org/abs/2412.07174

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