Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs
  on the Edge
Xuan Shen Peiyan Dong Lei Lu Zhenglun Kong Zhengang Li Ming Lin Chao Wu Yanzhi Wang
Large Language Models (LLMs) stand out for their impressive performance in
intricate language modeling tasks. However, their demanding computational and
memory needs pose obstacles for broad use on edge devices. Quantization is then
introduced to boost LLMs' on-device efficiency. Recent works show that 8-bit or
lower weight quantization is feasible with minimal impact on end-to-end task
performance, while the activation is still not quantized. On the other hand,
mainstream commodity edge devices still struggle to execute these sub-8-bit
quantized networks effectively. In this paper, we propose Agile-Quant, an
activation-guided quantization framework for popular Large Language Models
(LLMs), and implement an end-to-end accelerator on multiple edge devices for
faster inference. Considering the hardware profiling and activation analysis,
we first introduce a basic activation quantization strategy to balance the
trade-off of task performance and real inference speed. Then we leverage the
activation-aware token pruning technique to reduce the outliers and the adverse
impact on attentivity. Ultimately, we utilize the SIMD-based 4-bit multiplier
and our efficient TRIP matrix multiplication to implement the accelerator for
LLMs on the edge. We apply our framework on different scales of LLMs including
LLaMA, OPT, and BLOOM with 4-bit or 8-bit for the activation and 4-bit for the
weight quantization. Experiments show that Agile-Quant achieves simultaneous
quantization of model weights and activations while maintaining task
performance comparable to existing weight-only quantization methods. Moreover,
in the 8- and 4-bit scenario, Agile-Quant achieves an on-device speedup of up
to 2.55x compared to its FP16 counterparts across multiple edge devices,
marking a pioneering advancement in this domain.