FP=xINT:A Low-Bit Series Expansion Algorithm for Post-Training Quantization
This addresses the problem of maintaining model accuracy during low-bit quantization for efficient deployment, representing a novel approach rather than an incremental improvement.
The paper tackles the performance degradation in post-training quantization at extremely low-bit settings by introducing a series expansion framework that expands full-precision models into multiple low-bit basis models, achieving state-of-the-art results such as 77.03% accuracy with 4-bit ResNet-50 quantization.
Post-Training Quantization (PTQ) converts pre-trained Full-Precision (FP) models into quantized versions without training. While existing methods reduce size and computational costs, they also significantly degrade performance and quantization efficiency at extremely low settings due to quantization noise. We introduce a deep model series expansion framework to address this issue, enabling rapid and accurate approximation of unquantized models without calibration sets or fine-tuning. This is the first use of series expansion for neural network quantization. Specifically, our method expands the FP model into multiple low-bit basis models. To ensure accurate quantization, we develop low-bit basis model expansions at different granularities (tensor, layer, model), and theoretically confirm their convergence to the dense model, thus restoring FP model accuracy. Additionally, we design AbelianAdd/Mul operations between isomorphic models in the low-bit expansion, forming an Abelian group to ensure operation parallelism and commutativity. The experiments show that our algorithm achieves state-of-the-art performance in low-bit settings; for example, 4-bit quantization of ResNet-50 surpasses the original accuracy, reaching 77.03%. The code will be made public.