BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction (2102.05426v2)

Abstract: We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data but produces less powerful quantized models than Quantization-Aware Training (QAT). In this work, we propose a novel PTQ framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to INT2 for the first time. BRECQ leverages the basic building blocks in neural networks and reconstructs them one-by-one. In a comprehensive theoretical study of the second-order error, we show that BRECQ achieves a good balance between cross-layer dependency and generalization error. To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity. Extensive experiments on various handcrafted and searched neural architectures are conducted for both image classification and object detection tasks. And for the first time we prove that, without bells and whistles, PTQ can attain 4-bit ResNet and MobileNetV2 comparable with QAT and enjoy 240 times faster production of quantized models. Codes are available at https://github.com/yhhhli/BRECQ.

Insights into "Brecq: Pushing the Limit of Post-Training Quantization by Block Reconstruction"

The paper proposes an innovative Post-Training Quantization (PTQ) framework called Brecq, which aims to enhance neural network quantization without the need for end-to-end retraining. PTQ is particularly valuable in scenarios where access to full datasets is constrained or computational resources are limited. Unlike Quantization-Aware Training (QAT), which typically requires retraining with full datasets, PTQ addresses practical constraints by reducing precision post hoc while attempting to maintain model performance.

Contributions and Methodology

The primary contribution of this research is the introduction of Brecq, which enables network quantization to as low as 2-bit integer precision, a feat previously unattainable with PTQ methods without significant degradation in performance. The methodology hinges on a strategic block reconstruction approach to minimize quantization error while accommodating cross-layer dependencies and generalization error. This approach marks a departure from traditional layer-wise quantization techniques that assume layer independence.

The paper advances a comprehensive theoretical framework based on a second-order error analysis using the Gauss-Newton approximation. This framework underpins the block reconstruction process, addressing the pitfalls seen in previous works when quantizing to low precisions. Brecq extends this concept by incorporating a mixed precision strategy, assigning varying bits to different layers based on sensitivity approximations using the Fisher Information Matrix (FIM). Such an innovative combination allows Brecq to achieve high efficiency in quantized models production, integrating the strengths of both uniform and non-uniform precision configurations.

Numerical Results and Analysis

The numerical results validate Brecq’s effectiveness across various architectures and tasks, including image classification with ResNet and MobileNetV2, and object detection with Faster RCNN and RetinaNet models. Notably, quantizing ResNet models down to 4-bit while maintaining performance comparable to QAT is significant due to the drastic reduction in computational demand and increased production speed—up to 240 times faster. Moreover, the ability to achieve meaningful 2-bit quantization performance showcases the robustness of the proposed framework.

The researchers have demonstrated competitive results even against state-of-the-art QAT methods, which traditionally set the benchmark due to their ability to optimize across all parameters thoroughly. This not only positions Brecq as a formidable PTQ approach but also signifies a potential shift in how model quantization may be approached in resource-constrained environments.

Implications and Future Directions

The implications of Brecq are substantial, proving that post-training quantization can approach the performance levels of retraining-heavy QAT methods even at lower bit precision levels. The practical applications are extensive, ranging from deploying models on edge devices where computational resources and memory are limited, to accelerating the iterations in model development cycles by enabling more rapid quantization post-training.

Future developments could investigate further optimizations of the mixed precision configurations and explore adaptive techniques to dynamically adjust bit allocations based on model use cases or changes in data distributions. Work could also focus on expanding the framework to other types of neural network architectures, such as transformer models used in NLP, to verify the versatility of block reconstruction in quantization across domains.

Overall, "Brecq" provides a valuable contribution to PTQ literature and opens the door for further innovations in enabling efficient deep learning model deployment without sacrificing performance due to quantization.