Patch Similarity Aware Data-Free Quantization for Vision Transformers (2203.02250v3)

Abstract: Vision transformers have recently gained great success on various computer vision tasks; nevertheless, their high model complexity makes it challenging to deploy on resource-constrained devices. Quantization is an effective approach to reduce model complexity, and data-free quantization, which can address data privacy and security concerns during model deployment, has received widespread interest. Unfortunately, all existing methods, such as BN regularization, were designed for convolutional neural networks and cannot be applied to vision transformers with significantly different model architectures. In this paper, we propose PSAQ-ViT, a Patch Similarity Aware data-free Quantization framework for Vision Transformers, to enable the generation of "realistic" samples based on the vision transformer's unique properties for calibrating the quantization parameters. Specifically, we analyze the self-attention module's properties and reveal a general difference (patch similarity) in its processing of Gaussian noise and real images. The above insights guide us to design a relative value metric to optimize the Gaussian noise to approximate the real images, which are then utilized to calibrate the quantization parameters. Extensive experiments and ablation studies are conducted on various benchmarks to validate the effectiveness of PSAQ-ViT, which can even outperform the real-data-driven methods. Code is available at: https://github.com/zkkli/PSAQ-ViT.

• The paper introduces PSAQ-ViT, a framework that exploits patch similarity in self-attention to generate synthetic calibration data without using real images.
• It employs kernel density estimation and entropy metrics to transform Gaussian noise into effective samples for calibrating Vision Transformer quantization.
• Extensive experiments demonstrate that PSAQ-ViT outperforms conventional data-dependent methods, supporting privacy-preserving and efficient model deployment.

Insightful Overview of "Patch Similarity Aware Data-Free Quantization for Vision Transformers"

The paper "Patch Similarity Aware Data-Free Quantization for Vision Transformers" addresses the inherent challenges posed by Vision Transformers (ViTs) regarding their high computational and memory demands. The research presents a novel framework, PSAQ-ViT, to enable efficient data-free quantization of ViTs, which is crucial for deploying these models on resource-constrained devices without compromising data privacy.

Key Contributions

The research delineates a new perspective on quantization specifically tailored for Vision Transformers, particularly under scenarios where data privacy is a concern, and thus access to training data is restricted or infeasible. The paper's main contributions include:

1. Patch Similarity Awareness: PSAQ-ViT leverages the inherent properties of the self-attention mechanism in ViTs. It identifies and exploits the differential responses of the self-attention module to varying inputs, notably distinguishing between Gaussian noise and real images through patch similarity. This insight helps generate synthetic samples that mimic real data characteristics, facilitating effective quantization even without access to original datasets.
2. Quantization Framework Design: The framework utilizes a relative value metric, based on the entropy of patch similarity, to optimize Gaussian noise into useful synthetic samples. This is achieved through kernel density estimation, allowing for gradient back-propagation and thus enabling the generation of suitable data for the calibration of quantization parameters.
3. Competitive Performance: Extensive experiments demonstrate that PSAQ-ViT often surpasses real-data-dependent methods, highlighting its robustness and efficiency. The framework is tested on various benchmark models, including ViT and DeiT, and outperforms standard post-training quantization techniques, even those requiring large amounts of real data for calibration.

Implications and Future Directions

The proposed PSAQ-ViT framework provides a scalable solution for quantizing ViTs without the original data, addressing significant challenges in privacy-preserving machine learning applications. This advancement allows models to be more widely deployed, particularly in edge computing scenarios where privacy and computational resources are constraints.

Theoretically, this work sheds light on the potential for further exploration of intrinsic model properties—such as self-attention in transformers—that can be harnessed for various tasks beyond quantization. Additionally, the approach encourages future research in enhancing quantization methods, potentially integrating other model features or structures specific to transformers.

Future developments could extend the PSAQ-ViT approach by considering additional complexities, such as heterogeneity in transformer architectures or dynamic quantization strategies that adapt to varying deployment environments. Furthermore, exploring how PSAQ-ViT can be integrated into broader frameworks for efficient deployment of machine learning models, including aspects of hardware design and energy efficiency, could be promising.

In conclusion, the paper provides a meaningful contribution to the domain of model compression, specifically addressing the quantization of ViTs—a key area as these models gain prominence across various computer vision tasks. The framework effectively balances the need for model efficiency and data privacy, presenting a path forward for deploying advanced AI systems in real-world applications.