Efficient Compression of Hierarchical Vision Transformers through Data-independent Module-Aware Pruning

Core Concepts

A novel data-independent module-aware pruning method (DIMAP) is proposed to efficiently compress hierarchical vision transformers by considering their unique properties, including local attention and hierarchical feature extraction.

Abstract

The paper introduces a Data-independent Module-Aware Pruning (DIMAP) method to compress hierarchical vision transformers (ViTs) effectively. Hierarchical ViTs have two key advantages over conventional ViTs: linear computational complexity with respect to image size through local self-attention, and hierarchical feature maps for dense prediction tasks.
The authors identify two main issues with existing pruning methods for hierarchical ViTs: 1) Magnitude-based pruning compares "local" attention weights at a "global" level, leading to important "local" weights being pruned, and 2) Magnitude pruning fails to consider the distinct weight distributions across different hierarchical levels, which are essential for extracting coarse-to-fine features.
To address these problems, DIMAP takes a module-aware approach. It analyzes the Frobenius distortion incurred by pruning at the module level, rather than the window or layer level, to ensure fair comparison of weight importance across hierarchical levels. Furthermore, DIMAP introduces a novel data-independent weight importance metric based solely on weight values, eliminating the need for input data or complex pruning sensitivity analysis.
Experiments on Swin Transformer models of different sizes demonstrate the effectiveness of DIMAP. For example, when pruning Swin-B by 52.7% FLOPs and 52.5% parameters, the top-5 accuracy drop is only 0.07%. DIMAP also achieves better accuracy-computation trade-offs compared to state-of-the-art vision transformer compression methods.

Stats

When pruning Swin-B by 14.4% FLOPs and 14.3% parameters, the top-1 accuracy improves by 0.04%.
When pruning Swin-S by 33.2% FLOPs and 33.2% parameters, the top-5 accuracy improves by 0.03%.
When pruning Swin-T by 13.9% FLOPs and 13.7% parameters, the top-1 accuracy drop is only 0.01%.

Quotes

"Magnitude pruning results in unbalanced pruning outcomes for these layers. To ensure a fair comparison of weights across different layers, we employ the information distortion analysis as the weight metric for pruning."
"Our Swin-B-DIMAP3 model achieves 0.09% higher accuracy with 7.9M fewer parameters than the unpruned Swin-S model."
"Our Swin-S-DIMAP3 achieves 0.53% higher accuracy with 4.6M fewer parameters than the unpruned Swin-T model."

Key Insights Distilled From

Data-independent Module-aware Pruning for Hierarchical Vision Transformers

by Yang He,Joey... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13648.pdf

Deeper Inquiries

How can the proposed DIMAP method be extended to other types of vision transformers beyond the hierarchical architecture

The proposed DIMAP method can be extended to other types of vision transformers beyond the hierarchical architecture by adapting the module-aware pruning approach to suit the specific characteristics of different transformer models. While hierarchical vision transformers have unique properties such as local self-attention and patch merging, other vision transformer variants may have different architectural features that need to be considered during the pruning process.
To extend DIMAP to other vision transformers, researchers can analyze the specific structure and functionality of each model to define modules appropriately. By categorizing layers into modules based on their roles and interactions within the network, the weight importance metric can be tailored to capture the significance of weights within these modules. This customization ensures that the pruning process is optimized for the particular architecture of each vision transformer variant.
Additionally, researchers can explore the application of the data-independent weight importance metric to different types of vision transformers by conducting thorough experiments and validation on various models. By testing the effectiveness of DIMAP on a diverse range of vision transformer architectures, it is possible to refine and adapt the method to achieve efficient compression while maintaining or improving model performance across different variants.

What are the potential limitations of the data-independent weight importance metric, and how can it be further improved

The data-independent weight importance metric proposed in the DIMAP method may have potential limitations in certain scenarios that could impact its effectiveness in weight pruning. Some of these limitations include:

Sensitivity to Weight Distribution: The weight importance metric relies on the relative contribution of weights within a module, which may be influenced by the distribution of weights in the network. If the weight distribution is skewed or irregular, the metric may not accurately capture the importance of individual weights.

Lack of Contextual Information: The metric solely based on weight values may overlook contextual information or relationships between weights in different layers or modules. This limitation could lead to suboptimal pruning decisions, especially in complex neural network architectures.

To further improve the data-independent weight importance metric, researchers can consider the following strategies:

Incorporating Contextual Information: Enhancing the metric by incorporating contextual information such as inter-layer dependencies, attention patterns, or feature interactions can provide a more comprehensive understanding of weight importance within the network.

Adaptive Weight Importance Calculation: Developing adaptive algorithms that dynamically adjust the weight importance metric based on the network's characteristics and training dynamics can improve the robustness and accuracy of the pruning process.

Validation and Benchmarking: Conducting extensive validation and benchmarking studies on a wide range of datasets and models can help identify potential limitations of the metric and refine it to enhance its performance across different scenarios.

Can the module-aware pruning approach be applied to other neural network architectures beyond vision transformers to achieve efficient compression

The module-aware pruning approach can be applied to other neural network architectures beyond vision transformers to achieve efficient compression by adapting the concept of modules and weight importance analysis to suit the specific characteristics of different networks. While the module-aware pruning method was initially designed for hierarchical vision transformers, its principles can be extended to other architectures by following these steps:

Module Definition: Define modules within the neural network based on the functional roles and interactions of layers. Categorize layers into modules that represent coherent units of computation or information processing.

Weight Importance Metric: Develop a data-independent weight importance metric that evaluates the significance of weights within each module. Consider the unique properties of the network architecture to design a metric that captures the relative importance of weights accurately.

Pruning Strategy: Implement a pruning strategy that targets modules for compression based on the weight importance metric. Remove less important weights within modules while preserving the overall functionality and performance of the network.

By applying the module-aware pruning approach to other neural network architectures, researchers can achieve efficient compression, reduce computational costs, and improve the scalability of deep learning models across a variety of applications and domains.