Efficient Vision Transformer with Selective Attention Layer Removal

Core Concepts

The core message of this paper is that uninformative attention layers in vision transformers can be effectively integrated into their subsequent MLP layers, reducing computational load without compromising performance.

Abstract

The paper proposes a novel framework that selectively removes non-essential attention layers from vision transformers and integrates their knowledge into the subsequent MLP layers. The key insights are:

The entropy quantity of attention layers in the bottom blocks is lower than that of the top blocks, and their accompanying MLP layers exhibit similar entropy levels. This suggests that the MLP layers can elicit the same information as the attention layers in the bottom blocks.

The authors propose the Entropy-based Selection Strategy (NOSE) to identify the combination of attention layers that have the minimum interaction with the final output layer, measured by transfer entropy. This allows removing attention layers with fewer dependencies on the overall network.

A simple dilution learning technique is introduced to gradually degenerate the attention layers into identical mapping, which can then be integrated into the subsequent MLP layers. This yields transformer blocks with only MLP layers in certain cases.

Experiments on ImageNet-1k, CIFAR-100, and ADE20k demonstrate that the proposed method can effectively remove up to 50% of the attention layers without compromising performance. It also significantly improves throughput and memory efficiency compared to previous token pruning methods.

Stats

The paper provides the following key statistics:

Removing 40% of the attention layers in DeiT-B can reduce the model parameters by 13.7% and improve the working load by 20.5% without performance degradation on ImageNet-1k.
Removing 50% of the attention layers in DeiT-B can improve the throughput by 36.5% on a V100 GPU.
Combining the proposed method with unsupervised token merging can further improve the throughput by 69.6% on ImageNet-1k.

Quotes

"We identify that regarding the attention layer in bottom blocks, their subsequent MLP layers, i.e. two feed-forward layers, can elicit the same entropy quantity."
"Meanwhile, the accompanied MLPs are under-exploited since they exhibit smaller feature entropy compared to those MLPs in the top blocks."
"Therefore, we propose to integrate the uninformative attention layers into their subsequent counterparts by degenerating them into identical mapping, yielding only MLP in certain transformer blocks."

Key Insights Distilled From

MLP Can Be A Good Transformer Learner

by Sihao Lin,Pu... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05657.pdf

Deeper Inquiries

How can the proposed method be extended to other types of attention mechanisms beyond the standard softmax-based attention used in this work

The proposed method can be extended to other types of attention mechanisms by adapting the entropy-based selection strategy to accommodate different attention mechanisms. For instance, if the attention mechanism involves learnable parameters or different attention mechanisms like sparse attention or axial attention, the entropy calculation and selection criteria would need to be adjusted accordingly. The key is to analyze the information quantity carried by each layer and identify the layers with the least impact on the model's output. By customizing the entropy calculation and transfer entropy analysis for different attention mechanisms, the method can be effectively applied to a variety of architectures beyond the standard softmax-based attention.

What are the potential limitations of the entropy-based selection strategy, and how could it be further improved to handle more complex interactions between layers

One potential limitation of the entropy-based selection strategy is its reliance on transfer entropy to measure the interaction between layers. While transfer entropy provides valuable insights into the information flow between layers, it may not capture all aspects of complex interactions in deep neural networks. To address this limitation and handle more intricate layer interactions, the strategy could be further improved by incorporating additional metrics or techniques. For example, introducing higher-order statistics or dynamic analysis of layer interactions over time could provide a more comprehensive understanding of how different layers influence each other. Additionally, exploring graph-based approaches to model the network architecture and information flow could offer a more nuanced perspective on layer interactions and selection.

Given the findings on the feature expressivity of the proposed method, how could the insights be leveraged to design more efficient and effective vision transformer architectures from the ground up

The insights gained from the feature expressivity analysis of the proposed method can be leveraged to design more efficient and effective vision transformer architectures from the ground up. By focusing on encoding high-frequency components and enhancing feature richness, future architectures can prioritize learning representations that capture fine-grained details and global dependencies simultaneously. This can lead to improved generalization ability and performance across a wide range of tasks. Additionally, incorporating mechanisms to explicitly encourage the encoding of high-frequency components, such as specialized attention mechanisms or regularization techniques, can further enhance the feature expressivity of vision transformer architectures. By integrating these insights into the architectural design process, researchers can develop vision transformers that excel in capturing complex patterns and relationships in visual data.