Efficient Fusion of Pre-Trained Transformer Models Using Optimal Transport

Core Concepts

This paper presents a systematic approach for fusing two or more pre-trained transformer-based neural networks using Optimal Transport (OT) to effectively align and combine their capabilities. The proposed method allows for the fusion of models of different sizes (heterogeneous fusion) and outperforms vanilla fusion techniques.

Abstract

The paper introduces a methodology for efficiently fusing pre-trained transformer-based models, such as Vision Transformers (ViTs) and BERT, to combine their capabilities. The key contributions are:

1. A novel graph-based interpretation of the transportation map flow, which allows handling the idiosyncratic architectural components of transformers, such as multi-head self-attention, layer normalization, and residual connections.

2. An analysis showing that soft alignment using the Sinkhorn algorithm outperforms hard alignment (EMD) for transformers, contrary to previous findings for simpler architectures.

3. Extensive experiments on image classification tasks with ViTs and natural language modeling with BERT, demonstrating that the proposed fusion approach consistently outperforms vanilla fusion and can even surpass the performance of the individual converged parent models after a short finetuning.

4. The ability to fuse models of different sizes (heterogeneous fusion), providing an efficient alternative to knowledge distillation.

The authors showcase the potential of fusing multiple transformers to compound their expertise, offering a promising paradigm for model fusion and recombination.

Stats

"Transformer models consistently excel in different fields, including natural language processing, time series forecasting, and computer vision."
"Large Transformer foundation models continue to grow in size and complexity, with an exponential increase in parameters and compute for a fixed incremental improvement in performance."
"Fusing multiple Transformer models into a single entity can yield several advantages, such as enhanced performance, reduced inference complexity, and the ability to leverage existing pre-trained models."

Quotes

"Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities."
"Our approach consistently outperforms vanilla fusion, and, after a surprisingly short finetuning, also outperforms the individual converged parent models."
"Soft alignment plays a key role in successful one-shot fusion of Transformer models."

Key Insights Distilled From

Transformer Fusion with Optimal Transport

by Moritz Imfel... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2310.05719.pdf

Deeper Inquiries

How can the proposed fusion methodology be extended to handle transformer models of different depths?

The proposed fusion methodology can be extended to handle transformer models of different depths by incorporating a hierarchical approach to align and fuse the models. Since the current fusion technique is based on aligning individual layers of the models, extending it to models of different depths would require aligning not just the layers but also the hierarchical structures of the models. This can be achieved by developing a systematic approach to align and fuse the different levels of abstraction in the models, ensuring that the information flow and connections between layers of different depths are preserved during the fusion process. Additionally, the transport map flow graph concept introduced in the methodology can be expanded to visualize and manage the alignment and fusion of models with varying depths, providing a clear understanding of how the fusion process is carried out across different levels of the models.

What are the potential limitations or drawbacks of the soft alignment approach compared to hard alignment, and how can they be addressed?

One potential limitation of the soft alignment approach compared to hard alignment is the increased computational complexity due to the regularization parameter involved in soft alignment. Soft alignment requires tuning the regularization parameter to balance between alignment accuracy and computational efficiency, which can be a challenging task. Additionally, soft alignment may introduce a level of uncertainty in the alignment process compared to hard alignment, which can impact the overall fusion performance.
To address these limitations, one approach is to develop automated methods for tuning the regularization parameter in soft alignment, reducing the manual effort required to find the optimal balance. Machine learning techniques such as hyperparameter optimization or reinforcement learning can be employed to automatically adjust the regularization parameter based on the alignment performance metrics. Additionally, conducting thorough sensitivity analyses and robustness tests can help in understanding the impact of the regularization parameter on the fusion results and provide insights into optimizing the parameter for different scenarios.

Could the insights gained from fusing transformer models be applied to other complex neural network architectures beyond just transformers?

Yes, the insights gained from fusing transformer models can be applied to other complex neural network architectures beyond just transformers. The fusion methodology based on Optimal Transport can be generalized to various neural network architectures that exhibit similar challenges in aligning and fusing multiple models. For example, architectures with multiple branches, skip connections, or parallel pathways can benefit from the hierarchical fusion approach developed for transformers. By adapting the transport map flow graph concept and the layer alignment strategies to different architectural components, such as convolutional neural networks, recurrent neural networks, or graph neural networks, the fusion methodology can be extended to a wide range of complex neural network structures.
Furthermore, the principles of soft alignment, hierarchical fusion, and model combination learned from fusing transformer models can be applied to enhance the performance and efficiency of ensembling techniques in diverse neural network architectures. By leveraging the insights and techniques developed for transformer fusion, researchers can explore new avenues for improving model fusion and recombination in various domains of machine learning and artificial intelligence.