Efficient Cross-Architecture Transfer Learning for Low-Cost Inference Transformer Models

Core Concepts

Cross-Architecture Transfer Learning (XATL) can significantly reduce the training time and improve the performance of Low-Cost Inference (LCI) Transformer models by directly transferring compatible weights from pre-trained Transformer models, without the need to train the LCI models from scratch.

Abstract

The paper proposes a weight transfer learning paradigm called Cross-Architecture Transfer Learning (XATL) to address the costly pre-training requirement of Low-Cost Inference (LCI) Transformer models. LCI models, such as RetNet and Mamba, aim to improve the efficiency of Transformer Language Models by changing the design of the self-attention block to have linear-cost inference. However, this architectural change typically requires full pre-training of the weights from scratch, which incurs a significant cost. The key idea of XATL is to directly transfer the weights of the shared components between LCI and self-attention-based Transformers, such as layer norms, MLPs, and input/output embeddings, from a pre-trained Transformer model to the new LCI architecture. This allows the LCI models to benefit from the strong initialization and accelerated training, without the need for full pre-training. The authors conducted extensive experiments on varying model sizes and alternative attention architectures, including RetNet and Striped-Mamba. The results show that XATL can significantly reduce the training time by up to 2.5x and converge to a better minimum with up to 2.6% stronger performance on language modeling and commonsense benchmarks, compared to training the LCI models from scratch, within the same compute budget. The paper also explores the effects of freezing and unfreezing the transferred weights during training, as well as the benefits of using a hybrid architecture that combines attention and LCI components. The findings demonstrate the effectiveness of XATL in enabling the wider adoption of efficient LCI models by alleviating the costly pre-training requirement.

Stats

The authors report the following key figures: XATL can reduce the training time by up to 2.5x to reach the same performance as training from scratch. XATL can improve the performance by up to 2.6% on language modeling and commonsense benchmarks, compared to training from scratch, within the same compute budget.

Quotes

"Cross-Architecture Transfer Learning (XATL) can significantly reduce the training time and improve the performance of Low-Cost Inference (LCI) Transformer models by directly transferring compatible weights from pre-trained Transformer models, without the need to train the LCI models from scratch." "XATL can reduce the training time by up to 2.5x to reach the same performance as training from scratch." "XATL can improve the performance by up to 2.6% on language modeling and commonsense benchmarks, compared to training from scratch, within the same compute budget."

Key Insights Distilled From

Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers

by Sehyun Choi at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02684.pdf

Deeper Inquiries

What are the potential limitations of the XATL approach, and how could it be further improved or extended

The XATL approach, while effective in reducing training time and improving model performance, has some potential limitations. One limitation is the dependency on the compatibility of components between the source and target architectures. If the architectures differ significantly in their components or design, the transfer of weights may not be as effective. Additionally, freezing weights during training, as done in XATL, can limit the flexibility of the model to adapt to new data patterns. To improve XATL, one approach could be to incorporate more advanced techniques for aligning and adapting weights between architectures. This could involve fine-tuning the transferred weights dynamically during training to better fit the target architecture. Additionally, exploring methods to handle architectural differences more effectively, such as adapting the transferred weights through additional training steps, could enhance the robustness and applicability of XATL.

How do the performance and efficiency gains of XATL compare to other weight transfer or distillation techniques for Transformer models

In comparison to other weight transfer or distillation techniques for Transformer models, XATL offers unique advantages in terms of performance and efficiency gains. XATL stands out by directly transferring weights of shared components between architectures, enabling faster convergence and better model initialization. This direct transfer approach reduces the need for extensive retraining from scratch, saving computational resources and time. Moreover, XATL's ability to significantly reduce training time while maintaining or even improving model performance sets it apart from traditional weight transfer or distillation techniques. By leveraging the strengths of pre-trained models and efficiently transferring knowledge to new architectures, XATL offers a practical and effective solution for enhancing the efficiency and scalability of language model development.

What are the implications of XATL for the broader field of efficient and scalable language model development

The implications of XATL for the broader field of efficient and scalable language model development are significant. XATL provides a valuable framework for researchers and practitioners to leverage existing pre-trained models and efficiently transfer knowledge to new architectures. This approach not only accelerates the development of novel models but also reduces the computational burden associated with training large-scale language models from scratch. By enabling the reuse of pre-trained weights and components, XATL promotes a more sustainable and cost-effective approach to model development. This can lead to faster innovation, increased experimentation with different architectures, and ultimately, the advancement of state-of-the-art language models. Additionally, the efficiency gains offered by XATL contribute to the democratization of advanced language model development, making it more accessible to a wider range of researchers and organizations.