Efficient Multi-Level Training Framework for Accelerating Transformer Models

Core Concepts

A multi-level training framework that leverages the fast convergence of smaller models and the high expressiveness of larger models to significantly reduce the computational cost of training transformer-based models like BERT, GPT, and DeiT.

Abstract

The paper proposes an efficient multi-level training framework for accelerating the training of transformer-based models. The framework is built upon three key operators: Coalescing, De-coalescing, and Interpolation.
The Coalescing operator reduces the model complexity by decreasing the width and depth of the original model. The De-coalescing operator then maps the parameters back to the original model size. The Interpolation operator merges the de-coalesced parameters with the original model parameters to avoid the symmetry of neurons issue and further improve the convergence rate.
The framework follows a V-cycle training process. It first progressively coalesces the model to a smaller size, trains the smaller model quickly, and then de-coalesces and interpolates the parameters back to the original model size for further training. This approach leverages the fast convergence of smaller models to save computational cost while preserving the performance of the final large model.
The experiments on BERT, GPT, and DeiT models show that the proposed multi-level framework can reduce the training cost by 19-27% compared to training the models from scratch, while maintaining similar or even better performance on downstream tasks.

Stats

The paper reports the following key metrics:

For BERT-Base, the proposed framework saves 19.0% FLOPs and 10.8% walltime during pre-training.
For GPT-Base, the proposed framework saves 24.1% FLOPs and 16.5% walltime during pre-training.
For DeiT-B, the proposed framework saves 27.1% FLOPs and 24.3% walltime during pre-training on ImageNet.
For BERT-Large, the proposed 2-level and 3-level frameworks save 37.4% and 51.6% training cost, respectively, while maintaining similar or better performance on downstream tasks.

Quotes

"The fast growing capabilities of large-scale deep learning models, such as Bert, GPT and ViT, are revolutionizing the landscape of NLP, CV and many other domains. Training such models, however, poses an unprecedented demand for computing power, which incurs exponentially increasing energy cost and carbon dioxide emissions."
"Motivated by a set of key observations of inter- and intra-layer similarities among feature maps and attentions that can be identified from typical training processes, we propose a multi-level framework for training acceleration."

Key Insights Distilled From

A Multi-Level Framework for Accelerating Training Transformer Models

by Longwei Zou,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.07999.pdf

Deeper Inquiries

How can the multi-level training framework be extended to other types of deep learning models beyond transformers

The multi-level training framework can be extended to other types of deep learning models beyond transformers by adapting the Coalescing, De-coalescing, and Interpolation operators to suit the specific architecture of the model. For example, in convolutional neural networks (CNNs), the width and depth of the network can be reduced through coalescing operations, similar to what is done in transformers. The key is to identify the similarities within and between layers in the specific model architecture and design the operators accordingly. Additionally, the V-cycle training process can be applied to gradually scale up the model size and leverage the fast convergence of smaller models to train larger ones efficiently.

What are the potential limitations or drawbacks of the proposed framework, and how can they be addressed

One potential limitation of the proposed framework is the need for careful hyperparameter tuning, especially the interpolation factor α, to ensure optimal performance. If α is set too low, the larger model may not benefit enough from the knowledge of the smaller model, leading to suboptimal results. On the other hand, setting α too high may introduce noise and hinder convergence. This limitation can be addressed by conducting thorough experiments to find the optimal value of α for different models and datasets. Additionally, the scalability of the framework to extremely large models with billions of parameters may pose a challenge in terms of computational resources and training time. To address this, efficient parallel computing strategies and hardware acceleration techniques can be explored.

Can the multi-level training framework be combined with other orthogonal techniques, such as knowledge distillation or layer dropping, to further improve the training efficiency of large-scale models

The multi-level training framework can be combined with other orthogonal techniques, such as knowledge distillation or layer dropping, to further improve the training efficiency of large-scale models. Knowledge distillation can be used to transfer knowledge from a large model to a smaller model during the coalescing process, enhancing the quality of the intermediate solutions. Layer dropping can be integrated into the framework to selectively remove redundant or less informative layers in the larger model before coalescing, reducing the overall model complexity. By combining these techniques, the training efficiency and performance of large-scale models can be further optimized.