Deep Incubation: Training Large Models by Divide-and-Conquering (2212.04129v2)

Abstract: Recent years have witnessed a remarkable success of large deep learning models. However, training these models is challenging due to high computational costs, painfully slow convergence, and overfitting issues. In this paper, we present Deep Incubation, a novel approach that enables the efficient and effective training of large models by dividing them into smaller sub-modules that can be trained separately and assembled seamlessly. A key challenge for implementing this idea is to ensure the compatibility of the independently trained sub-modules. To address this issue, we first introduce a global, shared meta model, which is leveraged to implicitly link all the modules together, and can be designed as an extremely small network with negligible computational overhead. Then we propose a module incubation algorithm, which trains each sub-module to replace the corresponding component of the meta model and accomplish a given learning task. Despite the simplicity, our approach effectively encourages each sub-module to be aware of its role in the target large model, such that the finally-learned sub-modules can collaborate with each other smoothly after being assembled. Empirically, our method outperforms end-to-end (E2E) training in terms of both final accuracy and training efficiency. For example, on top of ViT-Huge, it improves the accuracy by 2.7% on ImageNet or achieves similar performance with 4x less training time. Notably, the gains are significant for downstream tasks as well (e.g., object detection and image segmentation on COCO and ADE20K). Code is available at https://github.com/LeapLabTHU/Deep-Incubation.

• The paper proposes a divide-and-conquer method that splits large models into independently trained sub-modules linked by a global meta model.
• It achieves a 2.7% improvement on ImageNet and reduces training time to one-fourth compared to traditional end-to-end training.
• The modular strategy enhances parallel training and paves the way for efficient distributed learning and adaptable transfer techniques.

An Overview of "Deep Incubation: Training Large Models by Divide-and-Conquering"

The paper introduces Deep Incubation, a novel methodology that aims to optimize the training efficiency and efficacy of large-scale neural networks by employing a divide-and-conquer strategy. This approach addresses prevalent challenges such as high computational costs, slow convergence rates, and overfitting, which are commonly associated with the training of sizeable deep learning models. Deep Incubation transitions the training process into a modular framework, facilitating smaller, independent training sessions for sub-modules before integratively assembling them into a cohesive model.

Core Methodology

At the center of Deep Incubation is the division of a large model into smaller sub-modules, thereby enabling parallel training and addressing convergence issues. However, ensuring compatibility among individually trained modules poses a significant challenge. This issue is innovatively resolved by introducing a globally shared meta model, which serves as a lightweight, shared framework linking sub-modules.

The training process involves a module incubation algorithm wherein each sub-module is trained to effectively replace its meta model counterpart while achieving specific learning objectives. This allows each module to adapt to its designated role within the larger model, promoting seamless cooperation upon assembly. The modular training optimally balances independence and collaboration, a balance difficult to achieve in traditional end-to-end (E2E) training.

Empirical Findings

Empirical evaluations demonstrate that Deep Incubation surpasses traditional E2E training methods in both accuracy and efficiency. On ViT-Huge models, the paper reports an accuracy improvement of 2.7% on the ImageNet dataset, with comparable performance achieved in one-fourth the training time. This demonstrates substantial gains in training speed without compromising model accuracy. Additionally, Deep Incubation exhibits significant improvements in downstream tasks such as object detection and image segmentation on datasets like COCO and ADE20K, further validating its versatility and effectiveness.

Implications

The divide-and-conquer approach proposed in Deep Incubation offers concrete advances in the domain of efficient training for large-scale models. Its modular nature presents opportunities for distributed learning systems where sub-module training can be parallelized across multiple computational resources, minimizing communication overhead. This could potentially revolutionize scenarios where computational resources are limited or costly.

From a theoretical standpoint, the introduction of a meta model to facilitate sub-module integration opens new avenues for exploring compatibility within modular training frameworks. This abstraction layer could be adapted or expanded to incorporate transfer learning techniques, where modules could be pre-trained across different datasets or tasks.

Future Developments

The Deep Incubation methodology significantly enhances the landscape of training large-scale models, albeit with room for further exploration. Future research may explore optimizing the design of the meta model, investigating adaptive methods for module training that could improve self-organizing capabilities, and extending the framework to encompass diverse types of neural networks beyond vision transformers.

In conclusion, Deep Incubation provides a pivotal step forward in addressing the intricate challenges of large model training, offering a structured yet adaptable framework that underscores collective and efficient learning strategies. This paradigm is poised to influence both practical applications and embrace more sophisticated theoretical advancements in AI model training paradigms.