Accelerating Training with Neuron Interaction and Nowcasting Networks (2409.04434v2)

Abstract: Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However, learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

• The paper introduces NiNo, a framework that constructs neuron graphs and uses direct multi-step nowcasting to forecast future parameter updates.
• It demonstrates up to a 50% reduction in training steps on both vision and language tasks compared to traditional methods like Adam.
• It integrates graph neural networks with layerwise scaling and a k-decay mechanism to enhance stability, scalability, and generalization.

Accelerating Training with Neuron Interaction and Nowcasting Networks

The paper "Accelerating Training with Neuron Interaction and Nowcasting Networks" presents a novel approach to enhance the efficiency and stability of neural network training by leveraging neuron connectivity and graph neural networks (GNNs). This method, termed Neuron Interaction and Nowcasting (NiNo), builds on the concept of periodically predicting future parameters, or "nowcasting," which augments traditional training methods like Adam.

Technical Contributions

The authors propose several key innovations:

1. Neuron Graph Construction: The NiNo model constructs neural graphs that accurately model neuron permutations, especially for complex architectures like transformers. This involves a more nuanced treatment of multi-head self-attention mechanisms, ensuring that the inherent symmetries of the neural network are preserved.
2. Direct Multi-Step Forecasting (DMS): NiNo leverages DMS to predict parameter updates for multiple future steps, thereby reducing the error accumulation typically associated with autoregressive forecasting methods.
3. Layerwise Scaling: The paper introduces a robust scaling mechanism for parameters that accounts for the varying scales across different layers and architectures, improving generalization and stability during prediction.
4. GNN Integration: A graph neural network processes the neuron graphs, providing a powerful inductive bias that captures the structural information and relationships between neurons, enhancing the accuracy of the nowcasting step.
5. $k$-Decay Mechanism: To adapt the prediction horizon dynamically, NiNo employs a $k$-decay schedule that adjusts the prediction horizon based on the stage of the optimization, promoting large steps early in training and smaller, more accurate predictions as the model converges.

Experimental Results

The authors conducted extensive experiments on both vision and language tasks to validate their approach:

• Vision Tasks: NiNo was tested on datasets like FashionMNIST and CIFAR-10 using convolutional neural networks (CNNs). The results showed significant speed-ups in the training process, with NiNo reducing the number of steps required to reach baseline performance by up to 50%.
• Language Tasks: For LLMing tasks using GPT-style transformers, NiNo again demonstrated substantial improvements, particularly excelling over state-of-the-art methods like Weight Nowcaster Networks (WNNs) and Learning to Optimize (L2O) models.

The reduction in training steps was consistently observed across all tasks, indicating the model's robustness and effectiveness. For instance, on the WikiText-103 dataset, NiNo achieved a 48% reduction in training steps compared to Adam.

Implications and Future Directions

The implications of this research are multifaceted:

• Practical Efficiency: By reducing the number of training steps, NiNo not only speeds up the training process but also decreases the computational resources required, which can lead to significant cost savings, particularly in large-scale models and datasets.
• Stability and Generalization: The use of neuron graphs and GNNs introduces a strong inductive bias that enhances the model's stability and allows it to generalize better across different tasks and architectures.
• Scalability: The demonstrated ability of NiNo to handle larger models, up to 29 million parameters, shows promise for its application in even larger neural networks, potentially benefiting the training of extensive models like those used in LLMs.

Conclusion

NiNo represents a significant advancement in neural network training methodologies. By effectively integrating neuron interactions and GNNs for parameter nowcasting, the approach achieves better training efficiency and generalization. Future research directions could explore scaling NiNo further, investigating its application in even more diverse architectures and tuning the GNN components for optimized performance across varying tasks. Additionally, NiNo's byproduct of providing low-dimensional encodings of network parameters could open new avenues for analyzing training dynamics and improving model interpretability.