DeMo: Decoupled Momentum Optimization (2411.19870v1)

Abstract: Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects. Drawing from the signal processing principles of frequency decomposition and energy compaction, we demonstrate that synchronizing full optimizer states and model parameters during training is unnecessary. By decoupling momentum updates and allowing controlled divergence in optimizer states across accelerators, we achieve improved convergence compared to state-of-the-art optimizers. We introduce {\textbf{De}}coupled {\textbf{Mo}}mentum (DeMo), a fused optimizer and data parallel algorithm that reduces inter-accelerator communication requirements by several orders of magnitude. This enables training of large neural networks even with limited network bandwidth and heterogeneous hardware. Our method is topology-agnostic and architecture-independent and supports scalable clock-synchronous distributed training with negligible compute and memory overhead. Empirical results show that models trained with DeMo match or exceed the performance of equivalent models trained with AdamW, while eliminating the need for high-speed interconnects when pre-training large scale foundation models. An open source reference PyTorch implementation is published on GitHub at https://github.com/bloc97/DeMo

• The paper presents a novel optimizer, DeMo, that decouples momentum updates to minimize inter-accelerator communication in training large neural networks.
• DeMo leverages a DCT-based approach to extract fast momentum components, enabling efficient synchronization with minimal bandwidth usage.
• Empirical results show that DeMo achieves comparable convergence to AdamW while significantly reducing per-GPU communication requirements.

An Analysis of Decoupled Momentum Optimization: Enhancing Neural Network Training Efficiency

The paper introduces a novel optimizer called Decoupled Momentum Optimization (DeMo), addressing the challenges of efficiently training large-scale neural networks across multiple accelerators with limited interconnect bandwidth. This method capitalizes on the compressibility of optimizer states, exemplifying significant improvements over state-of-the-art optimizers, such as AdamW, especially in bandwidth-constrained environments.

Core Concepts and Methodology

The authors present a methodology that emphasizes two principal innovations: decoupling momentum updates across accelerators and enabling controlled divergence in optimizer states. By reducing the synchronization of optimizer states and model parameters, the paper demonstrates a substantial reduction in inter-accelerator communication. The proposed method relies on frequency decomposition principles borrowed from signal processing, primarily employing Discrete Cosine Transform (DCT) to extract significant momentum components that are spatially auto-correlated.

The paper introduces three conjectures underpinning their approach:

1. Fast-moving components of momentum exhibit high spatial auto-correlation.
2. Fast-moving components have low temporal variance and should be applied immediately, while slow-moving ones benefit from temporal smoothing.
3. Slow-moving components are crucial for long-term convergence and must be preserved.

A noteworthy aspect of the DeMo algorithm is the use of DCT as an approximate mechanism for principal component extraction, facilitating efficient synchronization with minimal communication. The algorithm diverges from traditional optimization by removing the all-reduce operation on gradients, focusing instead on synchronizing only the extracted fast components.

Empirical Validation

Through empirical analysis in the pre-training of LLMs, the paper establishes DeMo's capability to match or surpass models trained with AdamW. The experiments utilize a highly reproducible framework, OLMo, and cover various hyperparameter configurations, demonstrating DeMo's robustness across different model scales. The results indicate that models trained via DeMo achieve comparable convergence and performance while drastically reducing per-GPU communication requirements—by several orders of magnitude.

The paper presents comprehensive experimental results, evidencing the efficacy of DeMo in terms of training loss and downstream task performance on benchmarks such as Hellaswag, ARC-Easy, and PIQA. Moreover, the utilization of a Signum variant of DeMo reveals potential for further optimization in memory usage without compromising efficacy.

Implications and Future Work

DeMo exemplifies a promising advancement in distributed training by reducing the dependency on high-speed interconnects, potentially enabling broader accessibility and cost efficiency in training large neural networks. Its topology-agnostic and architecture-independent design aids scalability, which is critical given the ever-increasing scale of contemporary neural networks.

The paper suggests numerous avenues for future developments. While DeMo presently leverages DCT for extracting principal momentum components efficiently, exploring alternative transformations or machine learning techniques for further optimization could enhance its compression capabilities. Additionally, extending the theoretical grounding by formally proving the conjectures could solidify its empirical success and widen its adoption in real-world applications.

In conclusion, DeMo represents a step forward in reducing communication bottlenecks in distributed deep learning training infrastructures. This work could inspire further research into compression techniques and innovative optimization algorithms that can exploit the inherent compressibility of neural network training processes.