SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum (1910.00643v2)

Abstract: Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SlowMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm. Experiments on image classification and machine translation tasks demonstrate that SlowMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SlowMo runtime is on par with that of the base optimizer. We provide theoretical convergence guarantees showing that SlowMo converges to a stationary point of smooth non-convex losses. Since BMUF can be expressed through the SlowMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.

An Examination of the SlowMo Framework for Distributed SGD Optimization

Distributed optimization techniques play a crucial role in training large-scale models across extensive datasets. The paper "SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum" by Wang et al. introduces a novel framework, namely SlowMo, aimed at enhancing the performance of distributed Stochastic Gradient Descent (SGD) by mitigating communication costs through optimized synchronization approaches and momentum updates.

Overview of the Study

Current distributed optimization approaches such as Local SGD or decentralized methods like stochastic gradient push (SGP) attempt to mitigate communication overhead by reducing synchronization frequency or using peer-to-peer communication structures. However, these methods might not always achieve superior model accuracy when compared to more conventional methods like AllReduce-SGD, which synchronizes after every update. The authors propose SlowMo, a framework that builds on BMUF's principles, whereby parameters are periodically synchronized across workers, followed by a momentum update rather than at each optimization step.

Experimental Validation

Wang et al. conducted empirical tests on image classification via CIFAR-10 and ImageNet datasets, and machine translation using WMT'16 En-De, to establish the efficacy of SlowMo. The experiments consistently showed that SlowMo improves both optimization speed and model generalization performance compared to baseline methods. Particularly notable is the fact that these improvements are achieved with comparable runtime to the base optimizer, thus asserting its practical effectiveness.

Theoretical Contributions

The authors provide theoretical convergence guarantees, asserting that SlowMo converges to a stationary point of smooth non-convex functions at a rate of $\mathcal{O}(1/\sqrt{m T\tau})$. This indicates that SlowMo performs no slower than stochastic gradient descent, scaling efficiently with the number of worker nodes. Importantly, this results in the first formal convergence proof for BMUF within the contexts of smooth and non-convex objectives.

Discussion and Implications

The insights drawn from this paper have substantial implications for enhancing distributed training paradigms. SlowMo's introduction of slow momentum proves instrumental in balancing communication efficiency with the need for model accuracy across a broad range of tasks. The potential to apply SlowMo alongside diverse base optimizers or even as an augmentative layer signals a promising direction for optimizing distributed machine learning frameworks.

Future Directions in AI Distributed Training

Given the robust empirical and theoretical backing of SlowMo, future research could investigate further reductions in communication cost, possibly by integrating gradient compression techniques or optimizing network topologies. Additional exploration could consider scaling SlowMo to even larger and more complex network architectures or datasets to validate and refine scaling efficiency claims.

In conclusion, the SlowMo framework delivers a compelling step towards optimized distributed training, reaffirming the necessity of sophisticated momentum and synchronization strategies in evolving distributed optimization methodologies. The paper reflects a significant contribution to the field, implying broader applications across machine learning systems that necessitate distributed architectures for model training.