Stochastic Gradient Push for Distributed Deep Learning (1811.10792v3)

Abstract: Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication delays. The PushSum gossip algorithm is robust to these issues, but only performs approximate distributed averaging. This paper studies Stochastic Gradient Push (SGP), which combines PushSum with stochastic gradient updates. We prove that SGP converges to a stationary point of smooth, non-convex objectives at the same sub-linear rate as SGD, and that all nodes achieve consensus. We empirically validate the performance of SGP on image classification (ResNet-50, ImageNet) and machine translation (Transformer, WMT'16 En-De) workloads. Our code will be made publicly available.

• The paper introduces Stochastic Gradient Push, which integrates the PushSum protocol with stochastic updates to mitigate communication delays and straggler issues.
• It details a novel overlapping approach that couples communication with computation while maintaining convergence rates comparable to traditional SGD.
• Empirical evaluations on benchmarks like ResNet-50 and Transformer architectures demonstrate significant speed-ups and robust performance over synchronous methods.

Analyzing Stochastic Gradient Push for Distributed Deep Learning

The paper "Stochastic Gradient Push for Distributed Deep Learning" introduces a novel approach for training deep neural networks over distributed systems, presented by Mahmoud Assran et al. The primary focus of this research lies in optimizing the communication efficiency during distributed deep learning, specifically addressing the challenges posed by stragglers and communication delays inherent in large-scale data-parallel environments.

Problem Context and Approach

Deep neural networks (DNNs) have gained wide application across multiple domains such as computer vision and natural language processing. As these models scale in complexity and data requirements, the computational demands also increase substantially. To achieve efficient training, distributed data-parallel algorithms are employed, where mini-batch gradient updates are computed across multiple nodes. Traditional synchronization methods, like AllReduce, are prone to bottlenecks and are negatively impacted by slower nodes (stragglers) and bandwidth limitations.

This paper investigates the Stochastic Gradient Push (SGP) algorithm, which combines the PushSum protocol—known for its tolerance to such issues—with stochastic gradient updates. SGP offers an approximate alternative to exact distributed averaging, facilitating the use of less stringent communication topologies. While prior methods constrained node interactions to symmetrical communications, SGP’s allowance for asymmetric and sparse topologies presents a broader scope of applicability. This augments both its scalability and robustness, mitigating the traditional challenges associated with distributed synchronization.

Theoretical Contributions

The paper serves three major theoretical advances:

1. Proposal of Overlap SGP: This variant overlaps communication with computation, aiming to further mask communication overhead without sacrificing the convergence rate.
2. Convergence Analysis: It rigorously demonstrates that SGP converges to a stationary point of smooth non-convex objectives at an $\mathcal{O}(1/\sqrt{nK})$ rate, aligning with the convergence characteristic of traditional SGD.
3. Consensus Achievement: It proves that all nodes reach consensus, offering not only theoretical assurances but also practical applicability in distributed setups.

Empirical Evaluation and Results

The empirical validation, carried out on tasks like image classification using ResNet-50 on the ImageNet dataset and machine translation on the WMT'16 En-De task with the Transformer architecture, indicates substantial practical benefits. Notably, SGP and its overlapping variant lead to acceleration in training times while maintaining or slightly improving predictive accuracy compared to the synchronous AllReduce SGD, especially under low-bandwidth conditions. For instance, in setups using 256 GPUs over 32 compute nodes, SGP achieved equivalent validation accuracy in one-third the time required by AllReduce and exceeded it with further iterations.

Implications and Future Directions

The introduction of the SGP algorithm addresses key hurdles in distributed machine learning, offering a technique that minimizes communication demands and counteracts the detrimental impact of stragglers. Its success introduces significant implications for enhancing the scalability of deep neural networks across more diverse and constrained environments.

For future advancements, this framework paves the path for integrating additional optimizations. Exploring combinations with quantized gradient techniques or federated architectures can further extend its utility. Additionally, adapting SGP for other widely used optimizers, such as Nesterov's accelerated gradient or Adam, and testing it across various heterogeneous network specifications could broaden its applicability and feedback resilience.

In conclusion, Stochastic Gradient Push provides a promising alternative to exact averaging strategies in distributed training, offering a balance between less communication overhead and robust algorithmic convergence, opening avenues for more scalable, distributed deep learning solutions.