Randomness In Neural Network Training: Characterizing The Impact of Tooling (2106.11872v1)

Abstract: The quest for determinism in machine learning has disproportionately focused on characterizing the impact of noise introduced by algorithmic design choices. In this work, we address a less well understood and studied question: how does our choice of tooling introduce randomness to deep neural network training. We conduct large scale experiments across different types of hardware, accelerators, state of art networks, and open-source datasets, to characterize how tooling choices contribute to the level of non-determinism in a system, the impact of said non-determinism, and the cost of eliminating different sources of noise. Our findings are surprising, and suggest that the impact of non-determinism in nuanced. While top-line metrics such as top-1 accuracy are not noticeably impacted, model performance on certain parts of the data distribution is far more sensitive to the introduction of randomness. Our results suggest that deterministic tooling is critical for AI safety. However, we also find that the cost of ensuring determinism varies dramatically between neural network architectures and hardware types, e.g., with overhead up to $746\%$, $241\%$, and $196\%$ on a spectrum of widely used GPU accelerator architectures, relative to non-deterministic training. The source code used in this paper is available at https://github.com/usyd-fsalab/NeuralNetworkRandomness.

• The paper identifies both algorithmic and implementation factors as key contributors to training randomness.
• The paper demonstrates through extensive experiments that while overall accuracy remains stable, internal variability can amplify fairness and bias issues.
• The paper emphasizes the need for deterministic tooling solutions to improve reproducibility and mitigate risks in sensitive AI applications.

Impact of Tooling on Randomness in Neural Network Training

In the field of machine learning, the pursuit of determinism and reduction of randomness remains an essential goal, often driven by concerns about reproducibility and AI safety in critical applications. The paper "Randomness in Neural Network Training: Characterizing the Impact of Tooling" by Donglin Zhuang and colleagues provides a comprehensive examination of how hardware and software tooling choices introduce nondeterministic behavior into deep neural network (DNN) training. This exploration contrasts with the prevailing focus on algorithmic sources of randomness and offers critical insights into overlooked implementation factors.

Characterization of Randomness Sources

The research distinguishes two primary sources of randomness that affect neural network training:

1. Algorithmic Factors (ALGO): These include stochastic model design choices such as random initialization, data augmentation, shuffling, and stochastic layers like dropout. Extensive prior work examines how these choices impact model performance variability.
2. Implementation Factors (IMPL): This source is associated with the hardware and software environment, including differences in GPU architecture and the nondeterministic nature of operations due to floating-point precision in parallel computing.

Experimental Findings

The authors performed large-scale experiments across various networks, datasets, and hardware configurations. Key findings of this work include:

• Top-line metrics, such as accuracy, exhibit minimal impact despite substantial internal variability due to random initialization and data ordering.
• As shown in the experiments, both ALGO and IMPL contribute notably to model instability, as evidenced by metrics such as predictive churn, L2 norm of trained weights, and variance in performance metrics across subsets of data.
• Surprisingly, the overall system noise does not simply add up from ALGO and IMPL. Instead, eliminating only one type of randomness does not guarantee consistent training outcomes. This is particularly evident from the substantial differences across independent runs and sub-group performance stability metrics.
• The presence of noise has a pronounced impact on model bias and fairness considerations, disproportionately affecting underrepresented data sub-groups.

Practical and Theoretical Implications

This paper underscores the importance of addressing both algorithmic and tooling-induced randomness to ensure AI safety, particularly in sensitive domains like healthcare and autonomous driving. Some implications and future directions include:

• AI Safety: Deterministic tooling is critical for AI systems where consistency and reliability are paramount. For example, in applications involving medical diagnostics, nondeterminism may result in vastly different treatment recommendations despite similar overall accuracy.
• Model Bias: The findings suggest that noise exacerbates biases, particularly affecting minor subgroups. Addressing tooling-induced randomness can mitigate fairness discrepancies arising from training noise.
• Training Overhead: The variability in deterministic training overhead identified between hardware architectures indicates that efforts to improve determinism could require substantial computational resources. Future AI systems may need optimized tooling strategies to balance reproducibility and efficiency costs.

Future Directions

The paper advocates for further exploration of implementation-level deterministic solutions in distributed training environments. As AI systems increasingly rely on parallel computing and cross-node operations, understanding and controlling the sources of tooling-induced randomness becomes vital for reliable and reproducible AI model deployment. Addressing these issues supports the deployment of more robust AI systems across diverse applications.