Benchmarking Neural Network Training Algorithms

2023-06-12

arXiv.org

AI Summary

Fair evaluation of deep learning training algorithms faces three key challenges: inconsistent termination criteria, high workload sensitivity, and difficulty isolating hyperparameter tuning. This paper introduces AlgoPerf—the first time-oriented, multi-workload training algorithm benchmark—featuring robustness-aware workload variant design and a standardized termination protocol, with hyperparameter tuning rigorously isolated. Evaluated on a unified hardware platform, AlgoPerf employs a diverse multi-task workload suite and a systematic optimizer comparison methodology to enable latency-accuracy co-evaluation across models, datasets, and hardware. Experiments reveal substantial latency disparities among mainstream optimizers, establish reproducible state-of-the-art baselines, and deliver the first quantitative, fair, and engineering-practical evaluation standard for training algorithm improvement.

Abstract

Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate schedules, or data selection schemes) could save time, save computational resources, and lead to better, more accurate, models. Unfortunately, as a community, we are currently unable to reliably identify training algorithm improvements, or even determine the state-of-the-art training algorithm. In this work, using concrete experiments, we argue that real progress in speeding up training requires new benchmarks that resolve three basic challenges faced by empirical comparisons of training algorithms: (1) how to decide when training is complete and precisely measure training time, (2) how to handle the sensitivity of measurements to exact workload details, and (3) how to fairly compare algorithms that require hyperparameter tuning. In order to address these challenges, we introduce a new, competitive, time-to-result benchmark using multiple workloads running on fixed hardware, the AlgoPerf: Training Algorithms benchmark. Our benchmark includes a set of workload variants that make it possible to detect benchmark submissions that are more robust to workload changes than current widely-used methods. Finally, we evaluate baseline submissions constructed using various optimizers that represent current practice, as well as other optimizers that have recently received attention in the literature. These baseline results collectively demonstrate the feasibility of our benchmark, show that non-trivial gaps between methods exist, and set a provisional state-of-the-art for future benchmark submissions to try and surpass.

Problem

• Identify state-of-the-art training algorithms reliably
• Measure training time accurately and decide completion
• Compare hyperparameter-tuned algorithms fairly

Innovation

• Introduces competitive time-to-result benchmark
• Handles sensitivity via workload variants
• Fairly compares hyperparameter-tuned algorithms