Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models

TL;DR

This paper compares six data-parallel strategies for training large NLP models like GPT-2, analyzing their performance and robustness to identify the most effective approaches for distributed deep learning.

Contribution

It introduces and evaluates six different data-parallel training strategies for NLP models, providing insights into their performance and robustness.

Findings

Distributed Parameter Server with Apex has best single-node performance.

Horovod with Apex is most robust across single and multiple nodes.

Quantitative analysis compares efficiency of each strategy.

Abstract

Distributed deep learning is becoming increasingly popular due to the expanding demand for computing resources for deep learning models with a larger amount of parameters. Different from traditional training approaches, data-parallel training allows multiple compute nodes to train large deep learning models simultaneously in order to boost the training efficiency. In this paper, we present and compare six strategies for data-parallel training using PyTorch on the language model GPT-2 with 100M parameters using a qualitative approach. These strategies are Single GPU, Single Parameter Server, Distributed Parameter Server, Horovod, Distributed Parameter Server with Apex mixed-precision strategy, and Horovod with Apex mixed-precision strategy. We also analyze the quantitative experiment results from each strategy. In the end, we draw the conclusion that the Distributed Parameter Server with Apex mixedprecision strategy has the best performance on single node training, while Horovod with Apex is the most robust approach to use when we have single or multiple nodes.