Asynchronous Local-SGD Training for Language Modeling

Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei Alex Rusu, Jiajun Shen, Arthur Szlam, MarcAurelio Ranzato

Keywords: Asynchronous Local-SGD training for Language Modeling

TL;DR: We investigate how to effectively apply asynchronous local-sgd for language modeling.

Abstract:

Local stochastic gradient descent (Local-SGD), also referred to as federated averaging, is an approach to distributed optimization where each device performs more than one SGD update per communication.
This work presents an empirical study of asynchronous Local-SGD for training language models; that is, each worker updates the global parameters as soon as it has finished its SGD steps. We conduct a comprehensive investigation by examining how worker hardware heterogeneity, model size, number of workers, and optimizer could impact the learning performance. We find that with naive implementations, asynchronous Local-SGD takes more iterations to converge than its synchronous counterpart despite updating the (global) model parameters more frequently. We identify momentum acceleration on the global parameters when worker gradients are stale as a key challenge. We propose a novel method that utilizes a delayed Nesterov momentum update and adjusts the workers' local training steps based on their computation speed. This approach, evaluated with models up to 150M parameters on the C4 dataset, matches the performance of synchronous Local-SGD in terms of perplexity per update step, and significantly surpasses it in terms of wall clock time.

Submission Number: 33