Efficient Large-Scale Language Model Training on GPU Clusters

Description

Large language models have led to state-of-the-art accuracies across a range of tasks. However, training these large models efficiently is challenging for two reasons: a) GPU memory capacity is limited, making it impossible to fit large models on a single GPU or even on a multi-GPU server; and b) the number of compute operations required to train these models can result in unrealistically long training times. New methods of model parallelism such as tensor and pipeline parallelism have been proposed to address these challenges; unfortunately, naive usage leads to fundamental scaling issues at thousands of GPUs due to various reasons, e.g., expensive cross-node communication or idle periods waiting on other devices. In this work, we show how to compose different types of parallelism methods (tensor, pipeline, and data parallelism) to scale to thousands of GPUs, achieving a two-order-of-magnitude increase in the sizes of models we can efficiently train compared to existing systems. We discuss various implementations of pipeline parallelism and propose a novel schedule that can improve throughput by more than 10% with comparable memory footprint compared to previously-proposed approa…

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