An Efficient 2D Method for Training Super-Large Deep Learning Models

Abstract

Since the rise of Transformer [22] and BERT [6], large language models [7], [12] have been proposed and shown unprecedented performance in tasks like translation, classification, and text generation. However, due to the memory constraint, model parallelism must be used to split the model across multiple processors. Inter-layer partition, intra-layer partition, and sparse activation are the major approaches to achieve model parallelism. Among them, inter-layer partition [10], [11] often requires the model to be explicitly expressed as a stack of sub-modules, the number of which equals to the number of processors, and would introduce either gradient staleness or bubble overhead; while the sparse activation [12] is primarily designed for Google TPU cluster and hard to deploy on GPU servers, intra-layer partition [17], especially Megatron-LM [18], can be easily deployed on GPU servers and has been adopted in subsequent works like Turing-NLG and M6. Though as pioneers of intra-layer parallelism, they still show memory redundancy and sub-optimal communication efficiency, which reveals the space for further improvements. In this work, we leverage SUMMA [21] and propose Optimus, a highly efficient and scalable paradigm for training super-large language models. In Optimus, activations and gradients are partitioned and distributed along processors all the way through forward and backward propagations, with hardly any memory redundancy. The isoefficiency of communication in pure model parallelism improves from W ~ p3 for Megatron-LM, to $W\sim {(\sqrt p \log p)^3}$ for our Optimus. This framework is implemented with open-source deep learning framework, PyTorch, and consolidates existing techniques such as mixed precision training [13], activation checkpointing [5], and data parallelism. In experiments on TACC Frontera supercomputers, Optimus shows 1.48× the speed for training, 1.78× speed for inference, and 8× the maximum batch size over Megatron-LM on 64 GPUs in pure model parallelism; and 1.73× speed for training, 2.32× speed for inference with data parallelism size equaling 2 on 128 GPUs. In pure model parallelism, Optimus surpasses Megatron-LM in weak scaling efficiency by a great margin, and shows an extraordinary increasing strong scaling efficiency. Optimus would facilitate the scaling of language models and serve as a strong thrust in the space exploration of artificial intelligence.