Foundation models are in the process of becoming the dominant deep learning technology. Pretraining a foundation model is always time-consuming due to the large scale of both the model parameter and training dataset. Besides being computing-intensive, the pretraining process is extremely memory- and communication-intensive. These challenges make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism, and tensor model parallelism, to achieve high training efficiency. However, current 3D parallelism frameworks still encounter two issues: i) they are not transparent to model developers, requiring manual model modification to parallelize training, and ii) their utilization of computation resources, GPU memory, and network bandwidth is insufficient. We proposeMerak, an automated 3D parallelism deep learning training framework with high resource utilization. Merak automatically deploys 3D parallelism with an automatic model partitioner, which includes a graph-sharding algorithm and proxy node-based model graph. Merak also offers a non-intrusive API to scale out foundation model training with minimal code modification. In addition, we design a high-performance 3D parallel runtime engine that employs several techniques to exploit available training resources, including a shifted critical path pipeline schedule that increases computation utilization, stage-aware recomputation that makes use of idle worker memory, and sub-pipelined tensor model parallelism that overlaps communication and computation. Experiments on 64 GPUs demonstrate Merak's capability to speed up training performance over state-of-the-art 3D parallelism frameworks of models with 1.5, 2.5, 8.3, and 20 billion parameters by up to 1.42, 1.39, 1.43, and 1.61×, respectively.

As the deep learning model grows larger, training model with a single computational resource becomes impractical. To solve this, hybrid parallelism, which combines data and pipeline parallelism emerges to train large models with multiple GPUs. In practice, using heterogeneous GPU clusters to train large models is a common need due to the upgrade of a part of hardware. However, existing hybrid parallelism approaches in the heterogeneous environment do not work well in communication efficacy, workload balance among GPUs and utilizing the memory constrained GPU. To address these problems, we present a parallel DNN training approach, Hybrid Parallelism on Heterogeneous clusters (HPH). In HPH, we propose a topology designer that minimizes the communication time cost. Furthermore, HPH uses a partition algorithm that automatically partitions DNN layers among workers to maximize throughput. Besides, HPH adopts recomputation-aware scheduling to reduce memory consumption and further reschedule the pipeline to eliminate the extra time overhead of recomputation. Our experimental results on a 32-GPU heterogeneous cluster show that HPH achieves up to 1.42x training speed-ups compared with the state-of-the-art approach.

Recently, pipeline parallelism has been widely used in training large DNN models. However, there are still two main challenges for efficient pipeline parallelism: i) a balanced model partition is crucial for pipeline efficiency, whereas prior works lack a sound solution to generate a balanced partition automatically. ii) the startup overhead is inevitable and especially significant for deep pipelines, which is an essential source of pipeline bubbles and severely affects pipeline scalability. We propose AutoPipe to solve these two problems, which contains i) a planner for automatically and quickly generating a balanced pipeline partition scheme with a fine-grained partitioner. This partitioner groups DNN in the sub-layer granularity and finds the balanced scheme with a heuristic search algorithm; and ii) a micro-batch slicer that reduces pipeline startup overhead according to the planner results by splitting the micro-batch evenly. This slicer automatically solves an appropriate number of micro-batches to split. The experimental results show that AutoPipe can accelerate training by up to 1.30x over the state-of-the-art distributed training framework Megatron-LM, with a 50% reduction in startup overhead and an order-of-magnitude reduction in pipeline planning time. Furthermore, AutoPipe Planner improves the partition balance by 2.73x-12.7x compared to DAPPLE Planner and Piper.

Distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like image classification models, we find that these DL frameworks have relatively low scalability for sparse models like natural language processing (NLP) models that have highly sparse embedding tables. Most existing works overlook the sparsity of model parameters thus suffering from significant but unnecessary communication overhead. In this paper, we propose EmbRace, an efficient communication framework to accelerate communications of distributed training for sparse models. EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters. To effectively overlap sparse communication with both backward and forward computation, EmbRace further designs a 2D Communication Scheduling approach which optimizes the model computation procedure, relaxes the dependency of embeddings, and schedules the sparse communications of each embedding row with a priority queue. We have implemented a prototype of EmbRace based on PyTorch and Horovod, and conducted comprehensive evaluations with four representative NLP models. Experimental results show that EmbRace achieves up to 2.41 × speedup compared to the state-of-the-art distributed training baselines.