EAGLE: Expedited Device Placement with Automatic Grouping for Large Models

Hao Lan, Li Chen, Baochun Li

Abstract: Advanced deep neural networks with large sizes are usually trained on a mixture of devices, including multiple CPUs and GPUs. The model training speed and efficiency are drastically impacted by the placement of operations on devices. To identify the optimal device placement, the state-of-the-art method is based on reinforcement learning with a hierarchical model, which partitions the operations into groups and then assigns each group to specific devices. However, due to the additional dimension of grouping decisions coupled with the placement, the reinforcement learning efficiency is greatly reduced. With modern neural networks growing in size and complexity, the issue of low efficiency and high cost in device placement is further aggravated. In this paper, we propose our design of EAGLE (Expedited Automatic Grouping for Large modEls), which integrates automatic grouping into reinforcement learning-based placement in an optimal way, to achieve the best possible training time performance for very large models. An extra RNN is introduced to transform parameters of the grouper into inputs of the placer, linking the originally separated parts together. Further optimizations have also been made in the network inputs. We have deployed and extensively evaluated EAGLE on InceptionV3, GNMT and BERT benchmarks. Compared with the state-of-the-art, the performance achieved by our design, measured by the per-step time with the resulted placement, is 2.7% and 18.7% better for GNMT and BERT, respectively. For Inception-V3, our design achieves the fastest speed in discovering the optimal placement.