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Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to over CPU execution and by up to compared to other commonly used baselines.
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Accelerated Device Placement Optimization with Contrastive Learning
models, it is difficult to fit and train a complete copy of the model on a single computational device with limited capability. Therefore, large neural networks are usually trained on a mixture of devices, including multiple CPUs and GPUs, of which the computational speed and efficiency are drastically affected by how these models are partitioned and placed on the devices. In this paper, we propose Mars, a novel design to find efficient placements for large models. Mars leverages a self-supervised graph neural network pre-training framework to generate node representations for operations, which is able to capture the topological properties of the computational graph. Then, a sequence-to-sequence neural network is applied to split large models into small segments so that Mars can predict the placements sequentially. Novel optimizations have been applied in the placer design to achieve the best possible performance in terms of the time needed to complete training the agent for placing models with very large sizes. We deployed and evaluated Mars on benchmarks involving Inception-V3, GNMT, and BERT models. Extensive experimental results show that Mars can achieve up to 27.2% and 2.7% speedup of per-step training time than the state-of-the-art for GNMT and BERT models, respectively. We also show that with self-supervised graph neural network pretraining, our design achieves the fastest speed in discovering the optimal placement for Inception-V3.
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- Award ID(s):
- 2019511
- PAR ID:
- 10338299
- Date Published:
- Journal Name:
- Proceedings of the 50th International Conference on Parallel Processing (ICPP 2021)
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3472456.3472523
