论文标题
迈向深层神经网络中计算图的有效视觉简化
Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks
论文作者
论文摘要
深神经网络（DNN）中的计算图表示由许多张量和操作员组成的特定数据流程图（DFD）。当结构高度复杂且大规模（例如Bert [1]）时，现有用于可视化计算图的工具包不适用于。为了解决这个问题，我们建议利用一套视觉简化技术，包括一种循环避难方法，基于模块的边缘变形算法和同构子图堆叠策略。我们设计和实施一个交互式可视化系统，该系统适用于最多10,000个元素的计算图。实验结果和用法方案表明，我们的工具平均减少了60％的元素，因此可以提高识别和诊断DNN模型的性能。我们的贡献被整合到开源DNN可视化工具包中，即MindinSight [2]。
A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].
