| 樊哲* **,南子渊* **,郝一帆*,杜子东*,陈云霁* **.基于自适应静态数据布局策略的深度学习张量程序自动生成框架[J].高技术通讯(中文),2023,33(11):1160~1171 |
| 基于自适应静态数据布局策略的深度学习张量程序自动生成框架 |
| A deep learning tensor program automatic generation framework based on adaptive layout of static data |
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| DOI：10. 3772/ j. issn. 1002-0470. 2023. 11. 004 |
| 中文关键词: 深度学习； 张量程序自动生成框架； 静态数据布局策略； 自适应策略； 性能预测模型 |
| 英文关键词: deep learning, tensor program automatic generation framework, layout of static/const data, adaptive strategy, performance cost model |
| 基金项目: |
| 作者 | 单位 | | 樊哲* ** | （*中国科学院计算技术研究所计算机体系结构国家重点实验室北京 100190）
（**中国科学院大学北京 100049） | | 南子渊* ** | | | 郝一帆* | | | 杜子东* | | | 陈云霁* ** | |
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| 中文摘要: |
| 如何确定静态数据布局是深度学习张量程序自动生成框架面临的重大挑战。Ansor作为目前应用最广泛、最具前景的此类框架，其根据预先指定的单一静态数据布局策略，训练性能预测模型，依据该模型搜索最佳性能的张量程序。但其存在单一策略非最优和性能预测模型不准确的问题。为此，本文提出基于自适应静态数据布局（AL）策略的深度学习张量程序自动生成框架AL-Ansor。AL-Ansor在搜索过程中自适应地选取多种静态数据布局策略，共同训练性能预测模型，从而搜索得到性能更高的张量程序。本文以32核Intel Xeon CPU为目标硬件平台，在多个卷积层上进行实验，结果表明，在同样的搜索次数下，相较于基于3种指定静态数据布局策略的Ansor，AL-Ansor生成的张量程序分别有13.81%、12.41%和16.59%的平均性能提升。 |
| 英文摘要: |
| How to determine the layout of static/const data is a big challenge faced by tensor program automatic generation frameworks. Ansor, the most broadly-used and promising framework among them, solves this issue by training a performance cost model according to a layout strategy specified in advance, then searching the tensor program with the optimal performance based on the cost model. However, there are two problems: a single strategy cannot be suitable for all tasks, and the performance cost model is not accurate. In order to solve these problems, AL-Ansor, a tensor program automatic generation framework based on the adaptive layout (AL) strategy of static data, is proposed. It adaptively chooses multiple layout strategies during the search process, and trains the performance cost model according to them. In this way, AL-Ansor can find a tensor program with higher performance. Taking convolutional layers as workloads, this work evaluates Ansor and AL-Ansor in a target server with a 32-core Intel Xeon CPU. The experimental results show that AL-Ansor improves the execution performance by 13.81%, 12.41%, and 16.59%, respectively, on average, compared against Ansor with three specified layout strategies. |
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