One-Shot Tuner for Deep Learning Compilers
CC'22: PROCEEDINGS OF THE 31ST ACM SIGPLAN INTERNATIONAL CONFERENCE ON COMPILER CONSTRUCTION(2022)
Abstract
Auto-tuning DL compilers are gaining ground as an optimizing back-end for DL frameworks. While existing work can generate deep learning models that exceed the performance of hand-tuned libraries, they still suffer from prohibitively long auto-tuning time due to repeated hardware measurements in large search spaces. In this paper, we take a neural-predictor inspired approach to reduce the auto-tuning overhead and show that a performance predictor model trained prior to compilation can produce optimized tensor operation codes without repeated search and hardware measurements. To generate a sample-efficient training dataset, we extend input representation to include task-specific information and to guide data sampling methods to focus on learning high-performing codes. We evaluated the resulting predictor model, One-Shot Tuner, against AutoTVM and other prior work, and the results show that One-Shot Tuner speeds up compilation by 2.81x to 67.7x compared to prior work while providing comparable or improved inference time for CNN and Transformer models.
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Key words
optimizing compilers,autotuning,performance models,deep neural networks
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