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Deep learning models rely on highly optimized tensor libraries for efficientinference on heterogeneous hardware. Current deep compilers typicallypredetermine layouts of tensors and then optimize loops of operators. However,such unidirectional and one-off workflow strictly separates graph-leveloptimization and operator-level optimization into different system layers,missing opportunities for unified tuning. This paper proposes ALT, a compilerthat performs joint graph- and operator-level optimizations for deep models.ALT provides a generic transformation module to manipulate layouts and loopswith easy-to-use primitive functions. ALT further integrates an auto-tuningmodule that jointly optimizes graph-level data layouts and operator-level loopswhile guaranteeing efficiency. Experimental results show that ALT significantlyoutperforms state-of-the-art compilers (e.g., Ansor) in terms of both singleoperator performance (e.g., 1.5x speedup on average) and end-to-end inferenceperformance (e.g., 1.4x speedup on average).

ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations