Tuning tensor program generation involves searching for various possible program transformation combinations for a given program on target hardware to optimize the tensor program execution. It is already a complex process because of the massive search space and exponential combinations of transformations make auto-tuning tensor program generation more challenging, especially when we have a heterogeneous target. In this research, we attempt to address these problems by learning the joint neural network and hardware features and transferring them to the new target hardware. We extensively study the existing state-of-the-art dataset, TenSet, perform comparative analysis on the test split strategies and propose methodologies to prune the dataset. We adopt an attention-inspired approach for tuning the tensor programs enabling them to embed neural network and hardware-specific features. Our approach could prune the dataset up to 45\% of the baseline without compromising the Pairwise Comparison Accuracy (PCA). Further, the proposed methodology can achieve on-par or improved mean inference time with 25%-40% of the baseline tuning time across different networks and target hardware.

本文追求解决自动优化张量程序生成的挑战，特别是在异构目标硬件的情况下，我们采用学习联合的神经网络和硬件特征以及基于注意力的方法来剪枝现有数据集，并将其应用于张量程序的调整中。我们的方法可以在不影响配对比较准确性 (PCA) 的情况下，将数据集缩减高达45％的基线。此外，与不同网络和目标硬件的基线调整时间相比，所提出的方法可以实现与基线相当或更好的平均推断时间，且时间只需要基线调整时间的 25％-40％。

跨异质特征的转移学习用于高效张量程序生成