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.

翻译：调优张量程序生成涉及为目标硬件上的给定程序搜索各种可能的程序变换组合，以优化张量程序执行。由于巨大的搜索空间和指数级的变换组合，这一过程已经非常复杂，尤其是在异构目标场景下，自动调优张量程序生成更具挑战性。在本研究中，我们尝试通过学习联合神经网络与硬件特征并将其迁移至新目标硬件来解决这些问题。我们深入研究了现有最先进数据集TenSet，对测试集划分策略进行了比较分析，并提出了数据集精简方法。我们采用受注意力机制启发的方法来调优张量程序，使其能够嵌入神经网络和硬件特定特征。我们的方法可在不牺牲成对比较准确率（PCA）的前提下，将数据集精简至基准的45%。此外，所提方法在不同网络和目标硬件上，能以基准调优时间25%-40%的代价实现与之相当甚至更优的平均推理时间。