GAS: A scheduling primitive dependency analysis-based cost model for tensor program optimization

Automatically generating high-performance tensor programs has become a promising approach for deploying deep neural networks. A key challenge lies in designing an effective cost model to navigate the vast scheduling search space. Existing approaches typically fall into two categories, each with limitations: offline learning cost models rely on large pre-collected datasets, which may be incomplete or device-specific, and online learning cost models depend on handcrafted features, requiring substantial manual effort and expertise. We propose GAS, a lightweight framework for generating tensor programs for deep learning applications. GAS reformulates feature extraction as a sequence-dependent analysis of scheduling primitives. Our cost model integrates three key factors to uncover performance-critical insights within scheduling sequences: (1) decision factors allocation, quantifying entropy and skewness of scheduling primitive factors to capture their dominance; (2) primitive contribution weights, measuring the relative impact of primitives on overall performance; and (3) structural semantic alignment, capturing correlations between scheduling primitive factors and hardware parallelism mechanisms. This approach reduces the complexity of handcrafted feature engineering and extensive pre-training datasets, significantly improving both efficiency and scalability. Experimental results on NVIDIA GPUs demonstrate that GAS achieves average speedups of 3.79 × over AMOS and 2.22 × over Ansor, while also consistently outperforming other state-of-the-art tensor compilers.