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A Sample-Free Compilation Framework for Efficient Dynamic Tensor Computation

Yangjie Zhou,Honglin Zhu,Qian Qiu,Weihao Cui,Zihan Liu,Peng Chen,Mohamed Wahib,Cong Guo,Siyuan Feng,Jintao Meng,Haidong Lan,Jingwen Leng,Yun Lin,Jinsong Dong,Wenxi Zhu,Minwen Deng

Published 2025 in International Conference on Software Composition

ABSTRACT

Dynamic-shape tensor computation poses challenges for shape-specific compilation due to variable input dimensions. Existing compilers rely on shape samples, incurring high tuning costs and performance degradation on unseen inputs. We present Helix, a dynamic tensor compilation framework with sample-free compilation and architecture-guided optimization to achieve both compilation efficiency and shape-general performance. To avoid shape sampling, Helix constructs shape-agnostic compilation by decomposing computations across architectural layers. A bidirectional strategy combines top-down abstraction to align tensor computations with architectural hierarchies, and bottom-up kernel construction to build efficient execution strategies from reusable, architecture-aligned micro-kernels. A hybrid analyzer ensures accuracy through profiling at lower architectural levels, and achieves scalability through architecture-informed modeling at higher levels and runtime. This hierarchical design eliminates shape-specific tuning and enables shape-adaptive execution. Evaluations conducted on x86 CPUs, ARM CPUs, and NVIDIA GPUs demonstrate that Helix reduces compilation time by 174 × over the existing compilers and delivers 2.26 × and 3.29 × execution speedups over vendor libraries and dynamic-shape compilers, respectively.

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