RedFuser: An Automatic Operator Fusion Framework for Cascaded Reductions on AI Accelerators
arXiv:2603.10026v1 Announce Type: cross Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been widely adopted in modern AI compilers. However, for cascaded reduction operations involving multiple loops with inter-loop data dependencies, such as the safe softmax followed by GEMM within attention mechanisms, existing compilers lack effective automated fusion and kernel generation capabilities. Although some works have addressed specific instances through hand-crafted fusion strategies, their solutions are limited in generality and difficult to extend to other similar structures. Given the prevalence of such computational patterns in deep learning models, there remains significant untapped potential in achieving general and automated fusion optimization. In this paper, we present a formal theoretical methodology for analyzing cascaded reductions which can fuse them into a single loo
arXiv:2603.10026v1 Announce Type: cross Abstract: Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been widely adopted in modern AI compilers. However, for cascaded reduction operations involving multiple loops with inter-loop data dependencies, such as the safe softmax followed by GEMM within attention mechanisms, existing compilers lack effective automated fusion and kernel generation capabilities. Although some works have addressed specific instances through hand-crafted fusion strategies, their solutions are limited in generality and difficult to extend to other similar structures. Given the prevalence of such computational patterns in deep learning models, there remains significant untapped potential in achieving general and automated fusion optimization. In this paper, we present a formal theoretical methodology for analyzing cascaded reductions which can fuse them into a single loop and introduce an incremental computation form. Based on this methodology, we design Reduction Fuser (RedFuser), a framework that automatically identifies supported cascaded reduction patterns and generates optimized fused kernels. Experiments show that RedFuser successfully fuses diverse workloads, achieving up to 2$\times$ to 5$\times$ speedup over state-of-the-art AI compilers and matching the performance of highly optimized hand-written kernels. The code is available at https://github.com/alibaba/redfuser
Executive Summary
The article introduces RedFuser, a framework for automatic operator fusion in AI models, specifically targeting cascaded reduction operations. RedFuser provides a formal methodology for analyzing and fusing these operations, resulting in significant performance improvements. The framework achieves up to 2-5 times speedup over state-of-the-art AI compilers and matches the performance of hand-optimized kernels. The code is made available, allowing for further development and application.
Key Points
- ▸ RedFuser is an automatic operator fusion framework for AI models
- ▸ It targets cascaded reduction operations with inter-loop data dependencies
- ▸ The framework achieves significant performance improvements over existing compilers
Merits
Automated Optimization
RedFuser provides automated fusion and kernel generation capabilities, reducing the need for manual optimization
Improved Performance
The framework achieves significant speedup over state-of-the-art AI compilers and matches hand-optimized kernel performance
Demerits
Limited Generality
The framework may not be applicable to all types of AI models or computational patterns
Dependence on Computational Patterns
RedFuser's effectiveness relies on the presence of specific computational patterns in the AI model
Expert Commentary
The introduction of RedFuser marks a significant advancement in AI compiler optimization, addressing a key challenge in the deployment of AI models. The framework's automated fusion capabilities and significant performance improvements make it an attractive solution for developers and researchers. However, further research is needed to fully explore the potential of RedFuser and its applications in various domains. The availability of the code and the potential for community development are also notable aspects, as they may lead to further innovations and improvements in the field.
Recommendations
- ✓ Further research should be conducted to explore the applicability of RedFuser to various AI models and computational patterns
- ✓ The development of RedFuser should be continued, with a focus on improving its generality and extending its capabilities to other areas of AI compiler optimization
