arXiv:2509.16248v4 Announce Type: replace-cross Abstract: This paper presents GraphMend, a compiler technique that automatically fixes FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to enable just-in-time graph compilation, certain code patterns still cause graph breaks that force execution to fall back to Python eager mode, introducing costly CPU-GPU synchronization and reducing optimization opportunities. Our investigation of 195 Hugging Face models reveals that 13.8% of models exhibit graph breaks. GraphMend automatically eliminates fixabl
The continuous evolution of AI frameworks like PyTorch 2 demands ongoing optimization to fully leverage underlying hardware, making performance improvements a constant focus.
This development significantly enhances the efficiency and performance of PyTorch 2 models, directly impacting AI development cycles and resource utilization for a wide range of applications.
Previously problematic code patterns causing 'graph breaks' in PyTorch 2 can now be automatically fixed, reducing execution fallbacks and enabling more consistent graph compilation.
- · AI developers and researchers
- · Cloud infrastructure providers
- · Companies deploying PyTorch models
- · Open-source AI community
Improved performance and reduced computational costs for PyTorch 2 users.
Accelerated development and experimentation with large-scale AI models due to better framework efficiency.
Potentially democratized access to high-performance AI deployment by lowering the technical barrier for optimization.
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Read at arXiv cs.LG