GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918476129501184 |
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| author | Kashmira, Savini Dantanarayana, Jayanaka Sathiyalogeswaran, Thamirawaran Flautner, Krisztian Tang, Lingjia Mars, Jason |
| author_facet | Kashmira, Savini Dantanarayana, Jayanaka Sathiyalogeswaran, Thamirawaran Flautner, Krisztian Tang, Lingjia Mars, Jason |
| contents | This paper presents GRAPHMEND, a high-level compiler technique that eliminates FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to enable just-in-time graph compilation, unresolved dynamic control flow and unsupported Python constructs often fragment models into multiple FX graphs. These fragments force frequent fallbacks to eager mode, introduce costly CPU-to-GPU synchronizations, and reduce optimization opportunities. GRAPHMEND addresses this limitation by analyzing and transforming source code before execution. Built on the Jaseci compilation framework, GRAPHMEND introduces two code transformations that remove graph breaks due to dynamic control flow and Python side effects. This design allows PyTorch's compilation pipeline to capture larger, uninterrupted FX graphs without requiring manual refactoring by developers. Evaluation across eight Hugging Face models shows that GRAPHMEND removes graph breaks due to dynamic control flow and Python side effects, reducing the break count to 0 in 6 models and reducing it from 5 to 2 in another model. On NVIDIA RTX 3090 and A40 GPUs, GRAPHMEND achieves up to 75% latency reductions and up to 8% higher end-to-end throughput. These results demonstrate that high-level code transformation is an effective complement to PyTorch's dynamic JIT compilation pipeline, substantially improving both usability and performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16248 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2 Kashmira, Savini Dantanarayana, Jayanaka Sathiyalogeswaran, Thamirawaran Flautner, Krisztian Tang, Lingjia Mars, Jason Programming Languages Machine Learning Software Engineering This paper presents GRAPHMEND, a high-level compiler technique that eliminates FX graph breaks in PyTorch 2 programs. Although PyTorch 2 introduced TorchDynamo and TorchInductor to enable just-in-time graph compilation, unresolved dynamic control flow and unsupported Python constructs often fragment models into multiple FX graphs. These fragments force frequent fallbacks to eager mode, introduce costly CPU-to-GPU synchronizations, and reduce optimization opportunities. GRAPHMEND addresses this limitation by analyzing and transforming source code before execution. Built on the Jaseci compilation framework, GRAPHMEND introduces two code transformations that remove graph breaks due to dynamic control flow and Python side effects. This design allows PyTorch's compilation pipeline to capture larger, uninterrupted FX graphs without requiring manual refactoring by developers. Evaluation across eight Hugging Face models shows that GRAPHMEND removes graph breaks due to dynamic control flow and Python side effects, reducing the break count to 0 in 6 models and reducing it from 5 to 2 in another model. On NVIDIA RTX 3090 and A40 GPUs, GRAPHMEND achieves up to 75% latency reductions and up to 8% higher end-to-end throughput. These results demonstrate that high-level code transformation is an effective complement to PyTorch's dynamic JIT compilation pipeline, substantially improving both usability and performance. |
| title | GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2 |
| topic | Programming Languages Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2509.16248 |