• depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers AuthorsKaichao You**â  , Runsheng Baiâ  , Meng Cao, Jianmin Wangâ  , Ion Stoicaâ ¡, Mingsheng Longâ View publication View source code (GitHub) Copy Bibtex PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. • However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. • The compiler operates at the Python bytecode level, making it appear as an opaque box. • To address this, we introduce \texttt{depyf}, a tool designed to demystify the inner workings of the PyTorch compiler. • \texttt{depyf} decompiles bytecode generated by PyTorch back into equivalent source code, and establishes connections between in-memory code objects and their on-disk source code counterparts. • This feature enables users to step through the source code line by line using debuggers, thus enhancing their understanding of the underlying processes.

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  • depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers AuthorsKaichao Youâ , Runsheng Baiâ , Meng Cao, Jianmin Wangâ , Ion Stoicaâ¡, Mingsheng Longâ depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers AuthorsKaichao Youâ , Runsheng Baiâ , Meng Cao, Jianmin Wangâ , Ion Stoicaâ¡, Mingsheng Longâ PyTorch \texttt{2.x} introduces a compiler designed to accelerate deep learning programs. However, for machine learning researchers, adapting to the PyTorch compiler to full potential can be challenging. The compiler operates at the Python bytecode

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