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PyTorch 2.0: The Journey to Bringing Compiler Technologies to the Core of PyTorch (Keynote)

Published: 22 February 2023 Publication History

Abstract

Four and a half years after PyTorch 1.0, we announced PyTorch 2.0 at the PyTorch Conference last December. The message was simple – introducing compiled mode, torch.compile(), to the core of PyTorch. This talk shares our 5-year journey of finding the right compiler solutions for PyTorch. We answer questions like: (i) Why did it take so long? (ii) What was the biggest challenge of designing compiler solutions for PyTorch? (iii) How did we co-design the compiler w/ the core of PyTorch? (iiii) What conventions did we break in the design of TorchDynamo and TorchInductor?
As an ML compiler, PyTorch 2.0 is unconventional in many ways. By sharing our thought processes, insights, and design decisions during the development of PT2, we hope to bring new thinking into the thriving landscape of ML compilers and inject a dose of real-world considerations into the research community.

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  • (2024)When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modelingHydrology and Earth System Sciences10.5194/hess-28-3051-202428:13(3051-3077)Online publication date: 15-Jul-2024
  • (2024)Couple-Domain Strategy for UAV Imagery Super-ResolutionProceedings of the 2024 7th International Conference on Signal Processing and Machine Learning10.1145/3686490.3686511(140-145)Online publication date: 12-Jul-2024

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  1. PyTorch 2.0: The Journey to Bringing Compiler Technologies to the Core of PyTorch (Keynote)

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        cover image ACM Conferences
        CGO '23: Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization
        February 2023
        262 pages
        ISBN:9798400701016
        DOI:10.1145/3579990
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        New York, NY, United States

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        Published: 22 February 2023

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        • (2024)When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modelingHydrology and Earth System Sciences10.5194/hess-28-3051-202428:13(3051-3077)Online publication date: 15-Jul-2024
        • (2024)Couple-Domain Strategy for UAV Imagery Super-ResolutionProceedings of the 2024 7th International Conference on Signal Processing and Machine Learning10.1145/3686490.3686511(140-145)Online publication date: 12-Jul-2024

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