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not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "PyTorch2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation", "url": "https://pytorch.org/assets/pytorch2-2.pdf" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "Separate compiler contribution from the inherited2019imperative PyTorch framework. Unsupported/data-dependent behavior can run outside compiled graphs; guards, recompilation and graph breaks have costs. Source-level conditional soundness reasoning is not a machine-checked proof of every handler/backend. Triton, CPython hooks and other reused components retain credit.", "domain": "Machine-learning compilation" }, "sources": [ { "citation": "PyTorch2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation", "url": "https://pytorch.org/assets/pytorch2-2.pdf" } ], "statement": "# PyTorch 2: guarded bytecode graph capture and compiler execution\n\nIntegrated TorchDynamo bytecode rewriting and guarded graph caching with graph-break continuations, AOTAutograd forward/backward capture and TorchInductor code generation for PyTorch programs.\n\n## Scientific scope\n\nSeparate compiler contribution from the inherited2019imperative PyTorch framework. Unsupported/data-dependent behavior can run outside compiled graphs; guards, recompilation and graph breaks have costs. Source-level conditional soundness reasoning is not a machine-checked proof of every handler/backend. Triton, CPython hooks and other reused components retain credit.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nJason Ansel; Edward Yang; Horace He; Natalia Gimelshein; Animesh Jain; Michael Voznesensky; Bin Bao; Peter Bell; David Berard; Evgeni Burovski; Geeta Chauhan; Anjali Chourdia; Will Constable; Alban Desmaison; Zachary DeVito; Elias Ellison; Will Feng; Jiong Gong; Michael Gschwind; Brian Hirsh; Sherlock Huang; Kshiteej Kalambarkar; Laurent Kirsch; Michael Lazos; Mario Lezcano; Yanbo Liang; Jason Liang; Yinghai Lu; CK Luk; Bert Maher; Yunjie Pan; Christian Puhrsch; Matthias Reso; Mark Saroufim; Marcos Yukio Siraichi; Helen Suk; Michael Suo; Phil Tillet; Eikan Wang; Xiaodong Wang; William Wen; Shunting Zhang; Xu Zhao; Keren Zhou; Richard Zou; Ajit Mathews; Gregory Chanan; Peng Wu; Soumith Chintala\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [PyTorch2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://pytorch.org/assets/pytorch2-2.pdf)\n", "summary": "Integrated TorchDynamo bytecode rewriting and guarded graph caching with graph-break continuations, AOTAutograd forward/backward capture and TorchInductor code generation for PyTorch programs.", "title": "PyTorch 2: guarded bytecode graph capture and compiler execution" }, "record_id": "urn:uuid:ec0d3518-62d4-4aa7-a212-1e60ade5f035", "version": 1, "source_ref": { "record_id": "urn:uuid:ec0d3518-62d4-4aa7-a212-1e60ade5f035", "version": 1, "content_hash": "sha256:f8cf646cf7386387ff77db8a4c2d3cb2102b89b017912d7cb3f4ecc476b84a82" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:f836d9c6678e41dbde0dd30d5d3dae96286c7616e1d18f48d60914e973e95c75" }, "target": { "record_id": "urn:uuid:ec0d3518-62d4-4aa7-a212-1e60ade5f035", "version": 1, "content_hash": "sha256:f8cf646cf7386387ff77db8a4c2d3cb2102b89b017912d7cb3f4ecc476b84a82" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "ansel-et-al-pytorch2-compiler-2024", "entry_id": "ansel-et-al-pytorch2-compiler-2024", "revision": 1, "title": "PyTorch 2: guarded bytecode graph capture and compiler execution", "context": "Machine-learning compilation", "track": "Human-designed research method or resource", "band": "A", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Integrated TorchDynamo bytecode rewriting and guarded graph caching with graph-break continuations, AOTAutograd forward/backward capture and TorchInductor code generation for PyTorch programs.", "scope": "Integrated TorchDynamo bytecode rewriting and guarded graph caching with graph-break continuations, AOTAutograd forward/backward capture and TorchInductor code generation for PyTorch programs. 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Triton, CPython hooks and other reused components retain credit.", "references": [ { "title": "PyTorch2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation", "url": "https://pytorch.org/assets/pytorch2-2.pdf", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. 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