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apache/tvm/52b9376d3f224ab624cc0d5f5df1b35e0161d9f7
commit
52b9376d3f224ab624cc0d5f5df1b35e0161d9f7
[log]
author
Shushi Hong <820958424@qq.com>
Thu Jul 23 20:02:48 2026 -0400
committer
GitHub <noreply@github.com>
Thu Jul 23 20:02:48 2026 -0400
tree
1a33aded871265a8b1ef300e7a4c297570967d7c
parent
277ae41efa503f899359b3a581f7c54b4b87d197 [diff]
[Fix][TIRx] Ignore statement spans in structural identity (#20043)

This PR excludes `tirx::StmtNode::span` from structural equality and
structural hash calculations.

Source locations are diagnostic metadata and should not affect the
structural identity of a TIRx statement. Other TIRx nodes with spans
already follow this behavior, but `StmtNode::span` was missing the
`SEqHashIgnore` field flag.

A regression test is added to verify that two otherwise identical
statements with different spans are structurally equal and produce the
same structural hash.
2 files changed
tree: 1a33aded871265a8b1ef300e7a4c297570967d7c
  1. .agents/
  2. .github/
  3. 3rdparty/
  4. apps/
  5. ci/
  6. cmake/
  7. docker/
  8. docs/
  9. include/
  10. jvm/
  11. licenses/
  12. python/
  13. src/
  14. tests/
  15. web/
  16. .asf.yaml
  17. .clang-format
  18. .gitattributes
  19. .gitignore
  20. .gitmodules
  21. .markdownlint-cli2.yaml
  22. .pre-commit-config.yaml
  23. .yamllint.yaml
  24. AGENTS.md
  25. CMakeLists.txt
  26. CONTRIBUTORS.md
  27. KEYS
  28. LICENSE
  29. NOTICE
  30. pyproject.toml
  31. README.md
README.md

Open Machine Learning Compiler Framework

Documentation | Contributors | Community | Release Notes

Apache TVM is an open machine learning compilation framework, following the following principles:

  • Python-first development that enables quick customization of machine learning compiler pipelines.
  • Universal deployment to bring models into minimum deployable modules.

License

TVM is licensed under the Apache-2.0 license.

Getting Started

Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.

Contribute to TVM

TVM adopts the Apache committer model. We aim to create an open-source project maintained and owned by the community. Check out the Contributor Guide.

History and Acknowledgement

TVM started as a research project for deep learning compilation. The first version of the project benefited a lot from the following projects:

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some parts of the lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.

Since then, the project has gone through several rounds of redesigns. The current design is also drastically different from the initial design, following the development trend of the ML compiler community.

The most recent version focuses on a cross-level design with TensorIR as the tensor-level representation and Relax as the graph-level representation and Python-first transformations. The project's current design goal is to make the ML compiler accessible by enabling most transformations to be customizable in Python and bringing a cross-level representation that can jointly optimize computational graphs, tensor programs, and libraries. The project is also a foundation infra for building Python-first vertical compilers for domains, such as LLMs.