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apache / tvm / 25a37e73fd696c76cdeebcf392953c5b1de0da04
commit25a37e73fd696c76cdeebcf392953c5b1de0da04[log] [tgz]
authorDayuxiaoshui <158081477+Dayuxiaoshui@users.noreply.github.com>Fri Nov 28 14:07:14 2025 +0800
committerGitHub <noreply@github.com>Fri Nov 28 01:07:14 2025 -0500
tree335cc9d36791e8e5cd8dd1a8ed2953212a7373f6
parent7fe876007683e55c49ff4aebc3a16280002265e1 [diff]
[Relax][PyTorch] Add support for sparse matrix multiplication and random number generation (#18499)

This commit adds support for sparse matrix multiplication and random
number generation in PyTorch frontend.

Changes:
- Add _sparse_mm() method to handle sparse matrix multiplication
- Add _sparse_addmm() method to handle sparse addmm operations
- Add _randn() method to handle torch.randn random number generation
- Register these operations in the convert_map

The fix ensures that PyTorch models containing sparse matrix operations
and random number generation can be successfully converted to TVM Relax
modules.

Fixes #18476
2 files changed
tree: 335cc9d36791e8e5cd8dd1a8ed2953212a7373f6
  1. .github/
  2. 3rdparty/
  3. apps/
  4. ci/
  5. cmake/
  6. conda/
  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. .pre-commit-config.yaml
  22. CMakeLists.txt
  23. conftest.py
  24. CONTRIBUTORS.md
  25. KEYS
  26. LICENSE
  27. Makefile
  28. mypy.ini
  29. NOTICE
  30. pyproject.toml
  31. README.md
  32. version.py
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.