Google Git
Sign in
apache / tvm / 4bc61a14452cdae09231f1085d40a4b04fbe1f75
commit4bc61a14452cdae09231f1085d40a4b04fbe1f75[log] [tgz]
authorMengshiun Yu <mengshyu@gmail.com>Sat Sep 14 23:07:06 2024 -0400
committerGitHub <noreply@github.com>Sat Sep 14 23:07:06 2024 -0400
tree2d86fb564b2766872c447475e09e25ba1e552712
parente0105e488dd99d5e153428bc1d8c3dec0c324086 [diff]
[Relax][Transform] Add SelectNode handling in SymbolicMatcher (#17368)

This PR added support for handling SelectNode in the SymbolicMatcher
class by modifying the VisitExpr_ function to match the true_value
and false_value expressions between the current SelectNode and the
other expression. If the other expression is not a SelectNode, the
matching condition is updated to ensure the current SelectNode
expression is equivalent to the other expression.
1 file changed
tree: 2d86fb564b2766872c447475e09e25ba1e552712
  1. .github/
  2. 3rdparty/
  3. apps/
  4. ci/
  5. cmake/
  6. conda/
  7. configs/
  8. docker/
  9. docs/
  10. gallery/
  11. golang/
  12. include/
  13. jvm/
  14. licenses/
  15. python/
  16. rust/
  17. src/
  18. tests/
  19. vta/
  20. web/
  21. .asf.yaml
  22. .clang-format
  23. .gitattributes
  24. .gitignore
  25. .gitmodules
  26. .pre-commit-config.yaml
  27. CMakeLists.txt
  28. conftest.py
  29. CONTRIBUTORS.md
  30. KEYS
  31. LICENSE
  32. Makefile
  33. mypy.ini
  34. NEWS.md
  35. NOTICE
  36. pyproject.toml
  37. README.md
  38. version.py
README.md

Open Deep Learning Compiler Stack

Documentation | Contributors | Community | Release Notes

Build Status WinMacBuild

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.

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 apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.

Acknowledgement

We learned a lot from the following projects when building TVM.

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some part of 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.