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apache / tvm / 4a5e22e869e92b9c12b3bda8b88a0ce8c69b8d30
commit4a5e22e869e92b9c12b3bda8b88a0ce8c69b8d30[log] [tgz]
authortsu-bin <81693503+tsu-bin@users.noreply.github.com>Fri Jun 28 12:55:06 2024 +0800
committerGitHub <noreply@github.com>Thu Jun 27 21:55:06 2024 -0700
treeaa25378570ee2ea67fe513c87bebba52e97d85e1
parenta84adaf0ff39a40ab4cd0867972b805c4733ca10 [diff]
[BugFix][MetaSchedule] MultiLevelTilingTensorCore generates inconsistent thread-binding sketch for batched matmul (#17012)

* [BugFix][MetaSchedule] MultiLevelTilingTensorCore generates inconsistent thread-binding sketch for batched matmul

* Update testcase test_meta_schedule_schedule_rule_mlt_tc.py::test_conv_1x1

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Co-authored-by: tsu-bin <tsubin@gmail.com>
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tree: aa25378570ee2ea67fe513c87bebba52e97d85e1
  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
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  23. .gitattributes
  24. .gitignore
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  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.