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apache / tvm / 18ff9ff89b4617d8925ef6afde233e8d1742a5bd
commit18ff9ff89b4617d8925ef6afde233e8d1742a5bd[log] [tgz]
authorYXY-0922 <50567910+YXY-0922@users.noreply.github.com>Tue Jul 23 02:48:57 2024 +0800
committerGitHub <noreply@github.com>Mon Jul 22 21:48:57 2024 +0300
treee3cae3b5066d6c1ed3479de36b052588fbc278dc
parente5bf56d1f4d4d46cfe4845e4f76c991be35cc332 [diff]
[MetaSchedule]Add a testcase for padded conv2d in meta_schedule (#17171)

### Bug Fix

In the `TileWithTensorIntrin` function, when the `allow_padding` parameter is enabled, the original implementation inlines all consumer blocks. This behavior can lead to incorrect inlining of output blocks, causing issues with block shapes and dependencies. To ensure correct inlining operations, only non-output consumer blocks should be inlined.
---------
Co-authored-by: yuxiyue <yuxiyue@CentOS7-Login3.future.cn>
2 files changed
tree: e3cae3b5066d6c1ed3479de36b052588fbc278dc
  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.