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apache / tvm / refs/tags/v0.13.dev0
tag6b753e16cb9fc41ce5952b7439aff5f2d3e519b6
taggerSiyuan Feng <hzfengsy@sjtu.edu.cn>Mon Apr 10 13:13:56 2023 +0800
object15f9be5449a1a351b65f327a3a5e2aaf578a7161 
commit15f9be5449a1a351b65f327a3a5e2aaf578a7161[log] [tgz]
authorChaofan Lin <1713833595@qq.com>Sun Apr 09 20:13:57 2023 +0800
committerGitHub <noreply@github.com>Sun Apr 09 08:13:57 2023 -0400
tree5ee331ac0848d28dfd9d06445cecd6ea221f3f88
parenta84a2cbe07dfe608acd79453affc53330ebab1f0 [diff]
[TOPI] Expose `topi::collapse_sum` to Python and support symbolic shape (#14541)

TOPI has an implementation of collapse_sum internally (tvm/topi/reduction.h) but it is not exposed to FFI and can not be called in Python side. This patch exposes it and adds related tests. And this PR lets the implementation of topi::collapse_sum support symbolic shape cases.
4 files changed
tree: 5ee331ac0848d28dfd9d06445cecd6ea221f3f88
  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.