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apache / tvm / d0a01945f77521f5d6dcb827fbe9e06b2de86af9
commitd0a01945f77521f5d6dcb827fbe9e06b2de86af9[log] [tgz]
authorZHANG Hao <zhanghao@4paradigm.com>Wed Apr 21 00:22:01 2021 +0800
committerGitHub <noreply@github.com>Tue Apr 20 09:22:01 2021 -0700
tree19568deda3e5a464287627578df041730a35c3f3
parentfbdffeb546b350eedb470f07b7915341610e3367 [diff]
[VTA][OpenCL] intelfocl (#6126)

* intelfocl support

* disable tsim test

* bugfix to vta autotvm

* disable tsim test in task_python_vta_tsim.sh

* fix integration test

* update vta submodule and re-enable tsim tests

* remove unnecessary comments
23 files changed
tree: 19568deda3e5a464287627578df041730a35c3f3
  1. .github/
  2. 3rdparty/
  3. apps/
  4. cmake/
  5. conda/
  6. docker/
  7. docs/
  8. golang/
  9. include/
  10. jvm/
  11. licenses/
  12. nnvm/
  13. python/
  14. rust/
  15. src/
  16. tests/
  17. tutorials/
  18. vta/
  19. web/
  20. .asf.yaml
  21. .clang-format
  22. .gitignore
  23. .gitmodules
  24. CMakeLists.txt
  25. conftest.py
  26. CONTRIBUTORS.md
  27. Jenkinsfile
  28. KEYS
  29. LICENSE
  30. Makefile
  31. NEWS.md
  32. NOTICE
  33. pyproject.toml
  34. pytest.ini
  35. README.md
  36. 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

© Contributors Licensed under an Apache-2.0 license.

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.