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apache / tvm / bd2520fdae8181a56a7e4bef8b7f8408b6553393
commitbd2520fdae8181a56a7e4bef8b7f8408b6553393[log] [tgz]
authorTristan Konolige <tristan.konolige@gmail.com>Tue Apr 20 10:21:54 2021 -0700
committerGitHub <noreply@github.com>Tue Apr 20 10:21:54 2021 -0700
tree9eb6927935b4749c1c574c4f832a86393268b5cc
parentd0a01945f77521f5d6dcb827fbe9e06b2de86af9 [diff]
[PROFILER] Add CSV output to profiler (#7797)

* [PROFILER] Add CSV output to profiler

This patch changes the profiler output from a string to a Report object.
A Report can either output CSV or the usual human-readable table.

* no spaces after commas

* Update src/runtime/profiling.cc

Co-authored-by: Leandro Nunes <leandro.nunes@arm.com>

* fix gcc

* fix test

* overall percent fix

* rename overall -> device_metrics

Co-authored-by: Leandro Nunes <leandro.nunes@arm.com>
9 files changed
tree: 9eb6927935b4749c1c574c4f832a86393268b5cc
  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.