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opensecura / 3p / openxla / iree / 9fa29980f2edf6a9f41e4379a96da0cb91a6f642
commit9fa29980f2edf6a9f41e4379a96da0cb91a6f642[log] [tgz]
authorNatashaKnk <natashaknk@google.com>Fri Aug 04 10:55:31 2023 -0700
committerGitHub <noreply@github.com>Fri Aug 04 10:55:31 2023 -0700
tree2449875cd087e63a784600c73919abcc9f093d27
parent3592c594ae611115987b943019ebbbaf61efce9b [diff]
Add canonicalizer that reorders unary elementwise ops and shape manipulation ops. (#14494)

The pass identifies shape manipulation operations (`reshape`,
`transpose`, `broadcast`) that feed into unary elementwise operations
and swaps their order. Since elementwise ops do not manipulate the input
shape (and there is no interaction between the elements), the reordering
doesn't affect the result.
This enables a future PR where some consecutive shape manipulation
operations should be collapsed together into a single operation.
`Broadcast_in_dim` is not included in this PR as the same thing is done
for it as part of an existing `StablehloToStablehlo` pass.
In the future, we might want to consider merging/more clearly defining
the difference between `Canonicalizations.cpp` and
`StablehloToStablehlo.cpp` files, as at the moment their function is
almost indistinguishable in some cases.
2 files changed
tree: 2449875cd087e63a784600c73919abcc9f093d27
  1. .devcontainer/
  2. .github/
  3. build_tools/
  4. compiler/
  5. docs/
  6. experimental/
  7. integrations/
  8. lib/
  9. llvm-external-projects/
  10. runtime/
  11. samples/
  12. tests/
  13. third_party/
  14. tools/
  15. .bazel_to_cmake.cfg.py
  16. .bazelignore
  17. .bazelrc
  18. .bazelversion
  19. .clang-format
  20. .dockerignore
  21. .git-blame-ignore-revs
  22. .gitignore
  23. .gitmodules
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. README.md
  33. WORKSPACE
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

CI Status

Project Status

IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!

Communication Channels

Related Project Channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture Overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and Talks

  • 2021-06-09: IREE Runtime Design Tech Talk (recording and slides)
  • 2020-08-20: IREE CodeGen: MLIR Open Design Meeting Presentation (recording and slides)
  • 2020-03-18: Interactive HAL IR Walkthrough (recording)
  • 2020-01-31: End-to-end MLIR Workflow in IREE: MLIR Open Design Meeting Presentation (recording and slides)

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.