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opensecura / 3p / openxla / iree / 57b9239e0d4988dc440cbe794f689003714e2bda
commit57b9239e0d4988dc440cbe794f689003714e2bda[log] [tgz]
authorScott Todd <scotttodd@google.com>Thu Aug 10 20:13:49 2023 -0700
committerGitHub <noreply@github.com>Thu Aug 10 20:13:49 2023 -0700
treec90a05fd117b77a5f82b0b7068cab53c419cfc27
parent50f099f964c83092c287cefcc840d115c0596646 [diff]
Refine release build CMake options. (#14611)

* We now rely on build system logic rather than script overrides for
enabling CUDA (so the CUDA compiler target is now enabled in the Windows
releases).
* Tested at https://github.com/openxla/iree/actions/runs/5813793199 and
verified that the expected runtime HAL drivers and compiler target
backends were enabled for each of the [Linux, macOS, Windows] x
[compiler, runtime] builds.
* Added a section to specify external HAL drivers in runtime/setup.py.
This project leaves the section empty and _unsets_ the variable with
`-UIREE_EXTERNAL_HAL_DRIVERS`, but downstream projects can provide a
list to _set_ the variable if they wish.
* Disabled samples and tests in compiler python package (to work around
path length limits on Windows, see logs at
https://github.com/nod-ai/SHARK-Runtime/actions/runs/5814626661/job/15764674157).
4 files changed
tree: c90a05fd117b77a5f82b0b7068cab53c419cfc27
  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.