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opensecura / 3p / openxla / iree / f726c8772d540f259d9d91619830b8db4d6a4be6
commitf726c8772d540f259d9d91619830b8db4d6a4be6[log] [tgz]
authorScott Todd <scotttodd@google.com>Fri Aug 18 13:38:40 2023 -0700
committerGitHub <noreply@github.com>Fri Aug 18 13:38:40 2023 -0700
treee342164b84e1048ea3ba52e9ca6c6edbfb7cd1ce
parentc4d76f278d52485bc840e2effeaf865d98c36254 [diff]
Build sample_webgpu in build_test_test_samples. (#14690)

This should help spot regressions in the webgpu work. The build script
runs the compiler and builds the runtime (HAL driver and "sample"
application code).

Note: we still don't have any alerting configured for the [samples.yml
workflow](https://github.com/openxla/iree/actions/workflows/samples.yml),
and it has been failing for around a month (TensorFlow Python issues).
Better than nothing though...

Testing this at (you_didnt_see_anything.gif) 
* ~~https://github.com/openxla/iree/actions/runs/5872478019~~
* ~~https://github.com/openxla/iree/actions/runs/5872744401~~
* ~~https://github.com/openxla/iree/actions/runs/5872858233~~
* ~~https://github.com/openxla/iree/actions/runs/5881255239~~
* ~~https://github.com/openxla/iree/actions/runs/5881744607~~
* https://github.com/openxla/iree/actions/runs/5881953294
11 files changed
tree: e342164b84e1048ea3ba52e9ca6c6edbfb7cd1ce
  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.