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opensecura/3p/openxla/iree/0f28d441bc07f137fea23fcc516463e461e02d93
commit
0f28d441bc07f137fea23fcc516463e461e02d93
[log]
author
Stella Laurenzo <stellaraccident@gmail.com>
Tue Oct 08 13:20:59 2024 -0700
committer
GitHub <noreply@github.com>
Tue Oct 08 13:20:59 2024 -0700
tree
d70782bb94285f58a61cb193bf239b4a2fa3d44b
parent
0889d13501b377decc2cc414dd2483dcddc45d33 [diff]
Use upstream dataflow tooling to build an arithmetic opt pass. (#18702)

This combines several things into one fixpoint iteration:

* Upstream IntRangeOptimizations for taking care of things like constant
replacement for unit ranges.
* Arith canonicalizations.
* Local adaptation of signed->unsigned conversion (upstream's version
can't compose since it is based on dialect conversion for some reason).
It also has 32bit bugs that have been corrected locally.
* Int64/unsigned index conversion.
* Common factor elision for integer division.
* Making the util.assume ops implement InferIntRangeInterface.

I have some additional advanced patterns to the side which simplify a
lot of torch cases, but they need some more baking/testing, so I'm just
landing the basic pass for now to start.

---------

Signed-off-by: Stella Laurenzo <stellaraccident@gmail.com>
13 files changed
tree: d70782bb94285f58a61cb193bf239b4a2fa3d44b
  1. .github/
  2. build_tools/
  3. compiler/
  4. docs/
  5. experimental/
  6. integrations/
  7. lib/
  8. llvm-external-projects/
  9. runtime/
  10. samples/
  11. tests/
  12. third_party/
  13. tools/
  14. .bazel_to_cmake.cfg.py
  15. .bazelignore
  16. .bazelrc
  17. .bazelversion
  18. .clang-format
  19. .git-blame-ignore-revs
  20. .gitattributes
  21. .gitignore
  22. .gitmodules
  23. .pre-commit-config.yaml
  24. .yamllint.yml
  25. AUTHORS
  26. BUILD.bazel
  27. CITATION.cff
  28. CMakeLists.txt
  29. configure_bazel.py
  30. CONTRIBUTING.md
  31. LICENSE
  32. MAINTAINERS.md
  33. README.md
  34. RELEASING.md
  35. 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.

IREE Discord Status pre-commit OpenSSF Best Practices

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

Release status

PackageRelease status
GitHub release (stable)GitHub Release
GitHub release (nightly)GitHub Release
Python iree-compilerPyPI version
Python iree-runtimePyPI version

Build status

CI PkgCI

Host platformBuild status
LinuxCI - Linux x64 clang
CI - Linux arm64 clang
macOSCI - macOS x64 clang
WindowsCI - Windows x64 MSVC

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

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

Community meeting recordings: IREE YouTube channel

  • 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.