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opensecura / 3p / openxla / iree / fb4d09470dc4be674de810dbfbf2d3764e2970ba
commitfb4d09470dc4be674de810dbfbf2d3764e2970ba[log] [tgz]
authorBenoit Jacob <jacob.benoit.1@gmail.com>Thu Dec 19 12:31:42 2024 -0500
committerGitHub <noreply@github.com>Thu Dec 19 12:31:42 2024 -0500
tree9f7f30cec1436536d5d5fdff4a9bdc765e65cccb
parent5c4bc678f9b7356fd083c20821fa2b92a48ab4fd [diff]
Ukernel lowering for data-tiled `multi_mma` with `mfma_i32_16x16x32_i8` (#19522)

This finishes implementing an initial ukernel for `multi_mma` for
`DataTiledMMAAttr` with `kind = mfma_i32_16x16x32_i8`.

The ukernel takes unroll and subgroup parameters as function parameters.
The idea is that once inlining works as intended, these function
parameters will be constants and the optimized code will be the same as
if we had hardcoded specific values. This inlining isn't happening at
the moment, but that is a bug that we should fix first. It is happening
in LLVMCPU, so that's probably something missing in LLVMGPU.

The ukernel file has a comment with a few TODOs to get from this initial
naive ukernel to something faster. The first step is to fix the
above-mentioned inlining problem, then get shared memory, then get
better instruction scheduling.

Signed-off-by: Benoit Jacob <jacob.benoit.1@gmail.com>
14 files changed
tree: 9f7f30cec1436536d5d5fdff4a9bdc765e65cccb
  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.

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Project news

Project status

Release status

Releases notes are published on GitHub releases.

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

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CI PkgCI

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Operating systemBuild status
LinuxCI - 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

DateTitleRecordingSlides
2021-06-09IREE Runtime Design Tech Talkrecordingslides
2020-08-20IREE CodeGen (MLIR Open Design Meeting)recordingslides
2020-03-18Interactive HAL IR Walkthroughrecording
2020-01-31End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting)recordingslides

License

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