[VectorDistribute] Refactor VectorLayoutAnalysis into 2-phase forward/backward design (#23611)

Restructure the layout analysis from an interleaved forward+backward
worklist into a clean two-phase design:

Phase 1 (forward): Multi-candidate propagation from ToLayoutOp anchors
through uses. No IR mutation.

Resolve: Pick first candidate per value. This is a placeholder cost
model for now which matches the old analysis. Eventually, we will
consider coalescing, compute ops, mma layout, etc.

Phase 2 (backward fixup): Walk operations in reverse program order via
recursive fixupRegion/fixupOp. For each op, derive operand layouts from
resolved result layouts. Assign missing layouts, clone cheap ops
(constants, create_mask, step), or insert to_layout conversions on
conflict.

This naturally handles conflicts better in a predictable manner. // The
forward analysis is the main driver of the analysis. The reason for this
is that for a program to be well-formed for vector distribution, there
must be some way for the final store/return to get a layout. Otherwise,
there is not enough information in the program to determine how
distribution should be done. The forward analysis ensures that the final
return/store gets a layout in a well-formed program. The rest of the
program can get their layouts from backward propagation, everything in
the program must eventually reach the store/return.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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tree: d78db22efd153caabd614ac76277491e00c08a29
  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
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  18. .clang-format
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  20. .gitattributes
  21. .gitignore
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  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. MODULE.bazel
  34. README.md
  35. RELEASING.md
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Eenvironment, 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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For more details on the release process, see https://iree.dev/developers/general/release-management/.

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  • 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.

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IREE Architecture IREE Architecture

See our website for more information.

Presentations and talks

Community meeting recordings: IREE YouTube channel

DateTitleRecordingSlides
2025-06-10Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM)recordingslides
2025-05-17Introduction to GPU architecture and IREE's GPU CodeGen Pipelinerecordingslides
2025-02-12The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised)recordingslides
2024-10-01Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardwarerecording
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.