[Codegen][PCF] Add foldForallIntoPCFLoop for split-k loop handling (#23452)

Adds a generic `foldForallIntoPCFLoop` function that folds an
`scf.forall` containing a `pcf.loop` into a single `pcf.generic`
operation. This enables incorporating user expressed extra levels of
parallelism into a scope. The immediate use case this enables is split-k
by folding the split-k loop into the workgroup loop.

Key changes:
- Add `foldForallIntoPCFLoop` API in PCF/Transforms/Transforms.h
- Implement structural matching helpers (matchFoldTerminator,
matchFoldPCFLoop, matchFoldWriteSlices) that validate requirements
without scope-specific logic
- Add TestFoldForallIntoPCFLoopPass for unit testing with local_mapping
+ sequential scope
- Add FoldSplitKWorkgroupLoop pattern in ConvertWorkgroupForallToPCF.cpp
that matches split_reduction_mapping + workgroup_scope and calls the
generic fold
- Run fold pattern as second pass in ConvertWorkgroupForallToPCFPass

The fold operation:
1. Creates pcf.generic with same scope as inner pcf.loop
2. Linearizes forall iteration space and delinearizes inside generic
3. Converts pcf.loop to a nested scf.forall loop to handle spillover
4. Composes tensor.parallel_insert_slice with pcf.write_slice ops

The reason this pattern has to generate a pcf.generic instead of
pcf.loop is to avoid extra execution of code that was inside
the scf.forall but not inside the pcf.loop.

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
12 files changed
tree: 1bfd038e70279be107759238783e0fa88518ffda
  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. 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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  • 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.