[codegen] more consumer fusion (#21521)

Enable consumer fusion to fuse more trailing operations, in particular
to fuse all operations used in layer normalization. The particular
enablement is to allow fusion of consumers of fused operations that are
not the "main" operation into which fusion happens.

This required three modifications:

1. Generate replacement values for any fused operation whose results are
used by operations dominated by the main operation (that will turn into
a loop). This is needed because the consumer fusion finds candidates
based on `insert_slice` operations in the loop that are not produced
unless the replacement values are generated. In the case of layer
normalizaiton, we also need actual replacement values so we can return
them. Spurious values are being cleaned up by canonicalization patterns
in the same pass.
2. Avoid generating replacement values for values used as DPS inits as
those cannot (currently) be fused.
3. Sort consumers to fuse by dominance and first most-dominating
consumer fuse to avoid triangular-use situation that prevents fusion.

This generally makes fusion more aggressive on the consumer side.

Signed-off-by: Alex Zinenko <git@ozinenko.com>
4 files changed
tree: 6d734064488bc9477a5474d8c127279392feee38
  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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iree-base-runtimePyPI version

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.