[ROCM] Add rocjitsu e2e validation (#24913)

I want to enable e2e testing across all recent amdgpu / rcom targets via
rocjitsu, an amdgpu simulator, so that anyone with an x86 machine can
run those tests regardless of the local GPU or lack thereof.

Add a CPU-hosted ROCm e2e test helper for gfx942, gfx950, gfx1100,
gfx1201, and gfx1250. Reconfigure a regular IREE build and rebuild
iree-test-deps for each target, then run HIP tests with the simulator
from a pinned TheRock nightly.

Run each test in a fresh Mirage session with a watchdog and per-test
JUnit output. Propagate configure/build failures so stale artifacts
cannot produce a successful run.

Make the default native-test timeout configurable and label tests that
are too expensive to simulate. Keep those tests enabled for hardware
runs. Exclude TopK v2 from ROCm tests because its missing thread
distribution causes overlapping output writes on all five targets.

Use the target's preferred subgroup size when an executable does not
specify one, so gfx1250 defaults to wave32. Add regression coverage for
the default and explicit subgroup sizes.

Assisted-by: codex (gpt-6-astra)
16 files changed
tree: f0076251d896b5a3a02dfcae4609d3ff58d14ba0
  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. GOVERNANCE.md
  32. LICENSE
  33. MAINTAINERS.md
  34. MODULE.bazel
  35. README.md
  36. RELEASING.md
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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Releases notes are published on GitHub releases.

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

For more details on the release process, see https://iree.dev/developers/general/release-management/.

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Operating systemBuild status
LinuxCI - Linux arm64 clang
macOSCI - macOS arm64 clang

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
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.