[GlobalOpt][Flow][Quant] Fix segfault on unlowered `quant.uniform` types (#24824)

`HoistIntoGlobalsPass`'s `HoistableTensorTypeInterface` unconditionally
called `getIntOrFloatBitWidth()` on a tensor's element type to decide
whether it is hoistable, assuming it is always a plain int/float. A
`!quant.uniform<...>` element type is neither, so
`getIntOrFloatBitWidth()` crashes instead of returning a sane answer.

`isHoistableType`/`isHoistableLeafType`, with this PR, check that the
element type is actually an int/float
before computing its bit width via a new helper `hasComputableBitWidth`,
and conservatively treat anything else as
non-hoistable.

Additonally, `quant.uniform` types leak into the pipeline because
`quant` is a transitive type dependency of TOSA, not because we have any
lowering support for `quant.qcast`/`quant.dcast`/`quant.scast` AFAIK.
TOSA's own conversion to `linalg`/`arith` is expected to fully resolve
these before `Flow`. `VerifyInputLegalityPass` already enforces that for
those some dialects, `quant` was simply missing from the list, so IR
where these ops survive past `GlobalOptimization` would segfault later.

Fixes #24814

---------

Signed-off-by: Federico Bruzzone <federico.bruzzone.i@gmail.com>
4 files changed
tree: 80d95cc7c388d4ade37d6fd6df817377b0cad266
  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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  • 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

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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

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