[GlobalOptimization] Rewrite QDQ contractions into integer math (#24902)

Rewrites contractions over `iree_linalg_ext.dequantize_affine` operands
into integer contractions, integer zero-point corrections, and
floating-point scaling.

The transformation uses indexing maps and iterator types to handle named
and generic contractions, including convolutions. It reduces in integer
arithmetic over dimensions where quantization parameters are invariant.
For blockwise quantization, it computes integer partial results, applies
each block's correction and scales, then accumulates across blocks in
floating point.

Zero-point correction terms are built as separate computations so
constant terms can be evaluated independently. Corrections are applied
before conversion to floating point. Scaling and accumulation use at
least f32, preserving wider scale or output types and narrowing only the
final result.

The rewrite requires a plain multiply-add body, zero initialization, and
positive static integer reduction extents whose products and corrections
fit in signed i32. Unsupported cases retain the original computation.
The pass is enabled by default; reassociation changes floating-point
rounding. It can be disabled with
`--iree-global-opt-enable-qdq-to-integer-math=false`.

Builds on the [affine QDQ operations PR
#24901](https://github.com/iree-org/iree/pull/24901) and implements the
contraction rewrite portion of [Linalg(_ext) native QDQ
#24862](https://github.com/iree-org/iree/issues/24862).

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---------

Signed-off-by: Thomas Ziereis <ziereis@roofline.ai>
Signed-off-by: Florian Walbroel <walbroel@roofline.ai>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: Florian Walbroel <walbroel@roofline.ai>
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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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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.

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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.