[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). --- <sub>Stack created with <a href="https://github.com/github/gh-stack">GitHub Stacks CLI</a> • <a href="https://gh.io/stacks-feedback">Give Feedback 💬</a></sub> --------- 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>
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.
Releases notes are published on GitHub releases.
| Package | Release status |
|---|---|
| GitHub release (stable) | |
| GitHub release (nightly) | |
iree-base-compiler | |
iree-base-runtime |
For more details on the release process, see https://iree.dev/developers/general/release-management/.
| Operating system | Build status |
|---|---|
| Linux | |
| macOS |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
See our website for more information.
Community meeting recordings: IREE YouTube channel
| Date | Title | Recording | Slides |
|---|---|---|---|
| 2025-06-10 | Data-Tiling in IREE: Achieving High Performance Through Compiler Design (AsiaLLVM) | recording | slides |
| 2025-05-17 | Introduction to GPU architecture and IREE's GPU CodeGen Pipeline | recording | slides |
| 2025-02-12 | The Long Tail of AI: SPIR-V in IREE and MLIR (Vulkanised) | recording | slides |
| 2024-10-01 | Unveiling the Inner Workings of IREE: An MLIR-Based Compiler for Diverse Hardware | recording | |
| 2021-06-09 | IREE Runtime Design Tech Talk | recording | slides |
| 2020-08-20 | IREE CodeGen (MLIR Open Design Meeting) | recording | slides |
| 2020-03-18 | Interactive HAL IR Walkthrough | recording | |
| 2020-01-31 | End-to-end MLIR Workflow in IREE (MLIR Open Design Meeting) | recording | slides |
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.