[Util] Implement InferIntDivisibilityOpInterface for affine ops (#22723) This PR implements the InferIntDivisibilityOpInterface for affine.apply, affine.min, and affine.max operations. Affine apply gets the divisibility of its result expression, and affine.min/max gets the GCD of all result expression divisibilities. The implementation supports the following divisibilities and any compositions of them: - Multiplication: product of operand divisibilities - Addition/Subtraction: GCD (union) of operand divisibilities - Division (floor/ceil): quotient when evenly divisible, else 1 - Modulo: falls back to minimum divisibility (1,1) This PR also adds the TestIntegerDivisibilityAnalysis pass to more directly test divisibility analysis without relying on IR optimizations. The pass probes values consumed by `"iree_unregistered.test_int_divisibility"` ops and annotates them with computed divisibility attributes. There is a small change to the arith.divui divisibility implementation to fallback to minimum divisibility when there is a remainder division, because we can't infer the divisibility when there is a remainder. --------- Signed-off-by: Max Dawkins <max.dawkins@gmail.com>
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 | |
| 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.