[DispatchCreation] Preserve the target of materialized tensor layouts (#24968) Once tensor layouts have been materialized for a device, dispatch creation must neither assign encodings to them again nor compile them for another device. Assigning encodings again re-encodes data-tiled convolutions when convolutions are data-tiled, and tile shapes chosen for one CPU can be unsupported or slow on another. Record the target in the `iree.encoding.materialized_layout_target` module attribute. Typed accessors live in `DispatchCreation/MaterializedLayoutTarget.h`; `Encoding/Utils` cannot depend on HAL because HAL/IR depends on Stream/IR, which depends on `Encoding/Utils`. The header lists every behavior the attribute changes. - `iree-dispatch-creation-assign-data-tiling-encodings` skips such modules. It is a module pass so that no function pass has to read the attribute. Its options carry the operation families and encoding strategy, so printed pipelines and crash reproducers keep them. The default operation families now come from one helper, `getDefaultDataTilingOpTypes`. - `iree-dispatch-creation-verify-materialized-layout-target` runs first in the dispatch-creation pipeline, independent of the data-tiling options. It rejects modules compiled for a different target, for several targets or for none. Resuming with `--compile-from` reuses the serialized device targets, so a normal `iree-compile` flow does not retarget. The check guards hand-edited IR and tools that rewrite device targets. Nothing sets the attribute until #24969. This is one of five PRs adding an experimental CPU path that materializes data-tiling layouts before dispatch creation, so that `linalg.mmt4d` and data-tiled convolutions fuse with their elementwise epilogues and result unpacks. Assisted by AI. Co-authored-by: Claude Opus 5.5 <noreply@anthropic.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 |
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.