[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>
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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.

IREE Discord Status pre-commit OpenSSF Best Practices

Project news

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Release status

Releases notes are published on GitHub releases.

PackageRelease status
GitHub release (stable)GitHub Release
GitHub release (nightly)GitHub Release
iree-base-compilerPyPI version
iree-base-runtimePyPI version

For more details on the release process, see https://iree.dev/developers/general/release-management/.

Build status

CI PkgCI

Nightly build status

Operating systemBuild status
LinuxCI - Linux arm64 clang
macOSCI - macOS arm64 clang

For the full list of workflows see https://iree.dev/developers/general/github-actions/.

Communication channels

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.

Architecture overview

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.