[DispatchCreation] Propagate tensor encodings outside dispatch regions (#24966) Encoding propagation is currently tied to dispatch hoisting, which prevents layout assignment from using the same rewrite rules on the surrounding tensor graph. Separate where propagation may happen from the rewrites themselves, and add the function pass `iree-dispatch-creation-propagate-data-tiling-encodings` for propagation outside dispatches. Hoisting keeps its existing behavior. The new pass stops at existing dispatches and block boundaries, and constant CSE stays disabled because Flow regions do not materialize constants themselves. - `iree-dispatch-creation-propagate-encodings` runs in the dispatch-time data-tiling pipeline and only swaps `set_encoding` with `tensor.cast` producers, so adding these rewrites to it would change that pipeline. They stay separate until bubbling through generic ops goes through the encoding propagation interfaces (TODO(#20179)). - Bubbling now stops at `set_encoding` ops whose encoding is not an `EncodingAttr`, such as `#iree_encoding.padding`, instead of crashing. `iree-dispatch-creation-hoist-encoding-ops` could already hit this within dispatches; both passes have a regression test. No pipeline runs the new pass yet. #24969 adds it to the experimental early data-tiling route. 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.
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.