[DispatchCreation] Fuse packed contractions with epilogues and unpack (#24967) Packed contractions lose their fused epilogues when they arrive at dispatch formation as mmt4d or a data-tiled convolution. The result unpack can also become a separate dispatch. Add opt-in fusion (`fuse-mmt4d`, `fuse-data-tiled-convolution`) of layout-preserving elementwise consumers and the result unpack. Notes for review: - Epilogues must read the contraction result and write their own result through identity maps. Epilogues that update the result through their init operand are not matched. - Only the unpack layouts that data-tiling materialization produces are matched. For mmt4d that is the row-major form and the narrow-N transposed form. For convolutions it is NHWC, NCHW and batchless HWC; batchless CHW is not matched. - The result unpack is the last op of the group, so a consumer of a claimed unpack gets its own dispatch instead of fusing with the unpack. - An unpack that its root does not claim becomes a root at its own position in program order, as without the options, so it fuses with its own consumers as before. - A group holds pack-like ops (`linalg.pack`, `set_encoding`) or the result unpack, but not both, whichever joins first. A unit-batch collapse only joins together with the unpack that consumes it, so a group never ends in the collapse. - The matchers hardcode the CPU layouts; a TODO describes moving the layout decision into its own component that dispatch formation queries. No pipeline enables the options 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. 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 |
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| 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.