| commit | 406392d08ee8746f474c82cd7d2489da3c91f6c8 | [log] [tgz] |
|---|---|---|
| author | Han-Chung Wang <hanhan0912@gmail.com> | Wed Dec 17 00:31:36 2025 +0800 |
| committer | GitHub <noreply@github.com> | Tue Dec 16 16:31:36 2025 +0000 |
| tree | 3792f7178dcb5e8a1273ba60f57f42d828a102a9 | |
| parent | bb6ec290fe145690db9ec15289dbca6179ef4ae3 [diff] |
[Stream] Selecting unified encodings from encoding resolvers. (#22898) Add a new `getUnifiedEncoding` method to the `LayoutResolverAttr` interface that returns a unified encoding given multiple candidate encodings. This is used by the UnifyEncodingForGlobals pass to select an appropriate encoding when the same source data has multiple encoded versions. Also refactor GlobalEncodingAnalyzer to: - Set up the layout resolver from dialect interfaces internally - Compute unified encodings as part of the analysis phase - Provide a `getUnifiedEncoding(name)` getter for querying results This simplifies the pass by moving analysis-related logic into the analyzer class, making the pass focused on applying transformations. Update lit tests to include device definitions and affinity attributes on stream.tensor.* ops, which are required for the pass to resolve layout attributes properly. It also switches identity_resolver to specialization_resolver, which improves the test quality. Identity encoding is used in fallback solution. It is a step towards https://github.com/iree-org/iree/issues/22485 --------- Signed-off-by: hanhanW <hanhan0912@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.