commit | f6b63359d27b7d72585274d69660bbc60082ce9e | [log] [tgz] |
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author | Nicolas Vasilache <nicolasvasilache@users.noreply.github.com> | Tue Jul 12 11:28:21 2022 +0200 |
committer | GitHub <noreply@github.com> | Tue Jul 12 02:28:21 2022 -0700 |
tree | d32af650907467a3dcef3a13ea2640569d70ba73 | |
parent | a9c91cdfa27a0d75aa9bea0c7bbc45fdfea86b48 [diff] |
Decouple Transform dialect usage in IREE from iree-dialects. (#9745) This revision creates a transform dialect interpreter pass in IREE with the proper dialect registrations to allow end-to-end examples from both iree-run-mlir and iree-opt. In the future, when the layering is right, only a single interpreter will be needed for both codegen and non-codegen rewrites, which will allow retiring the specialized interpreter that is used for dispatch region creation with the transform dialect. For now, the iree-dialects interpreter remain as a way to separate concerns between patterns and transform ops that are IREE-specific from one that will be upstreamed in the fullness of time. The GPU-specific transforms are relaxed to allow targeting either hal.executable or hal.executable.variant which lets them apply with either an iree-run-mlir or iree-opt flow.
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 is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.