| commit | e6354a3277e1790de063a519ac27945fbe539728 | [log] [tgz] |
|---|---|---|
| author | Boian Petkantchin <boian.petkantchin@amd.com> | Tue Feb 06 12:25:01 2024 -0800 |
| committer | GitHub <noreply@github.com> | Tue Feb 06 20:25:01 2024 +0000 |
| tree | eb4fd27292a0c1b7f9659a80f584f8e480aeaba4 | |
| parent | f1adc7897c620fd835da718c68ce0c4156d45688 [diff] |
[compiler] Mesh -> IREE conversion (#16199) Add conversion of the upstream MLIR Mesh Dialect to IREE Flow. Handle global collective channel construction through to the runtime. The supported operations are * retrieval of rank/process index * all-gather * all-reduce * all-to-all * reduce-scatter Dynamic device meshes are not supported. Some operations do not support dynamic tensor dimensions.
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.