| commit | 5a4e764189eea6039a7555a92d57c53777bdf889 | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Tue Dec 12 09:16:07 2023 -0800 |
| committer | GitHub <noreply@github.com> | Tue Dec 12 09:16:07 2023 -0800 |
| tree | 30438929d24d0fc1fa292f54d6c76730868a78d9 | |
| parent | fbbccdec1b9528577c654031b8a17262c648c162 [diff] |
Adding stream.dispatch.workgroup.* info ops. (#15889) These mirror the flow and hal ops and allow us to remove a few more flow ops from the flow->stream->hal path. Until we remove/replace the flow dispatch tensor load/store ops we can't do the trivial conversions but as of this PR those (and the workgroup count codegen op) are the only ops that still survive from 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.