| commit | 4a03eea3260dfe3946a5b667f4666f39ac4fad65 | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Thu Mar 07 10:08:01 2024 -0800 |
| committer | Ben Vanik <ben.vanik@gmail.com> | Mon Jul 29 20:32:21 2024 -0700 |
| tree | 145df1ea776a192490c5533d6403593dfc2053c2 | |
| parent | 3af1211b127f6c34bd8bd9a85b8595d9a23b4161 [diff] |
Working around data tiling limitations a bit. This changes the passes to be module-level and lookup their targets based on their function context. The passes are not long for this world in their current form and the spaghettification that happened with the VMVX and LLVM-CPU paths makes it near impossible to factor properly without a rewrite.
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.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.