commit | 50c851276008bcb2f2bb39dd98596d5e6aa7e245 | [log] [tgz] |
---|---|---|
author | Ben Vanik <ben.vanik@gmail.com> | Thu Feb 29 13:21:37 2024 -0800 |
committer | Ben Vanik <ben.vanik@gmail.com> | Mon Jul 29 20:32:21 2024 -0700 |
tree | 1343bfe5d3194441bea5483409bf98a102d09945 | |
parent | ba36e425db6eec0a32d7f20888709f7e3d743e33 [diff] |
Making MemoizeDeviceQueries support multiple devices. This will fail on cases where a query can't be tracked to a single device but it's possible in the future to hoist/propagate across CFG edges before running this pass such that it doesn't happen. Today we inline most things and don't deduplicate functions so it'll be rare that we end up being unable to memoize. Hopefully.
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.