| commit | 601ebbfa720571aead41d5acc135f3c1f2b8f630 | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Mon Jul 15 18:22:45 2024 -0700 |
| committer | Ben Vanik <ben.vanik@gmail.com> | Mon Jul 29 20:32:23 2024 -0700 |
| tree | 6fa504689bb1d4a0c40935d7846e14ca2fe4b793 | |
| parent | 710287135c92ed641529aca9c31d15d99f9ed043 [diff] |
Adding affinity analysis. This performs whole-program analysis to enable the querying of the ideal affinity for globals, execution ops, and resources. It can run at most phases of compilation (including on linalg/flow IR) though it's primarily used by the stream dialect passes such as conversion. The `AnnotateAffinitiesPass` has been added to aid debugging and the compiler `iree-stream-annotate-input-affinities` flag can be used to turn it on - it has no impact on the program generated but can be useful if affinity analysis fails during conversion.
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.