| commit | a36773a2420b48f0906c710389b0f9965504c10f | [log] [tgz] |
|---|---|---|
| author | Quinn Dawkins <quinn.dawkins@gmail.com> | Tue May 21 12:08:09 2024 -0400 |
| committer | GitHub <noreply@github.com> | Tue May 21 09:08:09 2024 -0700 |
| tree | 61b290f85061431dbab899228d6191345d329e21 | |
| parent | f6a38acdae433749a29ebc9b7712e1f8db1b8376 [diff] |
[Codegen][GPU] Add vectorization pattern for iree_gpu.multi_mma (#17453) This allows vectorizing a statically shaped `iree_gpu.multi_mma` op. To use this pattern with dynamically shaped tensors, a tiling implementation for this op will need to be added in the future. Additionally this adds a new set of `IREEGPUTransformExtensions` because `Codegen/CommonExtensions` has become a rather large file and this is specific to the dialect.
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.