commit | 362fd520cf8bdc6f4a3134f5cf03a8ea3b1e77e7 | [log] [tgz] |
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author | MaheshRavishankar <1663364+MaheshRavishankar@users.noreply.github.com> | Tue Jul 13 16:36:28 2021 -0700 |
committer | GitHub <noreply@github.com> | Tue Jul 13 16:36:28 2021 -0700 |
tree | 1f31d5879663f1580a0c043b238c36c35d99e7b3 | |
parent | c3dde7786ee50056e035bd126cfda21f9d0015b7 [diff] |
Initial Implementation of tile (+distribute) of LinalgExt ops using `TiledOpInterface` (#6423) This changes adds tiling transformations for LinalgExt using the TiledOpInterface. The linalg_ext.scatter and linalg_ext.sort (only parallel dims) operation is used as the candidate for tiling. The operations implements the TiledOpInterface which is used by the tiling transformation to tile + distribute the op. The tiling transformation is controlled similar to LinalgOps, using LinalgTilingOptions, LinalgTransformationFilter and LinalgLoopDistributionOptions.
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!
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.