commit | 9fe159d99d86f3292ae901427a159fb61898fa2c | [log] [tgz] |
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author | Kunwar Grover <groverkss@gmail.com> | Wed May 22 19:21:58 2024 +0100 |
committer | GitHub <noreply@github.com> | Wed May 22 19:21:58 2024 +0100 |
tree | 20a9096b9f6942f92221e8c51064eaca939738ba | |
parent | 900ec67677ccca3eaae79fa02fe78bec30603f3c [diff] |
[LinalgExt] Generalize attention tiling interface implementation (#17408) This patch generalizes tiling implementation for AttentionOp. Before, only the batch and M dimension of attention could be tiled. This patch instead, allows tiling of N dimension as well as allows transposition based on indexing maps (hardcoded for now). Tiling on dimension N is disabled in CPU backend for now, because TileAndDecomposeAttention pass is hardecoded with dimensions. This will be fixed once we implement reduction tiling interface for it (after https://github.com/llvm/llvm-project/pull/92624)
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.