commit | ff1149c9324abc039193e2e80700fea0b260b808 | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Fri Jan 14 18:03:28 2022 -0800 |
committer | GitHub <noreply@github.com> | Fri Jan 14 18:03:28 2022 -0800 |
tree | 9a9fdd1551ac3fb9e59e44eab27b6d476c5fec19 | |
parent | 144834601141faae51b8b4995c5b7c80ae07586b [diff] |
Create an e2e DoubleTilingExpert pass pipeline for CPU gemms. (#8118) - Add a DoubleTilingExpert pipline which uses sandbox codegen driver approaches. - Add a struct that mirrors all the options specified for the LinalgVectorLowering in codegen. This allows the pass pipeline to control these options. - This enables vectorization for other cases that the dim sizes are not multiples of tile sizes. - Verified that the final LLVM IRs are almost identical for some matmul cases, but there still are performance gaps. Thus, the option is not on by default. We might have to re-pick L1 tile sizes because they are larger than workgroup sizes in IREE, which is always a one-trip loop.
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.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.