commit | 907269844fd9497aa35b650221dba086ac404db4 | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Sat Nov 13 12:23:03 2021 -0800 |
committer | GitHub <noreply@github.com> | Sat Nov 13 12:23:03 2021 -0800 |
tree | b2a7affdddc7cb2e296f8ec9fb377c3f1b8f02ec | |
parent | 575509fcc692d0544d6709033c64419621b343b4 [diff] |
Enable more vectorizations in LLVMCPUTileFuseAndVectorizePass (#7652) Vectorization will turn Linalg ops to vector ops and arith ops. Since we don't propagate information through arith ops during bufferization. We can't unconditionally vectorize all the ops. To prevent creating extra memref.alloc ops, we can't tile along reduction dims. The next step is to get unroll vector pass in, so we can vectorize more ops. This PR improves the performance of transformer-benchmark from 58 ms to 33 ms.
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.