commit | 6a9ca642d6a22a021fca30eb27942ab42617ea7d | [log] [tgz] |
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author | Han-Chung Wang <hanchung@google.com> | Tue Mar 15 18:44:55 2022 -0700 |
committer | GitHub <noreply@github.com> | Tue Mar 15 18:44:55 2022 -0700 |
tree | 42b9b6c0f7ba378e27b1d5c347ce4d83bbaad6ea | |
parent | 4b889d91517d23fa7fb98d36e55e96442cc76554 [diff] |
Account all the element types to determine vector sizes. (#8552) The assumption was that all the element types have the same bitwidth. However, there are cases that element types do not match, e.g., matmul i8xi8 -> i32. It caused issues that large tiling sizes were selected, which kicked in heavy optimization in LLVM. This commit chooses the smallest vector size over all the element types. This also updates the logic of first level tiling, which follows what we've done for generic ops. The commit reduce compilation time from hours to 5 mins for mobilebert-baseline-tf2-quant.mlir when targeting ARM. Fixes https://github.com/google/iree/issues/8540
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.