commit | a7b981d777b9fa1d036f1a53d0ce1c2ddbfbb8e3 | [log] [tgz] |
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author | Lei Zhang <antiagainst@google.com> | Fri Apr 22 17:02:24 2022 -0400 |
committer | GitHub <noreply@github.com> | Fri Apr 22 17:02:24 2022 -0400 |
tree | 0b0fef0e55ce7241f1c096c514802a923aea2ace | |
parent | 761ba9c5fb6771d95621ddbd183ceb0708aecf6b [diff] |
Integrate llvm-project at 9b32886e7e705bb28aab57682e612375075a0ad7 (#8967) * Reset third_party/llvm-project: 9b32886e7e705bb28aab57682e612375075a0ad7 (2022-04-22 09:20:18 +0000): [mlir][Arithmetic] Use common constant fold function in RemSI and RemUI to cover splat. * Update mlir-hlo to bbd8077e020d4359ae0bcf8b5a895a5ebe2f020d * Update TensorFlow to 5abd3d88570e304f3d3f9bb99eb98620e2839a31 * Update FuncOp to func::FuncOp and update parseRegion API usages
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.