commit | cd8e3fcdf9a2f10500b217babb1b1d5c05c1e349 | [log] [tgz] |
---|---|---|
author | Geoffrey Martin-Noble <gcmn@google.com> | Wed Dec 22 18:33:43 2021 -0800 |
committer | Copybara-Service <iree-copybara-bot@google.com> | Wed Dec 22 18:34:49 2021 -0800 |
tree | 2d566f6338b6145608fd12ffa513f7bb616d8baf | |
parent | c9fe7e4b0464c43504328975cbf4313cf74d339c [diff] |
PR #53490: [MHLO] add BroadcastOp constant folding and identity folding Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/53490 * add BroadcastOp constant folding and identity folding Copybara import of the project: -- a8ffc6457a113dda840fad7e75687633e2d94524 by liuyuanqiang <yuanqiangliu@pku.edu.cn>: [MHLO] add BroadcastOp constant folding and identity folding PiperOrigin-RevId: 417920961
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.