commit | 24db592936b9f11802dcb6b3e54bfc54d0903d10 | [log] [tgz] |
---|---|---|
author | Lei Zhang <antiagainst@google.com> | Tue Mar 01 13:29:44 2022 -0500 |
committer | GitHub <noreply@github.com> | Tue Mar 01 10:29:44 2022 -0800 |
tree | 762e62eb7173dde752ee35e1f4031e131a8d65ed | |
parent | 0265b7256f880bb1e6a49b1c19b1b53ab13fd05d [diff] |
Integrate llvm-project at ba54ebeb5eba0f63de8ce2d73a85e9bf508008f6 (#8426) * Reset third_party/llvm-project: ba54ebeb5eba0f63de8ce2d73a85e9bf508008f6 (2022-03-01 20:55:28 +0800): [clang-tidy] Fix `readability-const-return-type` for pure virtual function. * Update mlir-hlo and TensorFlow to match the LLVM commit * Delete call to llvm::AddressSanitizerPass: this pass was removed in upstream as llvm/llvm-project@b7fd30e; the functionality was already incorporated into the module pass.
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.