commit | 4c1706aaf1b5232aaec72909f1392a6dd46513b3 | [log] [tgz] |
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author | Lei Zhang <antiagainst@google.com> | Wed Jun 01 16:31:15 2022 -0700 |
committer | GitHub <noreply@github.com> | Wed Jun 01 16:31:15 2022 -0700 |
tree | 684b4fa21196a675a0d5ff0867d055ab3b357b28 | |
parent | 8e5e20b1a79c436ae7786b871fc09d1ea3431604 [diff] |
Integrate llvm-project at 4cbfd2e7ebb50cb43024a6c37e5e28460e32d1a5 (#9261) Co-authored-by: Nicolas Vasilache <nicolas.vasilache@gmail.com> - Reset third_party/llvm-project: 4cbfd2e7ebb50cb43024a6c37e5e28460e32d1a5 (2022-06-01 09:09:43 +0000): [libc][mem*] - Address facility + test enum support - Fixed transform dialect op traits - Updated bufferization usages - Define empty LLVM_INITIALIZE_TARGET_VE() - Dedup transforms that have been copied to the upstream transform dialect - Temporary Transform dialect workaround to avoid triggering a spurious check when the transform IR is specified in a separate file that does not nest under the same toplevel op as the payload IR. - Drop transform.structured2.scalarize/interchange/pad that was upstreamed
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.