commit | e41e71c522e5bfdeb0220a2c24d34c4a70dee44f | [log] [tgz] |
---|---|---|
author | Han-Chung Wang <hanhan0912@gmail.com> | Mon Jun 24 15:01:14 2024 -0700 |
committer | GitHub <noreply@github.com> | Mon Jun 24 15:01:14 2024 -0700 |
tree | 54e936096f9f9c77d55918da299c12e02d3feae3 | |
parent | fe571e4d5efde141ec437cb4699307df67a38b9c [diff] |
[CPU] Limit the use of [8, 32, 16] gemm vector sizes to CPUs w/ avx512f feature (#17727) The tile sizes were tuned for targets that have avx512f features. The revision update the default vector sizes to [1, 1, vector_size] for targets w/o avx512f feature, which avoids large vector sizes. Fixes https://github.com/iree-org/iree/issues/17683 --------- Signed-off-by: hanhanW <hanhan0912@gmail.com>
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.
Community meeting recordings: IREE YouTube channel
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.