commit | a6c5ebfd3e80f306a5fdf2e232cac9cf7505778b | [log] [tgz] |
---|---|---|
author | Kunwar Grover <groverkss@gmail.com> | Mon Jun 17 18:40:09 2024 +0100 |
committer | GitHub <noreply@github.com> | Mon Jun 17 18:40:09 2024 +0100 |
tree | 6eb7d1a98201660dda463315d34bdd2091953375 | |
parent | 7b782a871e227c7049be6ebf59dde3e5288ae15e [diff] |
Remove attention transform dialect e2e tests (#17682) With https://github.com/iree-org/iree/pull/17681 , the state of attention codegen for llvm-cpu is at a good enough place where we don't need to rely on a transform dialect spec (at least for llvm-cpu) anymore. This patch removes e2e tests for the attention transform dialect spec as it adds more burden on a path that will probably not be maintained in the future. There are some e2e correctness tests in e2e/linalg_ext/ that check correctness for small test cases and pkgci running sdxl + attention tests.
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.