commit | 3fe8427e613659a3140162acdc4523e2becee4ad | [log] [tgz] |
---|---|---|
author | MaheshRavishankar <1663364+MaheshRavishankar@users.noreply.github.com> | Wed Mar 16 13:37:24 2022 -0700 |
committer | GitHub <noreply@github.com> | Wed Mar 16 13:37:24 2022 -0700 |
tree | 3cfb79b8409d014635605236cd7b9a33c9271fe8 | |
parent | cba0bc460d0529849703b4b1b37fe6f92d78cdb9 [diff] |
Do not fuse with elementwise operations that cant bufferize in-place. (#8526) Currently the backend cannot bufferize in-place dispatch regions that contain operations where the root operation like conv, etc. is fused with an elementwise operation, where in the latter the buffer for an output cannot be reused for the result of the root. Disable fusing such cases as a WAR. For now only do this for the convolution cases, more might be needed while the proper fix is worked out down stream. (Proper fix is to "vectorize always" even if the vector size is 1). Issue #8411
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.