| commit | 1daf7af04783702f675b9d710eec325f44277904 | [log] [tgz] |
|---|---|---|
| author | MaheshRavishankar <1663364+MaheshRavishankar@users.noreply.github.com> | Wed May 12 17:25:58 2021 -0700 |
| committer | GitHub <noreply@github.com> | Wed May 12 17:25:58 2021 -0700 |
| tree | 56373c3e348ab23a6700c3af800c0caeaf8bbbbf | |
| parent | 807f4573562472b1a58ce63a9d91b006cd1fb6cb [diff] |
Inline IndexCastOp and int/float ops into the dispatch region. (#5865) Allowing folding index_cast operation and other integer/float operations into the dispatch region avoids round-tripping to host to get values from a tensor on device required for some index computation. Note: This only works for MHLO lowering path. A more nuanced solution might be needed in general with explicit white-listing of operations, but that requires more use cases. Fixes #5620
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler that lowers Machine Learning (ML) models to a unified IR optimized for real-time inference on mobile/edge devices against heterogeneous hardware accelerators. IREE also provides flexible deployment solutions for its compiled ML models.
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!
IREE is licensed under the terms of the Apache license. See LICENSE for more information.