commit | 7f94a78b269e69167d4195270a4d1a5c6aafe68e | [log] [tgz] |
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author | MaheshRavishankar <1663364+MaheshRavishankar@users.noreply.github.com> | Wed Sep 01 13:42:53 2021 -0700 |
committer | GitHub <noreply@github.com> | Wed Sep 01 13:42:53 2021 -0700 |
tree | d5115357830a4a2b1dee95da3138696361fb02b5 | |
parent | e1a81541bc02ef0266d452b8e2d36dfec17ad9d8 [diff] |
NFC: Remove crufty insertion point search. (#6937) To start creating the memrefs for the output (when bufferizing a `flow.tensor.store`), the result buffer has to be allocated before any uses of it. Replace the crufty insertion point search with just cloning of operations. They get CSE-ed anyway.
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!
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.