| commit | 5efe034e5d24eb4425492f925509f8e1afd609bd | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Tue Nov 22 17:28:04 2022 -0800 |
| committer | Ben Vanik <ben.vanik@gmail.com> | Mon Dec 05 22:01:17 2022 -0800 |
| tree | 954ee51deee0311504037eacd5ac62d7f5f8782d | |
| parent | b71aa530ffbf7a00a0cac65dbbed4c230444e582 [diff] |
Adding examples of custom CUDA/SPIR-V/CPU dispatch code. This adds a skeleton workflow for declaring external objects that are able to be referenced by the compiler all the way from the high-level dialects (flow, at least) and a sample demonstrating how device functions in .cu, .glsl, and .o files can be connected end-to-end with execution. The samples are built extremely simple to allow users to hack together their own without IREE compiler or runtime changes or even a from source build of either. This layer of the stack is really best done by way of compiler extensions rewriting ops to calls and these samples show what such extensions should produce without dictating how to produce them.
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.