[Codegen][Tuner] Add support for default tuning specs (#19394) These default specs are target architecture-specific and will be shipped with the compiler. * Default specs belong to target plugins and get embedded in `libIREECompiler.so`, just like ukernels. * Plugins then register their default tuning specs with the default embedded directory. * We store them as mlir text. We can't easily assemble them as mlir bytecode without taking a circular dependency on iree-opt. We can revisit this in the future and add a new tool `iree-as` that will only link with dialects. * After the initial loading, we cache the default specs in the IREE codegen dialect transform library manager. * Add a placeholder spec for gfx942. * Document and test the inclusion order. User specs come before default specs. Issue: https://github.com/iree-org/iree/issues/19214
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
| Package | Release status |
|---|---|
| GitHub release (stable) | |
| GitHub release (nightly) | |
| Python iree-base-compiler | |
| Python iree-base-runtime |
| Host platform | Build status |
|---|---|
| Linux | |
| macOS | |
| Windows |
For the full list of workflows see https://iree.dev/developers/general/github-actions/.
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.