| # IREE: Intermediate Representation Execution Environment |
| |
| IREE (**I**ntermediate **R**epresentation **E**xecution **E**nvironment, |
| pronounced as "eerie") is an MLIR-based end-to-end compiler that lowers ML |
| models to a unified IR optimized for real-time mobile/edge inference against |
| heterogeneous hardware accelerators. IREE also provides flexible deployment |
| solutions for the compiled ML models. |
| |
| #### Project Status |
| |
| 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](#communication-channels)! |
| |
| ## Communication Channels |
| |
| * [GitHub Issues](https://github.com/google/iree/issues): Preferred for |
| specific technical issues and coordination on upcoming features. |
| * [Google IREE Discord Server](https://discord.gg/26P4xW4): The core team and |
| collaborators coordinate daily development here; good for low-latency |
| communication. |
| * [Google Groups Email List](https://groups.google.com/forum/#!forum/iree-discuss): |
| Good for general and low-priority discussion. |
| |
| #### Related Project Channels |
| |
| * [MLIR topic within LLVM Discourse](https://llvm.discourse.group/c/llvm-project/mlir/31): |
| IREE is enabled by and heavily relies on [MLIR](https://mlir.llvm.org). IREE |
| sometimes is referred to in certain MLIR discussions. Useful if you are also |
| interested in MLIR evolution. |
| |
| ## Getting Started |
| |
| ### Quick Start using Python |
| |
| Python packages are published on the |
| [releases](https://github.com/google/iree/releases) page. See the |
| [colab/](https://github.com/google/iree/tree/main/colab) directory for examples. |
| |
| ### Building from Source |
| |
| IREE can be built from source using both Bazel and CMake on Windows and Linux. |
| We also have experimental macOS support. |
| |
| Please see the [Getting Started](https://google.github.io/iree/get-started) |
| pages on IREE's [documentation hub](https://google.github.io/iree) to configure, |
| compile, and run IREE in your favorite development environment! |
| |
| ## Documentation and Talks |
| |
| IREE hosts all its documentation and project status dashboards on |
| [GitHub Pages](https://google.github.io/iree). We are still building up the |
| website; please feel free to |
| [create issues](https://github.com/google/iree/issues) for the documentation |
| you'd like to see! |
| |
| We also have some public talks that explain IREE's concepts and architecture: |
| |
| * 2020-03-18: Interactive HAL IR Walkthrough (Ben Vanik and core team) |
| ([recording](https://drive.google.com/file/d/1_sWDgAPDfrGQZdxAapSA90AD1jVfhp-f/view?usp=sharing)) |
| * 2020-01-31: End-to-end MLIR Workflow in IREE |
| ([recording](https://drive.google.com/open?id=1os9FaPodPI59uj7JJI3aXnTzkuttuVkR) |
| and |
| [slides](https://drive.google.com/open?id=1RCQ4ZPQFK9cVgu3IH1e5xbrBcqy7d_cEZ578j84OvYI)) |
| |
| ## Architecture and Goals |
| |
| IREE adopts a _holistic_ approach towards ML model compilation: the IR produced |
| contains both the _scheduling_ logic, required to communicate data dependencies |
| to low-level parallel pipelined hardware/API like Vulkan, and the _execution_ |
| logic, encoding dense computation on the hardware in the form of |
| hardware/API-specific binaries like SPIR-V. |
| |
| The architecture of IREE is best illustrated by the following picture: |
| |
|  |
| |
| Being compilation-based means IREE does not have a traditional runtime that |
| dispatches "ops" to their fat kernel implementations. What IREE provides is a |
| toolbox for different deployment scenarios. It scales from running generated |
| code on a particular API (such as emitting C code calling external DSP kernels), |
| to a HAL (**H**ardware **A**bstraction **L**ayer) that allows the same generated |
| code to target multiple APIs (like Vulkan and Direct3D 12), to a full VM |
| allowing runtime model loading for flexible deployment options and heterogeneous |
| execution. |
| |
| IREE aims to |
| |
| * Support advanced models on mobile/edge devices. Dynamic shapes, dynamic flow |
| control, dynamic multi-model dispatch, streaming models, tree-based search |
| algorithms, and other are all good examples of exciting ML evolution. We are |
| trying to build IREE from the ground-up to enable these models and run them |
| efficiently on modern hardware, especially on mobile/edge devices. |
| * Demonstrate MLIR's ability to develop non-traditional ML compiler backends |
| and runtimes. MLIR enables IREE's holistic approach of focusing on the math |
| being performed and how that math is scheduled rather than graphs of "ops". |
| * Embrace standard-based ML via Vulkan. The graphics world is shifting towards |
| favoring modern explicit APIs for performance and predictability and Vulkan |
| is emerging as the "compatibility" layer. We would love to allow hardware |
| vendors to be able to make ML efficient on their hardware without the need |
| for bespoke runtimes and special access. We also would love to let |
| developers and users utilize all the hardware available on as many platforms |
| as possible. |
| |
| ## Roadmap and Milestones |
| |
| IREE is in the early stages of development and not yet ready for broad adoption. |
| Check out the [long-term design roadmap](./docs/design_roadmap.md) to get |
| a sense of where we're headed. |
| |
| We plan on a quarterly basis using [OKRs](https://en.wikipedia.org/wiki/OKR). |
| Review our latest [objectives](./docs/objectives.md) to get a sense of what |
| we're up to in the near term. |
| |
| We use [GitHub Projects](https://github.com/google/iree/projects) to track |
| progress on IREE components and specific efforts. We use |
| [GitHub Milestones](https://github.com/google/iree/milestones) to track the |
| work associated with plans for each quarter. |
| |
| ## Build Status |
| |
| CI System | Build System | Platform | Architecture | Component | Status |
| :-------: | :-----------: | :------: | :-------------: | :------------------: | :----: |
| Kokoro | Bazel | Linux | x86 | Core | [](https://storage.googleapis.com/iree-oss-build-badges/bazel/linux/x86-swiftshader/core/main_result.html) |
| Kokoro | CMake & Bazel | Linux | x86-swiftshader | Integrations | [](https://storage.googleapis.com/iree-oss-build-badges/cmake_bazel/linux/x86-swiftshader/main_result.html) |
| Kokoro | CMake & Bazel | Linux | x86-turing | Integrations | [](https://storage.googleapis.com/iree-oss-build-badges/cmake_bazel/linux/x86-turing/main_result.html) |
| Kokoro | CMake | Linux | x86-swiftshader | Core + Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/cmake/linux/x86-swiftshader/main_result.html) |
| Kokoro | CMake | Linux | x86-turing | Core + Bindings | [](https://storage.googleapis.com/iree-oss-build-badges/cmake/linux/x86-turing/main_result.html) |
| Kokoro | CMake | Android | arm64-v8a | Runtime (build only) | [](https://storage.googleapis.com/iree-oss-build-badges/cmake/android/arm64-v8a/main_result.html) |
| BuildKite | CMake | Android | arm64-v8a | Runtime | [](https://buildkite.com/iree/iree-android-arm64-v8a/builds?branch=main) |
| |
| ## License |
| |
| IREE is licensed under the terms of the Apache license. See [LICENSE](LICENSE) |
| for more information. |
