README.md - 3p/openxla/iree - Git at Google

 # 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

 For development, IREE supports both Bazel and CMake on Windows and Linux. We are
 working on enabling macOS support. For deployment, IREE aims to additionally
 cover Android and iOS.

 Please see the [Getting Started](https://google.github.io/iree/GetStarted) 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:

 ![IREE Architecture](./docs/IREE-Architecture.svg)

 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 still at its early stage; we have lots of exciting future plans. Please
 check out the [long-term design roadmap](./docs/roadmap_design.md) and
 [short-term focus areas](./docs/roadmap.md).

 We use [GitHub Projects](https://github.com/google/iree/projects) to track
 various IREE components and
 [GitHub Milestones](https://github.com/google/iree/milestones) for major
 features and quarterly plans. Please check out for updated information.

 ## Build Status

 CI System | Platform | Build System | Component       | Status
 :-------: | :------: | :----------: | :-------------: | :----:
 Kokoro    | Linux    | Bazel        | Core            | [![kokoro-status-linux-bazel-core](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/main_result.html)
 Kokoro    | Linux    | Bazel        | Bindings        | [![kokoro-status-linux-bazel-bindings](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/main_result.html)
 Kokoro    | Linux    | Bazel        | Integrations    | [![kokoro-status-linux-bazel-integrations](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/main_result.html)
 Kokoro    | Linux    | CMake        | Core + Bindings | [![kokoro-status-linux-cmake](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/main_result.html)

 ## License

 IREE is licensed under the terms of the Apache license. See [LICENSE](LICENSE)
 for more information.
	# IREE: Intermediate Representation Execution Environment

	IREE (Intermediate Representation Execution Environment,
	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

	For development, IREE supports both Bazel and CMake on Windows and Linux. We are
	working on enabling macOS support. For deployment, IREE aims to additionally
	cover Android and iOS.

	Please see the [Getting Started](https://google.github.io/iree/GetStarted) 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:

	![IREE Architecture](./docs/IREE-Architecture.svg)

	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 (Hardware Abstraction Layer) 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 still at its early stage; we have lots of exciting future plans. Please
	check out the [long-term design roadmap](./docs/roadmap_design.md) and
	[short-term focus areas](./docs/roadmap.md).

	We use [GitHub Projects](https://github.com/google/iree/projects) to track
	various IREE components and
	[GitHub Milestones](https://github.com/google/iree/milestones) for major
	features and quarterly plans. Please check out for updated information.

	## Build Status

	CI System \| Platform \| Build System \| Component \| Status
	:-------: \| :------: \| :----------: \| :-------------: \| :----:
	Kokoro \| Linux \| Bazel \| Core \| [![kokoro-status-linux-bazel-core](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/core/main_result.html)
	Kokoro \| Linux \| Bazel \| Bindings \| [![kokoro-status-linux-bazel-bindings](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/bindings/main_result.html)
	Kokoro \| Linux \| Bazel \| Integrations \| [![kokoro-status-linux-bazel-integrations](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/bazel/integrations/main_result.html)
	Kokoro \| Linux \| CMake \| Core + Bindings \| [![kokoro-status-linux-cmake](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/main_status.svg)](https://storage.googleapis.com/iree-oss-build-badges/linux/cmake/main_result.html)

	## License

	IREE is licensed under the terms of the Apache license. See [LICENSE](LICENSE)
	for more information.