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
- GitHub Issues: Preferred for
specific technical issues and coordination on upcoming features. - Google IREE Discord Server: The core team and
collaborators coordinate daily development here; good for low-latency
communication. - Google Groups Email List:
Good for general and low-priority discussion.
Related Project Channels
- MLIR topic within LLVM Discourse:
IREE is enabled by and heavily relies on MLIR. 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
pages on IREE's documentation hub 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. We are still building up the
website; please feel free to
create 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) - 2020-01-31: End-to-end MLIR Workflow in IREE
(recording
and
slides)
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 (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 in the early stages of development and not yet ready for broad adoption.
Check out the long-term design roadmap to get
a sense of where we're headed.
We plan on a quarterly basis using OKRs.
Review our latest objectives to get a sense of what
we're up to in the near term.
We use GitHub Projects to track
progress on IREE components and specific efforts. We use
GitHub 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, 
Kokoro, Bazel, Linux, x86, Bindings, 
Kokoro, Bazel, Linux, x86-swiftshader, Integrations, 
Kokoro, Bazel, Linux, x86-turing, Integrations, 
Kokoro, CMake, Linux, x86-swiftshader, Core + Bindings, 
Kokoro, CMake, Linux, x86-turing, Core + Bindings, 
Kokoro, CMake, Android, arm64-v8a, Runtime (build only), 
BuildKite, CMake, Android, arm64-v8a, Runtime, 
License
IREE is licensed under the terms of the Apache license. See LICENSE
for more information.