Google Git
Sign in
opensecura / 3p / openxla / iree / b89191c4c319c208d018dfb665416ff7a19fc60a
commitb89191c4c319c208d018dfb665416ff7a19fc60a[log] [tgz]
authorScott Todd <scotttodd@google.com>Mon Sep 14 17:48:09 2020 -0700
committerGitHub <noreply@github.com>Mon Sep 14 17:48:09 2020 -0700
tree9734c482a12d5fb83fcddbed49ac181e2cf04b0d
parenta5063ef228aea1ee5b3529c077e29bd51308eb51 [diff]
parent72f9c3cd165bb0ba162ac193ac5df3c4a5fa43ac [diff]
Merge google -> main (#3143)

* 72f9c3cd Merge pull request #3142 from ScottTodd:main-to-google
* 2fbc097d Synchronize submodules
* 966ae6de Integrate TF at tensorflow/tensorflow@86b67b8c829a
* f17c5992 Synchronize submodules
* 06f383a2 Integrate LLVM at llvm/llvm-project@7841e21c9849
* 1ca3126f Synchronize submodules
* 637f25b6 Integrate LLVM at llvm/llvm-project@c799f873cb9f
* 7a15ffb7 Synchronize submodules
* 58126a1b Integrate LLVM at llvm/llvm-project@0008fb343704
* b0a428ef Synchronize submodules
* 37c516f4 Integrate LLVM at llvm/llvm-project@cb3e1dd6c31e
* a618224f Synchronize submodules
* 5aa4f0e3 Integrate LLVM at llvm/llvm-project@c0bcd11068fc
* 43a979fe Synchronize submodules
* 16f9bceb Integrate LLVM at llvm/llvm-project@f086e85eea94
* b0a497ad Synchronize submodules
* 5845ac5c Integrate LLVM at llvm/llvm-project@70daa353e2ae
* 43eeb1e5 Synchronize submodules
* 6e1f490d Integrate LLVM at llvm/llvm-project@c2f8bc986fb3
* 611c9a19 Synchronize submodules
* 8745b7a7 Integrate LLVM at llvm/llvm-project@0680a3d56d8b
* a93f413b Synchronize submodules
* 90305fdf Integrate LLVM at llvm/llvm-project@0e0d93e2f09a
* 57549978 Synchronize submodules
* fb7b8fec Integrate LLVM at llvm/llvm-project@f2bb4b88550a
tree: 9734c482a12d5fb83fcddbed49ac181e2cf04b0d
  1. .github/
  2. bindings/
  3. build_tools/
  4. colab/
  5. docs/
  6. experimental/
  7. integrations/
  8. iree/
  9. packaging/
  10. scripts/
  11. third_party/
  12. .bazelignore
  13. .bazelrc
  14. .bazelversion
  15. .clang-format
  16. .gitignore
  17. .gitmodules
  18. .style.yapf
  19. .yamllint.yml
  20. BUILD.bazel
  21. CMakeLists.txt
  22. configure_bazel.py
  23. CONTRIBUTING.md
  24. LICENSE
  25. README.md
  26. repo_utils.bzl
  27. SUBMODULE_VERSIONS
  28. WORKSPACE
README.md

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

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:

IREE Architecture

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 and short-term focus areas.

We use GitHub Projects to track various IREE components and GitHub Milestones for major features and quarterly plans. Please check out for updated information.

Build Status

CI SystemBuild SystemPlatformArchitectureComponentStatus
KokoroBazelLinuxx86Corekokoro_status_bazel_linux_x86_core
KokoroBazelLinuxx86Bindingskokoro_status_bazel_linux_x86_bindings
KokoroBazelLinuxx86-swiftshaderIntegrationskokoro_status_bazel_linux_x86-swiftshader_integrations
KokoroBazelLinuxx86-turingIntegrationskokoro_status_bazel_linux_x86-turing_integrations
KokoroCMakeLinuxx86-swiftshaderCore + Bindingskokoro_status_cmake_linux_x86-swiftshader
KokoroCMakeLinuxx86-turingCore + Bindingskokoro_status_cmake_linux_x86-turing
KokoroCMakeAndroidarm64-v8aRuntime (build only)kokoro_status_cmake_android_arm64-v8a
BuildKiteCMakeAndroidarm64-v8aRuntimebuildkite-status-cmake-android-arm

License

IREE is licensed under the terms of the Apache license. See LICENSE for more information.