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opensecura / 3p / openxla / iree / 3b7fd18058428e3a4b462460a7da67fd635e0fc8
commit3b7fd18058428e3a4b462460a7da67fd635e0fc8[log] [tgz]
authorScott Todd <scotttodd@google.com>Thu Jul 16 12:49:32 2020 -0700
committerGitHub <noreply@github.com>Thu Jul 16 12:49:32 2020 -0700
tree6713dfa2d93e656e5b90207e791bda709d755b90
parent3c8954c6358ff0ac848cda42e1f1266c7268e53e [diff]
parentb2145c7f2fd5dbd0625a477c5b5ae76238602753 [diff]
Merge google -> main (#2544)

* b2145c7f Synchronize submodules                                                          
* 13bdb7e0 Integrate TF at https://github.com/tensorflow/tensorflow/commit/33a4c1a0aba     
* c89e8ee2 Merge pull request #2543 from ScottTodd:main-to-google                          
* 8be92426 Synchronize submodules                                                          
* b0737d29 Integrate LLVM at https://github.com/llvm/llvm-project/commit/1067d3e176ea      
* 3e574a9e Synchronize submodules                                                          
* d875628e Integrate LLVM at https://github.com/llvm/llvm-project/commit/b2018198c32a      
* 28e7bc29 Synchronize submodules                                                          
* 3f7687c3 Integrate LLVM at https://github.com/llvm/llvm-project/commit/f233b92f92a6      
* af5f1502 Adds a way to register modules with the context through the java API            
* 79586bdd Synchronize submodules                                                          
* 95d8c9f5 Integrate TF at ebcd2928f5b0                                                    
* 3aadd21f Synchronize submodules                                                          
* 0ee95249 Integrate LLVM at https://github.com/llvm/llvm-project/commit/c11c78a1bd0b      
* 0bdd8f2a Synchronize submodules                                                          
* 89bf08f5 Integrate LLVM at https://github.com/llvm/llvm-project/commit/f782d9c7002e      
* 764c9dde Merge pull request #2512 from ScottTodd:main-to-google
tree: 6713dfa2d93e656e5b90207e791bda709d755b90
  1. .github/
  2. bindings/
  3. build_tools/
  4. colab/
  5. docs/
  6. experimental/
  7. integrations/
  8. iree/
  9. kokoro/
  10. packaging/
  11. scripts/
  12. third_party/
  13. .bazelignore
  14. .bazelrc
  15. .bazelversion
  16. .clang-format
  17. .gitignore
  18. .gitmodules
  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 SystemPlatformBuild SystemComponentStatus
KokoroLinuxBazelCorekokoro-status-linux-bazel-core
KokoroLinuxBazelBindingskokoro-status-linux-bazel-bindings
KokoroLinuxBazelIntegrationskokoro-status-linux-bazel-integrations
KokoroLinuxCMakeCore + Bindingskokoro-status-linux-cmake
KokoroAndroidCMakeRuntime (build only)kokoro-status-android-cmake

License

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