Google Git
Sign in
opensecura / 3p / openxla / iree / 32fed9deaa3836d8cec537a0b50bd35829b1f388
commit32fed9deaa3836d8cec537a0b50bd35829b1f388[log] [tgz]
authorGeoffrey Martin-Noble <gcmn@google.com>Tue Jul 07 10:21:23 2020 -0700
committerGitHub <noreply@github.com>Tue Jul 07 10:21:23 2020 -0700
tree462c5fe7727204568fbbf56c5e5e7992470e6aeb
parentc716c1d9857065b4be52128c03cd2383b828f1a0 [diff]
parent58d25ee8af1b0589bb0415386ebacd6931ff7792 [diff]
Merge google -> main (#2419)

* 58d25ee8 Synchronize submodules                                                          
* 6919f322 Update TF and CMake files for mlir-hlo move                                     
* a708d97e Move XLA-independent transforms to the new MLIR-HLO directory                   
* 8b6621e1 Merge pull request #2400 from iml130:tensorflow-cmake                           
* ec41a0a3 Cleanup and fix file ordering in TF CMakeLists                                  
* 87eaa3c6 Refactor TensorFlow CMakeLists                                                  
* 1dc5fa09 Synchronize submodules                                                          
* d7745277 Update TF submodule and LLVM BUILD files                                        
* f478074f Synchronize submodules                                                          
* 87455ee8 Integrate LLVM at https://github.com/llvm/llvm-project/commit/0607c8df7faf      
* 6c41ee6d Synchronize submodules                                                          
* 8a8a89fc Integrate LLVM at https://github.com/llvm/llvm-project/commit/cd209f1a3790      
* 7643d59d Synchronize submodules                                                          
* 59f35a14 Integrate LLVM at https://github.com/llvm/llvm-project/commit/edba2864a7a8      
* a624b25a Synchronize submodules                                                          
* c36ba20a Integrate LLVM at https://github.com/llvm/llvm-project/commit/91c320e9d852      
* d1fc80be Synchronize submodules                                                          
* a9baf19b Integrate LLVM at https://github.com/llvm/llvm-project/commit/01c4574a129e      
* 3b51a99b Synchronize submodules                                                          
* 8b1b7edd Integrate LLVM at https://github.com/llvm/llvm-project/commit/d6343e607ac8      
* c2ec2bd9 Synchronize submodules                                                          
* ed47aac9 Integrate LLVM at https://github.com/llvm/llvm-project/commit/68498ce8af37      
* 506be9e1 Synchronize submodules                                                          
* c5dcf953 Integrate LLVM at https://github.com/llvm/llvm-project/commit/0f9d623b63e8      
* 896de17d Move the HLO/LHLO dialects to a new directory: tensorflow/compiler/mlir/hlo
tree: 462c5fe7727204568fbbf56c5e5e7992470e6aeb
  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

License

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