Google Git
Sign in
opensecura / 3p / openxla / iree / 66578b09e2dde8e4bda62cf16dcfda8b16feb6a3
commit66578b09e2dde8e4bda62cf16dcfda8b16feb6a3[log] [tgz]
authorStella Laurenzo <stellaraccident@gmail.com>Mon Jul 20 17:34:44 2020 -0700
committerGitHub <noreply@github.com>Mon Jul 20 17:34:44 2020 -0700
tree01d4a1565afc92e9cc1084f3efb8946045057666
parent153a8c4d63e27cc1b9705291080e358d1fd7f421 [diff]
Step 1 of creating multi-python bindings. (#2576)

* Step 1 of creating multi-python bindings.

* Adds the cmake machinery to configure the build to produce native modules for all specified python versions (should also work for python cross compile).
* The default is as it has been (to use the identified default python config).
* I had hoped to make a common library for this that I could share with LLVM, but IREE's use of CMake is too quirky and it would be easier to just adapt vs share.
* This marks the end of the road for bazel_to_cmake support for python. I see no maintainable way to have a single sot that spans cmake, bazel and Google's bazel: they are all just too weird when it comes to shenanigans needed to built python extensions effectively.
* I'm going to transition all of the python packages to more of a CMake-normal syntax (versus this weird dep naming and other stuff we do to make it look like Bazel): Python will be divergent forever and I don't care if it is a bit different from the rest (and more consistent with upstream, which I am also working on).
* Also fixed some default device enumeration issues in tests that were keeping me from making progress (no GPU on this container).
* I intend to remove/simplify the current machinery for this stuff but have left it in place for now (to simplify rollbacks and diffs).

* Adopt name mangling scheme.

* Fix PYEXT_DEPS on iree_py_library.

* Remove obsolete files.

* Fix typo.

* Add pybind11 include dir.
14 files changed
tree: 01d4a1565afc92e9cc1084f3efb8946045057666
  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.