Google Git
Sign in
opensecura / 3p / openxla / iree / 1633aa345bbf25d231faf4ee98ce65635ec8a56e
commit1633aa345bbf25d231faf4ee98ce65635ec8a56e[log] [tgz]
authorPhoenix Meadowlark <meadowlark@google.com>Wed Jun 17 12:28:58 2020 -0700
committerCopybara-Service <copybara-worker@google.com>Wed Jun 17 12:30:34 2020 -0700
treebbb756a2df7a4c0a29e55a912e8ca83e0796a205
parent3301874d06d76cf71df206c7acbc875dfdd40371 [diff]
Adds MacOS support to run_lit.sh and adds MacOS getting started.

This puts MacOS at a point where all but 8 of the core IREE tests are passing on Bazel. Both the Bazel and Cmake builds complete successfully on my laptop.

The getting started documentation include disclaimers that we do not actively support MacOS.

Python3 numpy is added as a dependency to the Linux and MacOS Bazel builds to reflect the effects of using `python3 configure_bazel.py`.

This change also adds a lot of small fixes to the getting started docs more broadly, as they were requested by reviewers.

Closes https://github.com/google/iree/pull/2201

COPYBARA_INTEGRATE_REVIEW=https://github.com/google/iree/pull/2201 from phoenix-meadowlark:getting-started-macos a683048d31ede75e3a26bf3d83cf7b213f594f0a
PiperOrigin-RevId: 316941443
10 files changed
tree: bbb756a2df7a4c0a29e55a912e8ca83e0796a205
  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. BUILD.bazel
  20. CMakeLists.txt
  21. configure_bazel.py
  22. CONTRIBUTING.md
  23. LICENSE
  24. README.md
  25. repo_utils.bzl
  26. SUBMODULE_VERSIONS
  27. 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 SystemPlatformComponentStatus
KokoroBazelLinuxCorekokoro-status-bazel-linux
KokoroBazelLinuxBindingskokoro-status-bazel-linux
KokoroBazelLinuxIntegrationskokoro-status-bazel-linux
KokoroCMakeLinuxCore + Bindingskokoro-status-cmake-linux

License

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