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opensecura / 3p / openxla / iree / c2faf4870a5412a0dd290f0522e70cf5e9cb8dbd
commitc2faf4870a5412a0dd290f0522e70cf5e9cb8dbd[log] [tgz]
authorStella Laurenzo <laurenzo@google.com>Fri May 29 17:41:47 2020 -0700
committerCopybara-Service <copybara-worker@google.com>Fri May 29 17:43:58 2020 -0700
tree68b328e4a50995b1d27f125949fbd98208287387
parent7de3b7a2b915b0ddfa7510c361a01be4912324d0 [diff]
Upgrade to the IdentifyDispatchRegions2 pass.
* Has a nasty workaround for #2050, which was exposed by different DR formation.
* Removed the special case output fusion in favor of relying on optimization passes to do the right thing (which is necessary for dynamic shapes anyway). FoldCompatibleDispatchRegions is working reasonably for this, and giving it smaller atoms, in theory, makes more advanced optimization passes more possible).
* Added in the special cases for our current list of ops that don't support fusion.
* Added a special case to assign a lower benefit to ops that are known to materialize to a copy, increasing the chance that they fuse to ops which are doing useful work.
* Enabled the dynamic shape multi-layer-perceptron tests on VMLA. Tried enabling on vulkan but ran into an issue. Filed #2057

PiperOrigin-RevId: 313881073
7 files changed
tree: 68b328e4a50995b1d27f125949fbd98208287387
  1. .github/
  2. bindings/
  3. build_tools/
  4. colab/
  5. docs/
  6. experimental/
  7. integrations/
  8. iree/
  9. kokoro/
  10. scripts/
  11. third_party/
  12. .bazelignore
  13. .bazelrc
  14. .bazelversion
  15. .clang-format
  16. .gitignore
  17. .gitmodules
  18. BUILD.bazel
  19. CMakeLists.txt
  20. configure_bazel.py
  21. CONTRIBUTING.md
  22. LICENSE
  23. README.md
  24. repo_utils.bzl
  25. SUBMODULE_VERSIONS
  26. 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 logisitics. 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 implemenations. 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 mordern 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
GitHub ActionsBazelLinuxCoreWorkflow History
GitHub ActionsBazelLinuxBindingsWorkflow History
GitHub ActionsBazelLinuxIntegrationsWorkflow History
GitHub ActionsBazelLinuxOtherWorkflow History
KokoroBazelLinuxCorekokoro-status-bazel-linux
KokoroCMakeLinuxCore + Bindingskokoro-status-cmake-linux

License

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