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opensecura / 3p / openxla / iree / f80da2e8cdccdd976f14f70d33195a40e4d8d961
commitf80da2e8cdccdd976f14f70d33195a40e4d8d961[log] [tgz]
authorBen Vanik <ben.vanik@gmail.com>Mon Apr 13 10:33:52 2020 -0700
committerCopybara-Service <copybara-worker@google.com>Mon Apr 13 10:36:44 2020 -0700
treed2831bece49c94c8e22858668f9c29ec679b327c
parent6564d6c2db67515b9fa85cba7d1d09360b2520f1 [diff]
Merging hal.device.switch feature branch.

Adding a new `hal.device.switch` op that allows us to nicely generate backend/device-specific logic that can be transformed into efficient code for runtime. There's a lot of folders needed to help here, however this initial structure will let us begin work on #1168 as it exposes the ability to generate device-specific dispatch ops.

Future iteration will add more device matchers to allow more fine-grained codegen. For example, matchers that look for supported device-specific extensions, minimum allocation alignment, unified vs. discrete memory, maximum descriptor set count, etc can all be added in a generic way and efficiently lowered to runtime code.

Progress towards heterogeneous execution will cause the `!hal.device` type to change to a `hal.device<ordinal>` form, allowing better compile-time evaluation of the `hal.device.switch` blocks when placement happens. For now we still only have a single runtime-selected `hal.ex.shared_device` so this cannot happen.

Closes https://github.com/google/iree/pull/1393

COPYBARA_INTEGRATE_REVIEW=https://github.com/google/iree/pull/1393 from google:benvanik-hal-device-switch d0348ef32a6af9940d4f9243a1cce88921bd398e
PiperOrigin-RevId: 306259903
41 files changed
tree: d2831bece49c94c8e22858668f9c29ec679b327c
  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. CONTRIBUTING.md
  21. LICENSE
  22. README.md
  23. repo_utils.bzl
  24. SUBMODULE_VERSIONS
  25. 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 SystemPlatformStatus
GitHub ActionsBazelLinuxWorkflow History
KokoroBazelLinuxkokoro-status-bazel-linux
KokoroCMakeLinuxkokoro-status-cmake-linux

License

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