commit	6073d9142c724cd354c13b292f84c61b2b55701e	[log] [tgz]
author	Mahesh Ravishankar <ravishankarm@google.com>	Mon Jun 29 10:19:24 2020 -0700
committer	Copybara-Service <copybara-worker@google.com>	Mon Jun 29 10:21:09 2020 -0700
tree	c3907747f8b22f8f35b590ac66188022f7c9c98f
parent	b66ecc226249a0d79b46300268bcd456b211f524 [diff]

Resubmitting PR #2163 with fixes.

This commit is a squash of two commits. The first is the same change
PR #2163.

The second commit contains fixes that address failures on Resnet seen
from that P. The original commit seems to have a correctness
issue. The change is valid if the number of iterations of the loop is
less than or equal to the workgroup size, which doesnt seem to be the
case for convolution/pooling in all cases. More investigation is
needed. For now, falling back to the loop method for
convolution/pooling when there is no padding. With padding, tiling is
completely avoided and is executed in parallel by linearizing all the
parallel loops and distributing to threads using the global invocation
ID. The commit also enables the option to split the padding into a
separate option when needed. This is done to make it easier to
pin-point the issue, so that the complications from padding are
removed.

PiperOrigin-RevId: 318839822

27 files changed

tree: c3907747f8b22f8f35b590ac66188022f7c9c98f

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

GitHub Issues: Preferred for specific technical issues and coordination on upcoming features.
Google IREE Discord Server: The core team and collaborators coordinate daily development here; good for low-latency communication.
Google Groups Email List: Good for general and low-priority discussion.

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 System	Build System	Platform	Component
Kokoro	Bazel	Linux	Core
Kokoro	Bazel	Linux	Bindings
Kokoro	Bazel	Linux	Integrations
Kokoro	CMake	Linux	Core + Bindings

License

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