Users of MLIR
In alphabetical order below.
CIRCT: Circuit IR Compilers and Tools
The CIRCT project is an (experimental!) effort looking to apply MLIR and the LLVM development methodology to the domain of hardware design tools.
Flang is a ground-up implementation of a Fortran front end written in modern C++. It started off as the f18 project with an aim to replace the previous flang project and address its various deficiencies. F18 was subsequently accepted into the LLVM project and rechristened as Flang. The high level IR of the Fortran compiler is modeled using MLIR.
IREE (pronounced “eerie”) is a compiler and minimal runtime system for compiling ML models for execution against a HAL (Hardware Abstraction Layer) that is aligned with Vulkan. It aims to be a viable way to compile and run ML devices on a variety of small and medium sized systems, leveraging either the GPU (via Vulkan/SPIR-V), CPU or some combination. It also aims to interoperate seamlessly with existing users of Vulkan APIs, specifically focused on games and rendering pipelines.
Lumen: A new compiler and runtime for BEAM languages
Lumen is not only a compiler, but a runtime as well. It consists of two parts:
- A compiler for Erlang to native code for a given target (x86, ARM, WebAssembly)
- An Erlang runtime, implemented in Rust, which provides the core functionality needed to implement OTP
The primary motivator for Lumen’s development was the ability to compile Elixir applications that could target WebAssembly, enabling use of Elixir as a language for frontend development. It is also possible to use Lumen to target other platforms as well, by producing self-contained executables on platforms such as x86.
Nod Distributed Runtime: Asynchronous fine-grained op-level parallel runtime
Nod’s MLIR based Parallel Compiler and Distributed Runtime provide a way to easily scale out training and inference of very large models across multiple heterogeneous devices (CPUs/GPUs/Accelerators/FPGAs) in a cluster while exploiting fine-grained op-level parallelism.
NPComp: MLIR based compiler toolkit for numerical python programs
The NPComp project aims to provide tooling for compiling numerical python programs of various forms to take advantage of MLIR+LLVM code generation and backend runtime systems.
In addition to providing a bridge to a variety of Python based numerical programming frameworks, NPComp also directly develops components for tracing and compilation of generic Python program fragments.
To represent neural network models, users often use Open Neural Network Exchange (ONNX) which is an open standard format for machine learning interoperability. ONNX-MLIR is a MLIR-based compiler for rewriting a model in ONNX into a standalone binary that is executable on different target hardwares such as x86 machines, IBM Power Systems, and IBM System Z.
See also this paper: Compiling ONNX Neural Network Models Using MLIR.
PlaidML is a tensor compiler that facilitates reusable and performance portable ML models across various hardware targets including CPUs, GPUs, and accelerators.
RISE is a spiritual successor to the Lift project: “a high-level functional data parallel language with a system of rewrite rules which encode algorithmic and hardware-specific optimisation choices”.
TFRT aims to provide a unified, extensible infrastructure layer for an asynchronous runtime system.
MLIR is used as a Graph Transformation framework and the foundation for building many tools (XLA, TFLite converter, quantization, …).
Project Verona is a research programming language to explore the concept of concurrent ownership. They are providing a new concurrency model that seamlessly integrates ownership.