MLIR, or Multi-Level Intermediate Representation, is a representation format and library of compiler utilities that sits between the model representation and low-level compilers/executors that generate hardware-specific code.
MLIR is, at its heart, a flexible infrastructure for modern optimizing compilers. This means it consists of a specification for intermediate representations (IR) and a code toolkit to perform transformations on that representation. (In compiler parlance, as you move from higher-level representations to lower-level representations, these transformations can be called “lowerings”)
MLIR is highly influenced by LLVM and unabashedly reuses many great ideas from it. It has a flexible type system, and allows representing, analyzing and transforming graphs combining multiple levels of abstraction in the same compilation unit. These abstractions include TensorFlow operations, nested polyhedral loop regions, and even LLVM instructions and fixed hardware operations and types.
We expect MLIR to be of interest to many groups, including:
- Compiler researchers and implementers looking to optimize performance and memory consumption of machine learning models
- Hardware makers looking for a way to connect their hardware to TensorFlow, such as TPUs, portable neural hardware in phones, and other custom ASICs
- People writing language bindings that want to take advantage of optimizing compilers and hardware acceleration.
The TensorFlow ecosystem contains a number of compilers and optimizers that operate at multiple levels of the software and hardware stack. We expect the gradual adoption of MLIF to simplify every aspect of this stack.