MLIR

Website | Users -- that is to say, projects and languages that use MLIR

MLIR is intended to be a hybrid IR which can support multiple different requirements in a unified infrastructure. For example, this includes:

The ability to represent dataflow graphs (such as in TensorFlow), including dynamic shapes, the user-extensible op ecosystem, TensorFlow variables, etc.
Optimizations and transformations typically done on such graphs (e.g. in Grappler).
Ability to host high-performance-computing-style loop optimizations across kernels (fusion, loop interchange, tiling, etc.), and to transform memory layouts of data.
Code generation “lowering” transformations such as DMA insertion, explicit cache management, memory tiling, and vectorization for 1D and 2D register architectures.
Ability to represent target-specific operations, e.g. accelerator-specific high-level operations.
Quantization and other graph transformations done on a Deep-Learning graph.
Polyhedral primitives.
Hardware Synthesis Tools / HLS.

MLIR is a common IR that also supports hardware specific operations. Thus, any investment into the infrastructure surrounding MLIR (e.g. the compiler passes that work on it) should yield good returns; many targets can use that infrastructure and will benefit from it.

MLIR is a powerful representation, but it also has non-goals. We do not try to support low level machine code generation algorithms (like register allocation and instruction scheduling). They are a better fit for lower level optimizers (such as LLVM). Also, we do not intend MLIR to be a source language that end-users would themselves write kernels in (analogous to CUDA C++). On the other hand, MLIR provides the backbone for representing any such DSL and integrating it in the ecosystem.

Tags: platform library vm native

Last modified 22 November 2024

MLIR

A hybrid IR which can support multiple different requirements in a unified infrastructure.