A tour of MLIR: The Dialect Stack Everyone Depends On

Summary

If you train or serve models, you depend on MLIR whether or not you have ever written a line of it. XLA lowers through it, Triton is built on it, Mojo is MLIR-native, and Torch-MLIR, IREE, and ONNX-MLIR exist to funnel their respective frontends into it. The reason a single piece of infrastructure ended up underneath so many otherwise-competing stacks is worth understanding, because it explains a lot about how modern ML compilers are actually built, and where their seams are.This post is a tour of MLIR: what it is, the dialect idea that makes it different, how a tensor operation is progressively lowered to machine code, and what the infrastructure does and does not give you.What MLIR Actually IsThe common misconception is that MLIR is “another IR like LLVM IR.” It is better described as an IR construction kit. LLVM IR is a single, fixed, low-level representation: roughly a typed assembly with SSA values. That is the right abstraction for the last mile to machine code, and the wrong one for a matrix multiply over tensors. Historically, every domain compiler that needed a higher-level representation invented its own from scratch: XLA had HLO, Halide its own IR, TensorFlow its graph, each shipping with a separate pass manager, serialization format, verifier, and pile of bugs.MLIR’s premise is that those representations have far more in common than not, and that the common parts can be built once and shared: SSA, a CFG of regions and blocks, a pass infrastructure, a pattern rewriter, location tracking, and verification. What differs between domains is then expressed as a dialect.The unit of everything in MLIR is the Operation. An op has operands and results (SSA values), a set of typed attributes (compile-time constants like shapes or strides), and zero or more regions, which themselves contain blocks of further ops. That last property is what makes the IR genuinely multi-level: a single op can carry a whole nested computation, so a high-level linalg.generic and a low-l...