Staff Software Engineer Google
AI Frameworks & Compilers. Now: Vulkan compute, IREE, MLIR. Previous: Vulkan graphics, SPIR-V toolchain.
Previous blog posts overviewed the MLIR dialect hierarchy for kernel code generation (CodeGen) and zoomed in on the Linalg and Vector dialects among them. Now I will switch to discuss the runtime side a bit, in order to provide a holistic view of MLIR-based machine learning (ML) compilers. This one touches the foundation and basics, including the target landscape, runtime requirements and designs to meet thereof.
The vector dialect and related transformations are crucial components in the MLIR CodeGen flow for machine learning (ML). Today I will zoom in on it to explain its positioning in the overall picture, characteristics, important operations and transformations, and best practices of using it based on my experiences.
The initial blog post in this series captured my overall take on the evolution trends of compilers and IRs. It also touched on LLVM IR, SPIR-V, and MLIR, explaining the problems they are addressing and design focuses thereof. Today I will expand on MLIR and talk about its dialect hierarchy for machine learning (ML) compilers systematically.
Compilers are often critical components in various development toolchains that boosts developer productivity. A compiler is normally used as a monolithic black box that consumes a high-level source program and produces a semantically-equivalent low-level one. It is still structured inside though; what flows between internal layers are called intermediate representations (IRs). IRs are critical to compilers. Like there are many compilers, there are also many IRs in use. I’m fortunate to have direct experience with three major schools of IRs or infrastructures thus far—LLVM IR, SPIR-V, MLIR, particularly extensively for the last two, where I both joined development in an early stage. So I’d like to write a series of blog posts to log down my understanding of compilers and IRs. Hopefully it could be beneficial to others.