Time flies—almost 9 years have passed since I joined Google. Now the time has come for me to leave and move on. While here, I’m super lucky to mostly work on open source projects that I can publicly talk about. So at the end of my tenure with Google, I’d like to reflect and summarize the incredible journey, which I am super grateful for and thoroughly enjoyed, before I forget some details.

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.

I explained the Vector dialect and related patterns in the previous blog post. In this one let us look at a layer higher and talk about the Linalg dialect and transformations around it.

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.