In my previous MLIR compiler project, I built a small tensor compiler using the MLIR framework. While working on it, I realized how much complexity the framework handles for me, from lowering operations through existing compiler infrastructure to generating executable code. This made me curious about what happens underneath these abstractions, particularly when compiling for custom hardware rather than a conventional CPU. For this project, I want to explore two things: how AI

I'm writing this article to document my learning journey. This is my first compiler. I first discovered tensor compilers last year while digging through the PyTorch codebase. At the time, I wanted to transition into low-level programming, and since PyTorch was the only framework I was familiar with, I decided to explore their codebase. There, I discovered their compiler toolchain, TorchDynamo and TorchInductor, which, in simple terms, serve as PyTorch's machine learning compi