We all use torch.compile, but for many of us, it’s a "black box" that magically makes code go fast. This poster is designed to turn that magic into a mental map. I’ve distilled the complex journey of a PyTorch tensor—from the Python frontend through TorchDynamo, into the AOTAutograd, and finally being lowered into a Triton kernel.

Instead of dense code blocks, I’m using visual flowcharts to show where the "graph breaks" happen and how to avoid them. If you’ve ever wondered why your model isn't as fast as the benchmarks promised, this poster will give you the diagnostic tools to look under the hood and fix it yourself. This isn't just a talk; it's a "cheat sheet" for the modern PyTorch developer.