What makes PyTorch beloved makes it harder to compile. After almost five years, we finally cracked the technologies that made it possible to compile any PyTorch model, resulting in a step-function change in PyTorch’s approach to execution efficiency. We called it PyTorch 2.0.
PyTorch 2.0 delivers significant performance improvements over a wide variety of models, often with just a simple one-liner change. This talk focuses on the two critical technologies underlying PyTorch 2.0, TorchDynamo and TorchInductor.
PyTorch 2.0 was released in March. But do not mistake it as the end of the story. The first release of PyTorch 2.0 marks the beginning of a roadmap for improving PyTorch execution efficiency via compiled mode.