PyTorch Conference Europe 2026

torch.compile has been invaluable in accelerating many machine learning and scientific computing workflows. It has become a one-shot way to get free performance for many kinds of programs and models.

However, it comes with its own set of limitations. One of these limitations is that, for a long time, torch.compile didn't accept complex-valued tensors. These tensors have many uses, from quantum mechanics to simplifying the physics for world models. Support for such tensors would accelerate many of these workflows.

In this talk, we will take a journey into the current progress for supporting such tensors in torch.compile; some of the encountered challenges and what we hope to achieve, including some side-benefits for reducing binary size by JIT-ing kernels on demand.