Functorch brings JAX-like composable function transforms to PyTorch, enabling efficient per-sample gradients, ensemble computations, batched Jacobians/Hessians, and MAML inner-loop optimization. It integrates with PyTorch autograd for eager-mode performance and includes experimental FX tracing for ahead-of-time compilation of transform results.