PyTorch's automatic differentiation is a key feature that distinguishes it from other deep learning frameworks. It provides flexibility and control by allowing users to define custom gradients for non-standard operations, making it easier to experiment and implement novel research ideas.

PyTorch uses a technique called dynamic computational graphs to enable automatic differentiation. This means that the graph is constructed on the fly as you define operations, allowing for efficient tracking of gradients.

PyTorch provides a powerful autograd package that automatically computes gradients for tensors. By setting the `requires_grad` attribute to `True`, PyTorch keeps track of all operations performed on that tensor and builds a computational graph. Backpropagation is then performed by calling `backward()` on a scalar loss tensor. This allows for easy implementation of complex models and optimization algorithms.