Before we dive into the solution, it’s essential to understand why this issue occurs in the first place. When you’re working with PyTorch, you’re likely dealing with tensors as inputs to your model. These tensors can be thought of as multidimensional arrays that hold your data. The problem arises when you’re not using the entire input tensor during gradient computation. PyTorch’s automatic differentiation mechanism relies on the concept of gradients being computed with respect to the entire input tensor. If you’re only using a portion of the tensor, PyTorch gets confused and throws an error. This error can manifest in various ways, such as: “RuntimeError: gradient can be implicitly created only for scalar outputs” “RuntimeError: grad can be implicitly created only for scalar outputs” “TypeError: can’t compute grad of scalar” These errors can be misleading, making it challenging to diagnose the issue. But fear not, we’ve got you covered! By ErvPosted on July 21, 2024