In PyTorch, the permute() function is used to rearrange the dimensions of a tensor according to a specified order. This can be useful in various deep learning scenarios, such as when you need to change the dimension order of your input data to match the expected input format of a model. The function takes a […]

The code snippet using PyTorch library below is found in the Taming Transformers paper: This code snippet is performing a vectorized calculation to compute pairwise squared Euclidean distances between two sets of vectors: z_flattened and the rows of self.embedding.weight. Let’s break down the code: Now let’s analyze the calculations: Finally, the code adds the three […]

Let’s take a closer look at the detach() function in PyTorch, which plays a helpful role when working with Tensors. The detach() function creates a new Tensor that shares the same data as the original one but without the attached computation history. This essentially separates the new Tensor from the computation graph, making it independent […]

In PyTorch, the view() function is a tensor operation used to reshape a tensor without changing its underlying data. It allows you to change the dimensions of a tensor to fit your desired shape while preserving the original data and maintaining the same number of elements. This is especially useful when you need to change […]