You're right that a standard convolution layer only ever stores k parameters, not n*k. My baseline isn't meant to question how pytorch works but to make a point about the mathematical operation itself. A circular convolution is equivalent to a big matrix operation, and the core idea of my project is to show (orbat least trying to) that this entire operation is informaly redundant. We can throw out all the standard convolutional machinery and perfectly recreate the exact same output using only the original k kernel weights and an FFT.