Setting torch.nn.linear() diagonal elements zero
I am trying to build a model with a layer of torch.nn.linear with same input size and output size, so the layer would be square matrix. In this model, I want the diagonal elements of this matrix fixed to zero. Which means, during training, I don't want the diagonal elements to be changed from zero. I could only think of adding some kind of step that change diag elements to zero for each training epoch, but I am not sure if this is valid or efficient way. Is there a definite way of making this kind of layer which can ensure that the diagonal elements don't change?
Sorry if my question is weird.

You can always implement your own layers. Note that all custom layers should be implemented as classes derived from nn.Module. For example:
class LinearWithZeroDiagonal(nn.Module):
  def __init__(self, num_features, bias):
    super(LinearWithZeroDiagonal, self).__init__()
    self.base_linear_layer = nn.Linear(num_features, num_features, bias)
    
  def forward(self, x):
    # first, make sure the diagonal is zero
    with torch.no_grad():
      self.base_linear_layer.weight.fill_diagonal_(0.)
    return self.base_linear_layer(x)

