Hello,
I’m trying to implement Integrated Gradients (explainability method) for my seq2seq NMT model since there are no public implementations available. For that, I would be required to compute the gradient of my output w.r.t. my input, which would in turn require me to set requires_grad = True for my input variables. Consequently, I would need my input to be of dtype = torch.float (or else an error is thrown), but then I would not be able to use the inputs as inputs to the encoder/decoder embedding layers since those require a dtype = torch.long input. Any suggestions on how to go about this?
Thanks