Hi,
I’m trying to compute the sub hessian vector product of a function efficiently. By sub hessian, I mean that I’d like to exctract a sub matrix from the regular hessian and do a vector product with it.
I am aware of the standard trick for computing h.v.p. efficiently and I’d like to do something similar here.
I think one solution could be to stop the gradient for some entries of the tensor X.  Hence I could get the “sub” hessian vector product by double differentiation.
Is there a way in pytorch to stop gradient for some entries of a tensor ?
Thanks for your help