- torch.autograd.forward_ad.make_dual(tensor, tangent, *, level=None)[source]¶
Associates a tensor value with a forward gradient, the tangent, to create a “dual tensor”, which is used to compute forward AD gradients. The result is a new tensor aliased to
tangentembedded as an attribute as-is if it has the same storage layout or copied otherwise. The tangent attribute can be recovered with
This function is backward differentiable.
Given a function f whose jacobian is J, it allows one to compute the Jacobian-vector product (jvp) between J and a given vector v as follows.
>>> with dual_level(): ... inp = make_dual(x, v) ... out = f(inp) ... y, jvp = unpack_dual(out)
Please see the forward-mode AD tutorial for detailed steps on how to use this API.