In pytorch, how to calculate gradient for a element in a tensor when it is used to calculate another element in this tensor?
In this pytorch code:
import torch
a = torch.tensor([2.], requires_grad=True)
y = torch.zeros((10))
gt = torch.zeros((10))

y[0] = a
y[1] = y[0] * 2
y.retain_grad()

loss = torch.sum((y-gt) ** 2)
loss.backward()
print(y.grad)

I want y[0]'s gradient to consist 2 parts:

loss backward to y[0] itself.
y[0] is used to calculate y[1], so it should have the part of y[1]'s gradient.

but when I run this code, there is only part 1 in y[0]'s gradient.
So how to make y[0]'s gradient to have all 2 parts?
edit: the output is:
tensor([4., 8., 0., 0., 0., 0., 0., 0., 0., 0.])

but I expect:
tensor([20., 8., 0., 0., 0., 0., 0., 0., 0., 0.])


y[0] and y[1] are two different elements, therefore they have different grad. The only thing that &quot;binds&quot; them is the underlying relation to a. If you inspect the grad of a, you'll see:
print(a.grad)


tensor([20.])


That is, the two parts of the gradients are combined in a.grad.
