I’m tracking it down. Sorry I was working on some other tasks before. Here is a MWE we found. I’ll let you know if there is any update.
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F


from torch.autograd import Variable
from torch.autograd import grad


conv = nn.Conv2d(1, 10, 5).cuda()

input = Variable(torch.randn(1,1,32,32).cuda())

loss1 = conv(input).sum()
t, = torch.autograd.grad(loss1, conv.bias, create_graph=True)

loss2 = t.sum()
loss2.backward()