I am afraid it is not that easy to do.
The simplest way I see is to use:
layer_grad_fn.next_functions[1][0].variable that is the weights of the conv and layer_grad_fn.next_functions[2][0].variable that is the bias of the conv. By looking at the size of the weight matrix, you can know the size of the kernel as the weight matrix is: out_chan x in_chan x kern x kern.