Computing variance of an SGD iteration

It is known that SGD iteration has huge variance.
Given the iteration update:
$$
w^{k+1} := w^k – underbrace{alpha g_i(w^k)}_{p^k},
$$
where $w$ are model weights and $g_i(w^k)$ is gradient of loss function evaluated for sample $i$. How do I compute variance of each update $p^k$?
I would like to plot it for each iteration and study its behavior during minimization process.