Yeah, you are right, it was just Ipython pretty printing that confused me.
So you can evaluate the logp of your distributions via
with pm.Model() as m:
x = generate_normal({'name': 'x', 'mu': 0, 'sigma': 2})
x.logp({'x': 1}) # array(-1.73708571)
scipy.stats.norm(0, 2).logpdf(1) # -1.737085713764618