The compiled logprob graphs do not enforce the input shapes

[rlouf]

Let's consider the following artificial example. Although the variable being conditioned on is of shape (3,), the compiled logprob graph accepts any input shape as long as the input is 1-dimensional:

import aesara
import aeppl
import aesara.tensor as at

srng = at.random.RandomStream(0)
x_rv = srng.normal(0, 1, size=(10,))
y = x_rv[[1,2,5]]
y.name="y"

logprob, (y_vv,) = aeppl.joint_logprob(y)
aesara.dprint(logprob)
# Sum{acc_dtype=float64} [id A]
#  |Check{sigma > 0} [id B] 'y_logprob'
#    |Elemwise{sub,no_inplace} [id C]
#    | |Elemwise{sub,no_inplace} [id D]
#    | | |Elemwise{mul,no_inplace} [id E]
#    | | | |InplaceDimShuffle{x} [id F]
#    | | | | |TensorConstant{-0.5} [id G]
#    | | | |Elemwise{pow,no_inplace} [id H]
#    | | |   |Elemwise{true_div,no_inplace} [id I]
#    | | |   | |Elemwise{sub,no_inplace} [id J]
#    | | |   | | |y_vv [id K]
#    | | |   | | |InplaceDimShuffle{x} [id L]
#    | | |   | |   |TensorConstant{0} [id M]
#    | | |   | |InplaceDimShuffle{x} [id N]
#    | | |   |   |TensorConstant{1} [id O]
#    | | |   |InplaceDimShuffle{x} [id P]
#    | | |     |TensorConstant{2} [id Q]
#    | | |InplaceDimShuffle{x} [id R]
#    | |   |Elemwise{log,no_inplace} [id S]
#    | |     |Elemwise{sqrt,no_inplace} [id T]
#    | |       |TensorConstant{6.283185307179586} [id U]
#    | |InplaceDimShuffle{x} [id V]
#    |   |Elemwise{log,no_inplace} [id W]
#    |     |TensorConstant{1} [id O]
#    |All [id X]
#      |Elemwise{gt,no_inplace} [id Y]
#        |TensorConstant{1} [id O]
#        |TensorConstant{0.0} [id Z]

logprob_fn = aesara.function([y_vv], logprob)
try:
    print(logprob_fn(1.))
except Exception as e:
    print(e)
# Wrong number of dimensions: expected 1, got 0 with shape ().

print(logprob_fn([1.]))
# -1.4189385332046727
print(logprob_fn([1., 1., 1.]))
# -4.2568155996140185

I would however expect the compiled function to behave like the following:

y_at = at.tensor(shape=(3,), dtype="float32")
out = at.sum(y_at)
fn = aesara.function((y_at,), out)
try:
    print(fn([1.]))
except Exception as e:
    print(e)
# The type's shape ((3,)) is not compatible with the data's ((1,))

This was already noted here #51 (reply in thread), and is due to the fact that we do not enforce any type of shape constraints when cloning the random variables to get the value variables:

aeppl/aeppl/joint_logprob.py

Line 100 in eb55106

vv = rv.clone()

This constraint would have been difficult to enforce with the previous conditional_logprob interface that asked users to pass value variables. However, we now have full control over how value variables are created, and could thus specify their shape and dtype.