Support logp derivation of power(base, rv)

pymc/logprob/transforms.py

@@ -127,7 +127,7 @@
     cleanup_ir_rewrites_db,
     measurable_ir_rewrites_db,
 )
-from pymc.logprob.utils import check_potential_measurability
+from pymc.logprob.utils import CheckParameterValue, check_potential_measurability
 
 
 class TransformedVariable(Op):
@@ -617,6 +617,21 @@ def measurable_special_exp_to_exp(fgraph, node):
         return [1 / (1 + pt.exp(-inp))]
 
 
+@node_rewriter([pow])
+def measurable_power_exponent_to_exp(fgraph, node):
+    """Convert power(base, rv) of `MeasurableVariable`s to exp(log(base) * rv) form."""
+    base, inp_exponent = node.inputs
+
+    # When the base is measurable we have `power(rv, exponent)`, which should be handled by `PowerTransform` and needs no further rewrite.
+    # Here we change only the cases where exponent is measurable `power(base, rv)` which is not supported by the `PowerTransform`
+    if check_potential_measurability([base], fgraph.preserve_rv_mappings.rv_values.keys()):
+        return None
+
+    base = CheckParameterValue("base >= 0")(base, pt.all(pt.ge(base, 0.0)))
+
+    return [pt.exp(pt.log(base) * inp_exponent)]
+
+
 @node_rewriter(
     [
         exp,
@@ -693,7 +708,7 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> Optional[Li
         try:
             (power,) = other_inputs
             power = pt.get_underlying_scalar_constant_value(power).item()
-        # Power needs to be a constant
+        # Power needs to be a constant, if not then proceed to the other case power(base, rv)
         except NotScalarConstantError:
             return None
         transform_inputs = (measurable_input, power)
@@ -769,6 +784,12 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> Optional[Li
     "transform",
 )
 
+measurable_ir_rewrites_db.register(
+    "measurable_power_expotent_to_exp",
+    measurable_power_exponent_to_exp,
+    "basic",
+    "transform",
+)
 
 measurable_ir_rewrites_db.register(
     "find_measurable_transforms",

tests/logprob/test_transforms.py

@@ -72,6 +72,7 @@
     TransformValuesMapping,
     TransformValuesRewrite,
 )
+from pymc.logprob.utils import ParameterValueError
 from pymc.testing import Rplusbig, Vector, assert_no_rvs
 from tests.distributions.test_transform import check_jacobian_det
 
@@ -1159,6 +1160,61 @@ def test_special_log_exp_transforms(transform):
         assert equal_computations([logp_test], [logp_ref])
 
 
+def test_measurable_power_exponent_with_constant_base():
+    # test power(2, rv) = exp2(rv)
+    # test negative base fails
+    x_rv_pow = pt.pow(2, pt.random.normal())
+    x_rv_exp2 = pt.exp2(pt.random.normal())
+
+    x_vv_pow = x_rv_pow.clone()
+    x_vv_exp2 = x_rv_exp2.clone()
+
+    x_logp_fn_pow = pytensor.function([x_vv_pow], pt.sum(logp(x_rv_pow, x_vv_pow)))
+    x_logp_fn_exp2 = pytensor.function([x_vv_exp2], pt.sum(logp(x_rv_exp2, x_vv_exp2)))
+
+    np.testing.assert_allclose(x_logp_fn_pow(0.1), x_logp_fn_exp2(0.1))
+
+    with pytest.raises(ParameterValueError, match="base >= 0"):
+        x_rv_neg = pt.pow(-2, pt.random.normal())
+        x_vv_neg = x_rv_neg.clone()
+        logp(x_rv_neg, x_vv_neg)
+
+
+def test_measurable_power_exponent_with_variable_base():
+    # test with RV when logp(<0) we raise error
+    base_rv = pt.random.normal([2])
+    x_raw_rv = pt.random.normal()
+    x_rv = pt.power(base_rv, x_raw_rv)
+
+    x_rv.name = "x"
+    base_rv.name = "base"
+    base_vv = base_rv.clone()
+    x_vv = x_rv.clone()
+
+    res = conditional_logp({base_rv: base_vv, x_rv: x_vv})
+    x_logp = res[x_vv]
+    logp_vals_fn = pytensor.function([base_vv, x_vv], x_logp)
+
+    with pytest.raises(ParameterValueError, match="base >= 0"):
+        logp_vals_fn(np.array([-2]), np.array([2]))
+
+
+def test_base_exponent_non_measurable():
+    # test dual sources of measuravility fails
+    base_rv = pt.random.normal([2])
+    x_raw_rv = pt.random.normal()
+    x_rv = pt.power(base_rv, x_raw_rv)
+    x_rv.name = "x"
+
+    x_vv = x_rv.clone()
+
+    with pytest.raises(
+        RuntimeError,
+        match="The logprob terms of the following value variables could not be derived: {x}",
+    ):
+        conditional_logp({x_rv: x_vv})
+
+
 @pytest.mark.parametrize("shift", [1.5, np.array([-0.5, 1, 0.3])])
 @pytest.mark.parametrize("scale", [2.0, np.array([1.5, 3.3, 1.0])])
 def test_multivariate_rv_transform(shift, scale):