StandandardNormalRV is now just a helper function

pytensor/link/jax/dispatch/random.py

@@ -162,7 +162,6 @@ def jax_sample_fn_generic(op, node):
 @jax_sample_fn.register(ptr.LaplaceRV)
 @jax_sample_fn.register(ptr.LogisticRV)
 @jax_sample_fn.register(ptr.NormalRV)
-@jax_sample_fn.register(ptr.StandardNormalRV)
 def jax_sample_fn_loc_scale(op, node):
     """JAX implementation of random variables in the loc-scale families.
 

pytensor/tensor/random/basic.py

@@ -281,38 +281,24 @@ class NormalRV(RandomVariable):
 normal = NormalRV()
 
 
-class StandardNormalRV(NormalRV):
-    r"""A standard normal continuous random variable.
+def standard_normal(*, size=None, rng=None, dtype=None):
+    """Draw samples from a standard normal distribution.
 
-    The probability density function for `standard_normal` is:
+    Signature
+    ---------
 
-    .. math::
+    `nil -> ()`
 
-        f(x) = \frac{1}{\sqrt{2 \pi}} e^{-\frac{x^2}{2}}
+    Parameters
+    ----------
+    size
+        Sample shape. If the given size is, e.g. `(m, n, k)` then `m * n * k`
+        independent, identically distributed random variables are
+        returned. Default is `None` in which case a single random variable
+        is returned.
 
     """
-
-    def __call__(self, size=None, **kwargs):
-        """Draw samples from a standard normal distribution.
-
-        Signature
-        ---------
-
-        `nil -> ()`
-
-        Parameters
-        ----------
-        size
-            Sample shape. If the given size is, e.g. `(m, n, k)` then `m * n * k`
-            independent, identically distributed random variables are
-            returned. Default is `None` in which case a single random variable
-            is returned.
-
-        """
-        return super().__call__(loc=0.0, scale=1.0, size=size, **kwargs)
-
-
-standard_normal = StandardNormalRV()
+    return normal(0.0, 1.0, size=size, rng=rng, dtype=dtype)
 
 
 class HalfNormalRV(ScipyRandomVariable):

pytensor/tensor/random/utils.py

@@ -218,9 +218,9 @@ class RandomStream:
         if namespace is None:
             from pytensor.tensor.random import basic  # pylint: disable=import-self
 
-            self.namespaces = [basic]
+            self.namespaces = [(basic, set(basic.__all__))]
         else:
-            self.namespaces = [namespace]
+            self.namespaces = [(namespace, set(namespace.__all__))]
 
         self.default_instance_seed = seed
         self.state_updates = []
@@ -235,22 +235,20 @@ class RandomStream:
 
     def __getattr__(self, obj):
         ns_obj = next(
-            (getattr(ns, obj) for ns in self.namespaces if hasattr(ns, obj)), None
+            (
+                getattr(ns, obj)
+                for ns, all_ in self.namespaces
+                if obj in all_ and hasattr(ns, obj)
+            ),
+            None,
         )
 
         if ns_obj is None:
             raise AttributeError(f"No attribute {obj}.")
 
-        from pytensor.tensor.random.op import RandomVariable
-
-        if isinstance(ns_obj, RandomVariable):
-
-            @wraps(ns_obj)
-            def meta_obj(*args, **kwargs):
-                return self.gen(ns_obj, *args, **kwargs)
-
-        else:
-            raise AttributeError(f"No attribute {obj}.")
+        @wraps(ns_obj)
+        def meta_obj(*args, **kwargs):
+            return self.gen(ns_obj, *args, **kwargs)
 
         setattr(self, obj, meta_obj)
         return getattr(self, obj)

tests/tensor/random/test_utils.py

@@ -114,7 +114,7 @@ class TestSharedRandomStream:
         assert hasattr(random, "standard_normal")
 
         with pytest.raises(AttributeError):
-            np_random = RandomStream(namespace=np, rng_ctor=rng_ctor)
+            np_random = RandomStream(namespace=np.random, rng_ctor=rng_ctor)
             np_random.ndarray
 
         fn = function([], random.uniform(0, 1, size=(2, 2)), updates=random.updates())