Remove unused/deprecated variable attributes added by RandomStream

aesara/compile/nanguardmode.py

@@ -30,12 +30,13 @@ def _is_numeric_value(arr, var):
 
     """
     from aesara.link.c.type import _cdata_type
+    from aesara.tensor.random.type import RandomType
 
     if isinstance(arr, _cdata_type):
         return False
     elif isinstance(arr, (np.random.mtrand.RandomState, np.random.Generator)):
         return False
-    elif var and getattr(var.tag, "is_rng", False):
+    elif var and isinstance(var.type, RandomType):
         return False
     elif isinstance(arr, slice):
         return False

aesara/sandbox/rng_mrg.py

@@ -926,8 +926,6 @@ def uniform(
             size=size,
             nstreams=orig_nstreams,
         )
-        # Add a reference to distinguish from other shared variables
-        node_rstate.tag.is_rng = True
         r = u * (high - low) + low
 
         if u.type.broadcastable != r.type.broadcastable:

aesara/tensor/random/utils.py

@@ -251,20 +251,19 @@ def gen(self, op, *args, **kwargs):
 
         # Generate a new random state
         seed = int(self.gen_seedgen.integers(2**30))
-        random_state_variable = shared(self.rng_ctor(seed))
-
-        # Distinguish it from other shared variables (why?)
-        random_state_variable.tag.is_rng = True
+        rng = shared(self.rng_ctor(seed), borrow=True)
 
         # Generate the sample
-        out = op(*args, **kwargs, rng=random_state_variable)
-        out.rng = random_state_variable
+        out = op(*args, **kwargs, rng=rng)
+
+        # This is the value that should be used to replace the old state
+        # (i.e. `rng`) after `out` is sampled/evaluated.
+        # The updates mechanism in `aesara.function` is supposed to perform
+        # this replace action.
+        new_rng = out.owner.outputs[0]
 
-        # Update the tracked states
-        new_r = out.owner.outputs[0]
-        out.update = (random_state_variable, new_r)
-        self.state_updates.append(out.update)
+        self.state_updates.append((rng, new_rng))
 
-        random_state_variable.default_update = new_r
+        rng.default_update = new_rng
 
         return out

tests/tensor/random/test_utils.py

@@ -97,7 +97,6 @@ def test_tutorial(self):
         assert np.all(f() != f())
         assert np.all(g() == g())
         assert np.all(abs(nearly_zeros()) < 1e-5)
-        assert isinstance(rv_u.rng.get_value(borrow=True), np.random.Generator)
 
     @pytest.mark.parametrize("rng_ctor", [np.random.RandomState, np.random.default_rng])
     def test_basics(self, rng_ctor):
@@ -109,8 +108,7 @@ def test_basics(self, rng_ctor):
         with pytest.raises(AttributeError):
             random.blah
 
-        # test if standard_normal is available in the namespace, See: GH issue #528
-        random.standard_normal
+        assert hasattr(random, "standard_normal")
 
         with pytest.raises(AttributeError):
             np_random = RandomStream(namespace=np, rng_ctor=rng_ctor)
@@ -223,7 +221,7 @@ def test_default_updates(self, rng_ctor):
         # Explicit updates #2
         random_c = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
         out_c = random_c.uniform(0, 1, size=(2, 2))
-        fn_c = function([], out_c, updates=[out_c.update])
+        fn_c = function([], out_c, updates=random_c.state_updates)
         fn_c_val0 = fn_c()
         fn_c_val1 = fn_c()
         assert np.all(fn_c_val0 == fn_a_val0)
@@ -241,7 +239,7 @@ def test_default_updates(self, rng_ctor):
         # No updates for out
         random_e = RandomStream(utt.fetch_seed(), rng_ctor=rng_ctor)
         out_e = random_e.uniform(0, 1, size=(2, 2))
-        fn_e = function([], out_e, no_default_updates=[out_e.rng])
+        fn_e = function([], out_e, no_default_updates=[random_e.state_updates[0][0]])
         fn_e_val0 = fn_e()
         fn_e_val1 = fn_e()
         assert np.all(fn_e_val0 == fn_a_val0)