Numba RVs: Include output_bc_pattern in cache key

pytensor/link/numba/dispatch/random.py

@@ -485,16 +485,14 @@ def numba_funcify_RandomVariable(op: RandomVariableWithCoreShape, node, **kwargs
         # If the core RV can't be cached, then the whole RV can't be cached
         random_rv_key = None  # type: ignore[unreachable]
     else:
-        rv_op_props_dict = rv_op.props_dict() if hasattr(rv_op, "props_dict") else {}
         random_rv_key_contents = (
             type(op),
             type(rv_op),
-            rv_op,
-            tuple(rv_op_props_dict.items()),
+            tuple(rv_op._props_dict().items()),  # type: ignore[attr-defined]
             size_len,
             core_shape_len,
-            inplace,
             input_bc_patterns,
+            output_bc_patterns,
             core_cache_key,
         )
         random_rv_key = sha256(str(random_rv_key_contents).encode()).hexdigest()

tests/link/numba/test_random.py

@@ -689,6 +689,15 @@ def test_dirichlet_discrete_alpha():
     assert np.unique(res).size > 2  # Make sure we have more than just 0s and 1s
 
 
+def test_cache_size_bcast_change():
+    # Regression bug for caching with the same key in case where size is meaningful vs not
+    alpha = pt.dvector()
+    for s in (1, 2, 3):
+        x = ptr.dirichlet(alpha, size=(s,))
+        fn = function([alpha], x, mode=numba_mode)
+        assert fn([0.2, 0.3, 0.5]).shape == (s, 3)
+
+
 def test_rv_inside_ofg():
     rng_np = np.random.default_rng(562)
     rng = shared(rng_np)