Make DiracDelta a SymbolicRV

Author: ricardoV94

pymc/distributions/distribution.py

@@ -1048,7 +1048,11 @@ def change_custom_dist_size(op, rv, new_size, expand):
 
             return new_rv
 
-        rngs, rngs_updates = zip(*dummy_updates_dict.items())
+        if dummy_updates_dict:
+            rngs, rngs_updates = zip(*dummy_updates_dict.items())
+        else:
+            rngs, rngs_updates = (), ()
+
         inputs = [*dummy_params, *rngs]
         outputs = [dummy_rv, *rngs_updates]
         signature = cls._infer_final_signature(
@@ -1497,19 +1501,26 @@ def default_support_point(rv, size, *rv_inputs, rv_name=None, has_fallback=False
         )
 
 
-class DiracDeltaRV(RandomVariable):
+class DiracDeltaRV(SymbolicRandomVariable):
     name = "diracdelta"
-    signature = "()->()"
+    extended_signature = "[size],()->()"
     _print_name = ("DiracDelta", "\\operatorname{DiracDelta}")
 
+    def do_constant_folding(self, fgraph: "FunctionGraph", node: Apply) -> bool:
+        # Because the distribution does not have RNGs we have to prevent constant-folding
+        return False
+
     @classmethod
-    def rng_fn(cls, rng, c, size=None):
-        if size is None:
-            return c.copy()
-        return np.full(size, c)
+    def rv_op(cls, c, *, size=None, rng=None):
+        size = normalize_size_param(size)
+        c = pt.as_tensor(c)
 
+        if rv_size_is_none(size):
+            out = c.copy()
+        else:
+            out = pt.full(size, c)
 
-diracdelta = DiracDeltaRV()
+        return cls(inputs=[size, c], outputs=[out])(size, c)
 
 
 class DiracDelta(Discrete):
@@ -1524,14 +1535,15 @@ class DiracDelta(Discrete):
         that use DiracDelta, such as Mixtures.
     """
 
-    rv_op = diracdelta
+    rv_type = DiracDeltaRV
+    rv_op = DiracDeltaRV.rv_op
 
     @classmethod
     def dist(cls, c, *args, **kwargs):
         c = pt.as_tensor_variable(c)
         if c.dtype in continuous_types:
             c = floatX(c)
-        return super().dist([c], dtype=c.dtype, **kwargs)
+        return super().dist([c], **kwargs)
 
     def support_point(rv, size, c):
         if not rv_size_is_none(size):