Prepare switch from class to metaclass for Type

aesara/breakpoint.py

@@ -143,7 +143,7 @@ def perform(self, node, inputs, output_storage):
                 output_storage[i][0] = inputs[i + 1]
 
     def grad(self, inputs, output_gradients):
-        return [DisconnectedType()()] + output_gradients
+        return [DisconnectedType.subtype()()] + output_gradients
 
     def infer_shape(self, fgraph, inputs, input_shapes):
         # Return the shape of every input but the condition (first input)

aesara/gradient.py

@@ -90,7 +90,7 @@ def grad_not_implemented(op, x_pos, x, comment=""):
     """
 
     return (
-        NullType(
+        NullType.subtype(
             (
                 "This variable is Null because the grad method for "
                 f"input {x_pos} ({x}) of the {op} op is not implemented. {comment}"
@@ -113,7 +113,7 @@ def grad_undefined(op, x_pos, x, comment=""):
     """
 
     return (
-        NullType(
+        NullType.subtype(
             (
                 "This variable is Null because the grad method for "
                 f"input {x_pos} ({x}) of the {op} op is not implemented. {comment}"
@@ -158,7 +158,7 @@ def __str__(self):
         return "DisconnectedType"
 
 
-disconnected_type = DisconnectedType()
+disconnected_type = DisconnectedType.subtype()
 
 
 def Rop(
@@ -1803,7 +1803,7 @@ def verify_grad(
     )
 
     tensor_pt = [
-        aesara.tensor.type.TensorType(
+        aesara.tensor.type.TensorType.subtype(
             aesara.tensor.as_tensor_variable(p).dtype,
             aesara.tensor.as_tensor_variable(p).broadcastable,
         )(name=f"input {i}")

aesara/graph/null_type.py

@@ -42,4 +42,4 @@ def __str__(self):
         return "NullType"
 
 
-null_type = NullType()
+null_type = NullType.subtype()

aesara/graph/type.py

@@ -5,13 +5,23 @@
 
 from aesara.graph import utils
 from aesara.graph.basic import Constant, Variable
-from aesara.graph.utils import MetaObject
+from aesara.graph.utils import MetaType
 
 
 D = TypeVar("D")
 
 
-class Type(MetaObject, Generic[D]):
+class NewTypeMeta(type):
+    # pass
+    def __call__(cls, *args, **kwargs):
+        raise RuntimeError("Use subtype")
+        # return super().__call__(*args, **kwargs)
+
+    def subtype(cls, *args, **kwargs):
+        return super().__call__(*args, **kwargs)
+
+
+class Type(Generic[D], metaclass=NewTypeMeta):
     """
     Interface specification for variable type instances.
 
@@ -35,6 +45,12 @@ class Type(MetaObject, Generic[D]):
     The `Type` that will be created by a call to `Type.make_constant`.
     """
 
+    __props__: tuple[str, ...] = ()
+
+    @classmethod
+    def create(cls, **kwargs):
+        MetaType(f"{cls.__name__}[{kwargs}]", (cls,), kwargs)
+
     def in_same_class(self, otype: "Type") -> Optional[bool]:
         """Determine if another `Type` represents a subset from the same "class" of types represented by `self`.
 
@@ -214,7 +230,7 @@ def make_constant(self, value: D, name: Optional[Text] = None) -> constant_type:
 
     def clone(self, *args, **kwargs) -> "Type":
         """Clone a copy of this type with the given arguments/keyword values, if any."""
-        return type(self)(*args, **kwargs)
+        return type(self).subtype(*args, **kwargs)
 
     def __call__(self, name: Optional[Text] = None) -> variable_type:
         """Return a new `Variable` instance of Type `self`.
@@ -261,6 +277,41 @@ def values_eq_approx(cls, a: D, b: D) -> bool:
         """
         return cls.values_eq(a, b)
 
+    def _props(self):
+        """
+        Tuple of properties of all attributes
+        """
+        return tuple(getattr(self, a) for a in self.__props__)
+
+    def _props_dict(self):
+        """This return a dict of all ``__props__`` key-> value.
+
+        This is useful in optimization to swap op that should have the
+        same props. This help detect error that the new op have at
+        least all the original props.
+
+        """
+        return {a: getattr(self, a) for a in self.__props__}
+
+    def __hash__(self):
+        return hash((type(self), tuple(getattr(self, a) for a in self.__props__)))
+
+    def __eq__(self, other):
+        return type(self) == type(other) and tuple(
+            getattr(self, a) for a in self.__props__
+        ) == tuple(getattr(other, a) for a in self.__props__)
+
+    def __str__(self):
+        if self.__props__ is None or len(self.__props__) == 0:
+            return f"{self.__class__.__name__}()"
+        else:
+            return "{}{{{}}}".format(
+                self.__class__.__name__,
+                ", ".join(
+                    "{}={!r}".format(p, getattr(self, p)) for p in self.__props__
+                ),
+            )
+
 
 DataType = str
 

aesara/link/c/params_type.py

@@ -626,7 +626,7 @@ def extended(self, **kwargs):
         """
         self_to_dict = {self.fields[i]: self.types[i] for i in range(self.length)}
         self_to_dict.update(kwargs)
-        return ParamsType(**self_to_dict)
+        return ParamsType.subtype(**self_to_dict)
 
     # Returns a Params object with expected attributes or (in strict mode) checks that data has expected attributes.
     def filter(self, data, strict=False, allow_downcast=None):

aesara/link/c/type.py

@@ -115,7 +115,7 @@ def __str__(self):
         return self.__class__.__name__
 
 
-generic = Generic()
+generic = Generic.subtype()
 
 _cdata_type = None
 
@@ -497,7 +497,10 @@ def __repr__(self):
     def __getattr__(self, key):
         if key in self:
             return self[key]
-        return CType.__getattr__(self, key)
+        else:
+            raise AttributeError(
+                f"{self.__class__.__name__} object has no attribute or enum value {key}"
+            )
 
     def __setattr__(self, key, value):
         if key in self:
@@ -530,6 +533,9 @@ def __eq__(self, other):
             and all(self.aliases[a] == other.aliases[a] for a in self.aliases)
         )
 
+    def __ne__(self, other):
+        return not self == other
+
     # EnumType should be used to create constants available in both Python and C code.
     # However, for convenience, we make sure EnumType can have a value, like other common types,
     # such that it could be used as-is as an op param.

aesara/raise_op.py

@@ -22,7 +22,7 @@ def __hash__(self):
         return hash(type(self))
 
 
-exception_type = ExceptionType()
+exception_type = ExceptionType.subtype()
 
 
 class CheckAndRaise(COp):
@@ -38,7 +38,7 @@ class CheckAndRaise(COp):
     view_map = {0: [0]}
 
     check_input = False
-    params_type = ParamsType(exc_type=exception_type)
+    params_type = ParamsType.subtype(exc_type=exception_type)
 
     def __init__(self, exc_type, msg=""):
 
@@ -100,7 +100,7 @@ def perform(self, node, inputs, outputs, params):
             raise self.exc_type(self.msg)
 
     def grad(self, input, output_gradients):
-        return output_gradients + [DisconnectedType()()] * (len(input) - 1)
+        return output_gradients + [DisconnectedType.subtype()()] * (len(input) - 1)
 
     def connection_pattern(self, node):
         return [[1]] + [[0]] * (len(node.inputs) - 1)

aesara/sandbox/multinomial.py

@@ -71,7 +71,7 @@ def c_code(self, node, name, ins, outs, sub):
         if self.odtype == "auto":
             t = f"PyArray_TYPE({pvals})"
         else:
-            t = ScalarType(self.odtype).dtype_specs()[1]
+            t = ScalarType.subtype(self.odtype).dtype_specs()[1]
             if t.startswith("aesara_complex"):
                 t = t.replace("aesara_complex", "NPY_COMPLEX")
             else:
@@ -263,7 +263,7 @@ def c_code(self, node, name, ins, outs, sub):
         if self.odtype == "auto":
             t = "NPY_INT64"
         else:
-            t = ScalarType(self.odtype).dtype_specs()[1]
+            t = ScalarType.subtype(self.odtype).dtype_specs()[1]
             if t.startswith("aesara_complex"):
                 t = t.replace("aesara_complex", "NPY_COMPLEX")
             else:

aesara/sandbox/rng_mrg.py

@@ -325,7 +325,7 @@ def mrg_next_value(rstate, new_rstate, NORM, mask, offset):
 class mrg_uniform_base(Op):
     # TODO : need description for class, parameter
     __props__ = ("output_type", "inplace")
-    params_type = ParamsType(
+    params_type = ParamsType.subtype(
         inplace=bool_t,
         # following params will come from self.output_type.
         # NB: As output object may not be allocated in C code,
@@ -392,7 +392,7 @@ def new(cls, rstate, ndim, dtype, size):
         v_size = as_tensor_variable(size)
         if ndim is None:
             ndim = get_vector_length(v_size)
-        op = cls(TensorType(dtype, (False,) * ndim))
+        op = cls(TensorType.subtype(dtype, (False,) * ndim))
         return op(rstate, v_size)
 
     def perform(self, node, inp, out, params):

aesara/scalar/basic.py

@@ -298,7 +298,7 @@ def __init__(self, dtype):
     def clone(self, dtype=None, **kwargs):
         if dtype is None:
             dtype = self.dtype
-        return type(self)(dtype)
+        return type(self).subtype(dtype)
 
     @staticmethod
     def may_share_memory(a, b):
@@ -679,7 +679,7 @@ def get_scalar_type(dtype, cache: Dict[str, ScalarType] = {}) -> ScalarType:
 
     """
     if dtype not in cache:
-        cache[dtype] = ScalarType(dtype=dtype)
+        cache[dtype] = ScalarType.subtype(dtype=dtype)
     return cache[dtype]
 
 
@@ -2405,13 +2405,13 @@ def grad(self, inputs, gout):
         (gz,) = gout
         if y.type in continuous_types:
             # x is disconnected because the elements of x are not used
-            return DisconnectedType()(), gz
+            return DisconnectedType.subtype()(), gz
         else:
             # when y is discrete, we assume the function can be extended
             # to deal with real-valued inputs by rounding them to the
             # nearest integer. f(x+eps) thus equals f(x) so the gradient
             # is zero, not disconnected or undefined
-            return DisconnectedType()(), y.zeros_like()
+            return DisconnectedType.subtype()(), y.zeros_like()
 
 
 second = Second(transfer_type(1), name="second")

aesara/scalar/sharedvar.py

@@ -50,7 +50,7 @@ def shared(value, name=None, strict=False, allow_downcast=None):
 
     dtype = str(dtype)
     value = getattr(np, dtype)(value)
-    scalar_type = ScalarType(dtype=dtype)
+    scalar_type = ScalarType.subtype(dtype=dtype)
     rval = ScalarSharedVariable(
         type=scalar_type,
         value=value,

aesara/scan/op.py

@@ -793,7 +793,7 @@ def __init__(
         self.output_types = []
 
         def tensorConstructor(shape, dtype):
-            return TensorType(dtype=dtype, shape=shape)
+            return TensorType.subtype(dtype=dtype, shape=shape)
 
         if typeConstructor is None:
             typeConstructor = tensorConstructor
@@ -3033,7 +3033,7 @@ def compute_all_gradients(known_grads):
         if not isinstance(outputs, (list, tuple)):
             outputs = [outputs]
         # Re-order the gradients correctly
-        gradients = [DisconnectedType()()]
+        gradients = [DisconnectedType.subtype()()]
 
         offset = info.n_mit_mot + info.n_mit_sot + info.n_sit_sot + n_sitsot_outs
         for p, (x, t) in enumerate(
@@ -3057,7 +3057,7 @@ def compute_all_gradients(known_grads):
                 else:
                     gradients.append(x[::-1])
             elif t == "disconnected":
-                gradients.append(DisconnectedType()())
+                gradients.append(DisconnectedType.subtype()())
             elif t == "through_shared":
                 gradients.append(
                     grad_undefined(
@@ -3066,7 +3066,7 @@ def compute_all_gradients(known_grads):
                 )
             else:
                 # t contains the "why_null" string of a NullType
-                gradients.append(NullType(t)())
+                gradients.append(NullType.subtype(t)())
 
         end = info.n_mit_mot + info.n_mit_sot + info.n_sit_sot
         for p, (x, t) in enumerate(zip(outputs[:end], type_outs[:end])):
@@ -3085,7 +3085,7 @@ def compute_all_gradients(known_grads):
                 else:
                     gradients.append(x[::-1])
             elif t == "disconnected":
-                gradients.append(DisconnectedType()())
+                gradients.append(DisconnectedType.subtype()())
             elif t == "through_shared":
                 gradients.append(
                     grad_undefined(
@@ -3097,7 +3097,7 @@ def compute_all_gradients(known_grads):
                 )
             else:
                 # t contains the "why_null" string of a NullType
-                gradients.append(NullType(t)())
+                gradients.append(NullType.subtype(t)())
 
         start = len(gradients)
         node = outs[0].owner
@@ -3108,7 +3108,7 @@ def compute_all_gradients(known_grads):
                 if not isinstance(dC_dout.type, DisconnectedType) and connected:
                     disconnected = False
             if disconnected:
-                gradients.append(DisconnectedType()())
+                gradients.append(DisconnectedType.subtype()())
             else:
                 gradients.append(
                     grad_undefined(
@@ -3117,15 +3117,15 @@ def compute_all_gradients(known_grads):
                 )
 
         start = len(gradients)
-        gradients += [DisconnectedType()() for _ in range(info.n_nit_sot)]
+        gradients += [DisconnectedType.subtype()() for _ in range(info.n_nit_sot)]
         begin = end
 
         end = begin + n_sitsot_outs
         for p, (x, t) in enumerate(zip(outputs[begin:end], type_outs[begin:end])):
             if t == "connected":
                 gradients.append(x[-1])
             elif t == "disconnected":
-                gradients.append(DisconnectedType()())
+                gradients.append(DisconnectedType.subtype()())
             elif t == "through_shared":
                 gradients.append(
                     grad_undefined(
@@ -3137,7 +3137,7 @@ def compute_all_gradients(known_grads):
                 )
             else:
                 # t contains the "why_null" string of a NullType
-                gradients.append(NullType(t)())
+                gradients.append(NullType.subtype(t)())
 
         # Mask disconnected gradients
         # Ideally we would want to assert that the gradients we are
@@ -3153,7 +3153,7 @@ def compute_all_gradients(known_grads):
                 ):
                     disconnected = False
             if disconnected:
-                gradients[idx] = DisconnectedType()()
+                gradients[idx] = DisconnectedType.subtype()()
         return gradients
 
     def R_op(self, inputs, eval_points):