Unify Type with Python's type system

.pre-commit-config.yaml

@@ -20,7 +20,7 @@ repos:
           )$
       - id: check-merge-conflict
   - repo: https://github.com/psf/black
-    rev: 22.8.0
+    rev: 22.10.0
     hooks:
       - id: black
         language_version: python3
@@ -47,7 +47,7 @@ repos:
           )$
         args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variable']
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.971
+    rev: v0.982
     hooks:
       - id: mypy
         additional_dependencies:

aesara/__init__.py

@@ -151,7 +151,7 @@ def get_scalar_constant_value(v):
     """
     # Is it necessary to test for presence of aesara.sparse at runtime?
     sparse = globals().get("sparse")
-    if sparse and isinstance(v.type, sparse.SparseTensorType):
+    if sparse and isinstance(v.type, sparse.SparseTensorTypeMeta):
         if v.owner is not None and isinstance(v.owner.op, sparse.CSM):
             data = v.owner.inputs[0]
             return tensor.get_scalar_constant_value(data)

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/compile/builders.py

@@ -10,7 +10,7 @@
 from aesara.compile.mode import optdb
 from aesara.compile.sharedvalue import SharedVariable
 from aesara.configdefaults import config
-from aesara.gradient import DisconnectedType, Rop, grad
+from aesara.gradient import DisconnectedTypeMeta, Rop, grad
 from aesara.graph.basic import (
     Apply,
     Constant,
@@ -22,7 +22,7 @@
     replace_nominals_with_dummies,
 )
 from aesara.graph.fg import FunctionGraph
-from aesara.graph.null_type import NullType
+from aesara.graph.null_type import NullTypeMeta
 from aesara.graph.op import HasInnerGraph, Op
 from aesara.graph.rewriting.basic import in2out, node_rewriter
 from aesara.graph.utils import MissingInputError
@@ -210,7 +210,7 @@ def _filter_grad_var(grad, inp):
         #
         # For now, this converts NullType or DisconnectedType into zeros_like.
         # other types are unmodified: overrider_var -> None
-        if isinstance(grad.type, (NullType, DisconnectedType)):
+        if isinstance(grad.type, (NullTypeMeta, DisconnectedTypeMeta)):
             if hasattr(inp, "zeros_like"):
                 return inp.zeros_like(), grad
             else:
@@ -221,9 +221,9 @@ def _filter_grad_var(grad, inp):
     @staticmethod
     def _filter_rop_var(inpJ, out):
         # mostly similar to _filter_grad_var
-        if isinstance(inpJ.type, NullType):
+        if isinstance(inpJ.type, NullTypeMeta):
             return out.zeros_like(), inpJ
-        if isinstance(inpJ.type, DisconnectedType):
+        if isinstance(inpJ.type, DisconnectedTypeMeta):
             # since R_op does not have DisconnectedType yet, we will just
             # make them zeros.
             return out.zeros_like(), None
@@ -502,7 +502,7 @@ def lop_op(inps, grads):
             all_grads_l = list(all_grads_l)
             all_grads_ov_l = list(all_grads_ov_l)
         elif isinstance(lop_op, Variable):
-            if isinstance(lop_op.type, (DisconnectedType, NullType)):
+            if isinstance(lop_op.type, (NullTypeMeta, DisconnectedTypeMeta)):
                 all_grads_l = [inp.zeros_like() for inp in local_inputs]
                 all_grads_ov_l = [lop_op.type() for _ in range(inp_len)]
             else:
@@ -529,7 +529,7 @@ def lop_op(inps, grads):
                     all_grads_l.append(gnext)
                     all_grads_ov_l.append(gnext_ov)
                 elif isinstance(fn_gov, Variable):
-                    if isinstance(fn_gov.type, (DisconnectedType, NullType)):
+                    if isinstance(fn_gov.type, (NullTypeMeta, DisconnectedTypeMeta)):
                         all_grads_l.append(inp.zeros_like())
                         all_grads_ov_l.append(fn_gov.type())
                     else:
@@ -614,10 +614,10 @@ def _recompute_rop_op(self):
             all_rops_l = list(all_rops_l)
             all_rops_ov_l = list(all_rops_ov_l)
         elif isinstance(rop_op, Variable):
-            if isinstance(rop_op.type, NullType):
+            if isinstance(rop_op.type, NullTypeMeta):
                 all_rops_l = [inp.zeros_like() for inp in local_inputs]
                 all_rops_ov_l = [rop_op.type() for _ in range(out_len)]
-            elif isinstance(rop_op.type, DisconnectedType):
+            elif isinstance(rop_op.type, DisconnectedTypeMeta):
                 all_rops_l = [inp.zeros_like() for inp in local_inputs]
                 all_rops_ov_l = [None] * out_len
             else:
@@ -644,10 +644,10 @@ def _recompute_rop_op(self):
                     all_rops_l.append(rnext)
                     all_rops_ov_l.append(rnext_ov)
                 elif isinstance(fn_rov, Variable):
-                    if isinstance(fn_rov.type, NullType):
+                    if isinstance(fn_rov.type, NullTypeMeta):
                         all_rops_l.append(out.zeros_like())
                         all_rops_ov_l.append(fn_rov.type())
-                    if isinstance(fn_rov.type, DisconnectedType):
+                    if isinstance(fn_rov.type, DisconnectedTypeMeta):
                         all_rops_l.append(out.zeros_like())
                         all_rops_ov_l.append(None)
                     else:
@@ -857,7 +857,7 @@ def connection_pattern(self, node):
         # cpmat_self &= out_is_disconnected
         for i, t in enumerate(self._lop_op_stypes_l):
             if t is not None:
-                if isinstance(t.type, DisconnectedType):
+                if isinstance(t.type, DisconnectedTypeMeta):
                     for o in range(out_len):
                         cpmat_self[i][o] = False
             for o in range(out_len):

aesara/compile/compiledir.py

@@ -11,7 +11,7 @@
 
 from aesara.configdefaults import config
 from aesara.graph.op import Op
-from aesara.link.c.type import CType
+from aesara.link.c.type import CType, CTypeMeta
 from aesara.utils import flatten
 
 
@@ -131,7 +131,7 @@ def print_compiledir_content():
                     zeros_op += 1
                 else:
                     types = list(
-                        {x for x in flatten(keydata.keys) if isinstance(x, CType)}
+                        {x for x in flatten(keydata.keys) if isinstance(x, CTypeMeta)}
                     )
                     compile_start = compile_end = float("nan")
                     for fn in os.listdir(os.path.join(compiledir, dir)):

aesara/compile/debugmode.py

@@ -39,6 +39,7 @@
 from aesara.link.utils import map_storage, raise_with_op
 from aesara.printing import _debugprint
 from aesara.tensor import TensorType
+from aesara.tensor.type import TensorTypeMeta
 from aesara.utils import NoDuplicateOptWarningFilter, difference, get_unbound_function
 
 
@@ -792,7 +793,7 @@ def _get_preallocated_maps(
         for r in considered_outputs:
             # There is no risk to overwrite inputs, since r does not work
             # inplace.
-            if isinstance(r.type, TensorType):
+            if isinstance(r.type, TensorTypeMeta):
                 reuse_outputs[r][...] = np.asarray(def_val).astype(r.type.dtype)
 
         if reuse_outputs:
@@ -805,7 +806,7 @@ def _get_preallocated_maps(
     if "c_contiguous" in prealloc_modes or "ALL" in prealloc_modes:
         c_cont_outputs = {}
         for r in considered_outputs:
-            if isinstance(r.type, TensorType):
+            if isinstance(r.type, TensorTypeMeta):
                 # Build a C-contiguous buffer
                 new_buf = r.type.value_zeros(r_vals[r].shape)
                 assert new_buf.flags["C_CONTIGUOUS"]
@@ -822,7 +823,7 @@ def _get_preallocated_maps(
     if "f_contiguous" in prealloc_modes or "ALL" in prealloc_modes:
         f_cont_outputs = {}
         for r in considered_outputs:
-            if isinstance(r.type, TensorType):
+            if isinstance(r.type, TensorTypeMeta):
                 new_buf = np.zeros(
                     shape=r_vals[r].shape, dtype=r_vals[r].dtype, order="F"
                 )
@@ -850,7 +851,7 @@ def _get_preallocated_maps(
         max_ndim = 0
         rev_out_broadcastable = []
         for r in considered_outputs:
-            if isinstance(r.type, TensorType):
+            if isinstance(r.type, TensorTypeMeta):
                 if max_ndim < r.ndim:
                     rev_out_broadcastable += [True] * (r.ndim - max_ndim)
                     max_ndim = r.ndim
@@ -865,7 +866,7 @@ def _get_preallocated_maps(
         # Initial allocation
         init_strided = {}
         for r in considered_outputs:
-            if isinstance(r.type, TensorType):
+            if isinstance(r.type, TensorTypeMeta):
                 # Create a buffer twice as large in every dimension,
                 # except if broadcastable, or for dimensions above
                 # config.DebugMode__check_preallocated_output_ndim
@@ -944,7 +945,7 @@ def _get_preallocated_maps(
                 name = f"wrong_size{tuple(shape_diff)}"
 
                 for r in considered_outputs:
-                    if isinstance(r.type, TensorType):
+                    if isinstance(r.type, TensorTypeMeta):
                         r_shape_diff = shape_diff[: r.ndim]
                         out_shape = [
                             max((s + sd), 0)

aesara/compile/nanguardmode.py

@@ -7,6 +7,7 @@
 import aesara
 from aesara.compile.mode import Mode
 from aesara.configdefaults import config
+from aesara.tensor.random.type import RandomTypeMeta
 from aesara.tensor.type import discrete_dtypes
 
 
@@ -30,13 +31,12 @@ 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 isinstance(var.type, RandomType):
+    elif var and isinstance(var.type, RandomTypeMeta):
         return False
     elif isinstance(arr, slice):
         return False

aesara/compile/ops.py

@@ -13,7 +13,7 @@
 from aesara.graph.basic import Apply
 from aesara.graph.op import Op
 from aesara.link.c.op import COp
-from aesara.link.c.type import CType
+from aesara.link.c.type import CTypeMeta
 
 
 def register_view_op_c_code(type, code, version=()):
@@ -64,7 +64,7 @@ def c_code(self, node, nodename, inp, out, sub):
         (oname,) = out
         fail = sub["fail"]
 
-        itype = node.inputs[0].type.__class__
+        itype = node.inputs[0].type.base_type
         if itype in self.c_code_and_version:
             code, version = self.c_code_and_version[itype]
             return code % locals()
@@ -199,7 +199,7 @@ def c_code(self, node, name, inames, onames, sub):
         (oname,) = onames
         fail = sub["fail"]
 
-        itype = node.inputs[0].type.__class__
+        itype = node.inputs[0].type.base_type
         if itype in self.c_code_and_version:
             code, version = self.c_code_and_version[itype]
             return code % locals()
@@ -311,11 +311,11 @@ def numpy_dot(a, b):
     """
     if not isinstance(itypes, (list, tuple)):
         itypes = [itypes]
-    if not all(isinstance(t, CType) for t in itypes):
+    if not all(isinstance(t, CTypeMeta) for t in itypes):
         raise TypeError("itypes has to be a list of Aesara types")
     if not isinstance(otypes, (list, tuple)):
         otypes = [otypes]
-    if not all(isinstance(t, CType) for t in otypes):
+    if not all(isinstance(t, CTypeMeta) for t in otypes):
         raise TypeError("otypes has to be a list of Aesara types")
 
     # make sure they are lists and not tuples