Remove TensorType.value_zeros

aesara/compile/debugmode.py

@@ -807,7 +807,7 @@ def _get_preallocated_maps(
         for r in considered_outputs:
             if isinstance(r.type, TensorType):
                 # Build a C-contiguous buffer
-                new_buf = r.type.value_zeros(r_vals[r].shape)
+                new_buf = np.empty(r_vals[r].shape, dtype=r.type.dtype)
                 assert new_buf.flags["C_CONTIGUOUS"]
                 new_buf[...] = np.asarray(def_val).astype(r.type.dtype)
 
@@ -875,7 +875,8 @@ def _get_preallocated_maps(
                         buf_shape.append(s)
                     else:
                         buf_shape.append(s * 2)
-                new_buf = r.type.value_zeros(buf_shape)
+
+                new_buf = np.empty(buf_shape, dtype=r.type.dtype)
                 new_buf[...] = np.asarray(def_val).astype(r.type.dtype)
                 init_strided[r] = new_buf
 
@@ -950,7 +951,7 @@ def _get_preallocated_maps(
                             max((s + sd), 0)
                             for s, sd in zip(r_vals[r].shape, r_shape_diff)
                         ]
-                        new_buf = r.type.value_zeros(out_shape)
+                        new_buf = np.empty(out_shape, dtype=r.type.dtype)
                         new_buf[...] = np.asarray(def_val).astype(r.type.dtype)
                         wrong_size[r] = new_buf
 

aesara/scan/op.py

@@ -1380,11 +1380,13 @@ class Scan(Op, ScanMethodsMixin, HasInnerGraph):
                         # the output value, possibly inplace, at the end of the
                         # function execution. Also, since an update is defined,
                         # a default value must also be (this is verified by
-                        # DebugMode). Use an array of size 0 with the correct
-                        # ndim and dtype (use a shape of 1 on broadcastable
-                        # dimensions, and 0 on the others).
-                        default_shape = [1 if _b else 0 for _b in inp.broadcastable]
-                        default_val = inp.type.value_zeros(default_shape)
+                        # DebugMode).
+                        # TODO FIXME: Why do we need a "default value" here?
+                        # This sounds like a serious design issue.
+                        default_shape = tuple(
+                            s if s is not None else 0 for s in inp.type.shape
+                        )
+                        default_val = np.empty(default_shape, dtype=inp.type.dtype)
                         wrapped_inp = In(
                             variable=inp,
                             value=default_val,

aesara/sparse/type.py

@@ -224,14 +224,6 @@ class SparseTensorType(TensorType, HasDataType):
             + (shape_info[2] + shape_info[3]) * np.dtype("int32").itemsize
         )
 
-    def value_zeros(self, shape):
-        matrix_constructor = self.format_cls.get(self.format)
-
-        if matrix_constructor is None:
-            raise ValueError(f"Sparse matrix type {self.format} not found in SciPy")
-
-        return matrix_constructor(shape, dtype=self.dtype)
-
     def __eq__(self, other):
         res = super().__eq__(other)
 

aesara/tensor/type.py

@@ -331,13 +331,6 @@ class TensorType(CType[np.ndarray], HasDataType, HasShape):
             # `specify_shape` will combine the more precise shapes of the two types
             return aesara.tensor.specify_shape(var, self.shape)
 
-    def value_zeros(self, shape):
-        """Create an numpy ndarray full of 0 values.
-
-        TODO: Remove this trivial method.
-        """
-        return np.zeros(shape, dtype=self.dtype)
-
     @staticmethod
     def values_eq(a, b, force_same_dtype=True):
         # TODO: check to see if the shapes must match; for now, we err on safe

tests/compile/test_debugmode.py

@@ -725,10 +725,10 @@ class VecAsRowAndCol(Op):
         r, c = out
         lv = v.shape[0]
         if (r[0] is None) or (r[0].shape != (1, lv)):
-            r[0] = node.outputs[0].type.value_zeros((1, lv))
+            r[0] = np.empty((1, lv), dtype=node.outputs[0].type.dtype)
 
         if (c[0] is None) or (c[0].shape != (lv, 1)):
-            c[0] = node.outputs[1].type.value_zeros((lv, 1))
+            c[0] = np.empty((lv, 1), dtype=node.outputs[0].type.dtype)
 
         for i in range(lv):
             r[0][0, i] = v[i]

tests/tensor/test_type.py

@@ -234,7 +234,6 @@ def test_fixed_shape_basic():
     t1 = TensorType("float64", (2, 3))
     assert t1.shape == (2, 3)
     assert t1.broadcastable == (False, False)
-    assert t1.value_zeros(t1.shape).shape == t1.shape
 
     assert str(t1) == "TensorType(float64, (2, 3))"