Prevent broadcast_to from creating useless Ops

aesara/tensor/extra_ops.py

@@ -10,7 +10,7 @@ from aesara.gradient import (
     disconnected_type,
     grad_undefined,
 )
-from aesara.graph.basic import Apply, equal_computations
+from aesara.graph.basic import Apply, Variable, equal_computations
 from aesara.graph.op import COp, Op
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import EnumList, Generic
@@ -19,6 +19,7 @@ from aesara.raise_op import Assert
 from aesara.scalar import int32 as int_t
 from aesara.scalar import upcast
 from aesara.tensor import basic as at
+from aesara.tensor import get_vector_length
 from aesara.tensor.exceptions import NotScalarConstantError
 from aesara.tensor.math import abs as at_abs
 from aesara.tensor.math import all as at_all
@@ -1627,7 +1628,37 @@ class BroadcastTo(Op):
         return [node.inputs[1:]]
 
 
-broadcast_to = BroadcastTo()
+broadcast_to_ = BroadcastTo()
+
+
+def broadcast_to(
+    x: TensorVariable, shape: Union[TensorVariable, Tuple[Variable]]
+) -> TensorVariable:
+    """Broadcast an array to a new shape.
+
+    Parameters
+    ----------
+    array
+        The array to broadcast.
+    shape
+        The shape of the desired array.
+
+    Returns
+    -------
+    broadcast
+        A readonly view on the original array with the given shape. It is
+        typically not contiguous. Furthermore, more than one element of a
+        broadcasted array may refer to a single memory location.
+
+    """
+    x = at.as_tensor(x)
+    shape = at.as_tensor(shape, ndim=1, dtype="int64")
+    shape_len = get_vector_length(shape)
+
+    if x.ndim == 0 and shape_len == 0:
+        return x
+
+    return broadcast_to_(x, shape)
 
 
 def broadcast_arrays(*args: TensorVariable) -> Tuple[TensorVariable, ...]:

tests/tensor/test_basic_opt.py

@@ -51,14 +51,7 @@ from aesara.tensor.basic_opt import (
     register_specialize,
 )
 from aesara.tensor.elemwise import DimShuffle, Elemwise
-from aesara.tensor.extra_ops import (
-    BroadcastTo,
-    Repeat,
-    Unique,
-    broadcast_to,
-    repeat,
-    unique,
-)
+from aesara.tensor.extra_ops import BroadcastTo, Repeat, Unique, repeat, unique
 from aesara.tensor.math import (
     add,
     bitwise_and,
@@ -3359,7 +3352,6 @@ def test_local_Unique_Alloc_lift(
 @pytest.mark.parametrize(
     "x_val, axis, new_shape",
     [
-        (np.array(-10, dtype=np.int64), None, ()),
         (np.array(-10, dtype=np.int64), None, (2, 3)),
         (np.array([[-10, -3], [-10, 2], [-10, 2]], dtype=np.int64), None, (2, 3, 2)),
     ],
@@ -3372,7 +3364,7 @@ def test_local_Unique_BroadcastTo(
 ):
     x = as_tensor_variable(x_val).type()
     y = unique(
-        broadcast_to(x, tuple(new_shape)),
+        BroadcastTo()(x, tuple(new_shape)),
         return_index=return_index,
         return_counts=return_counts,
         return_inverse=return_inverse,

tests/tensor/test_extra_ops.py

@@ -1095,6 +1095,11 @@ class TestBroadcastTo(utt.InferShapeTester):
         self.op_class = BroadcastTo
         self.op = broadcast_to
 
+    def test_avoid_useless_scalars(self):
+        x = scalar()
+        y = broadcast_to(x, ())
+        assert y is x
+
     @config.change_flags(compute_test_value="raise")
     def test_perform(self):
         a = scalar()