Reuse dpctl_ext.tensor.broadcast_to() in dpnp

Author: vlad-perevezentsev

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -1156,9 +1156,9 @@ def __call__(
 
         # Broadcast shapes of input arrays
         if x1.shape != res_shape:
-            x1 = dpt.broadcast_to(x1, res_shape)
+            x1 = dpt_ext.broadcast_to(x1, res_shape)
         if x2.shape != res_shape:
-            x2 = dpt.broadcast_to(x2, res_shape)
+            x2 = dpt_ext.broadcast_to(x2, res_shape)
 
         # Call the binary function with input and output arrays
         ht_binary_ev, binary_ev = self.get_implementation_function()(

dpnp/dpnp_algo/dpnp_fill.py

@@ -28,13 +28,12 @@
 
 from numbers import Number
 
-import dpctl.tensor as dpt
 import dpctl.utils as dpu
 from dpctl.tensor._ctors import _cast_fill_val
 
 # TODO: revert to `from dpctl.tensor...`
 # when dpnp fully migrates dpctl/tensor
-import dpctl_ext.tensor as dpt_ext
+import dpctl_ext.tensor as dpt
 import dpnp
 from dpctl_ext.tensor._tensor_impl import (
     _copy_usm_ndarray_into_usm_ndarray,
@@ -56,7 +55,7 @@ def dpnp_fill(arr, val):
             raise dpu.ExecutionPlacementError(
                 "Input arrays have incompatible queues."
             )
-        a_val = dpt_ext.astype(val, arr.dtype)
+        a_val = dpt.astype(val, arr.dtype)
         a_val = dpt.broadcast_to(a_val, arr.shape)
         _manager = dpu.SequentialOrderManager[exec_q]
         dep_evs = _manager.submitted_events

dpnp/dpnp_iface_manipulation.py

@@ -1178,7 +1178,7 @@ def broadcast_to(array, /, shape, subok=False):
         raise NotImplementedError(f"subok={subok} is currently not supported")
 
     usm_array = dpnp.get_usm_ndarray(array)
-    new_array = dpt.broadcast_to(usm_array, shape)
+    new_array = dpt_ext.broadcast_to(usm_array, shape)
     return dpnp_array._create_from_usm_ndarray(new_array)