Fixed bug introduced in 5c1a961

This commit short-circuited broadcastability of shapes for zero-size
rhs in __setitem__.

Refix array API test failure without introducing this regression and
reuse _manipulation_functions._broadcast_strides as suggested by
@ndgrigorian

Author: oleksandr-pavlyk

dpctl/tensor/_copy_utils.py

@@ -246,6 +246,23 @@ def _broadcast_shapes(sh1, sh2):
         ).shape
 
 
+def _broadcast_strides(X_shape, X_strides, res_ndim):
+    """
+    Broadcasts strides to match the given dimensions;
+    returns tuple type strides.
+    """
+    out_strides = [0] * res_ndim
+    X_shape_len = len(X_shape)
+    str_dim = -X_shape_len
+    for i in range(X_shape_len):
+        shape_value = X_shape[i]
+        if not shape_value == 1:
+            out_strides[str_dim] = X_strides[i]
+        str_dim += 1
+
+    return tuple(out_strides)
+
+
 def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     if any(
         not isinstance(arg, dpt.usm_ndarray)
@@ -268,7 +285,7 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     except ValueError as exc:
         raise ValueError("Shapes of two arrays are not compatible") from exc
 
-    if dst.size < src.size:
+    if dst.size < src.size and dst.size < np.prod(common_shape):
         raise ValueError("Destination is smaller ")
 
     if len(common_shape) > dst.ndim:
@@ -279,9 +296,8 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
         common_shape = common_shape[ones_count:]
 
     if src.ndim < len(common_shape):
-        pad_count = len(common_shape) - src.ndim
-        new_src_strides = (0,) * pad_count + tuple(
-            s if d > 1 else 0 for s, d in zip(src.strides, src.shape)
+        new_src_strides = _broadcast_strides(
+            src.shape, src.strides, len(common_shape)
         )
         src_same_shape = dpt.usm_ndarray(
             common_shape, dtype=src.dtype, buffer=src, strides=new_src_strides

dpctl/tensor/_manipulation_functions.py

@@ -25,6 +25,7 @@
 import dpctl.tensor._tensor_impl as ti
 import dpctl.utils as dputils
 
+from ._copy_utils import _broadcast_strides
 from ._type_utils import _to_device_supported_dtype
 
 __doc__ = (
@@ -120,23 +121,6 @@ def __repr__(self):
         return self._finfo.__repr__()
 
 
-def _broadcast_strides(X_shape, X_strides, res_ndim):
-    """
-    Broadcasts strides to match the given dimensions;
-    returns tuple type strides.
-    """
-    out_strides = [0] * res_ndim
-    X_shape_len = len(X_shape)
-    str_dim = -X_shape_len
-    for i in range(X_shape_len):
-        shape_value = X_shape[i]
-        if not shape_value == 1:
-            out_strides[str_dim] = X_strides[i]
-        str_dim += 1
-
-    return tuple(out_strides)
-
-
 def _broadcast_shape_impl(shapes):
     if len(set(shapes)) == 1:
         return shapes[0]

dpctl/tensor/_usmarray.pyx

@@ -1175,8 +1175,6 @@ cdef class usm_ndarray:
         if adv_ind_start_p < 0:
             # basic slicing
             if isinstance(rhs, usm_ndarray):
-                if Xv.size == 0:
-                    return
                 _copy_from_usm_ndarray_to_usm_ndarray(Xv, rhs)
             else:
                 if hasattr(rhs, "__sycl_usm_array_interface__"):