Implementing iterator over array instead of numpy fallback (#1671)

Author: dinodeep

cupynumeric/_array/array.py

@@ -17,7 +17,7 @@
 import operator
 from functools import reduce
 from math import prod as builtin_prod
-from typing import TYPE_CHECKING, Any, Sequence, cast
+from typing import TYPE_CHECKING, Any, Iterator, Sequence, cast
 
 import legate.core.types as ty
 import numpy as np
@@ -1124,7 +1124,12 @@ def __iter__(self) -> Any:
         """a.__iter__(/)"""
         if settings.doctor():
             doctor.diagnose("__iter__", (self,), {})
-        return self.__array__().__iter__()
+
+        def iter_array(arr: ndarray) -> Iterator[Any]:
+            for i in range(len(arr)):
+                yield arr[i]
+
+        return iter_array(self)
 
     def __isub__(self, rhs: Any) -> ndarray:
         """a.__isub__(/)

tests/integration/test_index_routines.py

@@ -602,7 +602,9 @@ def test_axes_none(self) -> None:
     @pytest.mark.diff
     def test_extra_axes(self):
         # NumPy does not have axes arg
-        axes = num.arange(self.a.ndim + 1, dtype=int)
+        # Use NumPy vector axes because indexing it returns a hashable scalar
+        # which is necessary for _diag_helper
+        axes = np.arange(self.a.ndim + 1, dtype=int)
         with pytest.raises(ValueError):
             self.a._diag_helper(self.a, axes=axes)