Handle dpnp functions and tests to run on CUDA devices

dpnp/dpnp_iface.py

@@ -69,6 +69,7 @@
     "get_normalized_queue_device",
     "get_result_array",
     "get_usm_ndarray",
+    "is_cuda_backend",
     "get_usm_ndarray_or_scalar",
     "is_supported_array_or_scalar",
     "is_supported_array_type",
@@ -757,6 +758,40 @@ def get_usm_ndarray_or_scalar(a):
     return a if dpnp.isscalar(a) else get_usm_ndarray(a)
 
 
+def is_cuda_backend(obj=None):
+    """
+    Checks that object has a CUDA backend.
+
+    Parameters
+    ----------
+    obj : {Device, SyclDevice, SyclQueue, dpnp.ndarray, usm_ndarray, None},
+          optional
+        An input object with sycl_device property to check device backend.
+        If `obj` is ``None``, device backend will be checked for the default
+        queue.
+        Default: ``None``.
+
+    Returns
+    -------
+    out : bool
+        Return ``True`` if object has a cuda backend, otherwise``False``.
+
+    """
+
+    if obj is None:
+        sycl_device = dpctl.SyclQueue().sycl_device
+    elif isinstance(obj, dpctl.SyclDevice):
+        sycl_device = obj
+    else:
+        sycl_device = getattr(obj, "sycl_device", None)
+    if (
+        sycl_device is not None
+        and sycl_device.backend == dpctl.backend_type.cuda
+    ):
+        return True
+    return False
+
+
 def is_supported_array_or_scalar(a):
     """
     Return ``True`` if `a` is a scalar or an array of either

dpnp/dpnp_iface_indexing.py

@@ -173,6 +173,7 @@ def choose(x1, choices, out=None, mode="raise"):
     :obj:`dpnp.take_along_axis` : Preferable if choices is an array.
 
     """
+
     x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
 
     choices_list = []
@@ -192,6 +193,8 @@ def choose(x1, choices, out=None, mode="raise"):
             pass
         elif not choices_list:
             pass
+        elif dpnp.is_cuda_backend(x1):
+            pass
         else:
             size = x1_desc.size
             choices_size = choices_list[0].size

dpnp/dpnp_iface_libmath.py

@@ -81,7 +81,7 @@ def erf(in_array1):
     x1_desc = dpnp.get_dpnp_descriptor(
         in_array1, copy_when_strides=False, copy_when_nondefault_queue=False
     )
-    if x1_desc:
+    if x1_desc and not dpnp.is_cuda_backend(in_array1):
         return dpnp_erf(x1_desc).get_pyobj()
 
     result = create_output_descriptor_py(

dpnp/dpnp_iface_mathematical.py

@@ -2478,8 +2478,13 @@ def modf(x1, **kwargs):
     """
 
     x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
-    if x1_desc and not kwargs:
-        return dpnp_modf(x1_desc)
+    if x1_desc:
+        if not kwargs:
+            pass
+        elif dpnp.is_cuda_backend(x1):
+            pass
+        else:
+            return dpnp_modf(x1_desc)
 
     return call_origin(numpy.modf, x1, **kwargs)
 

dpnp/dpnp_iface_sorting.py

@@ -188,6 +188,8 @@ def partition(x1, kth, axis=-1, kind="introselect", order=None):
             pass
         elif order is not None:
             pass
+        elif dpnp.is_cuda_backend(x1):
+            pass
         else:
             return dpnp_partition(x1_desc, kth, axis, kind, order).get_pyobj()
 

dpnp/dpnp_iface_statistics.py

@@ -380,6 +380,8 @@ def correlate(x1, x2, mode="valid"):
             pass
         elif mode != "valid":
             pass
+        elif dpnp.is_cuda_backend(x1) or dpnp.is_cuda_backend(x2):
+            pass
         else:
             return dpnp_correlate(x1_desc, x2_desc).get_pyobj()
 
@@ -665,6 +667,8 @@ def median(x1, axis=None, out=None, overwrite_input=False, keepdims=False):
             pass
         elif keepdims:
             pass
+        elif dpnp.is_cuda_backend(x1):
+            pass
         else:
             result_obj = dpnp_median(x1_desc).get_pyobj()
             result = dpnp.convert_single_elem_array_to_scalar(result_obj)