[BUG] fix empty initialization of device_ndarray in pylibraft (#2061)

The `device_ndarray.empty()` function can be used to allocate device memory without initialization. Previously, the memory has been allocated (uninitialized) on the host and then been copied to the device.

This PR fixes the behavior for 'empty()' by allowing the `device_ndarray` to be initialized by an `array_interface` instead of an `numpy.ndarray` instance, which conditionally allows to skip the initialization of the `DeviceBuffer`.

CC @tfeher, @cjnolet

Authors:
  - Malte Förster (https://github.com/mfoerste4)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: #2061

python/pylibraft/pylibraft/common/device_ndarray.py

@@ -31,7 +31,9 @@ def __init__(self, np_ndarray):
 
         Parameters
         ----------
-        ndarray : A numpy.ndarray which will be copied and moved to the device
+        ndarray : Can be numpy.ndarray, array like or even directly
+            an __array_interface__. Only case it is a numpy.ndarray its
+            contents will be copied to the device.
 
         Examples
         --------
@@ -58,11 +60,38 @@ def __init__(self, np_ndarray):
             raft_array = device_ndarray.empty((100, 50))
             torch_tensor = torch.as_tensor(raft_array, device='cuda')
         """
-        self.ndarray_ = np_ndarray
+
+        if type(np_ndarray) is np.ndarray:
+            # np_ndarray IS an actual numpy.ndarray
+            self.__array_interface__ = np_ndarray.__array_interface__.copy()
+            self.ndarray_ = np_ndarray
+            copy = True
+        elif hasattr(np_ndarray, "__array_interface__"):
+            # np_ndarray HAS an __array_interface__
+            self.__array_interface__ = np_ndarray.__array_interface__.copy()
+            self.ndarray_ = np_ndarray
+            copy = False
+        elif all(
+            name in np_ndarray for name in {"typestr", "shape", "version"}
+        ):
+            # np_ndarray IS an __array_interface__
+            self.__array_interface__ = np_ndarray.copy()
+            self.ndarray_ = None
+            copy = False
+        else:
+            raise ValueError(
+                "np_ndarray should be or contain __array_interface__"
+            )
+
         order = "C" if self.c_contiguous else "F"
-        self.device_buffer_ = rmm.DeviceBuffer.to_device(
-            self.ndarray_.tobytes(order=order)
-        )
+        if copy:
+            self.device_buffer_ = rmm.DeviceBuffer.to_device(
+                self.ndarray_.tobytes(order=order)
+            )
+        else:
+            self.device_buffer_ = rmm.DeviceBuffer(
+                size=np.prod(self.shape) * self.dtype.itemsize
+            )
 
     @classmethod
     def empty(cls, shape, dtype=np.float32, order="C"):
@@ -82,7 +111,7 @@ def empty(cls, shape, dtype=np.float32, order="C"):
                 or column-major (Fortran-style) order in memory
         """
         arr = np.empty(shape, dtype=dtype, order=order)
-        return cls(arr)
+        return cls(arr.__array_interface__.copy())
 
     @property
     def c_contiguous(self):
@@ -104,23 +133,23 @@ def dtype(self):
         """
         Datatype of the current device_ndarray instance
         """
-        array_interface = self.ndarray_.__array_interface__
+        array_interface = self.__array_interface__
         return np.dtype(array_interface["typestr"])
 
     @property
     def shape(self):
         """
         Shape of the current device_ndarray instance
         """
-        array_interface = self.ndarray_.__array_interface__
+        array_interface = self.__array_interface__
         return array_interface["shape"]
 
     @property
     def strides(self):
         """
         Strides of the current device_ndarray instance
         """
-        array_interface = self.ndarray_.__array_interface__
+        array_interface = self.__array_interface__
         return array_interface.get("strides")
 
     @property
@@ -131,7 +160,7 @@ def __cuda_array_interface__(self):
         zero-copy semantics.
         """
         device_cai = self.device_buffer_.__cuda_array_interface__
-        host_cai = self.ndarray_.__array_interface__.copy()
+        host_cai = self.__array_interface__.copy()
         host_cai["data"] = (device_cai["data"][0], device_cai["data"][1])
 
         return host_cai