Add callback mechanism so that array_finalize can see if obj is Numba meminfo. (#214)

* change dparray to numpy_usm_shared.  add callback mechanism so that numba_dppy.numpy_usm_shared can register with dpctl.dptensor.numpy_usm_shared.ndarray a callback function to look and see if the object is a Numba MemInfo with USM allocator.

* Define sycl usm interface statically.

Co-authored-by: Sergey Pokhodenko <sergey.pokhodenko@intel.com>

Author: DrTodd13

dpctl/dptensor/numpy_usm_shared.py

@@ -1,4 +1,4 @@
-##===---------- dparray.py - dpctl  -------*- Python -*----===##
+##===---------- numpy_usm_shared.py - dpctl  -------*- Python -*----===##
 ##
 ##                      Data Parallel Control (dpCtl)
 ##
@@ -19,7 +19,7 @@
 ##===----------------------------------------------------------------------===##
 ###
 ### \file
-### This file implements a dparray - USM aware implementation of ndarray.
+### This file implements a numpy_usm_shared - USM aware implementation of ndarray.
 ##===----------------------------------------------------------------------===##
 
 import numpy as np
@@ -70,12 +70,17 @@ class ndarray(np.ndarray):
     with a foreign allocator.
     """
 
+    external_usm_checkers = []
+
+    def add_external_usm_checker(func):
+        ndarray.external_usm_checkers.append(func)
+
     def __new__(
         subtype, shape, dtype=float, buffer=None, offset=0, strides=None, order=None
     ):
         # Create a new array.
         if buffer is None:
-            dprint("dparray::ndarray __new__ buffer None")
+            dprint("numpy_usm_shared::ndarray __new__ buffer None")
             nelems = np.prod(shape)
             dt = np.dtype(dtype)
             isz = dt.itemsize
@@ -102,7 +107,7 @@ def __new__(
             return new_obj
         # zero copy if buffer is a usm backed array-like thing
         elif hasattr(buffer, array_interface_property):
-            dprint("dparray::ndarray __new__ buffer", array_interface_property)
+            dprint("numpy_usm_shared::ndarray __new__ buffer", array_interface_property)
             # also check for array interface
             new_obj = np.ndarray.__new__(
                 subtype,
@@ -124,7 +129,7 @@ def __new__(
                 )
             return new_obj
         else:
-            dprint("dparray::ndarray __new__ buffer not None and not sycl_usm")
+            dprint("numpy_usm_shared::ndarray __new__ buffer not None and not sycl_usm")
             nelems = np.prod(shape)
             # must copy
             ar = np.ndarray(
@@ -158,6 +163,9 @@ def __new__(
                 )
             return new_obj
 
+    def __sycl_usm_array_interface__(self):
+        return self._getter_sycl_usm_array_interface()
+
     def _getter_sycl_usm_array_interface_(self):
         ary_iface = self.__array_interface__
         _base = _get_usm_base(self)
@@ -186,6 +194,9 @@ def __array_finalize__(self, obj):
         # subclass of ndarray, including our own.
         if hasattr(obj, array_interface_property):
             return
+        for ext_checker in ndarray.external_usm_checkers:
+            if ext_checker(obj):
+                return
         if isinstance(obj, np.ndarray):
             ob = self
             while isinstance(ob, np.ndarray):
@@ -200,7 +211,7 @@ def __array_finalize__(self, obj):
         )
 
     # Tell Numba to not treat this type just like a NumPy ndarray but to propagate its type.
-    # This way it will use the custom dparray allocator.
+    # This way it will use the custom numpy_usm_shared allocator.
     __numba_no_subtype_ndarray__ = True
 
     # Convert to a NumPy ndarray.
@@ -234,8 +245,8 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                 else:
                     return NotImplemented
             # Have to avoid recursive calls to array_ufunc here.
-            # If no out kwarg then we create a dparray out so that we get
-            # USM memory.  However, if kwarg has dparray-typed out then
+            # If no out kwarg then we create a numpy_usm_shared out so that we get
+            # USM memory.  However, if kwarg has numpy_usm_shared-typed out then
             # array_ufunc is called recursively so we cast out as regular
             # NumPy ndarray (having a USM data pointer).
             if kwargs.get("out", None) is None:
@@ -246,7 +257,7 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                 out_as_np = np.ndarray(out.shape, out.dtype, out)
                 kwargs["out"] = out_as_np
             else:
-                # If they manually gave dparray as out kwarg then we have to also
+                # If they manually gave numpy_usm_shared as out kwarg then we have to also
                 # cast as regular NumPy ndarray to avoid recursion.
                 if isinstance(kwargs["out"], ndarray):
                     out = kwargs["out"]
@@ -271,7 +282,7 @@ def isdef(x):
     cname = c[0]
     if isdef(cname):
         continue
-    # For now we do the simple thing and copy the types from NumPy module into dparray module.
+    # For now we do the simple thing and copy the types from NumPy module into numpy_usm_shared module.
     new_func = "%s = np.%s" % (cname, cname)
     try:
         the_code = compile(new_func, "__init__", "exec")