Merge branch 'master' into build_target_cuda

Author: npolina4

.github/workflows/CODEOWNERS

@@ -0,0 +1 @@
+* @antonwolfy @npolina4 @vlad-perevezentsev @vtavana

.pre-commit-config.yaml

@@ -96,6 +96,7 @@ repos:
             [
             "-rn", # Only display messages
             "-sn", # Don't display the score
+            "--disable=c-extension-no-member",
             "--disable=import-error",
             "--disable=redefined-builtin",
             "--disable=unused-wildcard-import"

doc/reference/logic.rst

@@ -68,6 +68,7 @@ Comparison
    dpnp.allclose
    dpnp.isclose
    dpnp.array_equal
+   dpnp.array_equiv
    dpnp.greater
    dpnp.greater_equal
    dpnp.less

dpnp/dpnp_array.py

@@ -70,31 +70,30 @@ def __init__(
         usm_type="device",
         sycl_queue=None,
     ):
+        if order is None:
+            order = "C"
+
         if buffer is not None:
-            if not isinstance(buffer, dpt.usm_ndarray):
-                raise TypeError(
-                    "Expected dpctl.tensor.usm_ndarray, got {}"
-                    "".format(type(buffer))
-                )
-            if buffer.shape != shape:
-                raise ValueError(
-                    "Expected buffer.shape={}, got {}"
-                    "".format(shape, buffer.shape)
-                )
-            self._array_obj = dpt.asarray(buffer, copy=False, order=order)
+            buffer = dpnp.get_usm_ndarray(buffer)
+
+            if dtype is None:
+                dtype = buffer.dtype
         else:
-            sycl_queue_normalized = dpnp.get_normalized_queue_device(
-                device=device, sycl_queue=sycl_queue
-            )
-            self._array_obj = dpt.usm_ndarray(
-                shape,
-                dtype=dtype,
-                strides=strides,
-                buffer=usm_type,
-                offset=offset,
-                order=order,
-                buffer_ctor_kwargs={"queue": sycl_queue_normalized},
-            )
+            buffer = usm_type
+
+        sycl_queue_normalized = dpnp.get_normalized_queue_device(
+            device=device, sycl_queue=sycl_queue
+        )
+
+        self._array_obj = dpt.usm_ndarray(
+            shape,
+            dtype=dtype,
+            strides=strides,
+            buffer=buffer,
+            offset=offset,
+            order=order,
+            buffer_ctor_kwargs={"queue": sycl_queue_normalized},
+        )
 
     @property
     def __sycl_usm_array_interface__(self):
@@ -457,6 +456,8 @@ def __setitem__(self, key, val):
     # '__setstate__',
     # '__sizeof__',
 
+    __slots__ = ("_array_obj",)
+
     def __str__(self):
         """Return ``str(self)``."""
         return self._array_obj.__str__()
@@ -1275,6 +1276,9 @@ def shape(self, newshape):
 
         """
 
+        if not isinstance(newshape, (list, tuple)):
+            newshape = (newshape,)
+
         self._array_obj.shape = newshape
 
     @property

dpnp/dpnp_container.py

@@ -79,8 +79,7 @@ def arange(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-
-    return dpnp_array(array_obj.shape, buffer=array_obj)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def asarray(
@@ -132,7 +131,7 @@ def asarray(
         if array_obj is x1_obj and isinstance(x1, dpnp_array):
             return x1
 
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def copy(x1, /, *, order="K"):
@@ -141,7 +140,7 @@ def copy(x1, /, *, order="K"):
         order = "K"
 
     array_obj = dpt.copy(dpnp.get_usm_ndarray(x1), order=order)
-    return dpnp_array(array_obj.shape, buffer=array_obj, order="K")
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def empty(
@@ -169,7 +168,7 @@ def empty(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def eye(
@@ -202,7 +201,7 @@ def eye(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def full(
@@ -236,7 +235,7 @@ def full(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def ones(
@@ -264,19 +263,19 @@ def ones(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def tril(x1, /, *, k=0):
     """Creates `dpnp_array` as lower triangular part of an input array."""
     array_obj = dpt.tril(dpnp.get_usm_ndarray(x1), k=k)
-    return dpnp_array(array_obj.shape, buffer=array_obj, order="K")
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def triu(x1, /, *, k=0):
     """Creates `dpnp_array` as upper triangular part of an input array."""
     array_obj = dpt.triu(dpnp.get_usm_ndarray(x1), k=k)
-    return dpnp_array(array_obj.shape, buffer=array_obj, order="K")
+    return dpnp_array._create_from_usm_ndarray(array_obj)
 
 
 def zeros(
@@ -304,4 +303,4 @@ def zeros(
         usm_type=usm_type,
         sycl_queue=sycl_queue_normalized,
     )
-    return dpnp_array(array_obj.shape, buffer=array_obj, order=order)
+    return dpnp_array._create_from_usm_ndarray(array_obj)

dpnp/dpnp_iface.py

@@ -56,7 +56,6 @@
 
 __all__ = [
     "are_same_logical_tensors",
-    "array_equal",
     "asnumpy",
     "astype",
     "as_usm_ndarray",
@@ -173,24 +172,6 @@ def are_same_logical_tensors(ar1, ar2):
     )
 
 
-def array_equal(a1, a2, equal_nan=False):
-    """
-    True if two arrays have the same shape and elements, False otherwise.
-
-    For full documentation refer to :obj:`numpy.array_equal`.
-
-    See Also
-    --------
-    :obj:`dpnp.allclose` : Returns True if two arrays are element-wise equal
-                           within a tolerance.
-    :obj:`dpnp.array_equiv` : Returns True if input arrays are shape consistent
-                              and all elements equal.
-
-    """
-
-    return numpy.array_equal(a1, a2, equal_nan=equal_nan)
-
-
 def asnumpy(a, order="C"):
     """
     Returns the NumPy array with input data.

dpnp/dpnp_iface_bitwise.py

@@ -38,7 +38,6 @@
 """
 
 # pylint: disable=protected-access
-# pylint: disable=c-extension-no-member
 
 
 import dpctl.tensor._tensor_elementwise_impl as ti