Enable third party tests for dpnp.ndarray (#1964)

* Enable all supported use cases and address missing one

* Use dpnp_array._create_from_usm_ndarray() within dpnp container

* Allow shape setter to work with integer scalar

* Applied pre-commit hook

* Removed commented leftovers

Author: antonwolfy

.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"

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_bitwise.py

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

dpnp/dpnp_iface_logic.py

@@ -40,7 +40,6 @@
 """
 
 # pylint: disable=protected-access
-# pylint: disable=c-extension-no-member
 # pylint: disable=duplicate-code
 # pylint: disable=no-name-in-module
 

dpnp/dpnp_iface_mathematical.py

@@ -38,7 +38,6 @@
 """
 
 # pylint: disable=protected-access
-# pylint: disable=c-extension-no-member
 # pylint: disable=duplicate-code
 # pylint: disable=no-name-in-module
 

dpnp/fft/dpnp_utils_fft.py

@@ -34,7 +34,6 @@
 """
 
 # pylint: disable=protected-access
-# pylint: disable=c-extension-no-member
 # pylint: disable=no-name-in-module
 
 import dpctl

tests/third_party/cupy/core_tests/test_ndarray.py

@@ -42,50 +42,50 @@ def test_shape_not_integer(self):
             with pytest.raises(TypeError):
                 xp.ndarray((1.0,))
 
-    @pytest.mark.skip("passing buffer as dpnp array is not supported")
     def test_shape_int_with_strides(self):
         dummy = cupy.ndarray(3)
         a = cupy.ndarray(3, strides=(0,), buffer=dummy)
         assert a.shape == (3,)
         assert a.strides == (0,)
 
-    @pytest.mark.skip("passing buffer as dpnp array is not supported")
     def test_memptr(self):
         a = cupy.arange(6).astype(numpy.float32).reshape((2, 3))
         memptr = a
 
-        b = cupy.ndarray((2, 3), numpy.float32, buffer=memptr)
+        b = cupy.ndarray((2, 3), numpy.float32, memptr)
         testing.assert_array_equal(a, b)
 
         b += 1
         testing.assert_array_equal(a, b)
 
-    @pytest.mark.skip("self-overlapping strides are not supported")
+    @pytest.mark.skip(
+        "dpctl-1765: might lead to race condition (no plan to support that)"
+    )
     def test_memptr_with_strides(self):
         buf = cupy.ndarray(20, numpy.uint8)
         memptr = buf
 
         # self-overlapping strides
-        a = cupy.ndarray((2, 3), numpy.float32, buffer=memptr, strides=(8, 4))
-        assert a.strides == (8, 4)
+        a = cupy.ndarray((2, 3), numpy.float32, memptr, strides=(2, 1))
+        assert a.strides == (2, 1)
 
         a[:] = 1
         a[0, 2] = 4
         assert float(a[1, 0]) == 4
 
-    @pytest.mark.skip("no exception raised by dpctl")
+    @pytest.mark.skip(
+        "dpctl-1766: no exception raised by dpctl (no plan to support that)"
+    )
     def test_strides_without_memptr(self):
         for xp in (numpy, cupy):
             with pytest.raises(ValueError):
                 xp.ndarray((2, 3), numpy.float32, strides=(20, 4))
 
-    @pytest.mark.skip("passing buffer as dpnp array is not supported")
     def test_strides_is_given_and_order_is_ignored(self):
         buf = cupy.ndarray(20, numpy.uint8)
         a = cupy.ndarray((2, 3), numpy.float32, buf, strides=(2, 1), order="C")
         assert a.strides == (2, 1)
 
-    @pytest.mark.skip("dpctl-1724 issue")
     @testing.with_requires("numpy>=1.19")
     def test_strides_is_given_but_order_is_invalid(self):
         for xp in (numpy, cupy):
@@ -102,7 +102,6 @@ def test_order(self):
         assert a.flags.f_contiguous
         assert not a.flags.c_contiguous
 
-    @pytest.mark.skip("passing 'None' into order arguments is not supported")
     def test_order_none(self):
         shape = (2, 3, 4)
         a = cupy.ndarray(shape, order=None)
@@ -113,7 +112,6 @@ def test_order_none(self):
             i * a.itemsize == j for i, j in zip(a.strides, a_cpu.strides)
         )
 
-    @pytest.mark.skip("__slots__ is not supported")
     def test_slots(self):
         # Test for #7883.
         a = cupy.ndarray((2, 3))
@@ -130,7 +128,7 @@ class UserNdarray(cupy.ndarray):
 @testing.parameterize(
     *testing.product(
         {
-            "shape": [(), (1,), (1, 2), (1, 2, 3)],
+            "shape": [(), (1,), (2, 3), (1, 2, 3)],
             "order": ["C", "F"],
             "dtype": [
                 numpy.uint8,  # itemsize=1
@@ -139,13 +137,15 @@ class UserNdarray(cupy.ndarray):
         }
     )
 )
-@pytest.mark.skip("strides may vary")
 class TestNdarrayInitStrides(unittest.TestCase):
     # Check the strides given shape, itemsize and order.
     @testing.numpy_cupy_equal()
     def test_strides(self, xp):
         arr = xp.ndarray(self.shape, dtype=self.dtype, order=self.order)
-        return (arr.strides, arr.flags.c_contiguous, arr.flags.f_contiguous)
+        strides = arr.strides
+        if xp is cupy:
+            strides = tuple(i * arr.itemsize for i in strides)
+        return (strides, arr.flags.c_contiguous, arr.flags.f_contiguous)
 
 
 class TestNdarrayInitRaise(unittest.TestCase):
@@ -210,8 +210,8 @@ def test_deepcopy_multi_device(self):
 """
 
 
-@pytest.mark.skip()
 class TestNdarrayCopy:
+    @pytest.mark.skip("SAT-7167: add device keyword")
     @testing.multi_gpu(2)
     @testing.for_orders("CFA")
     def test_copy_multi_device_non_contiguous(self, order):
@@ -221,14 +221,16 @@ def test_copy_multi_device_non_contiguous(self, order):
         assert arr2.device == dev1
         testing.assert_array_equal(arr, arr2)
 
+    @pytest.mark.skip("SAT-7167: add device keyword")
     @testing.multi_gpu(2)
     def test_copy_multi_device_non_contiguous_K(self):
-        arr = _core.ndarray((20,))[::2]
-        with cuda.Device(1):
-            with pytest.raises(NotImplementedError):
-                arr.copy("K")
+        arr = cupy.ndarray((20,))[::2]
+        dev1 = dpctl.SyclDevice()
+        with pytest.raises(NotImplementedError):
+            arr.copy("K", device=dev1)
 
     # See cupy/cupy#5004
+    @pytest.mark.skip("RawKernel() is not supported")
     @testing.multi_gpu(2)
     def test_copy_multi_device_with_stream(self):
         # Kernel that takes long enough then finally writes values.
@@ -256,14 +258,16 @@ def test_copy_multi_device_with_stream(self):
                 )
 
 
-@pytest.mark.skip()
 class TestNdarrayShape(unittest.TestCase):
     @testing.numpy_cupy_array_equal()
     def test_shape_set(self, xp):
         arr = xp.ndarray((2, 3))
         arr.shape = (3, 2)
         return xp.array(arr.shape)
 
+    @pytest.mark.skip(
+        "dpctl-1699: shape setter does not work with negative shape"
+    )
     @testing.numpy_cupy_array_equal()
     def test_shape_set_infer(self, xp):
         arr = xp.ndarray((2, 3))
@@ -682,7 +686,7 @@ def __array_finalize__(self, obj):
         self.info = getattr(obj, "info", None)
 
 
-@pytest.mark.skip("explicit constructor call is not supported")
+@pytest.mark.skip("SAT-7168: explicit constructor call is not supported")
 class TestNdarraySubclass:
     def test_explicit_constructor_call(self):
         a = C([0, 1, 2, 3], info="information")