address comments - second round

dpnp/dpnp_container.py

@@ -218,6 +218,7 @@ def full(
 ):
     """Validate input parameters before passing them into `dpctl.tensor` module"""
     dpu.validate_usm_type(usm_type, allow_none=True)
+
     sycl_queue_normalized = dpnp.get_normalized_queue_device(
         fill_value, sycl_queue=sycl_queue, device=device
     )

dpnp/dpnp_iface_arraycreation.py

@@ -1,5 +1,3 @@
-# cython: language_level=3
-# distutils: language = c++
 # -*- coding: utf-8 -*-
 # *****************************************************************************
 # Copyright (c) 2016-2023, Intel Corporation
@@ -575,7 +573,7 @@ def copy(a, order="K", subok=False):
     return array(a, order=order, subok=subok, copy=True)
 
 
-def diag(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
+def diag(v, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
     """
     Extract a diagonal or construct a diagonal array.
 
@@ -588,9 +586,8 @@ def diag(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`.
     Parameter `k` is only supported as integer data type.
-    Otherwise the function will be executed sequentially on CPU.
+    Otherwise ``TypeError`` exception will be raised.
 
     See Also
     --------
@@ -623,56 +620,44 @@ def diag(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
 
     """
 
-    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
-        pass
-    elif not isinstance(k, int):
-        pass
+    if not isinstance(k, int):
+        raise TypeError("An integer is required, but got {}".format(type(k)))
     else:
-        _usm_type = x1.usm_type if usm_type is None else usm_type
-        _sycl_queue = dpnp.get_normalized_queue_device(
-            x1, sycl_queue=sycl_queue, device=device
-        )
-        x1 = (
-            x1.to_device(_sycl_queue.sycl_device)
-            if x1.sycl_queue != _sycl_queue
-            else x1
+        v = dpnp.asarray(
+            v, device=device, usm_type=usm_type, sycl_queue=sycl_queue
         )
 
         init0 = max(0, -k)
         init1 = max(0, k)
-        if x1.ndim == 1:
-            size = x1.shape[0] + abs(k)
+        if v.ndim == 1:
+            size = v.shape[0] + abs(k)
             m = dpnp.zeros(
                 (size, size),
-                dtype=x1.dtype,
-                usm_type=_usm_type,
-                sycl_queue=_sycl_queue,
+                dtype=v.dtype,
+                usm_type=v.usm_type,
+                sycl_queue=v.sycl_queue,
             )
-            for i in range(x1.shape[0]):
-                m[(init0 + i), init1 + i] = x1[i]
+            for i in range(v.shape[0]):
+                m[(init0 + i), init1 + i] = v[i]
             return m
-        elif x1.ndim == 2:
-            size = min(
-                x1.shape[0], x1.shape[0] + k, x1.shape[1], x1.shape[1] - k
-            )
+        elif v.ndim == 2:
+            size = min(v.shape[0], v.shape[0] + k, v.shape[1], v.shape[1] - k)
             if size < 0:
                 size = 0
             m = dpnp.zeros(
                 (size,),
-                dtype=x1.dtype,
-                usm_type=_usm_type,
-                sycl_queue=_sycl_queue,
+                dtype=v.dtype,
+                usm_type=v.usm_type,
+                sycl_queue=v.sycl_queue,
             )
             for i in range(size):
-                m[i] = x1[(init0 + i), init1 + i]
+                m[i] = v[(init0 + i), init1 + i]
             return m
         else:
             raise ValueError("Input must be a 1-D or 2-D array.")
 
-    return call_origin(numpy.diag, x1, k)
 
-
-def diagflat(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
+def diagflat(v, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
     """
     Create a two-dimensional array with the flattened input as a diagonal.
 
@@ -691,9 +676,8 @@ def diagflat(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`.
     Parameter `k` is only supported as integer data type.
-    Otherwise the function will be executed sequentially on CPU.
+    Otherwise ``TypeError`` exception will be raised.
 
     Examples
     --------
@@ -713,19 +697,15 @@ def diagflat(x1, /, k=0, *, device=None, usm_type=None, sycl_queue=None):
         [0, 0, 0, 0, 0]])
 
     """
-    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
-        pass
-    elif not isinstance(k, int):
-        pass
+
+    if not isinstance(k, int):
+        raise TypeError("An integer is required, but got {}".format(type(k)))
     else:
-        _usm_type = x1.usm_type if usm_type is None else usm_type
-        _sycl_queue = dpnp.get_normalized_queue_device(
-            x1, sycl_queue=sycl_queue, device=device
+        v = dpnp.asarray(
+            v, device=device, usm_type=usm_type, sycl_queue=sycl_queue
         )
-        v = dpnp.ravel(x1)
-        return dpnp.diag(v, k, usm_type=_usm_type, sycl_queue=_sycl_queue)
-
-    return call_origin(numpy.diagflat, x1, k)
+        v = dpnp.ravel(v)
+        return dpnp.diag(v, k, usm_type=v.usm_type, sycl_queue=v.sycl_queue)
 
 
 def empty(
@@ -1024,6 +1004,7 @@ def full(
     [10, 10, 10, 10]
 
     """
+
     if like is not None:
         pass
     elif order not in ("C", "c", "F", "f", None):
@@ -1824,8 +1805,8 @@ def vander(
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`.
-    Otherwise the function will be executed sequentially on CPU.
+    Parameter `N`, if it is not ``None``, is only supported as integer data type.
+    Otherwise ``TypeError`` exception will be raised.
 
     Examples
     --------
@@ -1852,44 +1833,34 @@ def vander(
            [  1,   5,  25, 125]])
     """
 
-    if not isinstance(x1, (dpnp.ndarray, dpt.usm_ndarray)):
-        pass
-    elif N is not None and not isinstance(N, int):
-        pass
+    x1 = dpnp.asarray(
+        x1, device=device, usm_type=usm_type, sycl_queue=sycl_queue
+    )
+
+    if N is not None and not isinstance(N, int):
+        raise TypeError("An integer is required, but got {}".format(type(N)))
     elif x1.ndim != 1:
         raise ValueError("x1 must be a one-dimensional array or sequence.")
     else:
         if N is None:
             N = x1.size
 
         _dtype = int if x1.dtype == bool else x1.dtype
-        _usm_type = x1.usm_type if usm_type is None else usm_type
-        _sycl_queue = dpnp.get_normalized_queue_device(
-            x1, sycl_queue=sycl_queue, device=device
-        )
-        x1 = (
-            x1.to_device(_sycl_queue.sycl_device)
-            if x1.sycl_queue != _sycl_queue
-            else x1
-        )
-
         m = empty(
             (x1.size, N),
             dtype=_dtype,
-            usm_type=_usm_type,
-            sycl_queue=_sycl_queue,
+            usm_type=x1.usm_type,
+            sycl_queue=x1.sycl_queue,
         )
         tmp = m[:, ::-1] if not increasing else m
         dpnp.power(
             x1.reshape(-1, 1),
-            dpnp.arange(N, dtype=_dtype, sycl_queue=_sycl_queue),
+            dpnp.arange(N, dtype=_dtype, sycl_queue=x1.sycl_queue),
             out=tmp,
         )
 
         return m
 
-    return call_origin(numpy.vander, x1, N=N, increasing=increasing)
-
 
 def zeros(
     shape,

dpnp/dpnp_iface_statistics.py

@@ -712,7 +712,6 @@ def ptp(
     Limitations
     -----------
     Input array is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`.
-    Otherwise the function will be executed sequentially on CPU.
 
     Examples
     --------
@@ -729,16 +728,11 @@ def ptp(
 
     """
 
-    if not isinstance(a, (dpnp.ndarray, dpt.usm_ndarray)):
-        pass
-    else:
-        return dpnp.subtract(
-            dpnp.max(a, axis=axis, keepdims=keepdims),
-            dpnp.min(a, axis=axis, keepdims=keepdims),
-            out=out,
-        )
-
-    return call_origin(numpy.ptp, a, axis, out, keepdims)
+    return dpnp.subtract(
+        dpnp.max(a, axis=axis, keepdims=keepdims, out=out),
+        dpnp.min(a, axis=axis, keepdims=keepdims),
+        out=out,
+    )
 
 
 def nanvar(x1, axis=None, dtype=None, out=None, ddof=0, keepdims=False):

tests/skipped_tests.tbl

@@ -158,11 +158,6 @@ tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_ones_like_s
 tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_like_subok
 tests/third_party/cupy/creation_tests/test_basic.py::TestBasic::test_zeros_strides
 
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_list
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_tuple
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_extraction_from_nested_list
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_extraction_from_nested_tuple
-
 tests/third_party/cupy/creation_tests/test_ranges.py::TestMeshgrid_param_0_{copy=False, indexing='xy', sparse=False}::test_meshgrid0
 tests/third_party/cupy/creation_tests/test_ranges.py::TestMeshgrid_param_0_{copy=False, indexing='xy', sparse=False}::test_meshgrid1
 tests/third_party/cupy/creation_tests/test_ranges.py::TestMeshgrid_param_0_{copy=False, indexing='xy', sparse=False}::test_meshgrid2

tests/skipped_tests_gpu.tbl

@@ -147,11 +147,6 @@ tests/third_party/cupy/core_tests/test_ndarray_conversion.py::TestNdarrayToBytes
 tests/third_party/cupy/core_tests/test_ndarray_conversion.py::TestNdarrayToBytes_param_3_{order='C', shape=(2, 3)}::test_item
 tests/third_party/cupy/core_tests/test_ndarray_conversion.py::TestNdarrayToBytes_param_4_{order='F', shape=(2, 3)}::test_item
 
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_list
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_construction_from_tuple
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_extraction_from_nested_list
-tests/third_party/cupy/creation_tests/test_matrix.py::TestMatrix::test_diag_extraction_from_nested_tuple
-
 tests/third_party/cupy/indexing_tests/test_insert.py::TestFillDiagonal_param_4_{shape=(3, 3), val=(2,), wrap=True}::test_1darray
 tests/third_party/cupy/indexing_tests/test_insert.py::TestFillDiagonal_param_4_{shape=(3, 3), val=(2,), wrap=True}::test_fill_diagonal
 tests/third_party/cupy/indexing_tests/test_insert.py::TestFillDiagonal_param_5_{shape=(3, 3), val=(2,), wrap=False}::test_1darray

tests/test_arraycreation.py

@@ -117,41 +117,40 @@ def test_arange(start, stop, step, dtype):
         "[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]",
     ],
 )
-def test_diag(v, k):
+def test_diag_diagflat(v, k):
     a = numpy.array(v)
     ia = dpnp.array(a)
     expected = numpy.diag(a, k)
     result = dpnp.diag(ia, k)
     assert_array_equal(expected, result)
 
+    expected = numpy.diagflat(a, k)
+    result = dpnp.diagflat(ia, k)
+    assert_array_equal(expected, result)
+
 
 @pytest.mark.parametrize(
-    "axis",
-    [None, 0, 1],
-    ids=["None", "0", "1"],
-)
-@pytest.mark.parametrize(
-    "v",
+    "seq",
     [
-        [[0, 0], [0, 0]],
-        [[1, 2], [1, 2]],
-        [[1, 2], [3, 4]],
+        [0, 1, 2, 3, 4],
+        (0, 1, 2, 3, 4),
         [[0, 1, 2], [3, 4, 5], [6, 7, 8]],
         [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]],
     ],
     ids=[
-        "[[0, 0], [0, 0]]",
-        "[[1, 2], [1, 2]]",
-        "[[1, 2], [3, 4]]",
+        "[0, 1, 2, 3, 4]",
+        "(0, 1, 2, 3, 4)",
         "[[0, 1, 2], [3, 4, 5], [6, 7, 8]]",
         "[[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]",
     ],
 )
-def test_ptp(v, axis):
-    a = numpy.array(v)
-    ia = dpnp.array(a)
-    expected = numpy.ptp(a, axis)
-    result = dpnp.ptp(ia, axis)
+def test_diag_diagflat_seq(seq):
+    expected = numpy.diag(seq)
+    result = dpnp.diag(seq)
+    assert_array_equal(expected, result)
+
+    expected = numpy.diagflat(seq)
+    result = dpnp.diagflat(seq)
     assert_array_equal(expected, result)
 
 
@@ -479,7 +478,7 @@ def test_triu_size_null(k):
 def test_vander(array, dtype, n, increase):
     if dtype in [dpnp.complex64, dpnp.complex128] and array == [0, 3, 5]:
         pytest.skip(
-            "dpnp.power(dpnp.array(complex(0,0)), dpnp.array(0)) returns nan+nanj while it should be 1+0j"
+            "per array API dpnp.power(complex(0,0)), 0) returns nan+nanj while NumPy returns 1+0j"
         )
     vander_func = lambda xp, x: xp.vander(x, N=n, increasing=increase)
 
@@ -489,6 +488,16 @@ def test_vander(array, dtype, n, increase):
     assert_allclose(vander_func(numpy, a_np), vander_func(dpnp, a_dpnp))
 
 
+@pytest.mark.parametrize(
+    "sequence",
+    [[1, 2, 3, 4], (1, 2, 3, 4)],
+    ids=["[1, 2, 3, 4]", "(1, 2, 3, 4)"],
+)
+def test_vander_seq(sequence):
+    vander_func = lambda xp, x: xp.vander(x)
+    assert_allclose(vander_func(numpy, sequence), vander_func(dpnp, sequence))
+
+
 @pytest.mark.parametrize(
     "shape",
     [(), 0, (0,), (2, 0, 3), (3, 2)],