Change dpnp.asfarray to run on Iris Xe

dpnp/dpnp_iface_manipulation.py

@@ -2,7 +2,7 @@
 # distutils: language = c++
 # -*- coding: utf-8 -*-
 # *****************************************************************************
-# Copyright (c) 2016-2022, Intel Corporation
+# Copyright (c) 2016-2023, Intel Corporation
 # All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
@@ -73,7 +73,7 @@
 ]
 
 
-def asfarray(x1, dtype=numpy.float64):
+def asfarray(x1, dtype=None):
     """
     Return an array converted to a float type.
 
@@ -82,14 +82,15 @@ def asfarray(x1, dtype=numpy.float64):
     Notes
     -----
     This function works exactly the same as :obj:`dpnp.array`.
+    If dtype is `None`, `bool` or one of the `int` dtypes, it is replaced with
+    the default floating type in DPNP depending on device capabilities.
 
     """
 
     x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
     if x1_desc:
-        # behavior of original function: int types replaced with float64
-        if numpy.issubdtype(dtype, numpy.integer):
-            dtype = numpy.float64
+        if dtype is None or not numpy.issubdtype(dtype, numpy.inexact):
+            dtype = dpnp.default_float_type(sycl_queue=x1.sycl_queue)
 
         # if type is the same then same object should be returned
         if x1_desc.dtype == dtype:

tests/test_arraymanipulation.py

@@ -1,32 +1,28 @@
 import pytest
+from .helper import get_all_dtypes
 
 import dpnp
 import numpy
 
 
 @pytest.mark.usefixtures("allow_fall_back_on_numpy")
-@pytest.mark.parametrize("dtype",
-                         [numpy.float64, numpy.float32, numpy.int64, numpy.int32],
-                         ids=["float64", "float32", "int64", "int32"])
-@pytest.mark.parametrize("data",
-                         [[1, 2, 3], [1., 2., 3.]],
-                         ids=["[1, 2, 3]", "[1., 2., 3.]"])
+@pytest.mark.parametrize("dtype", get_all_dtypes())
+@pytest.mark.parametrize(
+    "data", [[1, 2, 3], [1.0, 2.0, 3.0]], ids=["[1, 2, 3]", "[1., 2., 3.]"]
+)
 def test_asfarray(dtype, data):
     expected = numpy.asfarray(data, dtype)
     result = dpnp.asfarray(data, dtype)
 
     numpy.testing.assert_array_equal(result, expected)
 
 
-@pytest.mark.parametrize("dtype",
-                         [numpy.float64, numpy.float32, numpy.int64, numpy.int32],
-                         ids=["float64", "float32", "int64", "int32"])
-@pytest.mark.parametrize("data",
-                         [[1, 2, 3], [1., 2., 3.]],
-                         ids=["[1, 2, 3]", "[1., 2., 3.]"])
-def test_asfarray2(dtype, data):
-    expected = numpy.asfarray(numpy.array(data), dtype)
-    result = dpnp.asfarray(dpnp.array(data), dtype)
+@pytest.mark.parametrize("dtype", get_all_dtypes())
+@pytest.mark.parametrize("data", [[1.0, 2.0, 3.0]], ids=["[1., 2., 3.]"])
+@pytest.mark.parametrize("data_dtype", get_all_dtypes(no_none=True))
+def test_asfarray2(dtype, data, data_dtype):
+    expected = numpy.asfarray(numpy.array(data, dtype=data_dtype), dtype)
+    result = dpnp.asfarray(dpnp.array(data, dtype=data_dtype), dtype)
 
     numpy.testing.assert_array_equal(result, expected)
 
@@ -59,7 +55,9 @@ def test_concatenate(self):
         numpy.testing.assert_array_equal(dpnp.concatenate((r4, r3)), r4 + r3)
         # Mixed sequence types
         numpy.testing.assert_array_equal(dpnp.concatenate((tuple(r4), r3)), r4 + r3)
-        numpy.testing.assert_array_equal(dpnp.concatenate((dpnp.array(r4), r3)), r4 + r3)
+        numpy.testing.assert_array_equal(
+            dpnp.concatenate((dpnp.array(r4), r3)), r4 + r3
+        )
         # Explicit axis specification
         numpy.testing.assert_array_equal(dpnp.concatenate((r4, r3), 0), r4 + r3)
         # Including negative