Added more dtypes for minins tests and manipulation tests.

Author: npolina4

tests/test_manipulation.py

@@ -4,24 +4,29 @@
 
 import dpnp
 
-from .helper import has_support_aspect64
+from .helper import (
+    get_all_dtypes,
+    get_complex_dtypes,
+    get_float_dtypes,
+)
 
 testdata = []
 testdata += [
     ([True, False, True], dtype)
-    for dtype in ["float32", "int32", "int64", "bool"]
+    for dtype in get_all_dtypes(no_none=True, no_complex=True)
+]
+testdata += [
+    ([1, -1, 0], dtype)
+    for dtype in get_all_dtypes(no_none=True, no_bool=True, no_complex=True)
 ]
-testdata += [([1, -1, 0], dtype) for dtype in ["float32", "int32", "int64"]]
-testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float32"]]
-if has_support_aspect64():
-    testdata += [([True, False, True], dtype) for dtype in ["float64"]]
-    testdata += [([1, -1, 0], dtype) for dtype in ["float64"]]
-    testdata += [([0.1, 0.0, -0.1], dtype) for dtype in ["float64"]]
-    testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in ["complex128"]]
+testdata += [([0.1, 0.0, -0.1], dtype) for dtype in get_float_dtypes()]
+testdata += [([1j, -1j, 1 - 2j], dtype) for dtype in get_complex_dtypes()]
 
 
 @pytest.mark.parametrize("in_obj, out_dtype", testdata)
 def test_copyto_dtype(in_obj, out_dtype):
+    if out_dtype == dpnp.complex64:
+        pytest.skip("SAT-6016: dpnp.copyto() do not work with complex64 dtype")
     ndarr = numpy.array(in_obj)
     expected = numpy.empty(ndarr.size, dtype=out_dtype)
     numpy.copyto(expected, ndarr)

tests/test_mixins.py

@@ -4,72 +4,74 @@
 
 import dpnp as inp
 
+from .helper import get_float_dtypes
+
 
 class TestMatMul(unittest.TestCase):
     def test_matmul(self):
         array_data = [1.0, 2.0, 3.0, 4.0]
         size = 2
 
-        # DPNP
-        array1 = inp.reshape(
-            inp.array(array_data, dtype=inp.float32), (size, size)
-        )
-        array2 = inp.reshape(
-            inp.array(array_data, dtype=inp.float32), (size, size)
-        )
-        result = inp.matmul(array1, array2)
-        # print(result)
-
-        # original
-        array_1 = numpy.array(array_data, dtype=numpy.float32).reshape(
-            (size, size)
-        )
-        array_2 = numpy.array(array_data, dtype=numpy.float32).reshape(
-            (size, size)
-        )
-        expected = numpy.matmul(array_1, array_2)
-        # print(expected)
-
-        # passed
-        numpy.testing.assert_array_equal(expected, result)
-        # still failed
-        # self.assertEqual(expected, result)
+        for dtype in get_float_dtypes():
+            # DPNP
+            array1 = inp.reshape(
+                inp.array(array_data, dtype=dtype), (size, size)
+            )
+            array2 = inp.reshape(
+                inp.array(array_data, dtype=dtype), (size, size)
+            )
+            result = inp.matmul(array1, array2)
+            # print(result)
+
+            # original
+            array_1 = numpy.array(array_data, dtype=dtype).reshape((size, size))
+            array_2 = numpy.array(array_data, dtype=dtype).reshape((size, size))
+            expected = numpy.matmul(array_1, array_2)
+            # print(expected)
+
+            # passed
+            numpy.testing.assert_array_equal(expected, result)
+            # still failed
+            # self.assertEqual(expected, result)
 
     def test_matmul2(self):
         array_data1 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
         array_data2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
 
-        # DPNP
-        array1 = inp.reshape(inp.array(array_data1, dtype=inp.float32), (3, 2))
-        array2 = inp.reshape(inp.array(array_data2, dtype=inp.float32), (2, 4))
-        result = inp.matmul(array1, array2)
-        # print(result)
+        for dtype in get_float_dtypes():
+            # DPNP
+            array1 = inp.reshape(inp.array(array_data1, dtype=dtype), (3, 2))
+            array2 = inp.reshape(inp.array(array_data2, dtype=dtype), (2, 4))
+            result = inp.matmul(array1, array2)
+            # print(result)
 
-        # original
-        array_1 = numpy.array(array_data1, dtype=numpy.float32).reshape((3, 2))
-        array_2 = numpy.array(array_data2, dtype=numpy.float32).reshape((2, 4))
-        expected = numpy.matmul(array_1, array_2)
-        # print(expected)
+            # original
+            array_1 = numpy.array(array_data1, dtype=dtype).reshape((3, 2))
+            array_2 = numpy.array(array_data2, dtype=dtype).reshape((2, 4))
+            expected = numpy.matmul(array_1, array_2)
+            # print(expected)
 
-        numpy.testing.assert_array_equal(expected, result)
+            numpy.testing.assert_array_equal(expected, result)
 
     def test_matmul3(self):
         array_data1 = numpy.full((513, 513), 5)
         array_data2 = numpy.full((513, 513), 2)
-        out = numpy.empty((513, 513), dtype=numpy.float32)
 
-        # DPNP
-        array1 = inp.array(array_data1, dtype=inp.float32)
-        array2 = inp.array(array_data2, dtype=inp.float32)
-        out1 = inp.array(out, dtype=inp.float32)
-        result = inp.matmul(array1, array2, out=out1)
+        for dtype in get_float_dtypes():
+            out = numpy.empty((513, 513), dtype=dtype)
+
+            # DPNP
+            array1 = inp.array(array_data1, dtype=dtype)
+            array2 = inp.array(array_data2, dtype=dtype)
+            out1 = inp.array(out, dtype=dtype)
+            result = inp.matmul(array1, array2, out=out1)
 
-        # original
-        array_1 = numpy.array(array_data1, dtype=numpy.float32)
-        array_2 = numpy.array(array_data2, dtype=numpy.float32)
-        expected = numpy.matmul(array_1, array_2, out=out)
+            # original
+            array_1 = numpy.array(array_data1, dtype=dtype)
+            array_2 = numpy.array(array_data2, dtype=dtype)
+            expected = numpy.matmul(array_1, array_2, out=out)
 
-        numpy.testing.assert_array_equal(expected, result)
+            numpy.testing.assert_array_equal(expected, result)
 
 
 if __name__ == "__main__":