Add type checking in assert_dtype_allclose for inexact dtypes

tests/helper.py

@@ -7,25 +7,65 @@
 import dpnp
 
 
-def assert_dtype_allclose(dpnp_arr, numpy_arr, check_type=True):
+def assert_dtype_allclose(
+    dpnp_arr, numpy_arr, check_type=True, check_only_type_kind=False
+):
     """
     Assert DPNP and NumPy array based on maximum dtype resolution of input arrays
     for floating and complex types.
     For other dtypes the assertion is based on exact matching of the arrays.
-
-    """
-
+    When 'check_type' is True (default), the function asserts:
+    - Equal dtypes for exact types.
+    For inexact types:
+      - If the numpy array's dtype is `numpy.float16`, checks if the device
+        of the `dpnp_arr` supports 64-bit precision floating point operations.
+        If supported, asserts equal dtypes.
+        Otherwise, asserts equal type kinds.
+      - For other inexact types, asserts equal dtypes if the device of the `dpnp_arr`
+        supports 64-bit precision floating point operations or if the numpy array's inexact
+        dtype is not a double precision type.
+        Otherwise, asserts equal type kinds.
+    The 'check_only_type_kind' parameter (False by default) asserts only equal type kinds
+    for all data types supported by DPNP when set to True.
+    It is effective only when 'check_type' is also set to True.
+
+    """
+
+    list_64bit_types = [numpy.float64, numpy.complex128]
     is_inexact = lambda x: dpnp.issubdtype(x.dtype, dpnp.inexact)
     if is_inexact(dpnp_arr) or is_inexact(numpy_arr):
         tol = 8 * max(
             dpnp.finfo(dpnp_arr).resolution,
             numpy.finfo(numpy_arr.dtype).resolution,
         )
         assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol)
+        if check_type:
+            numpy_arr_dtype = numpy_arr.dtype
+            dpnp_arr_dtype = dpnp_arr.dtype
+            dpnp_arr_dev = dpnp_arr.sycl_device
+
+            if check_only_type_kind:
+                assert dpnp_arr_dtype.kind == numpy_arr_dtype.kind
+            else:
+                is_np_arr_f2 = numpy_arr_dtype == numpy.float16
+
+                if is_np_arr_f2:
+                    if has_support_aspect16(dpnp_arr_dev):
+                        assert dpnp_arr_dtype == numpy_arr_dtype
+                elif (
+                    numpy_arr_dtype not in list_64bit_types
+                    or has_support_aspect64(dpnp_arr_dev)
+                ):
+                    assert dpnp_arr_dtype == numpy_arr_dtype
+                else:
+                    assert dpnp_arr_dtype.kind == numpy_arr_dtype.kind
     else:
         assert_array_equal(dpnp_arr.asnumpy(), numpy_arr)
         if check_type:
-            assert dpnp_arr.dtype == numpy_arr.dtype
+            if check_only_type_kind:
+                assert dpnp_arr.dtype.kind == numpy_arr.dtype.kind
+            else:
+                assert dpnp_arr.dtype == numpy_arr.dtype
 
 
 def get_complex_dtypes(device=None):

tests/test_fft.py

@@ -16,7 +16,7 @@ def test_fft(dtype, norm):
     np_res = numpy.fft.fft(data, norm=norm)
     dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm)
 
-    assert_dtype_allclose(dpnp_res, np_res)
+    assert_dtype_allclose(dpnp_res, np_res, check_only_type_kind=True)
 
 
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
@@ -29,7 +29,7 @@ def test_fft_ndim(dtype, shape, norm):
     np_res = numpy.fft.fft(np_data, norm=norm)
     dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm)
 
-    assert_dtype_allclose(dpnp_res, np_res)
+    assert_dtype_allclose(dpnp_res, np_res, check_only_type_kind=True)
 
 
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
@@ -44,7 +44,7 @@ def test_fft_ifft(dtype, shape, norm):
     np_res = numpy.fft.ifft(np_data, norm=norm)
     dpnp_res = dpnp.fft.ifft(dpnp_data, norm=norm)
 
-    assert_dtype_allclose(dpnp_res, np_res)
+    assert_dtype_allclose(dpnp_res, np_res, check_only_type_kind=True)
 
 
 @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
@@ -58,7 +58,7 @@ def test_fft_rfft(dtype, shape):
     np_res = numpy.fft.rfft(np_data)
     dpnp_res = dpnp.fft.rfft(dpnp_data)
 
-    assert_dtype_allclose(dpnp_res, np_res)
+    assert_dtype_allclose(dpnp_res, np_res, check_only_type_kind=True)
 
 
 @pytest.mark.parametrize(

tests/test_mathematical.py

@@ -1105,16 +1105,19 @@ def test_out_dtypes(self, dtype):
         dp_array2 = dpnp.arange(size, dtype=dtype)
 
         dp_out = dpnp.empty(size, dtype=dpnp.complex64)
+        check_dtype = True
         if dtype != dpnp.complex64:
             # dtype of out mismatches types of input arrays
             with pytest.raises(TypeError):
                 dpnp.divide(dp_array1, dp_array2, out=dp_out)
 
             # allocate new out with expected type
             dp_out = dpnp.empty(size, dtype=dtype)
+            # Set check_dtype to False as dtype does not match
+            check_dtype = False
 
         result = dpnp.divide(dp_array1, dp_array2, out=dp_out)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, check_type=check_dtype)
 
     @pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings")
     @pytest.mark.parametrize("dtype", get_float_complex_dtypes())

tests/test_sum.py

@@ -27,10 +27,16 @@ def test_sum_float(dtype):
     )
     ia = dpnp.array(a)
 
+    # Flag for type check in special cases
+    # Use only type kinds checks when dpnp handles float32 arrays
+    # as `dpnp.sum()` and `numpy.sum()` return different dtypes
+    check_type_kind = dtype == dpnp.float32
     for axis in range(len(a)):
         result = dpnp.sum(ia, axis=axis)
         expected = numpy.sum(a, axis=axis)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(
+            result, expected, check_only_type_kind=check_type_kind
+        )
 
 
 def test_sum_int():

tests/test_sycl_queue.py

@@ -875,7 +875,7 @@ def test_fft_rfft(type, shape, device):
     np_res = numpy.fft.rfft(np_data)
     dpnp_res = dpnp.fft.rfft(dpnp_data)
 
-    assert_dtype_allclose(dpnp_res, np_res)
+    assert_dtype_allclose(dpnp_res, np_res, check_only_type_kind=True)
 
     expected_queue = dpnp_data.get_array().sycl_queue
     result_queue = dpnp_res.get_array().sycl_queue