update dpnp.fix docstring

Author: Vahid Tavanashad

CHANGELOG.md

@@ -19,6 +19,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 * Allowed input array of `uint64` dtype in `dpnp.bincount` [#2361](https://github.com/IntelPython/dpnp/pull/2361)
 * The vector norms `ord={None, 1, 2, inf}` and the matrix norms `ord={None, 1, 2, inf, "fro", "nuc"}` now consistently return zero for empty arrays, which are arrays with at least one axis of size zero. This change affects `dpnp.linalg.norm`, `dpnp.linalg.vector_norm`, and `dpnp.linalg.matrix_norm`. Previously, dpnp would either raise errors or return zero depending on the parameters provided [#2371](https://github.com/IntelPython/dpnp/pull/2371)
 * Improved performance of `dpnp.nansum`, `dpnp.nanprod`, `dpnp.nancumsum`, and `dpnp.nancumprod` by reusing `dpnp.nan_to_num` function in implementation of the functions [#2339](https://github.com/IntelPython/dpnp/pull/2339)
+* Updated `dpnp.fix` docstring to indicate that the output data-type will be a floating point dtype but not necessarily the default floating point dtype [#2392](https://github.com/IntelPython/dpnp/pull/2392)
 
 ### Fixed
 

dpnp/dpnp_iface_mathematical.py

@@ -1750,8 +1750,8 @@ def ediff1d(ary, to_end=None, to_begin=None):
 -------
 out : dpnp.ndarray
     An array with the rounded values and with the same dimensions as the input.
-    The returned array will have the default floating point data type for the
-    device where `a` is allocated.
+    The returned array will have a floating point data type that input can cast
+    to it safely considering device capabilities.
     If `out` is ``None`` then a float array is returned with the rounded values.
     Otherwise the result is stored there and the return value `out` is
     a reference to that array.

dpnp/dpnp_iface_statistics.py

@@ -797,8 +797,8 @@ def cov(
         Default: ``None``.
     dtype : {None, str, dtype object}, optional
         Data-type of the result. By default, the return data-type will have
-        at least floating point type based on the capabilities of the device on
-        which the input arrays reside.
+        the default floating point data-type of the device on which the input
+        arrays reside.
 
         Default: ``None``.
 

dpnp/tests/test_binary_ufuncs.py

@@ -684,6 +684,7 @@ def test_float_nan(self, dt):
         expected = numpy.nextafter(numpy.nan, a)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize("val", [0x7C00, 0x8000], ids=["val1", "val2"])
     def test_f16_strides(self, val):
         a = numpy.arange(val, dtype=numpy.uint16).astype(numpy.float16)
@@ -702,6 +703,7 @@ def test_f16_strides(self, val):
         expected = numpy.nextafter(a[1:], -hinf)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize("val", [0x7C00, 0x8000], ids=["val1", "val2"])
     def test_f16_array_inf(self, val):
         a = numpy.arange(val, dtype=numpy.uint16).astype(numpy.float16)
@@ -716,6 +718,7 @@ def test_f16_array_inf(self, val):
         expected = numpy.nextafter(-hinf, a)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize(
         "sign1, sign2",
         [
@@ -734,6 +737,7 @@ def test_f16_inf(self, sign1, sign2):
         expected = numpy.nextafter(hinf1, hinf2)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize("val", [0x7C00, 0x8000], ids=["val1", "val2"])
     def test_f16_array_nan(self, val):
         a = numpy.arange(val, dtype=numpy.uint16).astype(numpy.float16)
@@ -748,6 +752,7 @@ def test_f16_array_nan(self, val):
         expected = numpy.nextafter(nan, a)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize(
         "val1, val2",
         [
@@ -765,6 +770,7 @@ def test_f16_inf_nan(self, val1, val2):
         expected = numpy.nextafter(v1, v2)
         assert_equal(result, expected)
 
+    @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
     @pytest.mark.parametrize(
         "val, scalar",
         [

dpnp/tests/test_mathematical.py

@@ -36,30 +36,6 @@
 from .third_party.cupy import testing
 
 
-def _get_output_data_type(dtype):
-    """Return a data type specified by input `dtype` and device capabilities."""
-    dtype_float16 = any(
-        dpnp.issubdtype(dtype, t) for t in (dpnp.bool, dpnp.int8, dpnp.uint8)
-    )
-    dtype_float32 = any(
-        dpnp.issubdtype(dtype, t) for t in (dpnp.int16, dpnp.uint16)
-    )
-    if dtype_float16:
-        out_dtype = dpnp.float16 if has_support_aspect16() else dpnp.float32
-    elif dtype_float32:
-        out_dtype = dpnp.float32
-    elif dpnp.issubdtype(dtype, dpnp.complexfloating):
-        out_dtype = dpnp.complex64
-        if has_support_aspect64() and dtype != dpnp.complex64:
-            out_dtype = dpnp.complex128
-    else:
-        out_dtype = dpnp.float32
-        if has_support_aspect64() and dtype != dpnp.float32:
-            out_dtype = dpnp.float64
-
-    return out_dtype
-
-
 @pytest.mark.parametrize("deg", [True, False])
 class TestAngle:
     def test_angle_bool(self, deg):
@@ -775,6 +751,16 @@ def test_errors(self):
 
 
 class TestFix:
+    def get_numpy_output_dtype(self, dtype):
+        # this is used to determine the output dtype of numpy array
+        # which is on cpu so no need for checking has_support_aspect64
+        if has_support_aspect16() and dpnp.can_cast(dtype, dpnp.float16):
+            return dpnp.float16
+        if dpnp.can_cast(dtype, dpnp.float32):
+            return dpnp.float32
+        if dpnp.can_cast(dtype, dpnp.float64):
+            return dpnp.float64
+
     @pytest.mark.parametrize(
         "dt", get_all_dtypes(no_none=True, no_complex=True)
     )
@@ -794,28 +780,25 @@ def test_complex(self, xp, dt):
             xp.fix(a)
 
     @pytest.mark.parametrize(
-        "a_dt", get_all_dtypes(no_none=True, no_bool=True, no_complex=True)
+        "dt", get_all_dtypes(no_none=True, no_complex=True)
     )
-    def test_out(self, a_dt):
-        a = get_abs_array(
-            [[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]], a_dt
-        )
-        ia = dpnp.array(a)
-
-        out_dt = _get_output_data_type(a.dtype)
-        out = numpy.zeros_like(a, dtype=out_dt)
-        iout = dpnp.array(out)
+    def test_out(self, dt):
+        data = [[1.0, 1.1, 1.5, 1.8], [-1.0, -1.1, -1.5, -1.8]]
+        a = get_abs_array(data, dtype=dt)
+        # numpy output has the same dtype as input
+        # dpnp output always has a floating point dtype
+        dt_out = self.get_numpy_output_dtype(a.dtype)
+        out = numpy.zeros_like(a, dtype=dt_out)
+        ia, iout = dpnp.array(a), dpnp.array(out)
 
         result = dpnp.fix(ia, out=iout)
         expected = numpy.fix(a, out=out)
         assert_array_equal(result, expected)
 
     @pytest.mark.skipif(not has_support_aspect16(), reason="no fp16 support")
-    @pytest.mark.parametrize("dt", [bool, numpy.float16])
-    def test_out_float16(self, dt):
-        a = numpy.array(
-            [[1.0, 1.1], [1.5, 1.8], [-1.0, -1.1], [-1.5, -1.8]], dtype=dt
-        )
+    def test_out_float16(self):
+        data = [[1.0, 1.1], [1.5, 1.8], [-1.0, -1.1], [-1.5, -1.8]]
+        a = numpy.array(data, dtype=numpy.float16)
         out = numpy.zeros_like(a, dtype=numpy.float16)
         ia, iout = dpnp.array(a), dpnp.array(out)