Merge branch 'master' into dependabot/github_actions/github/codeql-action-3.28.13

Author: antonwolfy

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)
 * Extended `dpnp.fft.fftfreq` and `dpnp.fft.rfftfreq` functions to support `dtype` keyword per Python Array API spec 2024.12 [#2384](https://github.com/IntelPython/dpnp/pull/2384)
+* Updated `dpnp.fix` to return output with the same data-type of input [#2392](https://github.com/IntelPython/dpnp/pull/2392)
 
 ### Fixed
 

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -41,6 +41,7 @@
     "DPNPI0",
     "DPNPAngle",
     "DPNPBinaryFunc",
+    "DPNPFix",
     "DPNPImag",
     "DPNPReal",
     "DPNPRound",
@@ -511,6 +512,48 @@ def __call__(self, x, deg=False, out=None, order="K"):
         return res
 
 
+class DPNPFix(DPNPUnaryFunc):
+    """Class that implements dpnp.fix unary element-wise functions."""
+
+    def __init__(
+        self,
+        name,
+        result_type_resolver_fn,
+        unary_dp_impl_fn,
+        docs,
+    ):
+        super().__init__(
+            name,
+            result_type_resolver_fn,
+            unary_dp_impl_fn,
+            docs,
+        )
+
+    def __call__(self, x, out=None, order="K"):
+        if not dpnp.is_supported_array_type(x):
+            pass  # pass to raise error in main implementation
+        elif dpnp.issubdtype(x.dtype, dpnp.inexact):
+            pass  # for inexact types, pass to calculate in the backend
+        elif out is not None and not dpnp.is_supported_array_type(out):
+            pass  # pass to raise error in main implementation
+        elif out is not None and out.dtype != x.dtype:
+            # passing will raise an error but with incorrect needed dtype
+            raise ValueError(
+                f"Output array of type {x.dtype} is needed, got {out.dtype}"
+            )
+        else:
+            # for exact types, return the input
+            if out is None:
+                return dpnp.copy(x, order=order)
+
+            if isinstance(out, dpt.usm_ndarray):
+                out = dpnp_array._create_from_usm_ndarray(out)
+            out[...] = x
+            return out
+
+        return super().__call__(x, out=out, order=order)
+
+
 class DPNPI0(DPNPUnaryFunc):
     """Class that implements dpnp.i0 unary element-wise functions."""
 

dpnp/dpnp_iface_mathematical.py

@@ -63,6 +63,7 @@
     DPNPI0,
     DPNPAngle,
     DPNPBinaryFunc,
+    DPNPFix,
     DPNPImag,
     DPNPReal,
     DPNPRound,
@@ -1728,14 +1729,14 @@ def ediff1d(ary, to_end=None, to_begin=None):
 Round to nearest integer towards zero.
 
 Round an array of floats element-wise to nearest integer towards zero.
-The rounded values are returned as floats.
+The rounded values have the same data-type as the input.
 
 For full documentation refer to :obj:`numpy.fix`.
 
 Parameters
 ----------
 x : {dpnp.ndarray, usm_ndarray}
-    An array of floats to be rounded.
+    Input array, expected to have a real-valued data type.
 out : {None, dpnp.ndarray, usm_ndarray}, optional
     Output array to populate.
     Array must have the correct shape and the expected data type.
@@ -1749,19 +1750,12 @@ def ediff1d(ary, to_end=None, to_begin=None):
 Returns
 -------
 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.
-    If `out` is ``None`` then a float array is returned with the rounded values.
+    An array with the same dimensions and data-type as the input.
+    If `out` is ``None`` then a new array is returned
+    with the rounded values.
     Otherwise the result is stored there and the return value `out` is
     a reference to that array.
 
-Limitations
------------
-Parameters `where` and `subok` are supported with their default values.
-Keyword argument `kwargs` is currently unsupported.
-Otherwise ``NotImplementedError`` exception will be raised.
-
 See Also
 --------
 :obj:`dpnp.round` : Round to given number of decimals.
@@ -1782,7 +1776,7 @@ def ediff1d(ary, to_end=None, to_begin=None):
 array([ 2.,  2., -2., -2.])
 """
 
-fix = DPNPUnaryFunc(
+fix = DPNPFix(
     "fix",
     ufi._fix_result_type,
     ufi._fix,
@@ -1934,8 +1928,10 @@ def ediff1d(ary, to_end=None, to_begin=None):
 
 Notes
 -----
-Some spreadsheet programs calculate the "floor-towards-zero", in other words floor(-2.5) == -2.
-DPNP instead uses the definition of floor where floor(-2.5) == -3.
+Some spreadsheet programs calculate the "floor-towards-zero", where
+``floor(-2.5) == -2``. DPNP instead uses the definition of :obj:`dpnp.floor`
+where ``floor(-2.5) == -3``. The "floor-towards-zero" function is called
+:obj:`dpnp.fix` in DPNP.
 
 Examples
 --------

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",
         [