Backport gh-2048 & gh-2074 (#2076)

* Update `dpnp.clip()` with Numpy 2.0 (#2048)

* Update dpnp.clip to align with numpy 2.0

* Update cupy tests

* Compliance of dpnp.clip() with Array API

* Update CHANGELOG.md

* Update cupy tests for dpnp.linalg.solve() (#2074)

* Update cupy tests for dpnp.linalg.solve()

* Update CHANGELOG.md

Author: vlad-perevezentsev

CHANGELOG.md

@@ -106,6 +106,8 @@ In addition, this release completes implementation of `dpnp.fft` module and adds
 * Use `dpctl::tensor::alloc_utils::sycl_free_noexcept` instead of `sycl::free` in `host_task` tasks associated with life-time management of temporary USM allocations [#2058](https://github.com/IntelPython/dpnp/pull/2058)
 * Improved implementation of `dpnp.kron` to avoid unnecessary copy for non-contiguous arrays [#2059](https://github.com/IntelPython/dpnp/pull/2059)
 * Updated the test suit for `dpnp.fft` module [#2071](https://github.com/IntelPython/dpnp/pull/2071)
+* Reworked `dpnp.clip` implementation to align with Python Array API 2023.12 specification [#2048](https://github.com/IntelPython/dpnp/pull/2048)
+* Skipped outdated tests for `dpnp.linalg.solve` due to compatibility issues with NumPy 2.0 [#2074](https://github.com/IntelPython/dpnp/pull/2074)
 
 ### Fixed
 

dpnp/dpnp_iface_mathematical.py

@@ -627,7 +627,7 @@ def around(x, /, decimals=0, out=None):
 )
 
 
-def clip(a, a_min, a_max, *, out=None, order="K", **kwargs):
+def clip(a, /, min=None, max=None, *, out=None, order="K", **kwargs):
     """
     Clip (limit) the values in an array.
 
@@ -637,23 +637,27 @@ def clip(a, a_min, a_max, *, out=None, order="K", **kwargs):
     ----------
     a : {dpnp.ndarray, usm_ndarray}
         Array containing elements to clip.
-    a_min, a_max : {dpnp.ndarray, usm_ndarray, None}
+    min, max : {dpnp.ndarray, usm_ndarray, None}
         Minimum and maximum value. If ``None``, clipping is not performed on
-        the corresponding edge. Only one of `a_min` and `a_max` may be
-        ``None``. Both are broadcast against `a`.
+        the corresponding edge. If both `min` and `max` are ``None``,
+        the elements of the returned array stay the same.
+        Both are broadcast against `a`.
+        Default : ``None``.
     out : {None, dpnp.ndarray, usm_ndarray}, optional
         The results will be placed in this array. It may be the input array
         for in-place clipping. `out` must be of the right shape to hold the
         output. Its type is preserved.
+        Default : ``None``.
     order : {"C", "F", "A", "K", None}, optional
-        Memory layout of the newly output array, if parameter `out` is `None`.
+        Memory layout of the newly output array, if parameter `out` is ``None``.
         If `order` is ``None``, the default value ``"K"`` will be used.
+        Default: ``"K"``.
 
     Returns
     -------
     out : dpnp.ndarray
-        An array with the elements of `a`, but where values < `a_min` are
-        replaced with `a_min`, and those > `a_max` with `a_max`.
+        An array with the elements of `a`, but where values < `min` are
+        replaced with `min`, and those > `max` with `max`.
 
     Limitations
     -----------
@@ -687,15 +691,12 @@ def clip(a, a_min, a_max, *, out=None, order="K", **kwargs):
     if kwargs:
         raise NotImplementedError(f"kwargs={kwargs} is currently not supported")
 
-    if a_min is None and a_max is None:
-        raise ValueError("One of max or min must be given")
-
     if order is None:
         order = "K"
 
     usm_arr = dpnp.get_usm_ndarray(a)
-    usm_min = None if a_min is None else dpnp.get_usm_ndarray_or_scalar(a_min)
-    usm_max = None if a_max is None else dpnp.get_usm_ndarray_or_scalar(a_max)
+    usm_min = None if min is None else dpnp.get_usm_ndarray_or_scalar(min)
+    usm_max = None if max is None else dpnp.get_usm_ndarray_or_scalar(max)
 
     usm_out = None if out is None else dpnp.get_usm_ndarray(out)
     usm_res = dpt.clip(usm_arr, usm_min, usm_max, out=usm_out, order=order)

tests/third_party/cupy/linalg_tests/test_solve.py

@@ -50,14 +50,20 @@ def check_x(self, a_shape, b_shape, xp, dtype):
     def test_solve(self):
         self.check_x((4, 4), (4,))
         self.check_x((5, 5), (5, 2))
-        self.check_x((2, 4, 4), (2, 4))
         self.check_x((2, 5, 5), (2, 5, 2))
-        self.check_x((2, 3, 2, 2), (2, 3, 2))
         self.check_x((2, 3, 3, 3), (2, 3, 3, 2))
         self.check_x((0, 0), (0,))
         self.check_x((0, 0), (0, 2))
-        self.check_x((0, 2, 2), (0, 2))
         self.check_x((0, 2, 2), (0, 2, 3))
+        # In numpy 2.0 the broadcast ambiguity has been removed and now
+        # b is treaded as a single vector if and only if it is 1-dimensional;
+        # for other cases this signature must be followed
+        # (..., m, m), (..., m, n) -> (..., m, n)
+        # https://github.com/numpy/numpy/pull/25914
+        if numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0":
+            self.check_x((2, 4, 4), (2, 4))
+            self.check_x((2, 3, 2, 2), (2, 3, 2))
+            self.check_x((0, 2, 2), (0, 2))
 
     def check_shape(self, a_shape, b_shape, error_types):
         for xp, error_type in error_types.items():
@@ -90,7 +96,9 @@ def test_invalid_shape(self):
         self.check_shape((3, 3), (2,), value_errors)
         self.check_shape((3, 3), (2, 2), value_errors)
         self.check_shape((3, 3, 4), (3,), linalg_errors)
-        self.check_shape((2, 3, 3), (3,), value_errors)
+        # Since numpy >= 2.0, this case does not raise an error
+        if numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0":
+            self.check_shape((2, 3, 3), (3,), value_errors)
         self.check_shape((3, 3), (0,), value_errors)
         self.check_shape((0, 3, 4), (3,), linalg_errors)
 

tests/third_party/cupy/math_tests/test_misc.py

@@ -130,8 +130,17 @@ def test_clip_max_none(self, xp, dtype):
     def test_clip_min_max_none(self, dtype):
         for xp in (numpy, cupy):
             a = testing.shaped_arange((2, 3, 4), xp, dtype)
-            with pytest.raises(ValueError):
-                a.clip(None, None)
+            # According to Python Array API, clip() should return an array
+            # with the same elements in `a` if `min` and `max` are `None`.
+            # Numpy < 2.1 is not compatible with this and raises a ValueError
+            if (
+                xp is numpy
+                and numpy.lib.NumpyVersion(numpy.__version__) < "2.1.0"
+            ):
+                with pytest.raises(ValueError):
+                    a.clip(None, None)
+            else:
+                return a.clip(None, None)
 
     @testing.for_all_dtypes(no_complex=True)
     @testing.numpy_cupy_array_equal()
@@ -155,8 +164,18 @@ def test_external_clip3(self, xp, dtype):
     def test_external_clip4(self, dtype):
         for xp in (numpy, cupy):
             a = testing.shaped_arange((2, 3, 4), xp, dtype)
-            with pytest.raises(TypeError):
-                xp.clip(a, 3)
+            # Starting with numpy 2.1.0, it's possible to pass only one argument
+            # (min or max) as a keyword argument according to Python Array API.
+            # In older versions of numpy, both arguments must be positional;
+            # passing only one raises a TypeError.
+            if (
+                xp is numpy
+                and numpy.lib.NumpyVersion(numpy.__version__) < "2.1.0"
+            ):
+                with pytest.raises(TypeError):
+                    xp.clip(a, 3)
+            else:
+                return xp.clip(a, min=3)
 
     @testing.for_all_dtypes(no_complex=True)
     @testing.numpy_cupy_array_equal()