Add nd-support to dpnp.trim_zeros (#2241)

The PR extends `dpnp.trim_zeros` implement to align with changes
introduced in NumPy 2.2.

It adds support for trimming nd-arrays while preserving the old behavior
for 1-d input. The new parameter `axis` can specify a single dimension
to be trimmed (reducing all other dimensions to the envelope of absolute
values). By default (when `None` is specified), all dimensions are
trimmed iteratively.

dpnp/dpnp_iface_manipulation.py

@@ -3900,25 +3900,40 @@ def transpose(a, axes=None):
 permute_dims = transpose  # permute_dims is an alias for transpose
 
 
-def trim_zeros(filt, trim="fb"):
+def trim_zeros(filt, trim="fb", axis=None):
     """
-    Trim the leading and/or trailing zeros from a 1-D array.
+    Remove values along a dimension which are zero along all other.
 
     For full documentation refer to :obj:`numpy.trim_zeros`.
 
     Parameters
     ----------
     filt : {dpnp.ndarray, usm_ndarray}
-        Input 1-D array.
-    trim : str, optional
-        A string with 'f' representing trim from front and 'b' to trim from
-        back. By defaults, trim zeros from both front and back of the array.
+        Input array.
+    trim : {"fb", "f", "b"}, optional
+        A string with `"f"` representing trim from front and `"b"` to trim from
+        back. By default, zeros are trimmed on both sides. Front and back refer
+        to the edges of a dimension, with "front" referring to the side with
+        the lowest index 0, and "back" referring to the highest index
+        (or index -1).
         Default: ``"fb"``.
+    axis : {None, int}, optional
+        If ``None``, `filt` is cropped such, that the smallest bounding box is
+        returned that still contains all values which are not zero.
+        If an `axis` is specified, `filt` will be sliced in that dimension only
+        on the sides specified by `trim`. The remaining area will be the
+        smallest that still contains all values which are not zero.
+        Default: ``None``.
 
     Returns
     -------
     out : dpnp.ndarray
-        The result of trimming the input.
+        The result of trimming the input. The number of dimensions and the
+        input data type are preserved.
+
+    Notes
+    -----
+    For all-zero arrays, the first axis is trimmed first.
 
     Examples
     --------
@@ -3927,42 +3942,66 @@ def trim_zeros(filt, trim="fb"):
     >>> np.trim_zeros(a)
     array([1, 2, 3, 0, 2, 1])
 
-    >>> np.trim_zeros(a, 'b')
+    >>> np.trim_zeros(a, trim='b')
     array([0, 0, 0, 1, 2, 3, 0, 2, 1])
 
+    Multiple dimensions are supported:
+
+    >>> b = np.array([[0, 0, 2, 3, 0, 0],
+    ...               [0, 1, 0, 3, 0, 0],
+    ...               [0, 0, 0, 0, 0, 0]])
+    >>> np.trim_zeros(b)
+    array([[0, 2, 3],
+           [1, 0, 3]])
+
+    >>> np.trim_zeros(b, axis=-1)
+    array([[0, 2, 3],
+           [1, 0, 3],
+           [0, 0, 0]])
+
     """
 
     dpnp.check_supported_arrays_type(filt)
-    if filt.ndim == 0:
-        raise TypeError("0-d array cannot be trimmed")
-    if filt.ndim > 1:
-        raise ValueError("Multi-dimensional trim is not supported")
 
     if not isinstance(trim, str):
         raise TypeError("only string trim is supported")
 
-    trim = trim.upper()
-    if not any(x in trim for x in "FB"):
-        return filt  # no trim rule is specified
+    trim = trim.lower()
+    if trim not in ["fb", "bf", "f", "b"]:
+        raise ValueError(f"unexpected character(s) in `trim`: {trim!r}")
+
+    nd = filt.ndim
+    if axis is not None:
+        axis = normalize_axis_index(axis, nd)
 
     if filt.size == 0:
         return filt  # no trailing zeros in empty array
 
-    a = dpnp.nonzero(filt)[0]
-    a_size = a.size
-    if a_size == 0:
-        # 'filt' is array of zeros
-        return dpnp.empty_like(filt, shape=(0,))
+    non_zero = dpnp.argwhere(filt)
+    if non_zero.size == 0:
+        # `filt` has all zeros, so assign `start` and `stop` to the same value,
+        # then the resulting slice will be empty
+        start = stop = dpnp.zeros_like(filt, shape=nd, dtype=dpnp.intp)
+    else:
+        if "f" in trim:
+            start = non_zero.min(axis=0)
+        else:
+            start = (None,) * nd
 
-    first = 0
-    if "F" in trim:
-        first = a[0]
+        if "b" in trim:
+            stop = non_zero.max(axis=0)
+            stop += 1  # Adjust for slicing
+        else:
+            stop = (None,) * nd
 
-    last = filt.size
-    if "B" in trim:
-        last = a[-1] + 1
+    if axis is None:
+        # trim all axes
+        sl = tuple(slice(*x) for x in zip(start, stop))
+    else:
+        # only trim single axis
+        sl = (slice(None),) * axis + (slice(start[axis], stop[axis]),) + (...,)
 
-    return filt[first:last]
+    return filt[sl]
 
 
 def unique(

dpnp/tests/test_manipulation.py

@@ -1378,6 +1378,20 @@ def test_basic(self, dtype):
         expected = numpy.trim_zeros(a)
         assert_array_equal(result, expected)
 
+    @testing.with_requires("numpy>=2.2")
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
+    @pytest.mark.parametrize("trim", ["F", "B", "fb"])
+    @pytest.mark.parametrize("ndim", [0, 1, 2, 3])
+    def test_basic_nd(self, dtype, trim, ndim):
+        a = numpy.ones((2,) * ndim, dtype=dtype)
+        a = numpy.pad(a, (2, 1), mode="constant", constant_values=0)
+        ia = dpnp.array(a)
+
+        for axis in list(range(ndim)) + [None]:
+            result = dpnp.trim_zeros(ia, trim=trim, axis=axis)
+            expected = numpy.trim_zeros(a, trim=trim, axis=axis)
+            assert_array_equal(result, expected)
+
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize("trim", ["F", "B"])
     def test_trim(self, dtype, trim):
@@ -1398,6 +1412,19 @@ def test_all_zero(self, dtype, trim):
         expected = numpy.trim_zeros(a, trim)
         assert_array_equal(result, expected)
 
+    @testing.with_requires("numpy>=2.2")
+    @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
+    @pytest.mark.parametrize("trim", ["F", "B", "fb"])
+    @pytest.mark.parametrize("ndim", [0, 1, 2, 3])
+    def test_all_zero_nd(self, dtype, trim, ndim):
+        a = numpy.zeros((3,) * ndim, dtype=dtype)
+        ia = dpnp.array(a)
+
+        for axis in list(range(ndim)) + [None]:
+            result = dpnp.trim_zeros(ia, trim=trim, axis=axis)
+            expected = numpy.trim_zeros(a, trim=trim, axis=axis)
+            assert_array_equal(result, expected)
+
     def test_size_zero(self):
         a = numpy.zeros(0)
         ia = dpnp.array(a)
@@ -1416,17 +1443,11 @@ def test_overflow(self, a):
         expected = numpy.trim_zeros(a)
         assert_array_equal(result, expected)
 
-    # TODO: modify once SAT-7616
-    # numpy 2.2 validates trim rules
-    @testing.with_requires("numpy<2.2")
-    def test_trim_no_rule(self):
-        a = numpy.array([0, 0, 1, 0, 2, 3, 4, 0])
-        ia = dpnp.array(a)
-        trim = "ADE"  # no "F" or "B" in trim string
-
-        result = dpnp.trim_zeros(ia, trim)
-        expected = numpy.trim_zeros(a, trim)
-        assert_array_equal(result, expected)
+    @testing.with_requires("numpy>=2.2")
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    def test_trim_no_fb_in_rule(self, xp):
+        a = xp.array([0, 0, 1, 0, 2, 3, 4, 0])
+        assert_raises(ValueError, xp.trim_zeros, a, "ADE")
 
     def test_list_array(self):
         assert_raises(TypeError, dpnp.trim_zeros, [0, 0, 1, 0, 2, 3, 4, 0])

dpnp/tests/third_party/cupy/manipulation_tests/test_add_remove.py

@@ -387,17 +387,15 @@ def test_trim_back_zeros(self, xp, dtype):
         a = xp.array([1, 0, 2, 3, 0, 5, 0, 0, 0], dtype=dtype)
         return xp.trim_zeros(a, trim=self.trim)
 
-    # TODO: remove once SAT-7616
-    @testing.with_requires("numpy<2.2")
+    @pytest.mark.skip("0-d array is supported")
     @testing.for_all_dtypes()
     def test_trim_zero_dim(self, dtype):
         for xp in (numpy, cupy):
             a = testing.shaped_arange((), xp, dtype)
             with pytest.raises(TypeError):
                 xp.trim_zeros(a, trim=self.trim)
 
-    # TODO: remove once SAT-7616
-    @testing.with_requires("numpy<2.2")
+    @pytest.mark.skip("nd array is supported")
     @testing.for_all_dtypes()
     def test_trim_ndim(self, dtype):
         for xp in (numpy, cupy):