Leverage on dpctl.tensor implementation in dpnp.take_along_axis

dpnp/dpnp_iface_indexing.py

@@ -1174,7 +1174,7 @@ def put(a, ind, v, /, *, axis=None, mode="wrap"):
     v : {scalar, array_like}
          Values to be put into `a`. Must be broadcastable to the result shape
          ``a.shape[:axis] + ind.shape + a.shape[axis+1:]``.
-    axis {None, int}, optional
+    axis : {None, int}, optional
         The axis along which the values will be placed. If `a` is 1-D array,
         this argument is optional.
         Default: ``None``.
@@ -1502,7 +1502,7 @@ def take(a, indices, /, *, axis=None, out=None, mode="wrap"):
     return dpnp.get_result_array(result, out)
 
 
-def take_along_axis(a, indices, axis):
+def take_along_axis(a, indices, axis, mode="wrap"):
     """
     Take values from the input array by matching 1d index and data slices.
 
@@ -1523,15 +1523,24 @@ def take_along_axis(a, indices, axis):
         Indices to take along each 1d slice of `a`. This must match the
         dimension of the input array, but dimensions ``Ni`` and ``Nj``
         only need to broadcast against `a`.
-    axis : int
+    axis : {None, int}
         The axis to take 1d slices along. If axis is ``None``, the input
         array is treated as if it had first been flattened to 1d,
         for consistency with :obj:`dpnp.sort` and :obj:`dpnp.argsort`.
+    mode : {"wrap", "clip"}, optional
+        Specifies how out-of-bounds indices will be handled. Possible values
+        are:
+
+        - ``"wrap"``: clamps indices to (``-n <= i < n``), then wraps
+          negative indices.
+        - ``"clip"``: clips indices to (``0 <= i < n``).
+
+        Default: ``"wrap"``.
 
     Returns
     -------
     out : dpnp.ndarray
-        The indexed result.
+        The indexed result of the same data type as `a`.
 
     See Also
     --------
@@ -1591,12 +1600,21 @@ def take_along_axis(a, indices, axis):
 
     """
 
-    dpnp.check_supported_arrays_type(a, indices)
-
     if axis is None:
-        a = a.ravel()
+        dpnp.check_supported_arrays_type(indices)
+        if indices.ndim != 1:
+            raise ValueError(
+                "when axis=None, `indices` must have a single dimension."
+            )
 
-    return a[_build_along_axis_index(a, indices, axis)]
+        a = dpnp.ravel(a)
+        axis = 0
+
+    usm_a = dpnp.get_usm_ndarray(a)
+    usm_ind = dpnp.get_usm_ndarray(indices)
+
+    usm_res = dpt.take_along_axis(usm_a, usm_ind, axis=axis, mode=mode)
+    return dpnp_array._create_from_usm_ndarray(usm_res)
 
 
 def tril_indices(

tests/test_indexing.py

@@ -544,6 +544,13 @@ def test_values(self, arr_dt, idx_dt, ndim, values):
             dpnp.put_along_axis(dp_a, dp_ai, values, axis)
             assert_array_equal(np_a, dp_a)
 
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    @pytest.mark.parametrize("dt", [bool, numpy.float32])
+    def test_invalid_indices_dtype(self, xp, dt):
+        a = xp.ones((10, 10))
+        ind = xp.ones(10, dtype=dt)
+        assert_raises(IndexError, xp.put_along_axis, a, ind, 7, axis=1)
+
     @pytest.mark.parametrize("arr_dt", get_all_dtypes())
     @pytest.mark.parametrize("idx_dt", get_integer_dtypes())
     def test_broadcast(self, arr_dt, idx_dt):
@@ -673,66 +680,80 @@ def test_argequivalent(self, func, argfunc, kwargs):
     @pytest.mark.parametrize("idx_dt", get_integer_dtypes())
     @pytest.mark.parametrize("ndim", list(range(1, 4)))
     def test_multi_dimensions(self, arr_dt, idx_dt, ndim):
-        np_a = numpy.arange(4**ndim, dtype=arr_dt).reshape((4,) * ndim)
-        np_ai = numpy.array([3, 0, 2, 1], dtype=idx_dt).reshape(
+        a = numpy.arange(4**ndim, dtype=arr_dt).reshape((4,) * ndim)
+        ind = numpy.array([3, 0, 2, 1], dtype=idx_dt).reshape(
             (1,) * (ndim - 1) + (4,)
         )
-
-        dp_a = dpnp.array(np_a, dtype=arr_dt)
-        dp_ai = dpnp.array(np_ai, dtype=idx_dt)
+        ia, iind = dpnp.array(a), dpnp.array(ind)
 
         for axis in range(ndim):
-            expected = numpy.take_along_axis(np_a, np_ai, axis)
-            result = dpnp.take_along_axis(dp_a, dp_ai, axis)
+            result = dpnp.take_along_axis(ia, iind, axis)
+            expected = numpy.take_along_axis(a, ind, axis)
             assert_array_equal(expected, result)
 
     @pytest.mark.parametrize("xp", [numpy, dpnp])
-    def test_invalid(self, xp):
+    def test_not_enough_indices(self, xp):
         a = xp.ones((10, 10))
-        ai = xp.ones((10, 2), dtype=xp.intp)
-
-        # not enough indices
         assert_raises(ValueError, xp.take_along_axis, a, xp.array(1), axis=1)
 
-        # bool arrays not allowed
-        assert_raises(
-            IndexError, xp.take_along_axis, a, ai.astype(bool), axis=1
-        )
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    @pytest.mark.parametrize("dt", [bool, numpy.float32])
+    def test_invalid_indices_dtype(self, xp, dt):
+        a = xp.ones((10, 10))
+        ind = xp.ones((10, 2), dtype=dt)
+        assert_raises(IndexError, xp.take_along_axis, a, ind, axis=1)
 
-        # float arrays not allowed
-        assert_raises(
-            IndexError, xp.take_along_axis, a, ai.astype(numpy.float32), axis=1
-        )
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    def test_invalid_axis(self, xp):
+        a = xp.ones((10, 10))
+        ind = xp.ones((10, 2), dtype=xp.intp)
+        assert_raises(AxisError, xp.take_along_axis, a, ind, axis=10)
 
-        # invalid axis
-        assert_raises(AxisError, xp.take_along_axis, a, ai, axis=10)
+    @pytest.mark.parametrize("xp", [numpy, dpnp])
+    def test_indices_ndim_axis_none(self, xp):
+        a = xp.ones((10, 10))
+        ind = xp.ones((10, 2), dtype=xp.intp)
+        assert_raises(ValueError, xp.take_along_axis, a, ind, axis=None)
 
-    @pytest.mark.parametrize("arr_dt", get_all_dtypes())
+    @pytest.mark.parametrize("a_dt", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize("idx_dt", get_integer_dtypes())
-    def test_empty(self, arr_dt, idx_dt):
-        np_a = numpy.ones((3, 4, 5), dtype=arr_dt)
-        np_ai = numpy.ones((3, 0, 5), dtype=idx_dt)
-
-        dp_a = dpnp.array(np_a, dtype=arr_dt)
-        dp_ai = dpnp.array(np_ai, dtype=idx_dt)
+    def test_empty(self, a_dt, idx_dt):
+        a = numpy.ones((3, 4, 5), dtype=a_dt)
+        ind = numpy.ones((3, 0, 5), dtype=idx_dt)
+        ia, iind = dpnp.array(a), dpnp.array(ind)
 
-        expected = numpy.take_along_axis(np_a, np_ai, axis=1)
-        result = dpnp.take_along_axis(dp_a, dp_ai, axis=1)
+        result = dpnp.take_along_axis(ia, iind, axis=1)
+        expected = numpy.take_along_axis(a, ind, axis=1)
         assert_array_equal(expected, result)
 
-    @pytest.mark.parametrize("arr_dt", get_all_dtypes())
+    @pytest.mark.parametrize("a_dt", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize("idx_dt", get_integer_dtypes())
-    def test_broadcast(self, arr_dt, idx_dt):
-        np_a = numpy.ones((3, 4, 1), dtype=arr_dt)
-        np_ai = numpy.ones((1, 2, 5), dtype=idx_dt)
-
-        dp_a = dpnp.array(np_a, dtype=arr_dt)
-        dp_ai = dpnp.array(np_ai, dtype=idx_dt)
+    def test_broadcast(self, a_dt, idx_dt):
+        a = numpy.ones((3, 4, 1), dtype=a_dt)
+        ind = numpy.ones((1, 2, 5), dtype=idx_dt)
+        ia, iind = dpnp.array(a), dpnp.array(ind)
 
-        expected = numpy.take_along_axis(np_a, np_ai, axis=1)
-        result = dpnp.take_along_axis(dp_a, dp_ai, axis=1)
+        result = dpnp.take_along_axis(ia, iind, axis=1)
+        expected = numpy.take_along_axis(a, ind, axis=1)
         assert_array_equal(expected, result)
 
+    def test_mode_wrap(self):
+        a = numpy.array([-2, -1, 0, 1, 2])
+        ind = numpy.array([-2, 2, -5, 4])
+        ia, iind = dpnp.array(a), dpnp.array(ind)
+
+        result = dpnp.take_along_axis(ia, iind, axis=0, mode="wrap")
+        expected = numpy.take_along_axis(a, ind, axis=0)
+        assert_array_equal(result, expected)
+
+    def test_mode_clip(self):
+        a = dpnp.array([-2, -1, 0, 1, 2])
+        ind = dpnp.array([-2, 2, -5, 4])
+
+        # numpy does not support keyword `mode`
+        result = dpnp.take_along_axis(a, ind, axis=0, mode="clip")
+        assert (result == dpnp.array([-2, 0, -2, 2])).all()
+
 
 @pytest.mark.usefixtures("allow_fall_back_on_numpy")
 def test_choose():