Implement dpnp.put_along_axis through dpctl.tensor

Author: antonwolfy

dpnp/dpnp_iface_indexing.py

@@ -84,57 +84,6 @@
 ]
 
 
-def _build_along_axis_index(a, ind, axis):
-    """
-    Build a fancy index used by a family of `_along_axis` functions.
-
-    The fancy index consists of orthogonal arranges, with the
-    requested index inserted at the right location.
-
-    The resulting index is going to be used inside `dpnp.put_along_axis`
-    and `dpnp.take_along_axis` implementations.
-
-    """
-
-    if not dpnp.issubdtype(ind.dtype, dpnp.integer):
-        raise IndexError("`indices` must be an integer array")
-
-    # normalize array shape and input axis
-    if axis is None:
-        a_shape = (a.size,)
-        axis = 0
-    else:
-        a_shape = a.shape
-        axis = normalize_axis_index(axis, a.ndim)
-
-    if len(a_shape) != ind.ndim:
-        raise ValueError(
-            "`indices` and `a` must have the same number of dimensions"
-        )
-
-    # compute dimensions to iterate over
-    dest_dims = list(range(axis)) + [None] + list(range(axis + 1, ind.ndim))
-    shape_ones = (1,) * ind.ndim
-
-    # build the index
-    fancy_index = []
-    for dim, n in zip(dest_dims, a_shape):
-        if dim is None:
-            fancy_index.append(ind)
-        else:
-            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim + 1 :]
-            fancy_index.append(
-                dpnp.arange(
-                    n,
-                    dtype=ind.dtype,
-                    usm_type=ind.usm_type,
-                    sycl_queue=ind.sycl_queue,
-                ).reshape(ind_shape)
-            )
-
-    return tuple(fancy_index)
-
-
 def _ravel_multi_index_checks(multi_index, dims, order):
     dpnp.check_supported_arrays_type(*multi_index)
     ndim = len(dims)
@@ -1371,7 +1320,7 @@ def put(a, ind, v, /, *, axis=None, mode="wrap"):
         in_usm_a[:] = dpt.reshape(usm_a, in_usm_a.shape, copy=False)
 
 
-def put_along_axis(a, ind, values, axis):
+def put_along_axis(a, ind, values, axis, mode="wrap"):
     """
     Put values into the destination array by matching 1d index and data slices.
 
@@ -1395,9 +1344,16 @@ def put_along_axis(a, ind, values, axis):
     values : {scalar, array_like}, (Ni..., J, Nk...)
         Values to insert at those indices. Its shape and dimension are
         broadcast to match that of `ind`.
-    axis : int
+    axis : {None, int}
         The axis to take 1d slices along. If axis is ``None``, the destination
         array is treated as if a flattened 1d view had been created of it.
+    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"``.
 
     See Also
     --------
@@ -1426,12 +1382,26 @@ def put_along_axis(a, ind, values, axis):
 
     """
 
-    dpnp.check_supported_arrays_type(a, ind)
-
     if axis is None:
-        a = a.ravel()
+        dpnp.check_supported_arrays_type(ind)
+        if ind.ndim != 1:
+            raise ValueError(
+                "when axis=None, `ind` must have a single dimension."
+            )
+
+        a = dpnp.ravel(a)
+        axis = 0
+
+    usm_a = dpnp.get_usm_ndarray(a)
+    usm_ind = dpnp.get_usm_ndarray(ind)
+    if dpnp.is_supported_array_type(values):
+        usm_vals = dpnp.get_usm_ndarray(values)
+    else:
+        usm_vals = dpt.asarray(
+            values, usm_type=a.usm_type, sycl_queue=a.sycl_queue
+        )
 
-    a[_build_along_axis_index(a, ind, axis)] = values
+    dpt.put_along_axis(usm_a, usm_ind, usm_vals, axis=axis, mode=mode)
 
 
 def putmask(x1, mask, values):

tests/test_indexing.py

@@ -594,38 +594,51 @@ def test_replace_max(self, arr_dt, axis):
         ],
     )
     def test_values(self, arr_dt, idx_dt, ndim, values):
-        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):
-            numpy.put_along_axis(np_a, np_ai, values, axis)
-            dpnp.put_along_axis(dp_a, dp_ai, values, axis)
-            assert_array_equal(np_a, dp_a)
+            numpy.put_along_axis(a, ind, values, axis)
+            dpnp.put_along_axis(ia, iind, values, axis)
+            assert_array_equal(ia, 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)
+        ind = xp.ones_like(a, 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):
-        np_a = numpy.ones((3, 4, 1), dtype=arr_dt)
-        np_ai = numpy.arange(10, dtype=idx_dt).reshape((1, 2, 5)) % 4
+        a = numpy.ones((3, 4, 1), dtype=arr_dt)
+        ind = numpy.arange(10, dtype=idx_dt).reshape((1, 2, 5)) % 4
+        ia, iind = dpnp.array(a), dpnp.array(ind)
 
-        dp_a = dpnp.array(np_a, dtype=arr_dt)
-        dp_ai = dpnp.array(np_ai, dtype=idx_dt)
+        numpy.put_along_axis(a, ind, 20, axis=1)
+        dpnp.put_along_axis(ia, iind, 20, axis=1)
+        assert_array_equal(ia, a)
 
-        numpy.put_along_axis(np_a, np_ai, 20, axis=1)
-        dpnp.put_along_axis(dp_a, dp_ai, 20, axis=1)
-        assert_array_equal(np_a, dp_a)
+    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)
+
+        dpnp.put_along_axis(ia, iind, 3, axis=0, mode="wrap")
+        numpy.put_along_axis(a, ind, 3, axis=0)
+        assert_array_equal(ia, a)
+
+    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`
+        dpnp.put_along_axis(a, ind, 4, axis=0, mode="clip")
+        assert (a == dpnp.array([4, -1, 4, 1, 4])).all()
 
 
 class TestTake: