implement dpnp.apply_along_axis

doc/reference/fft.rst

@@ -1,5 +1,5 @@
-FFT Functions
-=============
+Discrete Fourier Transform
+==========================
 
 .. https://numpy.org/doc/stable/reference/routines.fft.html
 

doc/reference/functional.rst

@@ -0,0 +1,14 @@
+Functional programming
+======================
+
+.. https://numpy.org/doc/stable/reference/routines.functional.html
+
+.. autosummary::
+   :toctree: generated/
+   :nosignatures:
+
+   dpnp.apply_along_axis
+   dpnp.apply_over_axes
+   dpnp.vectorize
+   dpnp.frompyfunc
+   dpnp.piecewise

doc/reference/linalg.rst

@@ -1,4 +1,4 @@
-Linear Algebra
+Linear algebra
 ==============
 
 .. https://numpy.org/doc/stable/reference/routines.linalg.html

doc/reference/logic.rst

@@ -1,4 +1,4 @@
-Logic Functions
+Logic functions
 ===============
 
 .. https://numpy.org/doc/stable/reference/routines.logic.html

doc/reference/manipulation.rst

@@ -1,4 +1,4 @@
-Array Manipulation Routines
+Array manipulation routines
 ===========================
 
 .. https://numpy.org/doc/stable/reference/routines.array-manipulation.html

doc/reference/random.rst

@@ -1,4 +1,4 @@
-Random Sampling (``dpnp.random``)
+Random sampling (``dpnp.random``)
 =================================
 
 .. https://numpy.org/doc/stable/reference/random/legacy.html

doc/reference/routines.rst

@@ -4,7 +4,7 @@ Routines
 
 The following pages describe NumPy-compatible routines.
 These functions cover a subset of
-`NumPy routines <https://docs.scipy.org/doc/numpy/reference/routines.html>`_.
+`NumPy routines <https://numpy.org/doc/stable/reference/routines.html>`_.
 
 .. currentmodule:: dpnp
 
@@ -13,10 +13,11 @@ These functions cover a subset of
 
    creation
    manipulation
-   indexing
    binary
    dtype
    fft
+   functional
+   indexing
    linalg
    logic
    math

doc/reference/sorting.rst

@@ -1,4 +1,4 @@
-Sorting, Searching, and Counting
+Sorting, searching, and counting
 ================================
 
 .. https://numpy.org/doc/stable/reference/routines.sort.html

doc/reference/statistics.rst

@@ -1,5 +1,5 @@
-Statistical Functions
-=====================
+Statistics
+==========
 
 .. https://numpy.org/doc/stable/reference/routines.statistics.html
 

dpnp/dpnp_iface.py

@@ -81,6 +81,8 @@
 from dpnp.dpnp_iface_bitwise import __all__ as __all__bitwise
 from dpnp.dpnp_iface_counting import *
 from dpnp.dpnp_iface_counting import __all__ as __all__counting
+from dpnp.dpnp_iface_functional import *
+from dpnp.dpnp_iface_functional import __all__ as __all__functional
 from dpnp.dpnp_iface_histograms import *
 from dpnp.dpnp_iface_histograms import __all__ as __all__histograms
 from dpnp.dpnp_iface_indexing import *
@@ -116,6 +118,7 @@
 __all__ += __all__arraycreation
 __all__ += __all__bitwise
 __all__ += __all__counting
+__all__ += __all__functional
 __all__ += __all__histograms
 __all__ += __all__indexing
 __all__ += __all__libmath

dpnp/dpnp_iface_functional.py

@@ -0,0 +1,187 @@
+# *****************************************************************************
+# Copyright (c) 2024, Intel Corporation
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# - Redistributions of source code must retain the above copyright notice,
+#   this list of conditions and the following disclaimer.
+# - Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+# THE POSSIBILITY OF SUCH DAMAGE.
+# *****************************************************************************
+
+"""
+Interface of the functional programming routines part of the DPNP
+
+Notes
+-----
+This module is a face or public interface file for the library
+it contains:
+ - Interface functions
+ - documentation for the functions
+ - The functions parameters check
+
+"""
+
+
+import numpy
+from dpctl.tensor._numpy_helper import normalize_axis_index
+
+import dpnp
+
+__all__ = ["apply_along_axis"]
+
+
+def apply_along_axis(func1d, axis, arr, *args, **kwargs):
+    """
+    Apply a function to 1-D slices along the given axis.
+
+    Execute ``func1d(a, *args, **kwargs)`` where `func1d` operates on
+    1-D arrays and `a` is a 1-D slice of `arr` along `axis`.
+
+    This is equivalent to (but faster than) the following use of
+    :obj:`dpnp.ndindex` and :obj:`dpnp.s_`, which sets each of
+    ``ii``, ``jj``, and ``kk`` to a tuple of indices::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                f = func1d(arr[ii + s_[:,] + kk])
+                Nj = f.shape
+                for jj in ndindex(Nj):
+                    out[ii + jj + kk] = f[jj]
+
+    Equivalently, eliminating the inner loop, this can be expressed as::
+
+        Ni, Nk = a.shape[:axis], a.shape[axis+1:]
+        for ii in ndindex(Ni):
+            for kk in ndindex(Nk):
+                out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
+
+    For full documentation refer to :obj:`numpy.apply_along_axis`.
+
+    Parameters
+    ----------
+    func1d : function (M,) -> (Nj...)
+        This function should accept 1-D arrays. It is applied to 1-D
+        slices of `arr` along the specified axis.
+    axis : int
+        Axis along which `arr` is sliced.
+    arr : {dpnp.ndarray, usm_ndarray} (Ni..., M, Nk...)
+        Input array.
+    args : any
+        Additional arguments to `func1d`.
+    kwargs : any
+        Additional named arguments to `func1d`.
+
+    Returns
+    -------
+    out : dpnp.ndarray  (Ni..., Nj..., Nk...)
+        The output array. The shape of `out` is identical to the shape of
+        `arr`, except along the `axis` dimension. This axis is removed, and
+        replaced with new dimensions equal to the shape of the return value
+        of `func1d`.
+
+    See Also
+    --------
+    :obj:`dpnp.apply_over_axes` : Apply a function repeatedly over
+                                multiple axes.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> def my_func(a): # Average first and last element of a 1-D array
+    ...     return (a[0] + a[-1]) * 0.5
+    >>> b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> np.apply_along_axis(my_func, 0, b)
+    array([4., 5., 6.])
+    >>> np.apply_along_axis(my_func, 1, b)
+    array([2., 5., 8.])
+
+    For a function that returns a 1D array, the number of dimensions in
+    `out` is the same as `arr`.
+
+    >>> b = np.array([[8, 1, 7], [4, 3, 9], [5, 2, 6]])
+    >>> np.apply_along_axis(sorted, 1, b)
+    array([[1, 7, 8],
+           [3, 4, 9],
+           [2, 5, 6]])
+
+    For a function that returns a higher dimensional array, those dimensions
+    are inserted in place of the `axis` dimension.
+
+    >>> b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    >>> np.apply_along_axis(np.diag, -1, b)
+    array([[[1, 0, 0],
+            [0, 2, 0],
+            [0, 0, 3]],
+           [[4, 0, 0],
+            [0, 5, 0],
+            [0, 0, 6]],
+           [[7, 0, 0],
+            [0, 8, 0],
+            [0, 0, 9]]])
+
+    """
+
+    dpnp.check_supported_arrays_type(arr)
+    nd = arr.ndim
+    exec_q = arr.sycl_queue
+    usm_type = arr.usm_type
+    axis = normalize_axis_index(axis, nd)
+
+    # arr, with the iteration axis at the end
+    inarr_view = dpnp.moveaxis(arr, axis, -1)
+
+    # compute indices for the iteration axes, and append a trailing ellipsis to
+    # prevent 0d arrays decaying to scalars
+    # TODO: replace with dpnp.ndindex
+    inds = numpy.ndindex(inarr_view.shape[:-1])
+    inds = (ind + (Ellipsis,) for ind in inds)
+
+    # invoke the function on the first item
+    try:
+        ind0 = next(inds)
+    except StopIteration:
+        raise ValueError(
+            "Cannot apply_along_axis when any iteration dimensions are 0"
+        ) from None
+    res = dpnp.asanyarray(
+        func1d(inarr_view[ind0], *args, **kwargs),
+        sycl_queue=exec_q,
+        usm_type=usm_type,
+    )
+
+    # build a buffer for storing evaluations of func1d.
+    # remove the requested axis, and add the new ones on the end.
+    # laid out so that each write is contiguous.
+    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
+    buff = dpnp.empty_like(res, shape=inarr_view.shape[:-1] + res.shape)
+
+    # save the first result, then compute and save all remaining results
+    buff[ind0] = res
+    for ind in inds:
+        buff[ind] = dpnp.asanyarray(
+            func1d(inarr_view[ind], *args, **kwargs),
+            sycl_queue=exec_q,
+            usm_type=usm_type,
+        )
+
+    # restore the inserted axes back to where they belong
+    for _ in range(res.ndim):
+        buff = dpnp.moveaxis(buff, -1, axis)
+
+    return buff

dpnp/dpnp_iface_manipulation.py

@@ -25,7 +25,7 @@
 # *****************************************************************************
 
 """
-Interface of the Array manipulation routines part of the DPNP
+Interface of the array manipulation routines part of the DPNP
 
 Notes
 -----

tests/test_functional.py

@@ -0,0 +1,48 @@
+import numpy
+import pytest
+from numpy.testing import assert_array_equal, assert_equal, assert_raises
+
+import dpnp
+
+from .helper import get_all_dtypes
+
+
+class TestApplyAlongAxis:
+    def test_tuple_func1d(self):
+        def sample_1d(x):
+            return x[1], x[0]
+
+        a = numpy.array([[1, 2], [3, 4]])
+        ia = dpnp.array(a)
+
+        # 2d insertion along first axis
+        expected = numpy.apply_along_axis(sample_1d, 1, a)
+        result = dpnp.apply_along_axis(sample_1d, 1, ia)
+        assert_array_equal(result, expected)
+
+    @pytest.mark.parametrize("stride", [-1, 2, -3])
+    def test_stride(self, stride):
+        a = numpy.ones((20, 10), dtype="f")
+        ia = dpnp.array(a)
+
+        expected = numpy.apply_along_axis(len, 0, a[::stride, ::stride])
+        result = dpnp.apply_along_axis(len, 0, ia[::stride, ::stride])
+        assert_array_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", get_all_dtypes())
+    def test_args(self, dtype):
+        a = numpy.ones((20, 10))
+        ia = dpnp.array(a)
+
+        # kwargs
+        expected = numpy.apply_along_axis(
+            numpy.mean, 0, a, dtype=dtype, keepdims=True
+        )
+        result = dpnp.apply_along_axis(
+            dpnp.mean, 0, ia, dtype=dtype, keepdims=True
+        )
+        assert_array_equal(result, expected)
+
+        # positional args: axis, dtype, out, keepdims
+        result = dpnp.apply_along_axis(dpnp.mean, 0, ia, 0, dtype, None, True)
+        assert_array_equal(result, expected)

tests/test_sycl_queue.py

@@ -2178,6 +2178,18 @@ def test_split(func, data1, device):
     assert_sycl_queue_equal(result[1].sycl_queue, x1.sycl_queue)
 
 
+@pytest.mark.parametrize(
+    "device",
+    valid_devices,
+    ids=[device.filter_string for device in valid_devices],
+)
+def test_apply_along_axis(device):
+    x = dpnp.arange(9, device=device).reshape(3, 3)
+    result = dpnp.apply_along_axis(dpnp.sum, 0, x)
+
+    assert_sycl_queue_equal(result.sycl_queue, x.sycl_queue)
+
+
 @pytest.mark.parametrize(
     "device_x",
     valid_devices,

tests/test_usm_type.py

@@ -773,6 +773,14 @@ def test_2in_with_scalar_1out(func, data, scalar, usm_type):
     assert z.usm_type == usm_type
 
 
+@pytest.mark.parametrize("usm_type", list_of_usm_types, ids=list_of_usm_types)
+def test_apply_along_axis(usm_type):
+    x = dp.arange(9, usm_type=usm_type).reshape(3, 3)
+    y = dp.apply_along_axis(dp.sum, 0, x)
+
+    assert x.usm_type == y.usm_type
+
+
 @pytest.mark.parametrize("usm_type", list_of_usm_types, ids=list_of_usm_types)
 def test_broadcast_to(usm_type):
     x = dp.ones(7, usm_type=usm_type)