Leverage dpnp.cumsum through dpctl.tensor implementation

dpnp/dpnp_algo/dpnp_algo_mathematical.pxi

@@ -37,7 +37,6 @@ and the rest of the library
 
 __all__ += [
     "dpnp_cumprod",
-    "dpnp_cumsum",
     "dpnp_ediff1d",
     "dpnp_fabs",
     "dpnp_fmod",

dpnp/dpnp_array.py

@@ -533,7 +533,7 @@ def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True):
         """
         Copy the array with data type casting.
 
-        For full documentation refer to :obj:`numpy.ndarray.astype`.
+        Refer to :obj:`dpnp.astype` for full documentation.
 
         Parameters
         ----------
@@ -593,7 +593,12 @@ def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True):
     # 'byteswap',
 
     def choose(input, choices, out=None, mode="raise"):
-        """Construct an array from an index array and a set of arrays to choose from."""
+        """
+        Construct an array from an index array and a set of arrays to choose from.
+
+        Refer to :obj:`dpnp.choose` for full documentation.
+
+        """
 
         return dpnp.choose(input, choices, out, mode)
 
@@ -613,7 +618,7 @@ def conj(self):
         """
         Complex-conjugate all elements.
 
-        For full documentation refer to :obj:`numpy.ndarray.conj`.
+        Refer to :obj:`dpnp.conjugate` for full documentation.
 
         """
 
@@ -626,7 +631,7 @@ def conjugate(self):
         """
         Return the complex conjugate, element-wise.
 
-        For full documentation refer to :obj:`numpy.ndarray.conjugate`.
+        Refer to :obj:`dpnp.conjugate` for full documentation.
 
         """
 
@@ -683,9 +688,7 @@ def cumsum(self, axis=None, dtype=None, out=None):
         """
         Return the cumulative sum of the elements along the given axis.
 
-        See Also
-        --------
-        :obj:`dpnp.cumsum`
+        Refer to :obj:`dpnp.cumsum` for full documentation.
 
         """
 
@@ -697,9 +700,7 @@ def diagonal(input, offset=0, axis1=0, axis2=1):
         """
         Return specified diagonals.
 
-        See Also
-        --------
-        :obj:`dpnp.diagonal`
+        Refer to :obj:`dpnp.diagonal` for full documentation.
 
         """
 
@@ -709,7 +710,7 @@ def dot(self, b, out=None):
         """
         Dot product of two arrays.
 
-        For full documentation refer to :obj:`dpnp.dot`.
+        Refer to :obj:`dpnp.dot` for full documentation.
 
         Examples
         --------
@@ -1013,7 +1014,7 @@ def prod(
         """
         Returns the prod along a given axis.
 
-        For full documentation refer to :obj:`dpnp.prod`.
+        Refer to :obj:`dpnp.prod` for full documentation.
 
         """
 
@@ -1023,7 +1024,7 @@ def put(self, indices, vals, /, *, axis=None, mode="wrap"):
         """
         Puts values of an array into another array along a given axis.
 
-        For full documentation refer to :obj:`numpy.put`.
+        Refer to :obj:`dpnp.put` for full documentation.
 
         """
 
@@ -1033,7 +1034,7 @@ def ravel(self, order="C"):
         """
         Return a contiguous flattened array.
 
-        For full documentation refer to :obj:`dpnp.ravel`.
+        Refer to :obj:`dpnp.ravel` for full documentation.
 
         """
 
@@ -1083,7 +1084,7 @@ def repeat(self, repeats, axis=None):
         """
         Repeat elements of an array.
 
-        For full documentation refer to :obj:`dpnp.repeat`.
+        Refer to :obj:`dpnp.repeat` for full documentation.
 
         """
 
@@ -1093,7 +1094,7 @@ def reshape(self, *sh, **kwargs):
         """
         Returns an array containing the same data with a new shape.
 
-        For full documentation refer to :obj:`numpy.ndarray.reshape`.
+        Refer to :obj:`dpnp.reshape` for full documentation.
 
         Returns
         -------
@@ -1124,8 +1125,7 @@ def round(self, decimals=0, out=None):
         """
         Return array with each element rounded to the given number of decimals.
 
-        .. seealso::
-           :obj:`dpnp.around` for full documentation.
+        Refer to :obj:`dpnp.round` for full documentation.
 
         """
 
@@ -1271,7 +1271,7 @@ def sum(
         """
         Returns the sum along a given axis.
 
-        For full documentation refer to :obj:`dpnp.sum`.
+        Refer to :obj:`dpnp.sum` for full documentation.
 
         """
 
@@ -1289,7 +1289,7 @@ def swapaxes(self, axis1, axis2):
         """
         Interchange two axes of an array.
 
-        For full documentation refer to :obj:`numpy.swapaxes`.
+        Refer to :obj:`dpnp.swapaxes` for full documentation.
 
         """
 
@@ -1299,7 +1299,7 @@ def take(self, indices, /, *, axis=None, out=None, mode="wrap"):
         """
         Take elements from an array along an axis.
 
-        For full documentation refer to :obj:`numpy.take`.
+        Refer to :obj:`dpnp.take` for full documentation.
 
         """
 

dpnp/dpnp_iface_mathematical.py

@@ -855,42 +855,100 @@ def cumprod(x1, **kwargs):
     return call_origin(numpy.cumprod, x1, **kwargs)
 
 
-def cumsum(x1, **kwargs):
+def cumsum(a, axis=None, dtype=None, out=None):
     """
     Return the cumulative sum of the elements along a given axis.
 
     For full documentation refer to :obj:`numpy.cumsum`.
 
-    Limitations
-    -----------
-    Parameter `x` is supported as :obj:`dpnp.ndarray`.
-    Keyword argument `kwargs` is currently unsupported.
-    Otherwise the function will be executed sequentially on CPU.
-    Input array data types are limited by supported DPNP :ref:`Data types`.
+    Parameters
+    ----------
+    a : {dpnp.ndarray, usm_ndarray}
+        Input array.
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default (``None``)
+        is to compute the cumsum over the flattened array.
+    dtype : dtype, optional
+        Type of the returned array and of the accumulator in which the elements
+        are summed. If `dtype` is not specified, it defaults to the dtype of
+        `a`, unless `a` has an integer dtype with a precision less than that of
+        the default platform integer. In that case, the default platform
+        integer is used.
+    out : {dpnp.ndarray, usm_ndarray}, optional
+        Alternative output array in which to place the result. It must have the
+        same shape and buffer length as the expected output but the type will
+        be cast if necessary.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        A new array holding the result is returned unless `out` is specified as
+        :class:`dpnp.ndarray`, in which case a reference to `out` is returned.
+        The result has the same size as `a`, and the same shape as `a` if `axis`
+        is not ``None`` or `a` is a 1-d array.
 
     See Also
     --------
-    :obj:`dpnp.diff` : Calculate the n-th discrete difference along the given axis.
+    :obj:`dpnp.sum` : Sum array elements.
+    :obj:`dpnp.diff` : Calculate the n-th discrete difference along given axis.
 
     Examples
     --------
     >>> import dpnp as np
-    >>> a = np.array([1, 2, 4])
-    >>> result = np.cumsum(a)
-    >>> [x for x in result]
-    [1, 2, 7]
-    >>> b = np.array([[1, 2, 3], [4, 5, 6]])
-    >>> result = np.cumsum(b)
-    >>> [x for x in result]
-    [1, 2, 6, 10, 15, 21]
+    >>> a = np.array([[1, 2, 3], [4, 5, 6]])
+    >>> a
+    array([[1, 2, 3],
+           [4, 5, 6]])
+    >>> np.cumsum(a)
+    array([ 1,  3,  6, 10, 15, 21])
+    >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
+    array([ 1.,  3.,  6., 10., 15., 21.])
+
+    >>> np.cumsum(a, axis=0)     # sum over rows for each of the 3 columns
+    array([[1, 2, 3],
+           [5, 7, 9]])
+    >>> np.cumsum(a, axis=1)     # sum over columns for each of the 2 rows
+    array([[ 1,  3,  6],
+           [ 4,  9, 15]])
+
+    ``cumsum(b)[-1]`` may not be equal to ``sum(b)``
+
+    >>> b = np.array([1, 2e-9, 3e-9] * 10000)
+    >>> b.cumsum().dtype == b.sum().dtype == np.float64
+    True
+    >>> b.cumsum()[-1] == b.sum()
+    array(False)
 
     """
 
-    x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
-    if x1_desc and not kwargs:
-        return dpnp_cumsum(x1_desc).get_pyobj()
+    dpnp.check_supported_arrays_type(a)
+    if a.ndim > 1 and axis is None:
+        usm_a = dpnp.ravel(a).get_array()
+    else:
+        usm_a = dpnp.get_usm_ndarray(a)
+
+    input_out = out
+    if out is None:
+        usm_out = None
+    else:
+        dpnp.check_supported_arrays_type(out)
+
+        # get dtype used by dpctl for result array in cumulative_sum
+        if dtype is None:
+            res_dt = dtu._default_accumulation_dtype(a.dtype, a.sycl_queue)
+        else:
+            res_dt = dpnp.dtype(dtype)
+            res_dt = dtu._to_device_supported_dtype(res_dt, a.sycl_device)
+
+        # dpctl requires strict data type matching of out array with the result
+        if out.dtype != res_dt:
+            out = dpnp.astype(out, dtype=res_dt, copy=False)
+
+        usm_out = dpnp.get_usm_ndarray(out)
 
-    return call_origin(numpy.cumsum, x1, **kwargs)
+    res_usm = dpt.cumulative_sum(usm_a, axis=axis, dtype=dtype, out=usm_out)
+    res = dpnp_array._create_from_usm_ndarray(res_usm)
+    return dpnp.get_result_array(res, input_out, casting="unsafe")
 
 
 def diff(a, n=1, axis=-1, prepend=None, append=None):

tests/helper.py

@@ -37,7 +37,10 @@ def assert_dtype_allclose(
     """
 
     list_64bit_types = [numpy.float64, numpy.complex128]
-    is_inexact = lambda x: dpnp.issubdtype(x.dtype, dpnp.inexact)
+    is_inexact = lambda x: hasattr(x, "dtype") and dpnp.issubdtype(
+        x.dtype, dpnp.inexact
+    )
+
     if is_inexact(dpnp_arr) or is_inexact(numpy_arr):
         tol_dpnp = (
             dpnp.finfo(dpnp_arr).resolution
@@ -73,7 +76,7 @@ def assert_dtype_allclose(
                     assert dpnp_arr_dtype.kind == numpy_arr_dtype.kind
     else:
         assert_array_equal(dpnp_arr.asnumpy(), numpy_arr)
-        if check_type:
+        if check_type and hasattr(numpy_arr, "dtype"):
             if check_only_type_kind:
                 assert dpnp_arr.dtype.kind == numpy_arr.dtype.kind
             else:

tests/skipped_tests.tbl

@@ -479,21 +479,6 @@ tests/third_party/cupy/math_tests/test_misc.py::TestConvolve::test_convolve_diff
 
 tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_fix
 
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_ndarray_cumprod_2dim_with_axis
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_arraylike
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_huge_array
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_numpy_array
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_out_noncontiguous
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_1dim
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_2dim_without_axis
-
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_2_{axis=2}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_2_{axis=2}]
-
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim_with_discont
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim_with_period

tests/skipped_tests_gpu.tbl

@@ -586,28 +586,6 @@ tests/third_party/cupy/math_tests/test_misc.py::TestConvolve::test_convolve_diff
 
 tests/third_party/cupy/math_tests/test_rounding.py::TestRounding::test_fix
 
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_ndarray_cumprod_2dim_with_axis
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_arraylike
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_huge_array
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_numpy_array
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_out_noncontiguous
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_1dim
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumprod::test_cumprod_2dim_without_axis
-
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum[_param_2_{axis=2}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_2dim[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_2dim[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_2dim[_param_2_{axis=2}]
-
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_arraylike[_param_2_{axis=2}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_0_{axis=0}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_1_{axis=1}]
-tests/third_party/cupy/math_tests/test_sumprod.py::TestCumsum::test_cumsum_numpy_array[_param_2_{axis=2}]
-
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim_with_discont
 tests/third_party/cupy/math_tests/test_trigonometric.py::TestUnwrap::test_unwrap_1dim_with_period