Function cumsum

python/paddle/tensor/math.py:3055–3161

The cumulative sum of the elements along a given axis. Note: The first element of the result is the same as the first element of the input. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for

(
    x: Tensor,
    axis: int | None = None,
    dtype: DTypeLike | None = None,
    name: str | None = None,
    *,
    out: Tensor | None = None,
)

@param_two_alias(["x", "input"], ["axis", "dim"])
def cumsum(
    x: Tensor,
    axis: int | None = None,
    dtype: DTypeLike | None = None,
    name: str | None = None,
    *,
    out: Tensor | None = None,
) -> Tensor:
    """
    The cumulative sum of the elements along a given axis.

    Note:
        The first element of the result is the same as the first element of the input.

    .. note::
        Alias Support: The parameter name ``input`` can be used as an alias for ``x``, and ``dim`` can be used as an alias for ``axis``.
        For example, ``cumsum(input=tensor_x, dim=1, ...)`` is equivalent to ``cumsum(x=tensor_x, axis=1, ...)``.

    Args:
        x (Tensor): The input tensor needed to be cumsumed.
            alias: ``input``.
        axis (int, optional): The dimension to accumulate along. -1 means the last dimension. The default (None) is to compute the cumsum over the flattened array.
            alias: ``dim``.
        dtype (str|paddle.dtype|np.dtype|None, optional): The data type of the output tensor, can be bfloat16, float16, float32, float64, int32, int64, complex64, complex128. By default, it is int64 if the input x is int8/int16/int32; otherwise, it is None. If it is not None, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows.
        name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
        out (Tensor, optional): The output tensor. If provided, the result will be stored in this tensor.

    Returns:
        Tensor, the result of cumsum operator.

    Examples:
        .. code-block:: pycon

            >>> import paddle

            >>> data = paddle.arange(12)
            >>> data = paddle.reshape(data, (3, 4))

            >>> y = paddle.cumsum(data)
            >>> y
            Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True,
            [0 , 1 , 3 , 6 , 10, 15, 21, 28, 36, 45, 55, 66])

            >>> y = paddle.cumsum(data, axis=0)
            >>> y
            Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            [[0 , 1 , 2 , 3 ],
             [4 , 6 , 8 , 10],
             [12, 15, 18, 21]])

            >>> y = paddle.cumsum(data, axis=-1)
            >>> y
            Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            [[0 , 1 , 3 , 6 ],
             [4 , 9 , 15, 22],
             [8 , 17, 27, 38]])

            >>> y = paddle.cumsum(data, dtype='float64')