activation.py

#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

__all__ = []

from paddle import _C_ops, in_dynamic_mode
from paddle.fluid.framework import dygraph_only
from paddle.fluid.layer_helper import LayerHelper


def relu(x, name=None):
    """
    sparse relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        out = max(x, 0)

    Parameters:
        x (Tensor): The input Sparse Tensor with data type float32, float64.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        A Sparse Tensor with the same data type and shape as ``x`` .

    Examples:
        .. code-block:: python

            import paddle

            dense_x = paddle.to_tensor([-2., 0., 1.])
            sparse_x = dense_x.to_sparse_coo(1)
            out = paddle.sparse.nn.functional.relu(sparse_x)
            # [0., 0., 1.]
    """
    if in_dynamic_mode():
        return _C_ops.sparse_relu(x)
    else:
        op_type = 'sparse_relu'
        helper = LayerHelper(op_type)
        out = helper.create_sparse_variable_for_type_inference(x.dtype)
        helper.append_op(
            type=op_type, inputs={'x': x}, outputs={'out': out}, attrs={}
        )
        return out


def softmax(x, axis=-1, name=None):
    r"""
    sparse softmax activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    Note:
        Only support axis=-1 for SparseCsrTensor, which is faster when read data
        by row (axis=-1).

    From the point of view of dense matrix, for each row :math:`i` and each column :math:`j`
    in the matrix, we have:

    .. math::

        softmax_ij = \frac{\exp(x_ij - max_j(x_ij))}{\sum_j(exp(x_ij - max_j(x_ij))}

    Parameters:
        x (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
        axis (int, optional): The axis along which to perform softmax calculations. Only support -1 for SparseCsrTensor.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor: SparseCoo or SparseCsr, whose layout is the same with `x` .

    Examples:
        .. code-block:: python

            import paddle
            paddle.seed(100)

            mask = paddle.rand((3, 4)) < 0.5
            x = paddle.rand((3, 4)) * mask
            print(x)
            # Tensor(shape=[3, 4], dtype=float32, place=Place(gpu:0), stop_gradient=True,
            #        [[0.83438963, 0.70008713, 0.        , 0.88831252],
            #         [0.02200012, 0.        , 0.75432241, 0.65136462],
            #         [0.96088767, 0.82938021, 0.35367414, 0.86653489]])

            csr = x.to_sparse_csr()
            print(csr)
            # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
            #        crows=[0 , 3 , 6 , 10],
            #        cols=[0, 1, 3, 0, 2, 3, 0, 1, 2, 3],
            #        values=[0.83438963, 0.70008713, 0.88831252, 0.02200012, 0.75432241,
            #                0.65136462, 0.96088767, 0.82938021, 0.35367414, 0.86653489])

            out = paddle.sparse.nn.functional.softmax(csr)
            print(out)
            # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
            #        crows=[0 , 3 , 6 , 10],
            #        cols=[0, 1, 3, 0, 2, 3, 0, 1, 2, 3],
            #        values=[0.34132850, 0.29843223, 0.36023921, 0.20176248, 0.41964680,
            #                0.37859070, 0.30015594, 0.26316854, 0.16354506, 0.27313042])

            coo = x.to_sparse_coo(sparse_dim=2)
            print(coo)
            # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
            #        indices=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 2],
            #                 [0, 1, 3, 0, 2, 3, 0, 1, 2, 3]],
            #        values=[0.83438963, 0.70008713, 0.88831252, 0.02200012, 0.75432241,
            #                0.65136462, 0.96088767, 0.82938021, 0.35367414, 0.86653489])

            out = paddle.sparse.nn.functional.softmax(coo)
            print(out)
            # Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
            #        indices=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 2],
            #                 [0, 1, 3, 0, 2, 3, 0, 1, 2, 3]],
            #        values=[0.34132853, 0.29843226, 0.36023924, 0.20176250, 0.41964683,
            #                0.37859073, 0.30015597, 0.26316857, 0.16354507, 0.27313042])
    """
    if in_dynamic_mode():
        return _C_ops.sparse_softmax(x, axis)
    else:
        op_type = 'sparse_softmax'
        helper = LayerHelper(op_type)
        out = helper.create_sparse_variable_for_type_inference(x.dtype)
        helper.append_op(
            type=op_type,
            inputs={'x': x},
            outputs={'out': out},
            attrs={'axis': axis},
        )
        return out


@dygraph_only
def relu6(x, name=None):
    """
    sparse relu6 activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::

        relu6(x) = min(max(0, x), 6)

    Parameters:
        x (Tensor): The input Sparse Tensor with data type float32, float64.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        A Sparse Tensor with the same data type and shape as ``x`` .

    Examples:
        .. code-block:: python

            import paddle

            dense_x = paddle.to_tensor([-2., 0., 8.])
            sparse_x = dense_x.to_sparse_coo(1)
            out = paddle.sparse.nn.functional.relu6(sparse_x)
    """
    return _C_ops.sparse_relu6(x)


@dygraph_only
def leaky_relu(x, negative_slope=0.01, name=None):
    r"""
    sparse leaky_relu activation, requiring x to be a SparseCooTensor or SparseCsrTensor.

    .. math::
        leaky\_relu(x)=
        \left\{
            \begin{array}{rcl}
                x, & & if \ x >= 0 \\
                negative\_slope * x, & & otherwise \\
            \end{array}
        \right.

    Parameters:
        x (Tensor): The input Sparse Tensor with data type float32, float64.
        negative_slope (float, optional): Slope of the activation function at
            :math:`x < 0` . Default is 0.01.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        A Sparse Tensor with the same data type and shape as ``x`` .

    Examples:
        .. code-block:: python

            import paddle

            dense_x = paddle.to_tensor([-2., 0., 5.])
            sparse_x = dense_x.to_sparse_coo(1)
            out = paddle.sparse.nn.functional.leaky_relu(sparse_x, 0.5)
    """
    return _C_ops.sparse_leaky_relu(x, negative_slope)