activation.py

#   Copyright (c) 2020 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,
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# See the License for the specific language governing permissions and
# limitations under the License.

# TODO: define activation functions of neural network
from ...fluid.layers import brelu  #DEFINE_ALIAS
from ...fluid.layers import elu  #DEFINE_ALIAS
from ...fluid.layers import erf  #DEFINE_ALIAS
from ...fluid.layers import gelu  #DEFINE_ALIAS
from ...fluid.layers import hard_shrink  #DEFINE_ALIAS
from ...fluid.layers import hard_sigmoid  #DEFINE_ALIAS
from ...fluid.layers import hard_swish  #DEFINE_ALIAS
from ...fluid.layers import leaky_relu  #DEFINE_ALIAS
from ...fluid.layers import logsigmoid  #DEFINE_ALIAS
from ...fluid.layers import maxout  #DEFINE_ALIAS
from ...fluid.layers import relu6  #DEFINE_ALIAS
from ...fluid.layers import selu  #DEFINE_ALIAS
from ...fluid.layers import soft_relu  #DEFINE_ALIAS
from ...fluid.layers import softplus  #DEFINE_ALIAS
from ...fluid.layers import softshrink  #DEFINE_ALIAS
from ...fluid.layers import softsign  #DEFINE_ALIAS
from ...fluid.layers import swish  #DEFINE_ALIAS
from ...fluid.layers import tanh_shrink  #DEFINE_ALIAS
from ...fluid.layers import thresholded_relu  #DEFINE_ALIAS

__all__ = [
    'brelu',
    'elu',
    'erf',
    'gelu',
    'hard_shrink',
    'hard_sigmoid',
    'hard_swish',
    'hsigmoid',
    'leaky_relu',
    'logsigmoid',
    'maxout',
    #       'prelu',
    'relu',
    'relu6',
    'selu',
    'sigmoid',
    'soft_relu',
    'softmax',
    'softplus',
    'softshrink',
    'softsign',
    'swish',
    'tanh_shrink',
    'thresholded_relu',
    'log_softmax'
]

import warnings
from ...fluid.layer_helper import LayerHelper
from ...fluid.framework import in_dygraph_mode, convert_np_dtype_to_dtype_
from ...fluid import core
from ...fluid.data_feeder import check_variable_and_dtype
import paddle


def hsigmoid(input,
             label,
             weight,
             bias,
             num_classes,
             path_table=None,
             path_code=None,
             is_sparse=False):
    """
	:alias_main: paddle.nn.functional.hsigmoid
	:alias: paddle.nn.functional.hsigmoid,paddle.nn.functional.activation.hsigmoid

    The hierarchical sigmoid organizes the classes into a complete binary tree to reduce the computational complexity
    and speed up the model training, especially the training of language model.
    Each leaf node of the complete binary tree represents a class(word) and each non-leaf node acts as a binary classifier.
    For each class(word), there's a unique path from root to itself, hsigmoid calculate the cost for each non-leaf node on
    the path, and sum them to get a total cost.
    Comparing to softmax, the OP can reduce the computational complexity from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
    represents the number of classes or the size of word dict.

    The OP supports default tree and custom tree. For the default tree, you can refer to `Hierarchical Probabilistic Neural
    Network Language Model <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_. For the custom
    tree, you need to set :attr:`is_custom` to True, and do the following steps (take the language model as an example):

    1. Using a custom word dict to build a binary tree, each leaf node should be an word in the word dict.
    2. Creating a dict map word_id -> path that from the word to the root node, we call it path_table.
    3. Creating a dict map word_id -> code of path that from the word to the root node, we call it path_code.
       Code means the label of each binary classifier, 1 indicate true, 0 indicate false.
    4. Now, each word should has its path and code along the path, you can pass a batch of path and code related
       to the same batch of inputs.

    Parameters:
        input (Variable): A tensor with the shape [N, D], where N is the size of mini-batch,
            and D is the feature size. Its data type supports float32 and float64.
        label (Variable): A tensor contains the labels of training data. Its shape is [N, 1]
            and data type is int64.
        weight (Variable): A tensor with shape (num_classes - 1, D) if not using custom tree(path_code and path_table is None), or (num_classes, D) if using custom tree.
        bias (Variable): A tensor with shape (num_classes - 1, 1) if not using custom tree(path_code and path_table is None), or (num_classes, 1) if using custom tree.
        num_classes (int): The number of classes or the size of word dict, must be greater than 2.
            If the default tree is used (:attr:`is_custom` is set to False), :attr:`num_classes`
            should not be None. If the custom tree is used (:attr:`is_custom` is set to True),
            :attr:`num_classes` should be the number of non-leaf nodes, which indicates the num of
            classes using by the binary classifier.
        path_table (Variable, optional): A tensor that stores each batch of samples' path from leaf to root
            node, its shape is [N, L] and data type is int64, where L is the length of path. For each sample i,
            path_table[i] is a np.array like structure and each element in this array is the indexes in parent
            nodes' weight matrix. Default: None.
        path_code (Variable, optional): A tensor that stores each batch of samples' code of path from leaf
            to root node, its shape is [N, L] and data type is int64, which is the same as :attr:`path_table`.
            Each code of path is consisted with the code of nodes from leaf to root node. Default: None.
        is_sparse (bool, optional): Whether use sparse updating instead of dense updating, if it's True, the
            gradient of W and input will be sparse. Default: False.

    Returns:
        Variable: A tensor with the cost of hierarchical sigmoid, its shape is [N, 1] and data type is the same as :attr:`input`.

    Examples:

        .. code-block:: python

            from paddle import fluid, nn
            import paddle.fluid.dygraph as dg
            import paddle.nn.functional as F
            import numpy as np

            main = fluid.Program()
            start = fluid.Program()
            feature_size = 6
            num_classes = 8
            with fluid.unique_name.guard():
                with fluid.program_guard(main, start):
                    x = fluid.data("input", [-1, feature_size],
                                  dtype="float32")
                    label = fluid.data("labels", [-1, 1], dtype="int64")
                    w = fluid.data("weight", (num_classes -1, feature_size), dtype="float32")
                    b = fluid.data("bias", (num_classes -1, ), dtype="float32")
                    y = F.hsigmoid(x, label, w, b, num_classes)

            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            exe.run(start)
            feed_dict = {
                "input": np.random.randn(4, feature_size).astype(np.float32),
                "labels": np.random.randint(0, num_classes, (4, 1)).astype(np.int64),
                "weight": np.random.randn(num_classes - 1, feature_size).astype(np.float32),
                "bias": np.random.randn(num_classes - 1, ).astype(np.float32),
            }
            y_np, = exe.run(main, feed=feed_dict, fetch_list=[y])
            print(y_np.shape)

          # (4, 1)
    """

    attrs = {
        "num_classes": num_classes,
        "is_sparse": is_sparse,
        "remote_prefetch": is_sparse
    }

    inputs = {
        "X": input,
        "W": weight,
        "Bias": bias,
        "PathTable": path_table,
        "PathCode": path_code,
        "Label": label
    }

    helper = LayerHelper('hierarchical_sigmoid', **locals())
    dtype = helper.input_dtype()

    out = helper.create_variable_for_type_inference(dtype)
    pre_out = helper.create_variable_for_type_inference(dtype)
    outputs = {"Out": out, "PreOut": pre_out, "W_Out": weight}

    helper.append_op(
        type="hierarchical_sigmoid",
        inputs=inputs,
        outputs=outputs,
        attrs=attrs)
    return out


def relu(input, inplace=False, name=None):
    """
	:alias_main: paddle.nn.functional.relu
	:alias: paddle.nn.functional.relu,paddle.nn.functional.activation.relu

    ReLU Activation.

    .. math:

        out = max(x, 0)

    Parameters:
        input (Variable): The input variable. A multi-dimension Tensor with type float16, float32, or float64.
        inplace (bool, optional): If inplace is True, the input and output of ``ReLU`` are the same variable.
            Otherwise, the input and output of ``ReLU`` are different variables. Default: False. Note that if x is
            more than one OPs' input, inplace must be False.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Output of relu operator, a Tensor with shape same as input

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import paddle.nn.functional as functional
          import numpy as np

          data = np.array([-2, 0, 1]).astype('float32')
          with fluid.dygraph.guard():
              data = fluid.dygraph.to_variable(data)
              res = functional.relu(data)  # [0, 0, 1]
    """

    if in_dygraph_mode():
        if inplace:
            warnings.warn(
                "Inplace on ReLU is not allowed and will be discarded in dygraph mode currently."
            )
        return core.ops.relu(input)

    check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
                             'relu')

    helper = LayerHelper('relu', **locals())
    outs = input if inplace else helper.create_variable_for_type_inference(
        input.dtype)
    helper.append_op(type='relu', inputs={'X': [input]}, outputs={'Out': outs})
    return outs


def sigmoid(input, inplace=False, name=None):
    """
	:alias_main: paddle.nn.functional.sigmoid
	:alias: paddle.nn.functional.sigmoid,paddle.nn.functional.activation.sigmoid

    Sigmoid Activation.

    .. math:

        output = \frac{1}{1 + e^{-input}}
    
    Parameters:
        input (Variable): The input variable. A multi-dimension Tensor with type float16, float32, or float64.
        inplace (bool, optional): If inplace is True, the input and output are the same variable.
            Otherwise, the input and output of are different variables. Default: False. Note that if x is
            more than one OPs' input, inplace must be False.
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
    
    Returns:
        Output of sigmoid operator, a Tensor with shape same as input
    
    Examples:
        .. code-block:: python
          
          import paddle.fluid as fluid
          import paddle.nn.functional as functional
          import numpy as np
          # In the static graph mode
          input = fluid.data(name="input", shape=[None, 4])
          output = functional.sigmoid(input)
          place = fluid.CPUPlace()
          exe = fluid.Executor(place)
          exe.run(fluid.default_startup_program())
          input_data = np.array([1.0, 2.0, 3.0, 4.0]).astype('float32')
          output_data = exe.run(feed={"input": input_data},
                                fetch_list=[output])
          print(output_data) # [0.7310586, 0.880797, 0.95257413, 0.98201376]
          # In the dynamic graph mode
          with fluid.dygraph.guard():
              input = fluid.dygraph.to_variable(input_data)
              output = functional.sigmoid(input)
              print(output) # [0.7310586, 0.880797, 0.95257413, 0.98201376]
    """

    if in_dygraph_mode():
        if inplace:
            warnings.warn(
                "Inplace on sigmoid is not allowed and will be discarded in dygraph mode currently."
            )
        return core.ops.sigmoid(input)

    check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
                             'sigmoid')
    helper = LayerHelper("sigmoid", **locals())
    outputs = helper.create_variable_for_type_inference(input.dtype)
    helper.append_op(
        type='sigmoid', inputs={'X': [input]}, outputs={'Out': outputs})
    return outputs


def softmax(x, axis=-1, name=None):
    """
    This operator implements the softmax layer. The calculation process is as follows:

    1. The dimension :attr:`axis` of ``x`` will be permuted to the last.

    2. Then ``x`` will be logically flattened to a 2-D matrix. The matrix's second
    dimension(row length) is the same as the dimension :attr:`axis` of ``x``,
    and the first dimension(column length) is the product of all other dimensions
    of ``x``. For each row of the matrix, the softmax operator squashes the
    K-dimensional(K is the width of the matrix, which is also the size of ``x``'s
    dimension :attr:`axis`) vector of arbitrary real values to a K-dimensional
    vector of real values in the range [0, 1] that add up to 1.

    3. After the softmax operation is completed, the inverse operations of steps 1 and 2
    are performed to restore the two-dimensional matrix to the same dimension as the ``x`` .

    It computes the exponential of the given dimension and the sum of exponential
    values of all the other dimensions in the K-dimensional vector input.
    Then the ratio of the exponential of the given dimension and the sum of
    exponential values of all the other dimensions is the output of the softmax
    operator.

    For each row :math:`i` and each column :math:`j` in the matrix, we have:

    .. math::

        out[i, j] = \\frac{\exp(x[i, j])}{\sum_j(exp(x[i, j])}

    Example:

    .. code-block:: text

        Case 1:
          Input:
            x.shape = [2, 3, 4]
            x.data = [[[2.0, 3.0, 4.0, 5.0],
                       [3.0, 4.0, 5.0, 6.0],
                       [7.0, 8.0, 8.0, 9.0]],
                      [[1.0, 2.0, 3.0, 4.0],
                       [5.0, 6.0, 7.0, 8.0],
                       [6.0, 7.0, 8.0, 9.0]]]

          Attrs:
            axis = -1

          Output:
            out.shape = [2, 3, 4]
            out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
                        [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                         [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]

        Case 2:
          Input:
            x.shape = [2, 3, 4]
            x.data = [[[2.0, 3.0, 4.0, 5.0],
                       [3.0, 4.0, 5.0, 6.0],
                       [7.0, 8.0, 8.0, 9.0]],
                      [[1.0, 2.0, 3.0, 4.0],
                       [5.0, 6.0, 7.0, 8.0],
                       [6.0, 7.0, 8.0, 9.0]]]
          Attrs:
            axis = 1

          Output:
            out.shape = [2, 3, 4]
            out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783],
                         [0.01786798, 0.01786798, 0.04661262, 0.04661262],
                         [0.97555875, 0.97555875, 0.93623955, 0.93623955]],
                        [[0.00490169, 0.00490169, 0.00490169, 0.00490169],
                         [0.26762315, 0.26762315, 0.26762315, 0.26762315],
                         [0.72747516, 0.72747516, 0.72747516, 0.72747516]]]

    Args:
        x (Tensor): The input multi-dimension Tensor with data type float32, float64.
        axis (int, optional): The axis along which to perform softmax calculations.
            It should be in range [-D, D), where D is the dimensions of ``x`` .
            When ``axis`` < 0, it works the same way as :math:`axis + D` .
            Default is -1.
        name (str, optional): Name for the operation (optional, default is None).
            For more information, please refer to :ref:`api_guide_Name`.

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

    Examples:

        .. code-block:: python

        import paddle
        import paddle.nn.functional as F
        import numpy as np

        paddle.disable_static()

        x = np.array([[[2.0, 3.0, 4.0, 5.0],
                       [3.0, 4.0, 5.0, 6.0],
                       [7.0, 8.0, 8.0, 9.0]],
                      [[1.0, 2.0, 3.0, 4.0],
                       [5.0, 6.0, 7.0, 8.0],
                       [6.0, 7.0, 8.0, 9.0]]], 'float32')
        x = paddle.to_variable(x)
        out = F.softmax(x)
        # [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
        #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
        #   [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
        # [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
        #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
        #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]
    """
    return paddle.fluid.layers.softmax(input=x, axis=axis, name=name)


def log_softmax(input, axis=None, dtype=None, name=None):
    """
	:alias_main: paddle.nn.functional.log_softmax
	:alias: paddle.nn.functional.log_softmax,paddle.nn.functional.activation.log_softmax

    This operator implements the log_softmax layer. The calculation process is as follows:

    .. math::

        Out[i, j] = log(softmax(x)) 
                  = log(\\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})

    Parameters:
        input (Variable): The input variable. A multi-dimension Tensor with type float32, or float64.
        axis (int, optional): The index of dimension to perform softmax calculations, it should be in
            range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None. 
            None and -1 means the last dimension.
        dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
            the input tensor is casted to dtype before the operation is performed. This is useful for
            preventing data type overflows. Default: None. Supported dtype: float32 or float64
        name (str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
 
    Returns:
        Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input``.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          import paddle.nn.functional as F
          import numpy as np

          data = np.array([[[-2.0, 3.0, -4.0, 5.0],
                            [3.0, -4.0, 5.0, -6.0],
                            [-7.0, -8.0, 8.0, 9.0]],
                           [[1.0, -2.0, -3.0, 4.0],
                            [-5.0, 6.0, 7.0, -8.0],
                            [6.0, 7.0, 8.0, 9.0]]]).astype('float32')
          with fluid.dygraph.guard():
              data = fluid.dygraph.to_variable(data)
              res = F.log_softmax(data, -1)
              # [[[ -7.1278396   -2.1278396   -9.127839    -0.12783948]
              #   [ -2.1270514   -9.127051    -0.12705144 -11.127051  ]
              #   [-16.313261   -17.313261    -1.3132617   -0.31326184]]
              #  [[ -3.0518122   -6.051812    -7.051812    -0.051812  ]
              #   [-12.313267    -1.3132664   -0.3132665  -15.313267  ]
              #   [ -3.4401896   -2.4401896   -1.4401896   -0.44018966]]]
    """

    axis = -1 if axis is None else axis
    dtype = convert_np_dtype_to_dtype_(dtype) if dtype is not None else dtype

    if in_dygraph_mode():
        outs_cast = input if dtype is None \
            else core.ops.cast(input, 'in_dtype', input.dtype, 'out_dtype', dtype)
        outs_softmax = core.ops.softmax(outs_cast, 'axis', axis, 'use_cudnn',
                                        False)
        return core.ops.log(outs_softmax)

    if dtype is None:
        check_variable_and_dtype(
            input, 'input', ['float16', 'float32', 'float64'], 'log_softmax')

    helper = LayerHelper("log_softmax", **locals())
    outs_cast = input
    if dtype is not None:
        outs_cast = helper.create_variable_for_type_inference(dtype)
        helper.append_op(
            type='cast',
            inputs={'X': input},
            outputs={'Out': outs_cast},
            attrs={'in_dtype': input.dtype,
                   'out_dtype': dtype})

    outs_softmax = helper.create_variable_for_type_inference(outs_cast.dtype)
    helper.append_op(
        type='softmax',
        inputs={'X': outs_cast},
        outputs={'Out': outs_softmax},
        attrs={'axis': axis,
               'use_cudnn': False})

    outs_log = helper.create_variable_for_type_inference(outs_softmax.dtype)
    helper.append_op(
        type='log', inputs={'X': outs_softmax}, outputs={'Out': outs_log})

    return outs_log