nets.py

#   Copyright (c) 2018 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.
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#     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.
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from __future__ import print_function
import six
from . import layers

__all__ = [
    "simple_img_conv_pool",
    "sequence_conv_pool",
    "glu",
    "scaled_dot_product_attention",
    "img_conv_group",
]


def simple_img_conv_pool(input,
                         num_filters,
                         filter_size,
                         pool_size,
                         pool_stride,
                         pool_padding=0,
                         pool_type='max',
                         global_pooling=False,
                         conv_stride=1,
                         conv_padding=0,
                         conv_dilation=1,
                         conv_groups=1,
                         param_attr=None,
                         bias_attr=None,
                         act=None,
                         use_cudnn=True):
    """
    The simple_img_conv_pool is composed with one Convolution2d and one Pool2d.

    Args:
        input (Variable): The input image with [N, C, H, W] format.
        num_filters(int): The number of filter. It is as same as the output
            feature channel.
        filter_size (int|list|tuple): The filter size. If filter_size is a list or
            tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise,
            the filter_size_H = filter_size_W = filter_size.
        pool_size (int|list|tuple): The pooling size of Pool2d layer. If pool_size
            is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
            Otherwise, the pool_size_H = pool_size_W = pool_size.
        pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
            is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W).
            Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
        pool_padding (int|list|tuple): The padding of Pool2d layer. If pool_padding is a list or
            tuple, it must contain two integers, (pool_padding_H, pool_padding_W).
            Otherwise, the pool_padding_H = pool_padding_W = pool_padding. Default 0.
        pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
            average-pooling. Default :math:`max`.
        global_pooling (bool): Whether to use the global pooling. If global_pooling = true,
            pool_size and pool_padding while be ignored. Default False
        conv_stride (int|list|tuple): The stride size of the conv2d Layer. If stride is a
            list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise,
            the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1.
        conv_padding (int|list|tuple): The padding size of the conv2d Layer. If padding is
            a list or  tuple, it must contain two integers, (conv_padding_H, conv_padding_W).
            Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0.
        conv_dilation (int|list|tuple): The dilation size of the conv2d Layer. If dilation is
            a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W).
            Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1.
        conv_groups (int): The groups number of the conv2d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
            and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`.
            Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        act (str): Activation type for conv2d, if it is set to None, activation is not
            appended. Default: None.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True

    Return:
        Variable: The result of input after Convolution2d and Pool2d.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
            conv_pool = fluid.nets.simple_img_conv_pool(input=img,
                                                        filter_size=5,
                                                        num_filters=20,
                                                        pool_size=2,
                                                        pool_stride=2,
                                                        act="relu")
    """
    conv_out = layers.conv2d(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        stride=conv_stride,
        padding=conv_padding,
        dilation=conv_dilation,
        groups=conv_groups,
        param_attr=param_attr,
        bias_attr=bias_attr,
        act=act,
        use_cudnn=use_cudnn)

    pool_out = layers.pool2d(
        input=conv_out,
        pool_size=pool_size,
        pool_type=pool_type,
        pool_stride=pool_stride,
        pool_padding=pool_padding,
        global_pooling=global_pooling,
        use_cudnn=use_cudnn)
    return pool_out


def img_conv_group(input,
                   conv_num_filter,
                   pool_size,
                   conv_padding=1,
                   conv_filter_size=3,
                   conv_act=None,
                   param_attr=None,
                   conv_with_batchnorm=False,
                   conv_batchnorm_drop_rate=0.0,
                   pool_stride=1,
                   pool_type="max",
                   use_cudnn=True):
    """
    The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut,
    and Pool2d. According to the input arguments, img_conv_group will do serials of
    computation for Input using Convolution2d, BatchNorm, DropOut, and pass the last
    result to Pool2d.

    Args:
        input (Variable): The input image with [N, C, H, W] format.
        conv_num_filter(list|tuple): Indicates the numbers of filter of this group.
        pool_size (int|list|tuple): The pooling size of Pool2d Layer. If pool_size
            is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
            Otherwise, the pool_size_H = pool_size_W = pool_size.
        conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
            a list or tuple, its length must be equal to the length of conv_num_filter.
            Otherwise the conv_padding of all Conv2d Layers are the same. Default 1.
        conv_filter_size (int|list|tuple): The filter size. If filter_size is a list or
            tuple, its length must be equal to the length of conv_num_filter.
            Otherwise the conv_filter_size of all Conv2d Layers are the same. Default 3.
        conv_act (str): Activation type for Conv2d Layer that is not followed by BatchNorm.
            Default: None.
        param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
        conv_with_batchnorm (bool|list): Indicates whether to use BatchNorm after Conv2d Layer.
            If conv_with_batchnorm is a list, its length must be equal to the length of
            conv_num_filter. Otherwise, conv_with_batchnorm indicates whether all the
            Conv2d Layer follows a BatchNorm. Default False.
        conv_batchnorm_drop_rate (float|list): Indicates the drop_rate of Dropout Layer
            after BatchNorm. If conv_batchnorm_drop_rate is a list, its length must be
            equal to the length of conv_num_filter. Otherwise, drop_rate of all Dropout
            Layers is conv_batchnorm_drop_rate. Default 0.0.
        pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
            is a list or tuple, it must contain two integers, (pooling_stride_H,
            pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
            Default 1.
        pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
            average-pooling. Default :math:`max`.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True

    Return:
        Variable: The final result after serial computation using Convolution2d,
            BatchNorm, DropOut, and Pool2d.

    Examples:
        .. code-block:: python

            img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
            conv_pool = fluid.nets.img_conv_group(input=img,
                                                  conv_padding=1,
                                                  conv_num_filter=[3, 3],
                                                  conv_filter_size=3,
                                                  conv_act="relu",
                                                  pool_size=2,
                                                  pool_stride=2)
    """
    tmp = input
    assert isinstance(conv_num_filter, list) or \
        isinstance(conv_num_filter, tuple)

    def __extend_list__(obj):
        if not hasattr(obj, '__len__'):
            return [obj] * len(conv_num_filter)
        else:
            assert len(obj) == len(conv_num_filter)
            return obj

    conv_padding = __extend_list__(conv_padding)
    conv_filter_size = __extend_list__(conv_filter_size)
    param_attr = __extend_list__(param_attr)
    conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
    conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)

    for i in six.moves.range(len(conv_num_filter)):
        local_conv_act = conv_act
        if conv_with_batchnorm[i]:
            local_conv_act = None

        tmp = layers.conv2d(
            input=tmp,
            num_filters=conv_num_filter[i],
            filter_size=conv_filter_size[i],
            padding=conv_padding[i],
            param_attr=param_attr[i],
            act=local_conv_act,
            use_cudnn=use_cudnn)

        if conv_with_batchnorm[i]:
            tmp = layers.batch_norm(input=tmp, act=conv_act, in_place=True)
            drop_rate = conv_batchnorm_drop_rate[i]
            if abs(drop_rate) > 1e-5:
                tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)

    pool_out = layers.pool2d(
        input=tmp,
        pool_size=pool_size,
        pool_type=pool_type,
        pool_stride=pool_stride,
        use_cudnn=use_cudnn)
    return pool_out


def sequence_conv_pool(input,
                       num_filters,
                       filter_size,
                       param_attr=None,
                       act="sigmoid",
                       pool_type="max",
                       bias_attr=None):
    """
    The sequence_conv_pool is composed with Sequence Convolution and Pooling.

    Args:
        input (Variable): The input of sequence_conv, which supports variable-time
            length input sequence. The underlying of input is a matrix with shape
            (T, N), where T is the total time steps in this mini-batch and N is
            the input_hidden_size
        num_filters(int): The number of filter.
        filter_size (int): The filter size.
        param_attr (ParamAttr): The parameters to the Sequence_conv Layer. Default: None.
        act (str): Activation type for Sequence_conv Layer. Default: "sigmoid".
        pool_type (str): Pooling type can be :math:`max` for max-pooling, :math:`average` for
            average-pooling, :math:`sum` for sum-pooling, :math:`sqrt` for sqrt-pooling.
            Default :math:`max`.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of sequence_conv.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, sequence_conv
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.

    Return:
        Variable: The final result after Sequence Convolution and Pooling.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            input_dim = 100 #len(word_dict)
            emb_dim = 128
            hid_dim = 512
            data = fluid.layers.data(name="words", shape=[1], dtype="int64", lod_level=1)
            emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
            seq_conv = fluid.nets.sequence_conv_pool(input=emb,
                                                     num_filters=hid_dim,
                                                     filter_size=3,
                                                     act="tanh",
                                                     pool_type="sqrt")
    """
    conv_out = layers.sequence_conv(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        param_attr=param_attr,
        bias_attr=bias_attr,
        act=act)

    pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type)
    return pool_out


def glu(input, dim=-1):
    """
    The Gated Linear Units(GLU) composed by split, sigmoid activation and element-wise
    multiplication. Specifically, Split the input into two equal sized parts,
    :math:`a` and :math:`b`, along the given dimension and then compute as
    following:

        .. math::

            {GLU}(a, b)= a \otimes \sigma(b)

    Refer to `Language Modeling with Gated Convolutional Networks
    <https://arxiv.org/pdf/1612.08083.pdf>`_.

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
        dim (int): The dimension along which to split. If :math:`dim < 0`, the
            dimension to split along is :math:`rank(input) + dim`. Default -1.

    Returns:
        Variable: Variable with half the size of input.

    Examples:
        .. code-block:: python

            data = fluid.layers.data(
                name="words", shape=[-1, 6, 3, 9], dtype="float32")
            # shape of output: [-1, 3, 3, 9]
            output = fluid.nets.glu(input=data, dim=1)
    """

    a, b = layers.split(input, num_or_sections=2, dim=dim)
    act_b = layers.sigmoid(x=b)
    out = layers.elementwise_mul(x=a, y=act_b)
    return out


def scaled_dot_product_attention(queries,
                                 keys,
                                 values,
                                 num_heads=1,
                                 dropout_rate=0.):
    """
    The dot-product attention.

    Attention mechanism can be seen as mapping a query and a set of key-value
    pairs to an output. The output is computed as a weighted sum of the values,
    where the weight assigned to each value is computed by a compatibility
    function (dot-product here) of the query with the corresponding key.

    The dot-product attention can be implemented through (batch) matrix
    multipication as follows:

        .. math::

            Attention(Q, K, V)= softmax(QK^\mathrm{T})V

    Refer to `Attention Is All You Need
    <https://arxiv.org/pdf/1706.03762.pdf>`_.

    Args:
        queries (Variable): The input variable which should be a 3-D Tensor.
        keys (Variable): The input variable which should be a 3-D Tensor.
        values (Variable): The input variable which should be a 3-D Tensor.
        num_heads (int): Head number to compute the scaled dot product
            attention. Default: 1.
        dropout_rate (float): The dropout rate to drop the attention weight.
            Default: 0.0.

    Returns:
        Variable: A 3-D Tensor computed by multi-head scaled dot product\
            attention.

    Raises:
        ValueError: If input queries, keys, values are not 3-D Tensors.

    NOTES:
        1. When num_heads > 1, three linear projections are learned respectively
           to map input queries, keys and values into queries', keys' and values'.
           queries', keys' and values' have the same shapes with queries, keys
           and values.
        2. When num_heads == 1, scaled_dot_product_attention has no learnable
           parameters.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            queries = fluid.layers.data(name="queries",
                                        shape=[3, 5, 9],
                                        dtype="float32",
                                        append_batch_size=False)
            queries.stop_gradient = False
            keys = fluid.layers.data(name="keys",
                                     shape=[3, 6, 9],
                                     dtype="float32",
                                     append_batch_size=False)
            keys.stop_gradient = False
            values = fluid.layers.data(name="values",
                                       shape=[3, 6, 10],
                                       dtype="float32",
                                       append_batch_size=False)
            values.stop_gradient = False
            contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
            contexts.shape  # [3, 5, 10]
    """
    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
        raise ValueError(
            "Inputs quries, keys and values should all be 3-D tensors.")

    if queries.shape[-1] != keys.shape[-1]:
        raise ValueError(
            "The hidden size of queries and keys should be the same.")
    if keys.shape[-2] != values.shape[-2]:
        raise ValueError(
            "The max sequence length in query batch and in key batch "
            "should be the same.")
    if keys.shape[-1] % num_heads != 0:
        raise ValueError("The hidden size of keys (%d) must be divisible "
                         "by the number of attention heads (%d)." %
                         (keys.shape[-1], num_heads))
    if values.shape[-1] % num_heads != 0:
        raise ValueError("The hidden size of values (%d) must be divisible "
                         "by the number of attention heads (%d)." %
                         (values.shape[-1], num_heads))

    def __compute_qkv(queries, keys, values, num_heads):
        """
        Add linear projection to queries, keys, and values.

        Args:
            queries(Tensor): a 3-D input Tensor.
            keys(Tensor): a 3-D input Tensor.
            values(Tensor): a 3-D input Tensor.
            num_heads(int): The number of heads. Linearly project the inputs
                            ONLY when num_heads > 1.

        Returns:
            Tensor: linearly projected output Tensors: queries', keys' and
                    values'. They have the same shapes with queries, keys and
                    values.
        """

        if num_heads == 1:
            return queries, keys, values

        q = layers.fc(input=queries, size=queries.shape[-1], num_flatten_dims=2)
        k = layers.fc(input=keys, size=keys.shape[-1], num_flatten_dims=2)
        v = layers.fc(input=values, size=values.shape[-1], num_flatten_dims=2)
        return q, k, v

    def __split_heads(x, num_heads):
        """
        Reshape the last dimension of inpunt tensor x so that it becomes two
        dimensions.

        Args:
            x(Tensor): a 3-D input Tensor.
            num_heads(int): The number of heads.

        Returns:
            Tensor: a Tensor with shape [..., n, m/num_heads], where m is size
                    of the last dimension of x.
        """
        if num_heads == 1:
            return x

        hidden_size = x.shape[-1]
        # reshape the 3-D input: [batch_size, max_sequence_length, hidden_dim]
        # into a 4-D output:
        # [batch_size, max_sequence_length, num_heads, hidden_size_per_head].
        reshaped = layers.reshape(
            x=x,
            shape=list(x.shape[:-1]) + [num_heads, hidden_size // num_heads])

        # permuate the dimensions into:
        # [batch_size, num_heads, max_sequence_len, hidden_size_per_head]
        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])

    def __combine_heads(x):
        """
        Reshape the last two dimensions of inpunt tensor x so that it becomes
        one dimension.

        Args:
            x(Tensor): a 4-D input Tensor with shape
                       [bs, num_heads, max_sequence_length, hidden_dim].

        Returns:
            Tensor: a Tensor with shape
                    [bs, max_sequence_length, num_heads * hidden_dim].
        """

        if len(x.shape) == 3: return x
        if len(x.shape) != 4:
            raise ValueError("Input(x) should be a 4-D Tensor.")

        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
        return layers.reshape(
            x=trans_x,
            shape=list(
                map(int, [
                    trans_x.shape[0], trans_x.shape[1], trans_x.shape[2] *
                    trans_x.shape[3]
                ])))

    q, k, v = __compute_qkv(queries, keys, values, num_heads)

    q = __split_heads(q, num_heads)
    k = __split_heads(k, num_heads)
    v = __split_heads(v, num_heads)

    key_dim_per_head = keys.shape[-1] // num_heads
    scaled_q = layers.scale(x=q, scale=key_dim_per_head**-0.5)
    product = layers.matmul(x=scaled_q, y=k, transpose_y=True)

    weights = layers.reshape(
        x=layers.reshape(
            x=product, shape=[-1, product.shape[-1]], act="softmax"),
        shape=product.shape)
    if dropout_rate:
        weights = layers.dropout(
            weights, dropout_prob=dropout_rate, is_test=False)
    ctx_multiheads = layers.matmul(weights, v)
    return __combine_heads(ctx_multiheads)