nets.py 21.7 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# 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
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13
# 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.
14
import layers
F
fengjiayi 已提交
15

16 17 18
__all__ = [
    "simple_img_conv_pool",
    "sequence_conv_pool",
19
    "glu",
20
    "scaled_dot_product_attention",
21
]
D
dzhwinter 已提交
22

F
fengjiayi 已提交
23 24 25

def simple_img_conv_pool(input,
                         num_filters,
D
dzhwinter 已提交
26
                         filter_size,
F
fengjiayi 已提交
27 28
                         pool_size,
                         pool_stride,
C
chengduoZH 已提交
29
                         pool_padding=0,
C
chengduoZH 已提交
30
                         pool_type='max',
C
chengduoZH 已提交
31 32 33 34 35 36 37 38
                         global_pooling=False,
                         conv_stride=1,
                         conv_padding=0,
                         conv_dilation=1,
                         conv_groups=1,
                         param_attr=None,
                         bias_attr=None,
                         act=None,
39 40
                         use_cudnn=True,
                         use_mkldnn=False):
C
chengduoZH 已提交
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
    """
    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): The parameters to the Conv2d Layer. Default: None
        bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
        act (str): Activation type for Conv2d. Default: None
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
            with mkldnn library. Default: False

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

    Examples:
        .. code-block:: python

            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")
    """
F
fengjiayi 已提交
100 101 102 103
    conv_out = layers.conv2d(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
C
chengduoZH 已提交
104 105 106 107
        stride=conv_stride,
        padding=conv_padding,
        dilation=conv_dilation,
        groups=conv_groups,
F
fengjiayi 已提交
108
        param_attr=param_attr,
C
chengduoZH 已提交
109
        bias_attr=bias_attr,
C
chengduoZH 已提交
110
        act=act,
111 112
        use_cudnn=use_cudnn,
        use_mkldnn=use_mkldnn)
F
fengjiayi 已提交
113 114 115 116

    pool_out = layers.pool2d(
        input=conv_out,
        pool_size=pool_size,
Q
Qiao Longfei 已提交
117
        pool_type=pool_type,
C
chengduoZH 已提交
118
        pool_stride=pool_stride,
C
chengduoZH 已提交
119 120
        pool_padding=pool_padding,
        global_pooling=global_pooling,
121 122
        use_cudnn=use_cudnn,
        use_mkldnn=use_mkldnn)
Q
Qiao Longfei 已提交
123 124 125 126 127 128 129 130 131
    return pool_out


def img_conv_group(input,
                   conv_num_filter,
                   pool_size,
                   conv_padding=1,
                   conv_filter_size=3,
                   conv_act=None,
F
fengjiayi 已提交
132
                   param_attr=None,
Q
Qiao Longfei 已提交
133
                   conv_with_batchnorm=False,
W
wanghaoshuang 已提交
134
                   conv_batchnorm_drop_rate=0.0,
Q
Qiao Longfei 已提交
135
                   pool_stride=1,
C
chengduoZH 已提交
136
                   pool_type="max",
137 138
                   use_cudnn=True,
                   use_mkldnn=False):
Q
Qiao Longfei 已提交
139
    """
C
chengduoZH 已提交
140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194
    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
        use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
            with mkldnn library. Default: False

    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,
                                                  num_channels=3,
                                                  conv_padding=1,
                                                  conv_num_filter=[3, 3],
                                                  conv_filter_size=3,
                                                  conv_act="relu",
                                                  pool_size=2,
                                                  pool_stride=2)
Q
Qiao Longfei 已提交
195 196 197
    """
    tmp = input
    assert isinstance(conv_num_filter, list) or \
198
        isinstance(conv_num_filter, tuple)
Q
Qiao Longfei 已提交
199 200 201 202 203

    def __extend_list__(obj):
        if not hasattr(obj, '__len__'):
            return [obj] * len(conv_num_filter)
        else:
C
chengduoZH 已提交
204
            assert len(obj) == len(conv_num_filter)
Q
Qiao Longfei 已提交
205 206 207 208
            return obj

    conv_padding = __extend_list__(conv_padding)
    conv_filter_size = __extend_list__(conv_filter_size)
F
fengjiayi 已提交
209
    param_attr = __extend_list__(param_attr)
Q
Qiao Longfei 已提交
210 211 212 213 214 215 216 217 218 219 220 221 222
    conv_with_batchnorm = __extend_list__(conv_with_batchnorm)
    conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate)

    for i in xrange(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],
F
fengjiayi 已提交
223
            param_attr=param_attr[i],
C
chengduoZH 已提交
224
            act=local_conv_act,
225 226
            use_cudnn=use_cudnn,
            use_mkldnn=use_mkldnn)
Q
Qiao Longfei 已提交
227 228

        if conv_with_batchnorm[i]:
229
            tmp = layers.batch_norm(input=tmp, act=conv_act, in_place=True)
Q
Qiao Longfei 已提交
230 231
            drop_rate = conv_batchnorm_drop_rate[i]
            if abs(drop_rate) > 1e-5:
232
                tmp = layers.dropout(x=tmp, dropout_prob=drop_rate)
Q
Qiao Longfei 已提交
233 234 235 236 237

    pool_out = layers.pool2d(
        input=tmp,
        pool_size=pool_size,
        pool_type=pool_type,
C
chengduoZH 已提交
238
        pool_stride=pool_stride,
239 240
        use_cudnn=use_cudnn,
        use_mkldnn=use_mkldnn)
F
fengjiayi 已提交
241
    return pool_out
D
dzhwinter 已提交
242 243 244 245 246


def sequence_conv_pool(input,
                       num_filters,
                       filter_size,
F
fengjiayi 已提交
247
                       param_attr=None,
248
                       act="sigmoid",
249
                       pool_type="max"):
C
chengduoZH 已提交
250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282
    """
    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`.

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

    Examples:
        .. code-block:: python

            input_dim = len(word_dict)
            emb_dim = 128
            hid_dim = 512
            data = fluid.layers.data( ame="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")
    """
D
dzhwinter 已提交
283 284 285 286
    conv_out = layers.sequence_conv(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
F
fengjiayi 已提交
287
        param_attr=param_attr,
288
        act=act)
D
dzhwinter 已提交
289

290
    pool_out = layers.sequence_pool(input=conv_out, pool_type=pool_type)
D
dzhwinter 已提交
291
    return pool_out
G
guosheng 已提交
292 293 294 295


def glu(input, dim=-1):
    """
C
chengduoZH 已提交
296 297 298
    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
G
guosheng 已提交
299
    following:
G
guosheng 已提交
300 301 302 303 304

        .. math::

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

Y
ying 已提交
305
    Refer to `Language Modeling with Gated Convolutional Networks
G
guosheng 已提交
306
    <https://arxiv.org/pdf/1612.08083.pdf>`_.
Y
ying 已提交
307

G
guosheng 已提交
308 309
    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
Y
ying 已提交
310
        dim (int): The dimension along which to split. If :math:`dim < 0`, the
C
chengduoZH 已提交
311
            dimension to split along is :math:`rank(input) + dim`. Default -1.
G
guosheng 已提交
312 313

    Returns:
C
chengduoZH 已提交
314
        Variable: Variable with half the size of input.
G
guosheng 已提交
315 316 317 318

    Examples:
        .. code-block:: python

C
chengduoZH 已提交
319 320
            data = fluid.layers.data(name="words", shape=[3, 6, 9], dtype="float32")
            output = fluid.nets.glu(input=data, dim=1)  # shape of output: [3, 3, 9]
G
guosheng 已提交
321 322 323
    """

    a, b = layers.split(input, num_or_sections=2, dim=dim)
G
guosheng 已提交
324 325
    act_b = layers.sigmoid(x=b)
    out = layers.elementwise_mul(x=a, y=act_b)
G
guosheng 已提交
326
    return out
327 328


Y
ying 已提交
329 330 331
def scaled_dot_product_attention(queries,
                                 keys,
                                 values,
Y
ying 已提交
332
                                 num_heads=1,
Y
ying 已提交
333
                                 dropout_rate=0.):
334 335 336
    """
    The dot-product attention.

337 338 339
    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
340
    function (dot-product here) of the query with the corresponding key.
Y
ying 已提交
341 342

    The dot-product attention can be implemented through (batch) matrix
343 344 345 346
    multipication as follows:

        .. math::

347
            Attention(Q, K, V)= softmax(QK^\mathrm{T})V
348

Y
ying 已提交
349
    Refer to `Attention Is All You Need
350 351
    <https://arxiv.org/pdf/1706.03762.pdf>`_.

Y
ying 已提交
352 353 354 355 356
    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
C
chengduoZH 已提交
357
            attention. Default: 1.
Y
ying 已提交
358
        dropout_rate (float): The dropout rate to drop the attention weight.
C
chengduoZH 已提交
359
            Default: 0.0.
360 361

    Returns:
C
chengduoZH 已提交
362 363
        Variable: A 3-D Tensor computed by multi-head scaled dot product\
            attention.
364

Y
ying 已提交
365 366 367
    Raises:
        ValueError: If input queries, keys, values are not 3-D Tensors.

C
chengduoZH 已提交
368
    NOTES:
Y
ying 已提交
369
        1. When num_heads > 1, three linear projections are learned respectively
C
chengduoZH 已提交
370 371 372 373 374
           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.
Y
ying 已提交
375

376 377 378
    Examples:
        .. code-block:: python

C
chengduoZH 已提交
379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394
            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)
Y
ying 已提交
395
            contexts.shape  # [3, 5, 10]
396
    """
Y
ying 已提交
397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
    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))

Y
ying 已提交
417
    def __compute_qkv(queries, keys, values, num_heads):
Y
ying 已提交
418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433
        """
        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.
        """

Y
ying 已提交
434 435 436 437 438 439 440 441
        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

Y
ying 已提交
442 443 444 445 446 447
    def __split_heads(x, num_heads):
        """
        Reshape the last dimension of inpunt tensor x so that it becomes two
        dimensions.

        Args:
Y
ying 已提交
448 449
            x(Tensor): a 3-D input Tensor.
            num_heads(int): The number of heads.
Y
ying 已提交
450 451

        Returns:
Y
ying 已提交
452 453
            Tensor: a Tensor with shape [..., n, m/num_heads], where m is size
                    of the last dimension of x.
Y
ying 已提交
454
        """
Y
ying 已提交
455 456
        if num_heads == 1:
            return x
457

Y
ying 已提交
458
        hidden_size = x.shape[-1]
459 460 461
        # 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].
Y
ying 已提交
462
        reshaped = layers.reshape(
463 464
            x=x,
            shape=list(x.shape[:-1]) + [num_heads, hidden_size // num_heads])
465 466

        # permuate the dimensions into:
467 468 469 470
        # [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):
Y
ying 已提交
471 472 473 474 475 476 477 478 479 480 481 482 483
        """
        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].
        """

Y
ying 已提交
484
        if len(x.shape) == 3: return x
485 486 487
        if len(x.shape) != 4:
            raise ValueError("Input(x) should be a 4-D Tensor.")

Y
ying 已提交
488
        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
Y
ying 已提交
489
        return layers.reshape(
490
            x=trans_x,
Y
ying 已提交
491 492 493 494
            shape=map(int, [
                trans_x.shape[0], trans_x.shape[1],
                trans_x.shape[2] * trans_x.shape[3]
            ]))
495

Y
ying 已提交
496 497 498 499 500
    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)
Y
ying 已提交
501 502

    key_dim_per_head = keys.shape[-1] // num_heads
503 504
    scaled_q = layers.scale(x=q, scale=key_dim_per_head**-0.5)
    product = layers.matmul(x=k, y=scaled_q, transpose_y=True)
Y
ying 已提交
505

Y
ying 已提交
506
    weights = layers.reshape(
507
        x=layers.reshape(
Y
ying 已提交
508
            x=product, shape=[-1, product.shape[-1]], act="softmax"),
509
        shape=product.shape)
Y
ying 已提交
510
    if dropout_rate:
G
guosheng 已提交
511 512
        weights = layers.dropout(
            weights, dropout_prob=dropout_rate, is_test=False)
Y
ying 已提交
513 514
    ctx_multiheads = layers.matmul(weights, v)
    return __combine_heads(ctx_multiheads)