rnn_impl.py 34.5 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14
# Copyright (c) 2019 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.

15 16
import copy

17
from paddle.fluid import layers, unique_name
18
from paddle.fluid.dygraph import Layer
19
from paddle.fluid.dygraph.layer_object_helper import LayerObjectHelper
20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 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
from paddle.fluid.layers.control_flow import StaticRNN

__all__ = ['BasicGRUUnit', 'basic_gru', 'BasicLSTMUnit', 'basic_lstm']


class BasicGRUUnit(Layer):
    """
    ****
    BasicGRUUnit class, using basic operators to build GRU
    The algorithm can be described as the equations below.

        .. math::
            u_t & = actGate(W_ux xu_{t} + W_uh h_{t-1} + b_u)

            r_t & = actGate(W_rx xr_{t} + W_rh h_{t-1} + b_r)

            m_t & = actNode(W_cx xm_t + W_ch dot(r_t, h_{t-1}) + b_m)

            h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)

    Args:
        name_scope(string) : The name scope used to identify parameters and biases
        hidden_size (integer): The hidden size used in the Unit.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            weight matrix. Note:
            If it is set to None or one attribute of ParamAttr, gru_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The parameter attribute for the bias
            of GRU unit.
            If it is set to None or one attribute of ParamAttr, gru_unit will 
            create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        gate_activation (function|None): The activation function for gates (actGate).
                                  Default: 'fluid.layers.sigmoid'
        activation (function|None): The activation function for cell (actNode).
                             Default: 'fluid.layers.tanh'
        dtype(string): data type used in this unit

    Examples:

        .. code-block:: python

            import paddle.fluid.layers as layers
            from paddle.fluid.contrib.layers import BasicGRUUnit

            input_size = 128
            hidden_size = 256
            input = layers.data( name = "input", shape = [-1, input_size], dtype='float32')
            pre_hidden = layers.data( name = "pre_hidden", shape=[-1, hidden_size], dtype='float32')

            gru_unit = BasicGRUUnit( "gru_unit", hidden_size )

            new_hidden = gru_unit( input, pre_hidden )

    """

    def __init__(self,
                 name_scope,
                 hidden_size,
                 param_attr=None,
                 bias_attr=None,
                 gate_activation=None,
                 activation=None,
                 dtype='float32'):
        super(BasicGRUUnit, self).__init__(name_scope, dtype)
86 87 88 89
        # reserve old school _full_name and _helper for static graph save load
        self._full_name = unique_name.generate(name_scope + "/" +
                                               self.__class__.__name__)
        self._helper = LayerObjectHelper(self._full_name)
90 91 92 93 94 95 96 97 98 99 100 101 102

        self._name = name_scope
        self._hiden_size = hidden_size
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._gate_activation = gate_activation or layers.sigmoid
        self._activation = activation or layers.tanh
        self._dtype = dtype

    def _build_once(self, input, pre_hidden):
        self._input_size = input.shape[-1]
        assert (self._input_size > 0)

103 104 105 106 107 108 109 110 111
        if self._param_attr is not None and self._param_attr.name is not None:
            gate_param_attr = copy.deepcopy(self._param_attr)
            candidate_param_attr = copy.deepcopy(self._param_attr)
            gate_param_attr.name += "_gate"
            candidate_param_attr.name += "_candidate"
        else:
            gate_param_attr = self._param_attr
            candidate_param_attr = self._param_attr

112
        self._gate_weight = self.create_parameter(
113
            attr=gate_param_attr,
114 115 116 117
            shape=[self._input_size + self._hiden_size, 2 * self._hiden_size],
            dtype=self._dtype)

        self._candidate_weight = self.create_parameter(
118
            attr=candidate_param_attr,
119 120 121
            shape=[self._input_size + self._hiden_size, self._hiden_size],
            dtype=self._dtype)

122 123 124 125 126 127 128 129 130
        if self._bias_attr is not None and self._bias_attr.name is not None:
            gate_bias_attr = copy.deepcopy(self._bias_attr)
            candidate_bias_attr = copy.deepcopy(self._bias_attr)
            gate_bias_attr.name += "_gate"
            candidate_bias_attr.name += "_candidate"
        else:
            gate_bias_attr = self._bias_attr
            candidate_bias_attr = self._bias_attr

131
        self._gate_bias = self.create_parameter(
132
            attr=gate_bias_attr,
133 134 135 136
            shape=[2 * self._hiden_size],
            dtype=self._dtype,
            is_bias=True)
        self._candidate_bias = self.create_parameter(
137
            attr=candidate_bias_attr,
138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154
            shape=[self._hiden_size],
            dtype=self._dtype,
            is_bias=True)

    def forward(self, input, pre_hidden):
        concat_input_hidden = layers.concat([input, pre_hidden], 1)

        gate_input = layers.matmul(x=concat_input_hidden, y=self._gate_weight)

        gate_input = layers.elementwise_add(gate_input, self._gate_bias)

        gate_input = self._gate_activation(gate_input)
        r, u = layers.split(gate_input, num_or_sections=2, dim=1)

        r_hidden = r * pre_hidden

        candidate = layers.matmul(
155
            layers.concat([input, r_hidden], 1), self._candidate_weight)
156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178
        candidate = layers.elementwise_add(candidate, self._candidate_bias)

        c = self._activation(candidate)
        new_hidden = u * pre_hidden + (1 - u) * c

        return new_hidden


def basic_gru(input,
              init_hidden,
              hidden_size,
              num_layers=1,
              sequence_length=None,
              dropout_prob=0.0,
              bidirectional=False,
              batch_first=True,
              param_attr=None,
              bias_attr=None,
              gate_activation=None,
              activation=None,
              dtype='float32',
              name='basic_gru'):
    """
T
tianshuo78520a 已提交
179
    GRU implementation using basic operator, supports multiple layers and bidirectional gru.
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203

    .. math::
            u_t & = actGate(W_ux xu_{t} + W_uh h_{t-1} + b_u)

            r_t & = actGate(W_rx xr_{t} + W_rh h_{t-1} + b_r)

            m_t & = actNode(W_cx xm_t + W_ch dot(r_t, h_{t-1}) + b_m)

            h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)

    Args:
        input (Variable): GRU input tensor, 
                       if batch_first = False, shape should be ( seq_len x batch_size x input_size )  
                       if batch_first = True, shape should be ( batch_size x seq_len x hidden_size )
        init_hidden(Variable|None): The initial hidden state of the GRU
                       This is a tensor with shape ( num_layers x batch_size x hidden_size)
                       if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
                       and can be reshaped to tensor with ( num_layers x 2 x batch_size x hidden_size) to use.
                       If it's None, it will be set to all 0.
        hidden_size (int): Hidden size of the GRU
        num_layers (int): The total number of layers of the GRU
        sequence_length (Variabe|None): A Tensor (shape [batch_size]) stores each real length of each instance,
                        This tensor will be convert to a mask to mask the padding ids
                        If it's None means NO padding ids
T
tianshuo78520a 已提交
204
        dropout_prob(float|0.0): Dropout prob, dropout ONLY works after rnn output of each layers, 
205 206
                             NOT between time steps
        bidirectional (bool|False): If it is bidirectional
207 208 209 210 211
        batch_first (bool|True): The shape format of the input and output tensors. If true,
            the shape format should be :attr:`[batch_size, seq_len, hidden_size]`. If false,
            the shape format should be :attr:`[seq_len, batch_size, hidden_size]`. By default
            this function accepts input and emits output in batch-major form to be consistent
            with most of data format, though a bit less efficient because of extra transposes.
212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            weight matrix. Note:
            If it is set to None or one attribute of ParamAttr, gru_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The parameter attribute for the bias
            of GRU unit.
            If it is set to None or one attribute of ParamAttr, gru_unit will 
            create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        gate_activation (function|None): The activation function for gates (actGate).
                                  Default: 'fluid.layers.sigmoid'
        activation (function|None): The activation function for cell (actNode).
                             Default: 'fluid.layers.tanh'
        dtype(string): data type used in this unit
        name(string): name used to identify parameters and biases

    Returns:
        rnn_out(Tensor),last_hidden(Tensor)
            - rnn_out is result of GRU hidden, with shape (seq_len x batch_size x hidden_size) \
              if is_bidirec set to True, shape will be ( seq_len x batch_sze x hidden_size*2)
            - last_hidden is the hidden state of the last step of GRU \
              shape is ( num_layers x batch_size x hidden_size ) \
              if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size),
              can be reshaped to a tensor with shape( num_layers x 2 x batch_size x hidden_size)

    Examples:
        .. code-block:: python
            
            import paddle.fluid.layers as layers
            from paddle.fluid.contrib.layers import basic_gru

            batch_size = 20
            input_size = 128
            hidden_size = 256
            num_layers = 2
            dropout = 0.5
            bidirectional = True
            batch_first = False

            input = layers.data( name = "input", shape = [-1, batch_size, input_size], dtype='float32')
            pre_hidden = layers.data( name = "pre_hidden", shape=[-1, hidden_size], dtype='float32')
            sequence_length = layers.data( name="sequence_length", shape=[-1], dtype='int32')


            rnn_out, last_hidden = basic_gru( input, pre_hidden, hidden_size, num_layers = num_layers, \
                    sequence_length = sequence_length, dropout_prob=dropout, bidirectional = bidirectional, \
                    batch_first = batch_first)

    """

    fw_unit_list = []

    for i in range(num_layers):
        new_name = name + "_layers_" + str(i)
267 268 269 270 271 272 273 274 275 276
        if param_attr is not None and param_attr.name is not None:
            layer_param_attr = copy.deepcopy(param_attr)
            layer_param_attr.name += "_fw_w_" + str(i)
        else:
            layer_param_attr = param_attr
        if bias_attr is not None and bias_attr.name is not None:
            layer_bias_attr = copy.deepcopy(bias_attr)
            layer_bias_attr.name += "_fw_b_" + str(i)
        else:
            layer_bias_attr = bias_attr
277
        fw_unit_list.append(
278 279
            BasicGRUUnit(new_name, hidden_size, layer_param_attr,
                         layer_bias_attr, gate_activation, activation, dtype))
280 281 282 283 284
    if bidirectional:
        bw_unit_list = []

        for i in range(num_layers):
            new_name = name + "_reverse_layers_" + str(i)
285 286 287 288 289 290 291 292 293 294 295
            if param_attr is not None and param_attr.name is not None:
                layer_param_attr = copy.deepcopy(param_attr)
                layer_param_attr.name += "_bw_w_" + str(i)
            else:
                layer_param_attr = param_attr
            if bias_attr is not None and bias_attr.name is not None:
                layer_bias_attr = copy.deepcopy(bias_attr)
                layer_bias_attr.name += "_bw_b_" + str(i)
            else:
                layer_bias_attr = bias_attr

296
            bw_unit_list.append(
297 298 299
                BasicGRUUnit(new_name, hidden_size, layer_param_attr,
                             layer_bias_attr, gate_activation, activation,
                             dtype))
300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399

    if batch_first:
        input = layers.transpose(input, [1, 0, 2])

    mask = None
    if sequence_length:
        max_seq_len = layers.shape(input)[0]
        mask = layers.sequence_mask(
            sequence_length, maxlen=max_seq_len, dtype='float32')
        mask = layers.transpose(mask, [1, 0])

    direc_num = 1
    if bidirectional:
        direc_num = 2
    if init_hidden:
        init_hidden = layers.reshape(
            init_hidden, shape=[num_layers, direc_num, -1, hidden_size])

    def get_single_direction_output(rnn_input,
                                    unit_list,
                                    mask=None,
                                    direc_index=0):
        rnn = StaticRNN()
        with rnn.step():
            step_input = rnn.step_input(rnn_input)

            if mask:
                step_mask = rnn.step_input(mask)

            for i in range(num_layers):
                if init_hidden:
                    pre_hidden = rnn.memory(init=init_hidden[i, direc_index])
                else:
                    pre_hidden = rnn.memory(
                        batch_ref=rnn_input,
                        shape=[-1, hidden_size],
                        ref_batch_dim_idx=1)

                new_hidden = unit_list[i](step_input, pre_hidden)

                if mask:
                    new_hidden = layers.elementwise_mul(
                        new_hidden, step_mask, axis=0) - layers.elementwise_mul(
                            pre_hidden, (step_mask - 1), axis=0)
                rnn.update_memory(pre_hidden, new_hidden)

                rnn.step_output(new_hidden)

                step_input = new_hidden
                if dropout_prob != None and dropout_prob > 0.0:
                    step_input = layers.dropout(
                        step_input,
                        dropout_prob=dropout_prob, )

            rnn.step_output(step_input)

        rnn_out = rnn()

        last_hidden_array = []
        rnn_output = rnn_out[-1]
        for i in range(num_layers):
            last_hidden = rnn_out[i]
            last_hidden = last_hidden[-1]
            last_hidden_array.append(last_hidden)

        last_hidden_output = layers.concat(last_hidden_array, axis=0)
        last_hidden_output = layers.reshape(
            last_hidden_output, shape=[num_layers, -1, hidden_size])

        return rnn_output, last_hidden_output
        # seq_len, batch_size, hidden_size

    fw_rnn_out, fw_last_hidden = get_single_direction_output(
        input, fw_unit_list, mask, direc_index=0)

    if bidirectional:
        bw_input = layers.reverse(input, axis=[0])
        bw_mask = None
        if mask:
            bw_mask = layers.reverse(mask, axis=[0])
        bw_rnn_out, bw_last_hidden = get_single_direction_output(
            bw_input, bw_unit_list, bw_mask, direc_index=1)

        bw_rnn_out = layers.reverse(bw_rnn_out, axis=[0])

        rnn_out = layers.concat([fw_rnn_out, bw_rnn_out], axis=2)
        last_hidden = layers.concat([fw_last_hidden, bw_last_hidden], axis=1)

        last_hidden = layers.reshape(
            last_hidden, shape=[num_layers * direc_num, -1, hidden_size])

        if batch_first:
            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
        return rnn_out, last_hidden
    else:

        rnn_out = fw_rnn_out
        last_hidden = fw_last_hidden

        if batch_first:
400
            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421

        return rnn_out, last_hidden


def basic_lstm(input,
               init_hidden,
               init_cell,
               hidden_size,
               num_layers=1,
               sequence_length=None,
               dropout_prob=0.0,
               bidirectional=False,
               batch_first=True,
               param_attr=None,
               bias_attr=None,
               gate_activation=None,
               activation=None,
               forget_bias=1.0,
               dtype='float32',
               name='basic_lstm'):
    """
T
tianshuo78520a 已提交
422
    LSTM implementation using basic operators, supports multiple layers and bidirectional LSTM.
423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455

    .. math::
           i_t &= \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + b_i)

           f_t &= \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + b_f + forget_bias )

           o_t &= \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + b_o)

           \\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + b_c)

           c_t &= f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}

           h_t &= o_t \odot tanh(c_t)

    Args:
        input (Variable): lstm input tensor, 
                       if batch_first = False, shape should be ( seq_len x batch_size x input_size )  
                       if batch_first = True, shape should be ( batch_size x seq_len x hidden_size )
        init_hidden(Variable|None): The initial hidden state of the LSTM
                       This is a tensor with shape ( num_layers x batch_size x hidden_size)
                       if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
                       and can be reshaped to a tensor with shape ( num_layers x 2 x batch_size x hidden_size) to use.
                       If it's None, it will be set to all 0.
        init_cell(Variable|None): The initial hidden state of the LSTM
                       This is a tensor with shape ( num_layers x batch_size x hidden_size)
                       if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
                       and can be reshaped to a tensor with shape ( num_layers x 2 x batch_size x hidden_size) to use.
                       If it's None, it will be set to all 0.
        hidden_size (int): Hidden size of the LSTM
        num_layers (int): The total number of layers of the LSTM
        sequence_length (Variabe|None): A tensor (shape [batch_size]) stores each real length of each instance,
                        This tensor will be convert to a mask to mask the padding ids
                        If it's None means NO padding ids
T
tianshuo78520a 已提交
456
        dropout_prob(float|0.0): Dropout prob, dropout ONLY work after rnn output of each layers, 
457 458
                             NOT between time steps
        bidirectional (bool|False): If it is bidirectional
459 460 461 462 463
        batch_first (bool|True): The shape format of the input and output tensors. If true,
            the shape format should be :attr:`[batch_size, seq_len, hidden_size]`. If false,
            the shape format should be :attr:`[seq_len, batch_size, hidden_size]`. By default
            this function accepts input and emits output in batch-major form to be consistent
            with most of data format, though a bit less efficient because of extra transposes.
464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            weight matrix. Note:
            If it is set to None or one attribute of ParamAttr, lstm_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The parameter attribute for the bias
            of LSTM unit.
            If it is set to None or one attribute of ParamAttr, lstm_unit will 
            create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        gate_activation (function|None): The activation function for gates (actGate).
                                  Default: 'fluid.layers.sigmoid'
        activation (function|None): The activation function for cell (actNode).
                             Default: 'fluid.layers.tanh'
        forget_bias (float|1.0) : Forget bias used to compute the forget gate
        dtype(string): Data type used in this unit
        name(string): Name used to identify parameters and biases

    Returns:
        rnn_out(Tensor), last_hidden(Tensor), last_cell(Tensor)
            - rnn_out is the result of LSTM hidden, shape is (seq_len x batch_size x hidden_size) \
              if is_bidirec set to True, it's shape will be ( seq_len x batch_sze x hidden_size*2)
            - last_hidden is the hidden state of the last step of LSTM \
              with shape ( num_layers x batch_size x hidden_size ) \
              if is_bidirec set to True, it's shape will be ( num_layers*2 x batch_size x hidden_size),
              and can be reshaped to a tensor ( num_layers x 2 x batch_size x hidden_size)  to use.
            - last_cell is the hidden state of the last step of LSTM \
              with shape ( num_layers x batch_size x hidden_size ) \
              if is_bidirec set to True, it's shape will be ( num_layers*2 x batch_size x hidden_size),
              and can be reshaped to a tensor ( num_layers x 2 x batch_size x hidden_size)  to use.

    Examples:
        .. code-block:: python
            
            import paddle.fluid.layers as layers
            from paddle.fluid.contrib.layers import basic_lstm

            batch_size = 20
            input_size = 128
            hidden_size = 256
            num_layers = 2
            dropout = 0.5
            bidirectional = True
            batch_first = False

            input = layers.data( name = "input", shape = [-1, batch_size, input_size], dtype='float32')
            pre_hidden = layers.data( name = "pre_hidden", shape=[-1, hidden_size], dtype='float32')
            pre_cell = layers.data( name = "pre_cell", shape=[-1, hidden_size], dtype='float32')
            sequence_length = layers.data( name="sequence_length", shape=[-1], dtype='int32')

            rnn_out, last_hidden, last_cell = basic_lstm( input, pre_hidden, pre_cell, \
                    hidden_size, num_layers = num_layers, \
                    sequence_length = sequence_length, dropout_prob=dropout, bidirectional = bidirectional, \
                    batch_first = batch_first)

    """
    fw_unit_list = []

    for i in range(num_layers):
        new_name = name + "_layers_" + str(i)
524 525 526 527 528 529 530 531 532 533
        if param_attr is not None and param_attr.name is not None:
            layer_param_attr = copy.deepcopy(param_attr)
            layer_param_attr.name += "_fw_w_" + str(i)
        else:
            layer_param_attr = param_attr
        if bias_attr is not None and bias_attr.name is not None:
            layer_bias_attr = copy.deepcopy(bias_attr)
            layer_bias_attr.name += "_fw_b_" + str(i)
        else:
            layer_bias_attr = bias_attr
534 535 536 537
        fw_unit_list.append(
            BasicLSTMUnit(
                new_name,
                hidden_size,
538 539
                param_attr=layer_param_attr,
                bias_attr=layer_bias_attr,
540 541 542 543 544 545 546 547 548
                gate_activation=gate_activation,
                activation=activation,
                forget_bias=forget_bias,
                dtype=dtype))
    if bidirectional:
        bw_unit_list = []

        for i in range(num_layers):
            new_name = name + "_reverse_layers_" + str(i)
549 550 551 552 553 554 555 556 557 558
            if param_attr is not None and param_attr.name is not None:
                layer_param_attr = copy.deepcopy(param_attr)
                layer_param_attr.name += "_bw_w_" + str(i)
            else:
                layer_param_attr = param_attr
            if bias_attr is not None and bias_attr.name is not None:
                layer_bias_attr = copy.deepcopy(bias_attr)
                layer_bias_attr.name += "_bw_b_" + str(i)
            else:
                layer_bias_attr = param_attr
559 560 561 562
            bw_unit_list.append(
                BasicLSTMUnit(
                    new_name,
                    hidden_size,
563 564
                    param_attr=layer_param_attr,
                    bias_attr=layer_bias_attr,
565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779
                    gate_activation=gate_activation,
                    activation=activation,
                    forget_bias=forget_bias,
                    dtype=dtype))

    if batch_first:
        input = layers.transpose(input, [1, 0, 2])

    mask = None
    if sequence_length:
        max_seq_len = layers.shape(input)[0]
        mask = layers.sequence_mask(
            sequence_length, maxlen=max_seq_len, dtype='float32')

        mask = layers.transpose(mask, [1, 0])

    direc_num = 1
    if bidirectional:
        direc_num = 2
        # convert to [num_layers, 2, batch_size, hidden_size]
    if init_hidden:
        init_hidden = layers.reshape(
            init_hidden, shape=[num_layers, direc_num, -1, hidden_size])
        init_cell = layers.reshape(
            init_cell, shape=[num_layers, direc_num, -1, hidden_size])

    # forward direction
    def get_single_direction_output(rnn_input,
                                    unit_list,
                                    mask=None,
                                    direc_index=0):
        rnn = StaticRNN()
        with rnn.step():
            step_input = rnn.step_input(rnn_input)

            if mask:
                step_mask = rnn.step_input(mask)

            for i in range(num_layers):
                if init_hidden:
                    pre_hidden = rnn.memory(init=init_hidden[i, direc_index])
                    pre_cell = rnn.memory(init=init_cell[i, direc_index])
                else:
                    pre_hidden = rnn.memory(
                        batch_ref=rnn_input, shape=[-1, hidden_size])
                    pre_cell = rnn.memory(
                        batch_ref=rnn_input, shape=[-1, hidden_size])

                new_hidden, new_cell = unit_list[i](step_input, pre_hidden,
                                                    pre_cell)

                if mask:
                    new_hidden = layers.elementwise_mul(
                        new_hidden, step_mask, axis=0) - layers.elementwise_mul(
                            pre_hidden, (step_mask - 1), axis=0)
                    new_cell = layers.elementwise_mul(
                        new_cell, step_mask, axis=0) - layers.elementwise_mul(
                            pre_cell, (step_mask - 1), axis=0)

                rnn.update_memory(pre_hidden, new_hidden)
                rnn.update_memory(pre_cell, new_cell)

                rnn.step_output(new_hidden)
                rnn.step_output(new_cell)

                step_input = new_hidden
                if dropout_prob != None and dropout_prob > 0.0:
                    step_input = layers.dropout(
                        step_input,
                        dropout_prob=dropout_prob,
                        dropout_implementation='upscale_in_train')

            rnn.step_output(step_input)

        rnn_out = rnn()

        last_hidden_array = []
        last_cell_array = []
        rnn_output = rnn_out[-1]
        for i in range(num_layers):
            last_hidden = rnn_out[i * 2]
            last_hidden = last_hidden[-1]
            last_hidden_array.append(last_hidden)
            last_cell = rnn_out[i * 2 + 1]
            last_cell = last_cell[-1]
            last_cell_array.append(last_cell)

        last_hidden_output = layers.concat(last_hidden_array, axis=0)
        last_hidden_output = layers.reshape(
            last_hidden_output, shape=[num_layers, -1, hidden_size])
        last_cell_output = layers.concat(last_cell_array, axis=0)
        last_cell_output = layers.reshape(
            last_cell_output, shape=[num_layers, -1, hidden_size])

        return rnn_output, last_hidden_output, last_cell_output
        # seq_len, batch_size, hidden_size

    fw_rnn_out, fw_last_hidden, fw_last_cell = get_single_direction_output(
        input, fw_unit_list, mask, direc_index=0)

    if bidirectional:
        bw_input = layers.reverse(input, axis=[0])
        bw_mask = None
        if mask:
            bw_mask = layers.reverse(mask, axis=[0])
        bw_rnn_out, bw_last_hidden, bw_last_cell = get_single_direction_output(
            bw_input, bw_unit_list, bw_mask, direc_index=1)

        bw_rnn_out = layers.reverse(bw_rnn_out, axis=[0])

        rnn_out = layers.concat([fw_rnn_out, bw_rnn_out], axis=2)
        last_hidden = layers.concat([fw_last_hidden, bw_last_hidden], axis=1)
        last_hidden = layers.reshape(
            last_hidden, shape=[num_layers * direc_num, -1, hidden_size])

        last_cell = layers.concat([fw_last_cell, bw_last_cell], axis=1)
        last_cell = layers.reshape(
            last_cell, shape=[num_layers * direc_num, -1, hidden_size])

        if batch_first:
            rnn_out = layers.transpose(rnn_out, [1, 0, 2])
        return rnn_out, last_hidden, last_cell
    else:

        rnn_out = fw_rnn_out
        last_hidden = fw_last_hidden
        last_cell = fw_last_cell

        if batch_first:
            rnn_out = layers.transpose(rnn_out, [1, 0, 2])

        return rnn_out, last_hidden, last_cell


class BasicLSTMUnit(Layer):
    """
    ****
    BasicLSTMUnit class, Using basic operator to build LSTM
    The algorithm can be described as the code below.

        .. math::

           i_t &= \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + b_i)

           f_t &= \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + b_f + forget_bias )

           o_t &= \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + b_o)

           \\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + b_c)

           c_t &= f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}

           h_t &= o_t \odot tanh(c_t)

        - $W$ terms denote weight matrices (e.g. $W_{ix}$ is the matrix
          of weights from the input gate to the input)
        - The b terms denote bias vectors ($bx_i$ and $bh_i$ are the input gate bias vector).
        - sigmoid is the logistic sigmoid function.
        - $i, f, o$ and $c$ are the input gate, forget gate, output gate,
          and cell activation vectors, respectively, all of which have the same size as
          the cell output activation vector $h$.
        - The :math:`\odot` is the element-wise product of the vectors.
        - :math:`tanh` is the activation functions.
        - :math:`\\tilde{c_t}` is also called candidate hidden state,
          which is computed based on the current input and the previous hidden state.

    Args:
        name_scope(string) : The name scope used to identify parameter and bias name
        hidden_size (integer): The hidden size used in the Unit.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            weight matrix. Note:
            If it is set to None or one attribute of ParamAttr, lstm_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The parameter attribute for the bias
            of LSTM unit.
            If it is set to None or one attribute of ParamAttr, lstm_unit will 
            create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized as zero. Default: None.
        gate_activation (function|None): The activation function for gates (actGate).
                                  Default: 'fluid.layers.sigmoid'
        activation (function|None): The activation function for cells (actNode).
                             Default: 'fluid.layers.tanh'
        forget_bias(float|1.0): forget bias used when computing forget gate
        dtype(string): data type used in this unit

    Examples:

        .. code-block:: python

            import paddle.fluid.layers as layers
            from paddle.fluid.contrib.layers import BasicLSTMUnit

            input_size = 128
            hidden_size = 256
            input = layers.data( name = "input", shape = [-1, input_size], dtype='float32')
            pre_hidden = layers.data( name = "pre_hidden", shape=[-1, hidden_size], dtype='float32')
            pre_cell = layers.data( name = "pre_cell", shape=[-1, hidden_size], dtype='float32')

            lstm_unit = BasicLSTMUnit( "gru_unit", hidden_size)

            new_hidden, new_cell = lstm_unit( input, pre_hidden, pre_cell )

    """

    def __init__(self,
                 name_scope,
                 hidden_size,
                 param_attr=None,
                 bias_attr=None,
                 gate_activation=None,
                 activation=None,
                 forget_bias=1.0,
                 dtype='float32'):
        super(BasicLSTMUnit, self).__init__(name_scope, dtype)
780 781 782 783
        # reserve old school _full_name and _helper for static graph save load
        self._full_name = unique_name.generate(name_scope + "/" +
                                               self.__class__.__name__)
        self._helper = LayerObjectHelper(self._full_name)
784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824

        self._name = name_scope
        self._hiden_size = hidden_size
        self._param_attr = param_attr
        self._bias_attr = bias_attr
        self._gate_activation = gate_activation or layers.sigmoid
        self._activation = activation or layers.tanh
        self._forget_bias = layers.fill_constant(
            [1], dtype=dtype, value=forget_bias)
        self._forget_bias.stop_gradient = False
        self._dtype = dtype

    def _build_once(self, input, pre_hidden, pre_cell):
        self._input_size = input.shape[-1]
        assert (self._input_size > 0)

        self._weight = self.create_parameter(
            attr=self._param_attr,
            shape=[self._input_size + self._hiden_size, 4 * self._hiden_size],
            dtype=self._dtype)

        self._bias = self.create_parameter(
            attr=self._bias_attr,
            shape=[4 * self._hiden_size],
            dtype=self._dtype,
            is_bias=True)

    def forward(self, input, pre_hidden, pre_cell):
        concat_input_hidden = layers.concat([input, pre_hidden], 1)
        gate_input = layers.matmul(x=concat_input_hidden, y=self._weight)

        gate_input = layers.elementwise_add(gate_input, self._bias)
        i, j, f, o = layers.split(gate_input, num_or_sections=4, dim=-1)
        new_cell = layers.elementwise_add(
            layers.elementwise_mul(
                pre_cell,
                layers.sigmoid(layers.elementwise_add(f, self._forget_bias))),
            layers.elementwise_mul(layers.sigmoid(i), layers.tanh(j)))
        new_hidden = layers.tanh(new_cell) * layers.sigmoid(o)

        return new_hidden, new_cell