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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """Encoder of Seq2seq."""
- import copy
- from mindspore import nn
- from mindspore.ops import operations as P
- from mindspore.common import dtype as mstype
-
- from config.config import Seq2seqConfig
- from .dynamic_rnn import DynamicRNNNet
-
- class Seq2seqEncoder(nn.Cell):
- """
- Implements of Seq2seq encoder.
-
- Args:
- config (Seq2seqConfig): Configuration of Seq2seq network.
- is_training (bool): Whether to train.
- compute_type (mstype): Mindspore data type.
-
- Returns:
- Tensor, shape of (2, T, D).
- """
-
- def __init__(self,
- config: Seq2seqConfig,
- is_training: bool,
- compute_type=mstype.float32):
- super(Seq2seqEncoder, self).__init__()
-
- config = copy.deepcopy(config)
-
- if not is_training:
- config.hidden_dropout_prob = 0.0
-
- self.num_layers = config.num_hidden_layers
- self.hidden_dropout_prob = config.hidden_dropout_prob
- self.seq_length = config.seq_length
- self.batch_size = config.batch_size
- self.word_embed_dim = config.hidden_size
-
- encoder_layers = []
- for _ in range(0, self.num_layers):
- layer = DynamicRNNNet(seq_length=self.seq_length,
- batchsize=self.batch_size,
- word_embed_dim=self.word_embed_dim,
- hidden_size=self.word_embed_dim)
- encoder_layers.append(layer)
-
- self.encoder_layers = nn.CellList(encoder_layers)
- self.dropout = nn.Dropout(keep_prob=1.0 - config.hidden_dropout_prob)
- self.reverse_v2 = P.ReverseV2(axis=[0])
-
- def construct(self, inputs):
- """Encoder."""
- inputs_r = self.reverse_v2(inputs)
- encoder_outputs = inputs_r
- state = 0
-
- for i in range(0, self.num_layers):
- encoder_outputs = self.dropout(encoder_outputs)
- # [T,N,D] -> [T,N,D]
- encoder_outputs, state = self.encoder_layers[i](encoder_outputs)
-
- return encoder_outputs, state
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