84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.fluid.dygraph as dg
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import paddle.fluid.layers as layers
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import paddle.fluid as fluid
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import math
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from parakeet.modules.customized import Conv1D
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class PositionwiseFeedForward(dg.Layer):
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''' A two-feed-forward-layer module '''
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def __init__(self,
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d_in,
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num_hidden,
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filter_size,
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padding=0,
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use_cudnn=True,
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dropout=0.1):
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super(PositionwiseFeedForward, self).__init__()
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self.num_hidden = num_hidden
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self.use_cudnn = use_cudnn
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self.dropout = dropout
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k = math.sqrt(1 / d_in)
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self.w_1 = Conv1D(
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num_channels=d_in,
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num_filters=num_hidden,
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filter_size=filter_size,
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padding=padding,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)),
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use_cudnn=use_cudnn)
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k = math.sqrt(1 / num_hidden)
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self.w_2 = Conv1D(
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num_channels=num_hidden,
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num_filters=d_in,
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filter_size=filter_size,
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padding=padding,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)),
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use_cudnn=use_cudnn)
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self.layer_norm = dg.LayerNorm(d_in)
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def forward(self, input):
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"""
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Feed Forward Network.
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Args:
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input (Variable): Shape(B, T, C), dtype: float32. The input value.
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Returns:
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output (Variable), Shape(B, T, C), the result after FFN.
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"""
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x = layers.transpose(input, [0, 2, 1])
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#FFN Networt
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x = self.w_2(layers.relu(self.w_1(x)))
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# dropout
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x = layers.dropout(x, self.dropout)
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x = layers.transpose(x, [0, 2, 1])
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# residual connection
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x = x + input
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#layer normalization
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output = self.layer_norm(x)
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return output
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