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91 lines
3.3 KiB
91 lines
3.3 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2019 Mobvoi Inc. 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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# Modified from wenet(https://github.com/wenet-e2e/wenet)
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"""Positionwise feed forward layer definition."""
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import paddle
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from paddle import nn
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from paddle.nn import initializer as I
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from paddlespeech.s2t.modules.align import Linear
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["PositionwiseFeedForward"]
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class PositionwiseFeedForward(nn.Layer):
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"""Positionwise feed forward layer."""
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def __init__(self,
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idim: int,
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hidden_units: int,
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dropout_rate: float,
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activation: nn.Layer=nn.ReLU(),
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adaptive_scale: bool=False,
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init_weights: bool=False):
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"""Construct a PositionwiseFeedForward object.
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FeedForward are appied on each position of the sequence.
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The output dim is same with the input dim.
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Args:
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idim (int): Input dimenstion.
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hidden_units (int): The number of hidden units.
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dropout_rate (float): Dropout rate.
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activation (paddle.nn.Layer): Activation function
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"""
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super().__init__()
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self.idim = idim
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self.hidden_units = hidden_units
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self.w_1 = Linear(idim, hidden_units)
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self.activation = activation
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self.dropout = nn.Dropout(dropout_rate)
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self.w_2 = Linear(hidden_units, idim)
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self.adaptive_scale = adaptive_scale
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if self.adaptive_scale:
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ada_scale = self.create_parameter(
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[1, 1, idim], default_initializer=I.XavierUniform())
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self.add_parameter('ada_scale', ada_scale)
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ada_bias = self.create_parameter(
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[1, 1, idim], default_initializer=I.XavierUniform())
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self.add_parameter('ada_bias', ada_bias)
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if init_weights:
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self.init_weights()
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def init_weights(self):
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ffn1_max = self.idim**-0.5
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ffn2_max = self.hidden_units**-0.5
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self.w_1._param_attr = paddle.nn.initializer.Uniform(
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low=-ffn1_max, high=ffn1_max)
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self.w_1._bias_attr = paddle.nn.initializer.Uniform(
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low=-ffn1_max, high=ffn1_max)
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self.w_2._param_attr = paddle.nn.initializer.Uniform(
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low=-ffn2_max, high=ffn2_max)
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self.w_2._bias_attr = paddle.nn.initializer.Uniform(
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low=-ffn2_max, high=ffn2_max)
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def forward(self, xs: paddle.Tensor) -> paddle.Tensor:
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"""Forward function.
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Args:
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xs: input tensor (B, Lmax, D)
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Returns:
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output tensor, (B, Lmax, D)
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"""
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if self.adaptive_scale:
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xs = self.ada_scale * xs + self.ada_bias
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return self.w_2(self.dropout(self.activation(self.w_1(xs))))
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