57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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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, d_in, num_hidden, filter_size, padding=0, use_cudnn=True, 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(in_channels = d_in,
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out_channels = num_hidden,
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filter_size = filter_size,
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padding=padding,
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
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use_cudnn = use_cudnn,
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data_format = "NTC")
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k = math.sqrt(1 / num_hidden)
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self.w_2 = Conv1D(in_channels = num_hidden,
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out_channels = d_in,
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filter_size = filter_size,
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padding=padding,
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param_attr = fluid.ParamAttr(initializer=fluid.initializer.XavierInitializer()),
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bias_attr = fluid.ParamAttr(initializer=fluid.initializer.Uniform(low=-k, high=k)),
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use_cudnn = use_cudnn,
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data_format = "NTC")
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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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#FFN Networt
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x = self.w_2(layers.relu(self.w_1(input)))
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# dropout
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x = layers.dropout(x, self.dropout)
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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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