#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Positionwise feed forward layer definition."""
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import torch
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from funasr.modules.layer_norm import LayerNorm
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class PositionwiseFeedForward(torch.nn.Module):
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"""Positionwise feed forward layer.
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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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"""
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def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
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"""Construct an PositionwiseFeedForward object."""
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super(PositionwiseFeedForward, self).__init__()
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self.w_1 = torch.nn.Linear(idim, hidden_units)
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self.w_2 = torch.nn.Linear(hidden_units, idim)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.activation = activation
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def forward(self, x):
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"""Forward function."""
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return self.w_2(self.dropout(self.activation(self.w_1(x))))
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class PositionwiseFeedForwardDecoderSANM(torch.nn.Module):
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"""Positionwise feed forward layer.
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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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"""
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def __init__(self, idim, hidden_units, dropout_rate, adim=None, activation=torch.nn.ReLU()):
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"""Construct an PositionwiseFeedForward object."""
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super(PositionwiseFeedForwardDecoderSANM, self).__init__()
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self.w_1 = torch.nn.Linear(idim, hidden_units)
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self.w_2 = torch.nn.Linear(hidden_units, idim if adim is None else adim, bias=False)
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.activation = activation
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self.norm = LayerNorm(hidden_units)
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def forward(self, x):
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"""Forward function."""
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return self.w_2(self.norm(self.dropout(self.activation(self.w_1(x)))))
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