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Running
on
Zero
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """Positionwise feed forward layer definition.""" | |
| import torch | |
| from funasr_detach.models.transformer.layer_norm import LayerNorm | |
| class PositionwiseFeedForwardDecoderSANM(torch.nn.Module): | |
| """Positionwise feed forward layer. | |
| Args: | |
| idim (int): Input dimenstion. | |
| hidden_units (int): The number of hidden units. | |
| dropout_rate (float): Dropout rate. | |
| """ | |
| def __init__( | |
| self, idim, hidden_units, dropout_rate, adim=None, activation=torch.nn.ReLU() | |
| ): | |
| """Construct an PositionwiseFeedForward object.""" | |
| super(PositionwiseFeedForwardDecoderSANM, self).__init__() | |
| self.w_1 = torch.nn.Linear(idim, hidden_units) | |
| self.w_2 = torch.nn.Linear( | |
| hidden_units, idim if adim is None else adim, bias=False | |
| ) | |
| self.dropout = torch.nn.Dropout(dropout_rate) | |
| self.activation = activation | |
| self.norm = LayerNorm(hidden_units) | |
| def forward(self, x): | |
| """Forward function.""" | |
| return self.w_2(self.norm(self.dropout(self.activation(self.w_1(x))))) | |