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#!/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))))) | |