| 122 | |
| 123 | |
| 124 | class Decoder(nn.Module): |
| 125 | __constants__ = ['norm'] |
| 126 | |
| 127 | def __init__(self, decoder_layer, num_layers, norm): |
| 128 | super().__init__() |
| 129 | self.layers = transformer._get_clones(decoder_layer, num_layers) |
| 130 | self.num_layers = num_layers |
| 131 | self.norm = norm |
| 132 | |
| 133 | def forward( |
| 134 | self, |
| 135 | query, |
| 136 | content, |
| 137 | memory, |
| 138 | query_mask: Optional[Tensor] = None, |
| 139 | content_mask: Optional[Tensor] = None, |
| 140 | content_key_padding_mask: Optional[Tensor] = None, |
| 141 | ): |
| 142 | for i, mod in enumerate(self.layers): |
| 143 | last = i == len(self.layers) - 1 |
| 144 | query, content = mod( |
| 145 | query, |
| 146 | content, |
| 147 | memory, |
| 148 | query_mask, |
| 149 | content_mask, |
| 150 | content_key_padding_mask, |
| 151 | update_content=not last, |
| 152 | ) |
| 153 | query = self.norm(query) |
| 154 | return query |
| 155 | |
| 156 | |
| 157 | class TokenEmbedding(nn.Module): |