| 936 | |
| 937 | |
| 938 | class Decoder(nn.Module): |
| 939 | __constants__ = ['norm'] |
| 940 | |
| 941 | def __init__(self, decoder_layer, num_layers, norm): |
| 942 | super().__init__() |
| 943 | self.layers = transformer._get_clones(decoder_layer, num_layers) |
| 944 | self.num_layers = num_layers |
| 945 | self.norm = norm |
| 946 | |
| 947 | def forward( |
| 948 | self, |
| 949 | query, |
| 950 | content, |
| 951 | memory, |
| 952 | query_mask: Optional[Tensor] = None, |
| 953 | content_mask: Optional[Tensor] = None, |
| 954 | content_key_padding_mask: Optional[Tensor] = None, |
| 955 | ): |
| 956 | for i, mod in enumerate(self.layers): |
| 957 | last = i == len(self.layers) - 1 |
| 958 | query, content = mod( |
| 959 | query, |
| 960 | content, |
| 961 | memory, |
| 962 | query_mask, |
| 963 | content_mask, |
| 964 | content_key_padding_mask, |
| 965 | update_content=not last, |
| 966 | ) |
| 967 | query = self.norm(query) |
| 968 | return query |
| 969 | |
| 970 | |
| 971 | class TokenEmbedding(nn.Module): |