(
self,
hidden_size,
num_layers,
num_attention_heads,
padded_vocab_size,
max_position_embeddings,
)
| 924 | """ |
| 925 | |
| 926 | def __init__( |
| 927 | self, |
| 928 | hidden_size, |
| 929 | num_layers, |
| 930 | num_attention_heads, |
| 931 | padded_vocab_size, |
| 932 | max_position_embeddings, |
| 933 | ): |
| 934 | super(TransformerLanguageModel, self).__init__() |
| 935 | self.hidden_size = hidden_size |
| 936 | self.num_layers = num_layers |
| 937 | self.num_attention_heads = num_attention_heads |
| 938 | self.padded_vocab_size = padded_vocab_size |
| 939 | self.max_position_embeddings = max_position_embeddings |
| 940 | |
| 941 | # Embeddings |
| 942 | self.embedding = Embedding(self.hidden_size, |
| 943 | self.padded_vocab_size, |
| 944 | self.max_position_embeddings) |
| 945 | self._embedding_key = 'embedding' |
| 946 | |
| 947 | # Query embeddings |
| 948 | self.topQueryEmbedding = QueryEmbedding(self.hidden_size, |
| 949 | self.padded_vocab_size, |
| 950 | self.max_position_embeddings) |
| 951 | self._topQueryEmbedding_key = 'topQueryEmbedding' |
| 952 | |
| 953 | # Transformer |
| 954 | self.transformer = Transformer(self.hidden_size, |
| 955 | self.num_attention_heads, |
| 956 | self.num_layers) |
| 957 | self._transformer_key = 'transformer' |
| 958 | |
| 959 | def forward( |
| 960 | self, |
nothing calls this directly
no test coverage detected