| from transformers import PretrainedConfig | |
| class HLMEncoderConfig(PretrainedConfig): | |
| def __init__( | |
| self, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_dropout_prob=0.1, | |
| layer_norm_eps=1e-7, | |
| sandwich=False, | |
| sandwich_size=0, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.dropout_prob = hidden_dropout_prob | |
| self.layer_norm_eps = layer_norm_eps | |
| if sandwich: | |
| self.sandwich_size = num_hidden_layers // 6 | |
| else: | |
| self.sandwich_size = sandwich_size | |
| class HLMConfig(PretrainedConfig): | |
| model_type = "hlm" | |
| def __init__( | |
| self, | |
| vocab_size=512, | |
| type_vocab_size=2, | |
| embedding_size=-1, | |
| max_seq_length=256, | |
| max_word_length=16, | |
| initializer_range=0.02, | |
| pad_token_id=0, | |
| intra_word_encoder={}, | |
| inter_word_encoder={}, | |
| residual_word_embedding=False, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.type_vocab_size = type_vocab_size | |
| self.embedding_size = embedding_size | |
| self.initializer_range = initializer_range | |
| self.max_seq_length = max_seq_length | |
| self.max_word_length = max_word_length | |
| self.pad_token_id = pad_token_id | |
| self.intra_word_encoder = HLMEncoderConfig(**intra_word_encoder) | |
| self.inter_word_encoder = HLMEncoderConfig(**inter_word_encoder) | |
| self.hidden_size = self.inter_word_encoder.hidden_size | |
| self.residual_word_embedding = residual_word_embedding | |