| from typing import Tuple, Union | |
| import torch | |
| from transformers import PretrainedConfig | |
| from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions | |
| from transformers.models.bert.modeling_bert import BertModel | |
| class GoldenRetrieverConfig(PretrainedConfig): | |
| model_type = "bert" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=512, | |
| type_vocab_size=2, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| pad_token_id=0, | |
| position_embedding_type="absolute", | |
| use_cache=True, | |
| classifier_dropout=None, | |
| projection_dim=None, | |
| **kwargs, | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.use_cache = use_cache | |
| self.classifier_dropout = classifier_dropout | |
| self.projection_dim = projection_dim | |
| class GoldenRetrieverModel(BertModel): | |
| config_class = GoldenRetrieverConfig | |
| def __init__(self, config, *args, **kwargs): | |
| super().__init__(config) | |
| self.layer_norm_layer = torch.nn.LayerNorm( | |
| config.hidden_size, eps=config.layer_norm_eps | |
| ) | |
| self.projection: torch.nn.Module | None = None | |
| if config.projection_dim is not None: | |
| self.projection = torch.nn.Sequential( | |
| torch.nn.Linear(config.hidden_size, config.projection_dim), | |
| torch.nn.LayerNorm(config.projection_dim), | |
| ) | |
| def forward( | |
| self, **kwargs | |
| ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: | |
| attention_mask = kwargs.get("attention_mask", None) | |
| model_outputs = super().forward(**kwargs) | |
| if attention_mask is None: | |
| pooler_output = model_outputs.pooler_output | |
| else: | |
| token_embeddings = model_outputs.last_hidden_state | |
| input_mask_expanded = ( | |
| attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() | |
| ) | |
| pooler_output = torch.sum( | |
| token_embeddings * input_mask_expanded, 1 | |
| ) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) | |
| pooler_output = self.layer_norm_layer(pooler_output) | |
| if self.projection is not None: | |
| pooler_output = self.projection(pooler_output) | |
| if not kwargs.get("return_dict", True): | |
| return (model_outputs[0], pooler_output) + model_outputs[2:] | |
| return BaseModelOutputWithPoolingAndCrossAttentions( | |
| last_hidden_state=model_outputs.last_hidden_state, | |
| pooler_output=pooler_output, | |
| past_key_values=model_outputs.past_key_values, | |
| hidden_states=model_outputs.hidden_states, | |
| attentions=model_outputs.attentions, | |
| cross_attentions=model_outputs.cross_attentions, | |
| ) | |