| 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, |
| ) |
|
|
