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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,
**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
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
)
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 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,
)
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