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from typing import Optional, Tuple
import torch
from transformers import BertConfig, BertModel, BertPreTrainedModel, PreTrainedModel
from transformers.models.bert.modeling_bert import BertOnlyMLMHead
class BertEmbeddingConfig(BertConfig):
n_output_dims: int
distance_func: str = "euclidean"
class BiEncoderConfig(BertEmbeddingConfig):
max_length1: int
max_length2: int
class BiEncoder(PreTrainedModel):
config_class = BiEncoderConfig
def __init__(self, config: BiEncoderConfig):
super().__init__(config)
config1 = _replace_max_length(config, "max_length1")
self.bert1 = BertForEmbedding(config1)
config2 = _replace_max_length(config, "max_length2")
self.bert2 = BertForEmbedding(config2)
self.post_init()
def forward(self, x1, x2):
y1 = self.forward1(x1)
y2 = self.forward2(x2)
return {"y1": y1, "y2": y2}
def forward2(self, x2):
y2 = self.bert2(input_ids=x2["input_ids"])
return y2
def forward1(self, x1):
y1 = self.bert1(input_ids=x1["input_ids"])
return y1
class BiEncoderWithMaskedLM(PreTrainedModel):
config_class = BiEncoderConfig
def __init__(self, config: BiEncoderConfig):
super().__init__(config=config)
config1 = _replace_max_length(config, "max_length1")
self.bert1 = BertForEmbedding(config1)
self.lm_head1 = BertOnlyMLMHead(config=config1)
config2 = _replace_max_length(config, "max_length2")
self.bert2 = BertForEmbedding(config2)
self.lm_head2 = BertOnlyMLMHead(config=config2)
self.post_init()
def forward(self, x1, x2):
y1, state1 = self.bert1.forward_with_state(input_ids=x1["input_ids"])
y2, state2 = self.bert2.forward_with_state(input_ids=x2["input_ids"])
scores1 = self.lm_head1(state1)
scores2 = self.lm_head2(state2)
outputs = {"y1": y1, "y2": y2, "scores1": scores1, "scores2": scores2}
return outputs
def _replace_max_length(config, length_key):
c1 = config.__dict__.copy()
c1["max_position_embeddings"] = c1.pop(length_key)
config1 = BertEmbeddingConfig(**c1)
return config1
class L2Norm:
def __call__(self, x):
return x / torch.norm(x, p=2, dim=-1, keepdim=True)
class BertForEmbedding(BertPreTrainedModel):
config_class = BertEmbeddingConfig
def __init__(self, config: BertEmbeddingConfig):
super().__init__(config)
n_output_dims = config.n_output_dims
self.fc = torch.nn.Linear(config.hidden_size, n_output_dims)
self.bert = BertModel(config)
self.activation = _get_activation(config.distance_func)
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.Tensor:
embedding, _ = self.forward_with_state(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return embedding
def forward_with_state(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
encoded = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooler_output = encoded.pooler_output
logits = self.fc(pooler_output)
embedding = self.activation(logits)
return embedding, encoded.last_hidden_state
def _get_activation(distance_func: str):
if distance_func == "euclidean":
activation = torch.nn.Tanh()
elif distance_func == "angular":
activation = L2Norm() # type: ignore
else:
raise NotImplementedError()
return activation
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