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from transformers import PreTrainedModel, DPRQuestionEncoder, DPRContextEncoder
import torch
import torch.nn as nn
from .configuration_dpr import CustomDPRConfig
from typing import Union, List, Dict
class OBSSDPRModel(PreTrainedModel):
config_class = CustomDPRConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = DPRModel()
def forward(self, input):
return self.model(input)
class DPRModel(nn.Module):
def __init__(self,
question_model_name='facebook/dpr-question_encoder-single-nq-base',
context_model_name='facebook/dpr-ctx_encoder-single-nq-base',
freeze_params=12.0):
super(DPRModel, self).__init__()
self.freeze_params = freeze_params
self.question_model = DPRQuestionEncoder.from_pretrained(question_model_name)
self.context_model = DPRContextEncoder.from_pretrained(context_model_name)
# self.freeze_layers(freeze_params)
def freeze_layers(self, freeze_params):
num_layers_context = sum(1 for _ in self.context_model.parameters())
num_layers_question = sum(1 for _ in self.question_model.parameters())
for parameters in list(self.context_model.parameters())[:int(freeze_params * num_layers_context)]:
parameters.requires_grad = False
for parameters in list(self.context_model.parameters())[int(freeze_params * num_layers_context):]:
parameters.requires_grad = True
for parameters in list(self.question_model.parameters())[:int(freeze_params * num_layers_question)]:
parameters.requires_grad = False
for parameters in list(self.question_model.parameters())[int(freeze_params * num_layers_question):]:
parameters.requires_grad = True
def batch_dot_product(self, context_output, question_output):
mat1 = torch.unsqueeze(question_output, dim=1)
mat2 = torch.unsqueeze(context_output, dim=2)
result = torch.bmm(mat1, mat2)
result = torch.squeeze(result, dim=1)
result = torch.squeeze(result, dim=1)
return result
def forward(self, batch: Union[List[Dict], Dict]):
context_tensor = batch['context_tensor']
question_tensor = batch['question_tensor']
context_model_output = self.context_model(input_ids=context_tensor['input_ids'],
attention_mask=context_tensor['attention_mask']) # (bsz, hdim)
question_model_output = self.question_model(input_ids = question_tensor['input_ids'],
attention_mask=question_tensor['attention_mask'])
embeddings_context = context_model_output['pooler_output']
embeddings_question = question_model_output['pooler_output']
scores = self.batch_dot_product(embeddings_context, embeddings_question) # self.scale
return scores
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