| |
| |
| import torch |
| import torch.nn as nn |
| import torch |
| from torch.autograd import Variable |
| import copy |
| import torch.nn.functional as F |
| from torch.nn import CrossEntropyLoss, MSELoss |
|
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|
|
| |
| class Model(nn.Module): |
| def __init__(self, encoder,config,tokenizer,args): |
| super(Model, self).__init__() |
| self.encoder = encoder |
| self.config=config |
| self.tokenizer=tokenizer |
| self.args=args |
| |
| |
| def forward(self, input_ids=None,p_input_ids=None,n_input_ids=None,labels=None): |
| bs,_=input_ids.size() |
| input_ids=torch.cat((input_ids,p_input_ids,n_input_ids),0) |
| |
| outputs=self.encoder(input_ids,attention_mask=input_ids.ne(1)) |
| if len(outputs) > 1: |
| outputs = outputs[1] |
| else: |
| outputs = outputs[0][:, 0, :] |
| outputs=outputs.split(bs,0) |
| |
| prob_1=(outputs[0]*outputs[1]).sum(-1) |
| prob_2=(outputs[0]*outputs[2]).sum(-1) |
| temp=torch.cat((outputs[0],outputs[1]),0) |
| temp_labels=torch.cat((labels,labels),0) |
| prob_3= torch.mm(outputs[0],temp.t()) |
| mask=labels[:,None]==temp_labels[None,:] |
| prob_3=prob_3*(1-mask.float())-1e9*mask.float() |
| |
| prob=torch.softmax(torch.cat((prob_1[:,None],prob_2[:,None],prob_3),-1),-1) |
| loss=torch.log(prob[:,0]+1e-10) |
| loss=-loss.mean() |
| return loss,outputs[0] |
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