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