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import torch
import torch.nn as nn
import torch
from torch.autograd import Variable
import copy
# from transformers.modeling_bert import BertLayerNorm
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss, MSELoss
# from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
# BertConfig, BertForMaskedLM, BertTokenizer,
# GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
# OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
# RobertaConfig, RobertaModel, RobertaTokenizer,
# DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
from transformers.modeling_utils import PreTrainedModel
class Model(PreTrainedModel):
def __init__(self, encoder, config, tokenizer, args):
super(Model, self).__init__(config)
self.encoder = encoder
self.config = config
self.tokenizer = tokenizer
self.mlp = nn.Sequential(nn.Linear(768*4, 768),
nn.Tanh(),
nn.Linear(768, 1),
nn.Sigmoid())
self.loss_func = nn.BCELoss()
self.args = args
def forward(self, code_inputs, nl_inputs, labels, return_vec=False, do_my_test=False):
bs = code_inputs.shape[0]
inputs = torch.cat((code_inputs, nl_inputs), 0)
encoder_output = self.encoder(inputs, attention_mask=inputs.ne(1))
outputs = encoder_output[1]
code_vec = outputs[:bs]
nl_vec = outputs[bs:]
code_feature = encoder_output.pooler_output[:bs]
nl_feature = encoder_output.pooler_output[bs:]
if return_vec:
return code_vec, nl_vec
logits = self.mlp(torch.cat((nl_vec, code_vec, nl_vec-code_vec, nl_vec*code_vec), 1))
loss = self.loss_func(logits, labels.float().unsqueeze(1))
if do_my_test:
return loss, code_feature, nl_feature
predictions = (logits > 0.5).int() # (Batch, )
# predictions = logits.float()
return loss, predictions
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