| |
| |
| 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 |
|
|
|
|
| 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, code_inputs, nl_inputs, return_vec=False, return_scores=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:] |
|
|
| if return_vec: |
| return code_vec, nl_vec |
| scores = (nl_vec[:, None, :] * code_vec[None, :, :]).sum(-1) |
| if return_scores: |
| return scores |
| loss_fct = CrossEntropyLoss() |
| loss = loss_fct(scores, torch.arange(bs, device=scores.device)) |
| return loss, code_vec, nl_vec |
|
|
| def feature(self, code_inputs, nl_inputs): |
| bs = code_inputs.shape[0] |
| inputs = torch.cat((code_inputs, nl_inputs), 0) |
| encoder_output = self.encoder(inputs, attention_mask=inputs.ne(1)) |
| code_feature = encoder_output.pooler_output[:bs] |
| nl_feature = encoder_output.pooler_output[bs:] |
| return code_feature, nl_feature |
