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