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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
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 |