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Update retrain.py #77

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11 changes: 7 additions & 4 deletions bert_pytorch/trainer/pretrain.py
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Expand Up @@ -59,8 +59,11 @@ def __init__(self, bert: BERT, vocab_size: int,
self.optim_schedule = ScheduledOptim(self.optim, self.bert.hidden, n_warmup_steps=warmup_steps)

# Using Negative Log Likelihood Loss function for predicting the masked_token
self.criterion = nn.NLLLoss(ignore_index=0)

self.criterion_mask_lm = nn.NLLLoss(ignore_index=0)

# Using Negative Log Likelihood Loss function for predicting the is_next
self.criterion_is_next = nn.NLLLoss()

self.log_freq = log_freq

print("Total Parameters:", sum([p.nelement() for p in self.model.parameters()]))
Expand Down Expand Up @@ -102,10 +105,10 @@ def iteration(self, epoch, data_loader, train=True):
next_sent_output, mask_lm_output = self.model.forward(data["bert_input"], data["segment_label"])

# 2-1. NLL(negative log likelihood) loss of is_next classification result
next_loss = self.criterion(next_sent_output, data["is_next"])
next_loss = self.criterion_is_next(next_sent_output, data["is_next"])

# 2-2. NLLLoss of predicting masked token word
mask_loss = self.criterion(mask_lm_output.transpose(1, 2), data["bert_label"])
mask_loss = self.criterion_mask_lm(mask_lm_output.transpose(1, 2), data["bert_label"])

# 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure
loss = next_loss + mask_loss
Expand Down