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bart.py
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bart.py
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import torch
import torch.nn.functional as F
from torch import Tensor, nn
from transformers import T5ForConditionalGeneration, BartForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from transformers.modeling_outputs import Seq2SeqLMOutput
from utils import label_smoothed_nll_loss
class MyBart(BartForConditionalGeneration):
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
is_training=False,
**model_kwargs,
):
if is_training:
_decoder_input_ids = shift_tokens_right(decoder_input_ids, self.config.pad_token_id, self.config.decoder_start_token_id)
else:
_decoder_input_ids = decoder_input_ids
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=_decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
**model_kwargs,
)
# lm_logits = F.linear(outputs[0], self.model.shared.weight, bias=self.final_logits_bias)
lm_logits = F.linear(outputs[0], self.model.shared.weight, bias=self.final_logits_bias)
lprobs = F.log_softmax(lm_logits, dim=-1)
masked_lm_loss , _ = label_smoothed_nll_loss(lprobs, decoder_input_ids, epsilon=0.1, ignore_index=self.config.pad_token_id)
if is_training:
return masked_lm_loss
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
# return (lm_logits, ) + outputs[1:]