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from transformers.modeling_outputs import TokenClassifierOutput
import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel, AutoConfig
from torch.nn import CrossEntropyLoss
from typing import Optional, Tuple, Union
import logging
from configuration_extended_multitask import ImpressoConfig
logger = logging.getLogger(__name__)
class ExtendedMultitaskModelForTokenClassification(PreTrainedModel):
config_class = ImpressoConfig
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config, num_token_labels_dict):
super().__init__(config)
self.num_token_labels_dict = num_token_labels_dict
self.config = config
# self.bert = AutoModel.from_config(config)
self.bert = AutoModel.from_pretrained(
config.name_or_path, config=config.pretrained_config
)
if "classifier_dropout" not in config.__dict__:
classifier_dropout = 0.1
else:
classifier_dropout = (
config.classifier_dropout
if config.classifier_dropout is not None
else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
# Additional transformer layers
self.transformer_encoder = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=config.hidden_size, nhead=config.num_attention_heads
),
num_layers=2,
)
# For token classification, create a classifier for each task
self.token_classifiers = nn.ModuleDict(
{
task: nn.Linear(config.hidden_size, num_labels)
for task, num_labels in num_token_labels_dict.items()
}
)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
token_labels: Optional[dict] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
r"""
token_labels (`dict` of `torch.LongTensor` of shape `(batch_size, seq_length)`, *optional*):
Labels for computing the token classification loss. Keys should match the tasks.
"""
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
bert_kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
"position_ids": position_ids,
"head_mask": head_mask,
"inputs_embeds": inputs_embeds,
"output_attentions": output_attentions,
"output_hidden_states": output_hidden_states,
"return_dict": return_dict,
}
if any(
keyword in self.config.name_or_path.lower()
for keyword in ["llama", "deberta"]
):
bert_kwargs.pop("token_type_ids")
bert_kwargs.pop("head_mask")
outputs = self.bert(**bert_kwargs)
# For token classification
token_output = outputs[0]
token_output = self.dropout(token_output)
# Pass through additional transformer layers
token_output = self.transformer_encoder(token_output.transpose(0, 1)).transpose(
0, 1
)
# Collect the logits and compute the loss for each task
task_logits = {}
total_loss = 0
for task, classifier in self.token_classifiers.items():
logits = classifier(token_output)
task_logits[task] = logits
if token_labels and task in token_labels:
loss_fct = CrossEntropyLoss()
loss = loss_fct(
logits.view(-1, self.num_token_labels_dict[task]),
token_labels[task].view(-1),
)
total_loss += loss
if not return_dict:
output = (task_logits,) + outputs[2:]
return ((total_loss,) + output) if total_loss != 0 else output
return TokenClassifierOutput(
loss=total_loss,
logits=task_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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