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from typing import Optional, Sequence, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, functional
from transformers import RobertaConfig
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.models.roberta.modeling_roberta import RobertaModel, RobertaPooler, RobertaPreTrainedModel
class MishActivation(nn.Module):
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x * torch.tanh(torch.nn.functional.softplus(x))
class NFQAClassificationHead(nn.Module):
def __init__(
self, input_dim: int, num_labels: int, hidden_dims: Sequence[int] = (768, 512), dropout: float = 0.0,
) -> None:
super().__init__()
self.linear_layers = nn.Sequential(*(nn.Linear(input_dim, dim) for dim in hidden_dims))
self.classification_layer = torch.nn.Linear(hidden_dims[-1], num_labels)
self.activations = [MishActivation()] * len(hidden_dims)
self.dropouts = [torch.nn.Dropout(p=dropout)] * len(hidden_dims)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
output = inputs
for layer, activation, dropout in zip(self.linear_layers, self.activations, self.dropouts):
output = dropout(activation(layer(output)))
return self.classification_layer(output)
class RobertaNFQAClassification(RobertaPreTrainedModel):
_keys_to_ignore_on_load_missing = [r'position_ids']
_DROPOUT = 0.0
def __init__(self, config: RobertaConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.embedder = RobertaModel(config, add_pooling_layer=True)
self.pooler = RobertaPooler(config)
self.feedforward = NFQAClassificationHead(config.hidden_size, config.num_labels)
self.dropout = torch.nn.Dropout(self._DROPOUT)
self.init_weights()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.embedder(
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,
)
sequence_output = outputs[0]
logits = self.feedforward(self.dropout(self.pooler(sequence_output)))
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
)
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