323 lines
12 KiB
Python
323 lines
12 KiB
Python
from typing import Optional, Union, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss
|
|
from transformers import RobertaForSequenceClassification, RobertaModel
|
|
from transformers.modeling_outputs import SequenceClassifierOutput
|
|
|
|
# Simple version #
|
|
|
|
class RobertaClassificationHeadCustomSimple(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
hidden_size = config.hidden_size
|
|
self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
|
|
self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
|
|
x = self.dense_1(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.dense_2(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
class RobertaForSequenceClassificationCustomSimple(RobertaForSequenceClassification):
|
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.classifier = RobertaClassificationHeadCustomSimple(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
|
|
# Version with custom forward 1 #
|
|
####### EDITED #######
|
|
class RobertaClassificationHeadCustom(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.use_hidden_states = config.use_hidden_states
|
|
hidden_size = config.hidden_size
|
|
if self.use_hidden_states:
|
|
hidden_size *= 2
|
|
|
|
self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size)
|
|
self.dense_2 = nn.Linear(2 * hidden_size, hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.leaky_relu = nn.LeakyReLU()
|
|
self.out_proj = nn.Linear(hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
if 'hidden_states' in kwargs and kwargs['hidden_states'] is not None:
|
|
if self.use_hidden_states:
|
|
x = torch.cat(
|
|
(
|
|
features[:, 0, :],
|
|
# take <s> token (equiv. to [CLS]) from hidden states from second from the end
|
|
kwargs['hidden_states'][-2][:, 0, :]
|
|
),
|
|
dim=1
|
|
)
|
|
else:
|
|
x = features[:, 0, :] + kwargs['hidden_states'][-2][:, 0, :]
|
|
del kwargs['hidden_states']
|
|
else:
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
if self.use_hidden_states:
|
|
x = torch.cat(
|
|
(
|
|
features[:, 0, :],
|
|
torch.zeros(x.size(), dtype=x.dtype, device=x.device)
|
|
),
|
|
dim=1
|
|
)
|
|
|
|
x = self.dense_1(x)
|
|
x = self.leaky_relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.dense_2(x)
|
|
x = self.leaky_relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
class RobertaForSequenceClassificationCustom(RobertaForSequenceClassification):
|
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
####### EDITED #######
|
|
self.classifier = RobertaClassificationHeadCustom(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
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.roberta(
|
|
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 or self.config.use_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
logits = self.classifier(sequence_output, hidden_states=outputs.hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
# Version with custom forward 2 #
|
|
|
|
class RobertaClassificationHeadCustomAlternative(nn.Module):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
hidden_size = config.hidden_size
|
|
|
|
self.dense_1_input = nn.Linear(hidden_size, 2 * hidden_size)
|
|
self.dense_1_hidden = nn.Linear(hidden_size, 2 * hidden_size)
|
|
self.dense_2 = nn.Linear(4 * hidden_size, hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
if 'hidden_states' in kwargs and kwargs['hidden_states'] is not None:
|
|
# take <s> token (equiv. to [CLS]) from hidden states from second from the end
|
|
hidden = kwargs['hidden_states'][-2][:, 0, :]
|
|
del kwargs['hidden_states']
|
|
else:
|
|
hidden = torch.zeros(x.size(), dtype=x.dtype, device=x.device)
|
|
|
|
x = self.dense_1_input(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
hidden = self.dense_1_hidden(hidden)
|
|
hidden = torch.relu(hidden)
|
|
hidden = self.dropout(hidden)
|
|
|
|
x = torch.cat((x, hidden), dim=1)
|
|
x = self.dense_2(x)
|
|
x = torch.relu(x)
|
|
x = self.dropout(x)
|
|
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
class RobertaForSequenceClassificationCustomAlternative(RobertaForSequenceClassification):
|
|
_keys_to_ignore_on_load_missing = [r"position_ids"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.classifier = RobertaClassificationHeadCustomAlternative(config)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
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.roberta(
|
|
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 or self.config.use_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
logits = self.classifier(sequence_output, hidden_states=outputs.hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
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,
|
|
)
|