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import importlib
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers import AutoModel, PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
from transformers.utils import ModelOutput, logging
from .configuration_multitask import MultiTaskClsConfig
logger = logging.get_logger(__name__)
@dataclass
class MultiTaskSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
logits_list: List[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
class MultiTaskClsModel(PreTrainedModel):
config_class = MultiTaskClsConfig
def __init__(self, config: MultiTaskClsConfig):
super().__init__(config)
model_cls_str = MODEL_MAPPING_NAMES[config.model_type]
model_cls = getattr(importlib.import_module("transformers"), model_cls_str)
transformer_encoder = model_cls._from_config(config)
self.model_prefix = transformer_encoder.base_model_prefix
# create a variable with the same name as the prefix
setattr(self, self.model_prefix, transformer_encoder)
classifier_dropout = (
config.classifier_dropout
if config.classifier_dropout is not None
else config.hidden_dropout_prob
)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(classifier_dropout)
self.num_tasks = len(config.problem_types)
self.labels_list = config.labels_list
self.num_labels = [
len(labels) if labels is not None else 1 for labels in self.labels_list
]
self.problem_types = (
[None] * self.num_tasks
if config.problem_types is None
else config.problem_types
)
self.cls_task_heads = nn.ModuleList(
[
nn.Linear(self.config.hidden_size, _num_labels)
for _num_labels in self.num_labels
]
)
# Initialize weights and apply final processing
self.post_init()
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
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[List[torch.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], List[MultiTaskSequenceClassifierOutput]]:
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
)
# get attributes from the self.model_prefix
transformer_encoder = getattr(self, self.model_prefix)
outputs = transformer_encoder(
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,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# List of logits for each task
logits_list = [task_head(pooled_output) for task_head in self.cls_task_heads]
losses = []
loss = None
if labels is not None:
for logits, task_labels, task_type, num_labels in zip(
logits_list, labels, self.problem_types, self.num_labels
):
if task_type is None:
if num_labels == 1:
task_type = "regression"
elif num_labels > 1 and (
task_labels.dtype == torch.long
or task_labels.dtype == torch.int
):
task_type = "single_label_classification"
else:
task_type = "multi_label_classification"
if task_type == "regression":
loss_fct = nn.MSELoss()
if num_labels == 1:
loss = loss_fct(logits.squeeze(), task_labels.squeeze())
else:
loss = loss_fct(logits, task_labels)
elif task_type == "single_label_classification":
loss_fct = nn.CrossEntropyLoss()
if task_labels.shape == logits.view(-1, num_labels).shape:
loss = loss_fct(logits.view(-1, num_labels), task_labels)
else:
loss = loss_fct(
logits.view(-1, num_labels), task_labels.view(-1)
)
elif task_type == "multi_label_classification":
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits, task_labels)
else:
raise ValueError(f"Task type '{task_type}' not supported")
losses.append(loss)
loss = torch.stack(losses).sum()
if not return_dict:
output = (logits_list,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultiTaskSequenceClassifierOutput(
loss=loss,
logits_list=logits_list,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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