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""" |
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Implementation borrowed from transformers package and extended to support multiple prediction heads: |
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https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py |
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""" |
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import math |
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import torch |
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import torch.utils.checkpoint |
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from packaging import version |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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from transformers import PretrainedConfig |
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from transformers.activations import ACT2FN, gelu |
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from transformers.file_utils import ( |
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add_code_sample_docstrings, |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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replace_return_docstrings, |
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) |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPastAndCrossAttentions, |
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BaseModelOutputWithPoolingAndCrossAttentions, |
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CausalLMOutputWithCrossAttentions, |
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MaskedLMOutput, |
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MultipleChoiceModelOutput, |
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QuestionAnsweringModelOutput, |
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SequenceClassifierOutput, |
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TokenClassifierOutput, |
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) |
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from transformers.modeling_utils import ( |
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PreTrainedModel, |
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apply_chunking_to_forward, |
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find_pruneable_heads_and_indices, |
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prune_linear_layer, |
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) |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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_CHECKPOINT_FOR_DOC = "roberta-base" |
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_CONFIG_FOR_DOC = "RobertaConfig" |
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_TOKENIZER_FOR_DOC = "RobertaTokenizer" |
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from transformers.models.roberta.modeling_roberta import ( |
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RobertaPreTrainedModel, |
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RobertaClassificationHead, |
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RobertaModel, |
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) |
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class RobertaClassificationHead(nn.Module): |
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"""Head for sentence-level classification tasks.""" |
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def __init__(self, config, num_labels): |
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super().__init__() |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size) |
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classifier_dropout = ( |
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config.classifier_dropout |
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if config.classifier_dropout is not None |
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else config.hidden_dropout_prob |
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) |
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self.dropout = nn.Dropout(classifier_dropout) |
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self.out_proj = nn.Linear(config.hidden_size, num_labels) |
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def forward(self, features, **kwargs): |
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x = features[:, 0, :] |
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x = self.dropout(x) |
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x = self.dense(x) |
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x = torch.tanh(x) |
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x = self.dropout(x) |
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x = self.out_proj(x) |
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return x |
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class RobertaForMultitaskQA(RobertaPreTrainedModel): |
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_keys_to_ignore_on_load_missing = [r"position_ids"] |
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def __init__(self, config, **kwargs): |
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super().__init__(PretrainedConfig()) |
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self.num_labels = kwargs.get("task_labels_map", {}) |
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self.config = config |
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self.roberta = RobertaModel(config, add_pooling_layer=False) |
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self.qa_outputs = nn.Linear( |
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config.hidden_size, list(self.num_labels.values())[0] |
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) |
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self.classifier = RobertaClassificationHead( |
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config, num_labels=list(self.num_labels.values())[1] |
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) |
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self.init_weights() |
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def forward( |
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self, |
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input_ids=None, |
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attention_mask=None, |
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token_type_ids=None, |
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position_ids=None, |
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head_mask=None, |
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inputs_embeds=None, |
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labels=None, |
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start_positions=None, |
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end_positions=None, |
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output_attentions=None, |
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output_hidden_states=None, |
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return_dict=None, |
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task_name=None, |
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): |
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r""" |
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labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): |
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Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., |
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config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), |
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If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
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""" |
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return_dict = ( |
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return_dict if return_dict is not None else self.config.use_return_dict |
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) |
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outputs = self.roberta( |
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input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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sequence_output = outputs[0] |
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logits = self.qa_outputs(sequence_output) |
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start_logits, end_logits = logits.split(1, dim=-1) |
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start_logits = start_logits.squeeze(-1).contiguous() |
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end_logits = end_logits.squeeze(-1).contiguous() |
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total_loss = None |
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if start_positions is not None and end_positions is not None: |
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if len(start_positions.size()) > 1: |
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start_positions = start_positions.squeeze(-1) |
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if len(end_positions.size()) > 1: |
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end_positions = end_positions.squeeze(-1) |
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ignored_index = start_logits.size(1) |
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start_positions = start_positions.clamp(0, ignored_index) |
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end_positions = end_positions.clamp(0, ignored_index) |
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loss_fct = CrossEntropyLoss(ignore_index=ignored_index) |
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start_loss = loss_fct(start_logits, start_positions) |
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end_loss = loss_fct(end_logits, end_positions) |
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total_loss = (start_loss + end_loss) / 2 |
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if not return_dict: |
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output = (start_logits, end_logits) + outputs[2:] |
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return ((total_loss,) + output) if total_loss is not None else output |
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qa_result = QuestionAnsweringModelOutput( |
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loss=total_loss, |
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start_logits=start_logits, |
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end_logits=end_logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
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loss = None |
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logits = self.classifier(sequence_output) |
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if labels is not None: |
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if self.config.problem_type is None: |
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if list(self.num_labels.values())[1] == 1: |
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self.config.problem_type = "regression" |
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elif list(self.num_labels.values())[1] > 1 and ( |
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labels.dtype == torch.long or labels.dtype == torch.int |
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): |
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self.config.problem_type = "single_label_classification" |
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else: |
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self.config.problem_type = "multi_label_classification" |
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if self.config.problem_type == "regression": |
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loss_fct = MSELoss() |
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if list(self.num_labels.values())[1] == 1: |
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loss = loss_fct(logits.squeeze(), labels.squeeze()) |
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else: |
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loss = loss_fct(logits, labels) |
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elif self.config.problem_type == "single_label_classification": |
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loss_fct = CrossEntropyLoss() |
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loss = loss_fct( |
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logits.view(-1, list(self.num_labels.values())[1]), |
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labels.view(-1), |
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) |
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elif self.config.problem_type == "multi_label_classification": |
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loss_fct = BCEWithLogitsLoss() |
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loss = loss_fct(logits, labels) |
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if not return_dict: |
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output = (logits,) + outputs[2:] |
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return ((loss,) + output) if loss is not None else output |
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classifier_result = SequenceClassifierOutput( |
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loss=loss, |
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logits=logits, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
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return qa_result, classifier_result |
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