Delete BERTWithDateLayerTokenClassification.py

BERTWithDateLayerTokenClassification.py

@@ -1,154 +0,0 @@
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers.models.bert.modeling_bert import BertPreTrainedModel, BertModel, \
-    BERT_INPUTS_DOCSTRING, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, TokenClassifierOutput, _CONFIG_FOR_DOC
-from transformers.file_utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings_to_model_forward,
-)
-
-
-class DateEmebdding(nn.Module):
-    """Construct the embeddings the creation date"""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.date_vocab_size, config.date_hidden_size,
-                                            padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.date_max_position_embeddings, config.date_hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.date_hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.register_buffer("position_ids", torch.arange(config.date_max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        self.dense = nn.Linear(config.date_hidden_size, config.date_hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(
-            self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
-    ):
-
-        try:
-
-            if input_ids is not None:
-                input_shape = input_ids.shape
-            else:
-                input_shape = inputs_embeds.size()[:-1]
-
-            seq_length = input_shape[1]
-
-            if position_ids is None:
-                position_ids = self.position_ids[:, past_key_values_length: seq_length + past_key_values_length]
-
-            if inputs_embeds is None:
-                inputs_embeds = self.word_embeddings(input_ids)
-
-            embeddings = inputs_embeds
-            if self.position_embedding_type == "absolute":
-                position_embeddings = self.position_embeddings(position_ids)
-                embeddings += position_embeddings
-            embeddings = self.LayerNorm(embeddings)
-            embeddings = self.dropout(embeddings)
-            max_over_time = torch.max(embeddings, 1)[0]
-        except Exception as ex:
-            print(type(ex).__name__, ex.args)
-            import pdb
-            pdb.set_trace()
-        return max_over_time
-
-
-class BERTWithDateLayerTokenClassification(BertPreTrainedModel):
-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.bert = BertModel(config, add_pooling_layer=False)
-        self.date_embedding = DateEmebdding(config)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.classifier = nn.Linear(config.date_hidden_size + config.hidden_size, config.num_labels)
-        # self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids=None,
-            input_date_ids=None,
-            attention_mask=None,
-            token_type_ids=None,
-            position_ids=None,
-            head_mask=None,
-            inputs_embeds=None,
-            labels=None,
-            output_attentions=None,
-            output_hidden_states=None,
-            return_dict=None,
-    ):
-        r"""
-        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
-            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
-            1]``.
-        """
-        try:
-            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-            _, seq_length = input_ids.shape
-            outputs = self.bert(
-                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,
-            )
-            date_output = self.date_embedding(input_date_ids)
-            sequence_output = torch.cat((outputs[0], date_output.unsqueeze(1).repeat(1, seq_length, 1)), 2)
-
-            sequence_output = self.dropout(sequence_output)
-            logits = self.classifier(sequence_output)
-
-            loss = None
-            if labels is not None:
-                loss_fct = CrossEntropyLoss()
-                # Only keep active parts of the loss
-                if attention_mask is not None:
-                    active_loss = attention_mask.view(-1) == 1
-                    active_logits = logits.view(-1, self.num_labels)
-                    active_labels = torch.where(
-                        active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
-                    )
-                    loss = loss_fct(active_logits, active_labels)
-                else:
-                    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
-        except:
-            import pdb
-            pdb.set_trace()
-            raise BrokenPipeError("Problems in forward pass")
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )