Merge pull request #15 from boostcampaitech6/feat/cnn

feat : cnn layer 추가

code/custom_model.py

@@ -0,0 +1,126 @@
+
+from typing import Optional, Union, Tuple,List
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import RobertaPreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions,QuestionAnsweringModelOutput
+from transformers.models.roberta.modeling_roberta import RobertaEmbeddings, RobertaEncoder, RobertaPooler, RobertaModel
+
+class CNN_block(nn.Module):
+    def __init__(self, input_size, hidden_size):
+        super(CNN_block, self).__init__()
+        self.conv1 = nn.Conv1d(in_channels = input_size, out_channels = input_size * 2, kernel_size = 3, padding = 1)
+        self.conv2 = nn.Conv1d(in_channels = input_size*2, out_channels = input_size, kernel_size = 1)
+        self.relu = nn.ReLU()
+        self.layer_norm = nn.LayerNorm(hidden_size)
+
+    def forward(self, x):
+        output = self.conv1(x)
+        output = self.conv2(output)
+        output = x + self.relu(output)
+        output = self.layer_norm(output)
+
+        return output
+
+
+class CNN_RobertaForQuestionAnswering(RobertaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.roberta = RobertaModel(config, add_pooling_layer=False)
+        self.cnn_block1 = CNN_block( config.max_seq_len, config.hidden_size)
+        self.cnn_block2 = CNN_block( config.max_seq_len, config.hidden_size)
+        self.cnn_block3 = CNN_block( config.max_seq_len, config.hidden_size)
+        self.cnn_block4 = CNN_block( config.max_seq_len, config.hidden_size)
+        self.cnn_block5 = CNN_block( config.max_seq_len, config.hidden_size)
+
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # 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,
+        start_positions: Optional[torch.LongTensor] = None,
+        end_positions: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        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,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        # CNN layer 5개
+        sequence_output = self.cnn_block1(sequence_output)
+        sequence_output = self.cnn_block2(sequence_output)
+        sequence_output = self.cnn_block3(sequence_output)
+        sequence_output = self.cnn_block4(sequence_output)
+        sequence_output = self.cnn_block5(sequence_output)
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+