Update custom_heads_flash_t5.py

custom_heads_flash_t5.py

@@ -1,3 +1,47 @@
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+Update custom_heads_flash_t5.py
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+16.9 kB
 import torch
 import torch.nn as nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
@@ -220,19 +264,114 @@ class FlashT5ForSequenceClassification(FlashT5PreTrainedModel):
         )
 
 
-
-################## Seq2Seq head ##################
 class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
     _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]
 
     def __init__(self, config: FlashT5Config):
         super().__init__(config)
         self.transformer = FlashT5EncoderModel(config)
+
+        self.num_labels = config.num_labels
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], 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.
+        Returns:
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_outputs = self.transformer(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = encoder_outputs[0]
+
+        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).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # 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) + encoder_outputs[1:]
+            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=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+
+class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"encoder.embed_tokens.weight"]
+
+    def __init__(self, config: FlashT5Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = FlashT5Stack(encoder_config, self.shared)
         self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
 
         # Initialize weights and apply final processing
         self.post_init()
 
+        self.qa_outputs.weight.data.normal_(mean=0.0, std=config.initializer_factor * 1.0)
+        self.qa_outputs.bias.data.zero_()
+
         self.model_parallel = False
 
     def forward(
@@ -249,12 +388,9 @@ class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
     ) -> Union[Tuple, QuestionAnsweringModelOutput]:
         r"""
         Returns:
-
         Example:
-
         ```python
         >>> from transformers import AutoTokenizer, MTxEncoderForQuestionAnswering
-
         >>> tokenizer = AutoTokenizer.from_pretrained("MTx-small")
         >>> model = MTxEncoderForQuestionAnswering.from_pretrained("MTx-small")
         >>> input_ids = tokenizer(
@@ -265,15 +401,11 @@ class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
         >>> end_logits = outputs.end_logits
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
-        outputs = self.transformer(
+
+        outputs = self.encoder(
             input_ids,
             attention_mask=attention_mask,
-            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]
 
@@ -309,4 +441,4 @@ class FlashT5ForQuestionAnswering(FlashT5PreTrainedModel):
             end_logits=end_logits,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
-        )
+        )