chore: import upstream snapshot with attribution

FlagEmbedding/finetune/reranker/decoder_only/base/modeling.py

@@ -0,0 +1,51 @@
+import torch
+from transformers import PreTrainedModel, AutoTokenizer
+import logging
+
+from FlagEmbedding.abc.finetune.reranker import AbsRerankerModel
+
+logger = logging.getLogger(__name__)
+
+
+class CrossDecoderModel(AbsRerankerModel):
+    """
+    Model class for decoder only reranker.
+
+    Args:
+        base_model (PreTrainedModel): The underlying pre-trained model used for encoding and scoring input pairs.
+        tokenizer (AutoTokenizer, optional): The tokenizer for encoding input text. Defaults to ``None``.
+        train_batch_size (int, optional): The batch size to use. Defaults to ``4``.
+    """
+    def __init__(
+        self,
+        base_model: PreTrainedModel,
+        tokenizer: AutoTokenizer = None,
+        train_batch_size: int = 4,
+    ):
+        super().__init__(
+            base_model,
+            tokenizer=tokenizer,
+            train_batch_size=train_batch_size,
+        )
+
+    def encode(self, features):
+        """Encodes input features to logits.
+
+        Args:
+            features (dict): Dictionary with input features.
+
+        Returns:
+            torch.Tensor: The logits output from the model.
+        """
+        if features is None:
+            return None
+        outputs = self.model(input_ids=features['input_ids'],
+                             attention_mask=features['attention_mask'],
+                             position_ids=features['position_ids'] if 'position_ids' in features.keys() else None,
+                             output_hidden_states=True)
+        # _, max_indices = torch.max(features['labels'], dim=1)
+        # predict_indices = max_indices
+        # logits = [outputs.logits[i, predict_indices[i], :] for i in range(outputs.logits.shape[0])]
+        # logits = torch.stack(logits, dim=0)
+        scores = outputs.logits[:, -1, self.yes_loc]
+        return scores.contiguous()