mod

hft.py

@@ -110,9 +110,8 @@ def custom_collate_fn(batch):
     attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
     labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
     
-    # Dodajemy domyślne source_idx, jeśli nie istnieje
     source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
-
+    print("source_idx shape:", source_idx.shape)  # Debugowanie
     return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
 
 class CustomModel(AutoModelForCausalLM):
@@ -133,18 +132,19 @@ class CustomModel(AutoModelForCausalLM):
         )
         
         if source_idx is not None:
-            # Dodajemy embedding źródła do hidden states
-            source_embeds = self.source_embedding(source_idx).unsqueeze(1)
-            outputs.logits += source_embeds
-            
+            # Tutaj dodaj logikę obsługi source_idx
+            pass
+        
         return outputs
 
+
 class CustomTrainer(Trainer):
-    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
         labels = inputs.pop("labels")
-        source_idx = inputs.pop("source_idx")
+        source_idx = inputs.pop("source_idx", None)
         outputs = model(**inputs, labels=labels, source_idx=source_idx)
-        return (outputs.loss, outputs) if return_outputs else outputs.loss
+        loss = outputs.loss
+        return (loss, outputs) if return_outputs else loss
 
 # Inicjalizacja komponentów
 source_mapper = SourceMapper()