mod

hft.py

@@ -107,11 +107,10 @@ def tokenize_function(examples):
     return tokenized
 
 def custom_collate_fn(batch):
-    device = next(model.parameters()).device
+    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch]).cpu()
-    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch]).to(device)
+    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch]).cpu()
-    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch]).to(device)
+    labels = torch.stack([torch.tensor(b["labels"]) for b in batch]).cpu()
-    labels = torch.stack([torch.tensor(b["labels"]) for b in batch]).to(device)
+    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long).cpu()
-    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long).to(device)
     return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
 
 class CustomModel(nn.Module):
@@ -137,6 +136,8 @@ class CustomModel(nn.Module):
 
 class CustomTrainer(Trainer):
     def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+        device = next(model.parameters()).device
+        inputs = {k: v.to(device) for k, v in inputs.items()}
         labels = inputs.pop("labels")
         source_idx = inputs.pop("source_idx", None)
         outputs = model(**inputs, labels=labels, source_idx=source_idx)
@@ -196,6 +197,7 @@ training_args = TrainingArguments(
     save_steps=1000,
     logging_strategy="no",
     report_to="none",
+    pin_memory=True,
 )
 
 # Trening