fixed checkpoint

src/aiunn/finetune.py

@@ -14,6 +14,7 @@ from torch.utils.data import random_split, DataLoader
 from aiia import AIIA, AIIAConfig, AIIABase, AIIABaseShared, AIIAmoe, AIIAchunked, AIIArecursive
 from torch.cuda.amp import autocast, GradScaler
 from tqdm import tqdm
+from torch.utils.checkpoint import checkpoint
 
 class aiuNNDataset(torch.utils.data.Dataset):
     def __init__(self, parquet_path):
@@ -86,57 +87,48 @@ def finetune_model(model: AIIA, datasets: list[str], batch_size=1, epochs=10, ac
     )
     
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # Fix: Pass the current device index (an integer) rather than a torch.device without index.
     if device.type == 'cuda':
-        torch.cuda.set_per_process_memory_fraction(0.95, device=device)
+        current_device = torch.cuda.current_device()
-    model = model.to(device)
+        torch.cuda.set_per_process_memory_fraction(0.95, device=current_device)
     
+    model = model.to(device)
     criterion = nn.MSELoss()
     optimizer = torch.optim.Adam(model.parameters(), lr=model.config.learning_rate)
     scaler = GradScaler()
     best_val_loss = float('inf')
     
-    # Import checkpoint if gradient checkpointing is desired
-    from torch.utils.checkpoint import checkpoint
-    
     for epoch in range(epochs):
         model.train()
         train_loss = 0.0
         optimizer.zero_grad()
-        # Gradient accumulation over several steps (effective batch size = accumulation_steps)
         for i, batch in enumerate(tqdm(train_loader, desc=f"Epoch {epoch+1}/Training"), start=1):
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
             low_res = batch['low_res'].to(device)
             high_res = batch['high_res'].to(device)
-            
             with autocast():
                 if use_checkpoint:
-                    # Wrap the forward pass with checkpointing to save memory.
+                    # Use checkpointing to save intermediate activations if needed.
                     outputs = checkpoint(lambda x: model(x), low_res)
                 else:
                     outputs = model(low_res)
-                # Divide loss to average over accumulation steps.
                 loss = criterion(outputs, high_res) / accumulation_steps
-            
             scaler.scale(loss).backward()
-            train_loss += loss.item() * accumulation_steps  # recover actual loss value
+            train_loss += loss.item() * accumulation_steps
-            
-            # Update the optimizer every accumulation_steps iterations.
             if i % accumulation_steps == 0:
                 scaler.step(optimizer)
                 scaler.update()
                 optimizer.zero_grad()
-                
+        # Handle leftover gradients
-        # In case remaining gradients are present from an incomplete accumulation round.
         if (i % accumulation_steps) != 0:
             scaler.step(optimizer)
             scaler.update()
             optimizer.zero_grad()
-        
+            
         avg_train_loss = train_loss / len(train_loader)
         print(f"Epoch {epoch+1}, Training Loss: {avg_train_loss:.4f}")
         
-        # Validation loop (without accumulation, using standard precision)
         model.eval()
         val_loss = 0.0
         with torch.no_grad():
@@ -151,22 +143,20 @@ def finetune_model(model: AIIA, datasets: list[str], batch_size=1, epochs=10, ac
                 val_loss += loss.item()
         avg_val_loss = val_loss / len(val_loader)
         print(f"Epoch {epoch+1}, Validation Loss: {avg_val_loss:.4f}")
-        
         if avg_val_loss < best_val_loss:
             best_val_loss = avg_val_loss
             model.save("best_model")
-            
     return model
 
 def main():
-    BATCH_SIZE = 1           # Use a batch size of 1.
+    BATCH_SIZE = 2           # Use a batch size of 2.
-    ACCUMULATION_STEPS = 8   # Accumulate gradients over 8 iterations for an effective batch size of 8.
+    ACCUMULATION_STEPS = 8   # Accumulate gradients to simulate a larger batch.
-    USE_CHECKPOINT = False   # Set to True to enable gradient checkpointing instead.
+    USE_CHECKPOINT = True   # Set to True to enable gradient checkpointing if needed.
     
     model = AIIABase.load("/root/vision/AIIA/AIIA-base-512")
     if hasattr(model, 'chunked_'):
         model.add_module('final_upsample', nn.Upsample(scale_factor=2, mode='bilinear'))
-        
+    
     finetune_model(
         model=model,
         datasets=[
@@ -178,6 +168,6 @@ def main():
         accumulation_steps=ACCUMULATION_STEPS,
         use_checkpoint=USE_CHECKPOINT
     )
-    
+
 if __name__ == '__main__':
     main()