Fixing bug with multi epoch training

olmocr/train/train.py

@@ -189,6 +189,46 @@ def evaluate_model(
     return eval_metrics
 
 
+def create_train_dataloader(
+    train_dataset,
+    config,
+    data_collator,
+    seed_worker,
+    epoch_num: int = 0,
+) -> DataLoader:
+    """Create a training dataloader with epoch-specific shuffling.
+    
+    Args:
+        train_dataset: The training dataset
+        config: Training configuration
+        data_collator: Data collator for batching
+        seed_worker: Worker initialization function
+        epoch_num: Current epoch number for seed generation
+    
+    Returns:
+        DataLoader with epoch-specific shuffling
+    """
+    # Create generator with epoch-specific seed for different shuffling each epoch
+    epoch_generator = torch.Generator()
+    if config.training.data_seed is not None:
+        # Use epoch number to ensure different shuffling each epoch while maintaining reproducibility
+        epoch_generator.manual_seed(config.training.data_seed + epoch_num)
+    else:
+        # Use a random seed if no data_seed specified
+        epoch_generator.manual_seed(int(torch.randint(0, 2**32 - 1, (1,)).item()))
+    
+    return DataLoader(
+        train_dataset,
+        batch_size=config.training.per_device_train_batch_size,
+        shuffle=True,
+        collate_fn=data_collator,
+        num_workers=config.training.dataloader_num_workers,
+        drop_last=config.training.dataloader_drop_last,
+        worker_init_fn=seed_worker,
+        generator=epoch_generator,
+    )
+
+
 def main():
     parser = argparse.ArgumentParser(description="Train OlmOCR model")
     parser.add_argument("--config", type=str, default="olmocr/train/configs/example_config.yaml", help="Path to YAML configuration file")
@@ -313,12 +353,6 @@ def main():
 
         random.seed(worker_seed)
 
-    # Create generator for data loader
-    generator = None
-    if config.training.data_seed is not None:
-        generator = torch.Generator()
-        generator.manual_seed(config.training.data_seed)
-
     # Device setup
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
@@ -418,16 +452,14 @@ def main():
         best_metric = state["best_metric"]
         samples_seen = state["samples_seen"]
 
-    # Create dataloaders
-    train_dataloader = DataLoader(
+    # Create dataloaders - use epoch 0 initially (will be recreated with proper epoch if resuming)
+    current_epoch_num = int(samples_seen / len(train_dataset)) if samples_seen > 0 else 0
+    train_dataloader = create_train_dataloader(
         train_dataset,
-        batch_size=config.training.per_device_train_batch_size,
-        shuffle=True,
-        collate_fn=data_collator,
-        num_workers=config.training.dataloader_num_workers,
-        drop_last=config.training.dataloader_drop_last,
-        worker_init_fn=seed_worker,
-        generator=generator,
+        config,
+        data_collator,
+        seed_worker,
+        epoch_num=current_epoch_num,
     )
 
     eval_dataloaders = {
@@ -467,14 +499,26 @@ def main():
             samples_to_skip = samples_seen % len(train_dataset)
             batches_to_skip = samples_to_skip // config.training.per_device_train_batch_size
             logger.info(f"Resuming training: skipping {batches_to_skip} batches ({samples_to_skip} samples) to reach position {samples_seen}")
+            
+            # Skip batches to resume from the correct position within the epoch
             for _ in range(batches_to_skip):
                 try:
                     next(epoch_iterator)
                 except StopIteration:
-                    # We've reached the end of the epoch while skipping, create new iterator
+                    # We've reached the end of the epoch while skipping
+                    # This shouldn't normally happen, but handle it gracefully
+                    logger.warning(f"Reached end of epoch while skipping batches. Creating new epoch.")
+                    current_epoch_num += 1
+                    train_dataloader = create_train_dataloader(
+                        train_dataset,
+                        config,
+                        data_collator,
+                        seed_worker,
+                        epoch_num=current_epoch_num,
+                    )
                     epoch_iterator = iter(train_dataloader)
                     break
-
+        
         # Create progress bar
         pbar = tqdm(total=max_train_samples - samples_seen, desc=f"Training from step {global_step}", unit="samples")
 
@@ -482,9 +526,21 @@ def main():
             try:
                 batch = next(epoch_iterator)
             except StopIteration:
-                # End of epoch, create new iterator
+                # End of epoch, create new dataloader with fresh shuffle
                 current_epoch = samples_seen / len(train_dataset)
                 logger.info(f"Completed epoch {current_epoch:.2f}")
+                
+                # Increment epoch number for new shuffle seed
+                current_epoch_num += 1
+                
+                # Recreate dataloader with new generator for fresh shuffle
+                train_dataloader = create_train_dataloader(
+                    train_dataset,
+                    config,
+                    data_collator,
+                    seed_worker,
+                    epoch_num=current_epoch_num,
+                )
                 epoch_iterator = iter(train_dataloader)
                 batch = next(epoch_iterator)