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run_transformers_training.py

@@ -43,7 +43,7 @@ import torch
 transformers_available = find_spec("transformers") is not None
 if transformers_available:
     import transformers
-    from transformers import AutoTokenizer, TrainingArguments, Trainer, set_seed
+    from transformers import AutoTokenizer, TrainingArguments, Trainer, TrainerCallback, set_seed
     from torch.utils.data import DataLoader
 
 peft_available = find_spec("peft") is not None
@@ -627,8 +627,26 @@ def log_gpu_memory_usage(step=None, frequency=50, clear_cache_threshold=0.9, lab
     log_info(f"{prefix}GPU Memory: {', '.join(memory_info)}")
 
 class LoggingCallback(TrainerCallback):
+    """
+    Custom callback for logging training progress and metrics.
+    Provides detailed information about training status, GPU memory usage, and model performance.
+    """
     def __init__(self, model=None, dataset=None):
-        super().__init__()
+        # Ensure we have TrainerCallback
+        try:
+            super().__init__()
+        except Exception as e:
+            # Try to import directly if initial import failed
+            try:
+                from transformers.trainer_callback import TrainerCallback
+                self.__class__.__bases__ = (TrainerCallback,)
+                super().__init__()
+                log_info("Successfully imported TrainerCallback directly")
+            except ImportError as ie:
+                log_info(f"❌ Error: Could not import TrainerCallback: {str(ie)}")
+                log_info("Please ensure transformers is properly installed")
+                raise
+        
         self.training_started = time.time()
         self.last_log_time = time.time()
         self.last_step_time = None
@@ -1490,6 +1508,17 @@ def main():
     logger.info("Starting training process")
     
     try:
+        # Verify critical imports are available
+        if not transformers_available:
+            log_info("❌ Error: transformers library not available. Please install it with: pip install transformers")
+            return 1
+            
+        # Check for required classes
+        for required_class in ["Trainer", "TrainingArguments", "TrainerCallback"]:
+            if not hasattr(transformers, required_class):
+                log_info(f"❌ Error: {required_class} not found in transformers. Please update transformers.")
+                return 1
+        
         # Check for potential import order issue and warn early
         if "transformers" in sys.modules and "unsloth" in sys.modules:
             if list(sys.modules.keys()).index("transformers") < list(sys.modules.keys()).index("unsloth"):