fix: Consistent FIM examples between evals

eval/run_eval.py

@@ -397,6 +397,10 @@ def run_eval(config: DictConfig, model_provider: ModelProvider):
     else:
         logger.info("No benchmarks enabled.")
 
+    # Make a random seed that will be used for both pre- and post-finetune eval
+    # This is required to keep the FIM examples the same for both runs
+    eval_seed = random.randint(0, 2**32 - 1)
+
     # Load name of all eval tasks
     task_metrics = []
     for task_name, task_info in load_task_info():
@@ -409,6 +413,7 @@ def run_eval(config: DictConfig, model_provider: ModelProvider):
         ### Run eval ###
 
         logger.info(f"Running pre-finetune eval...")
+        set_seed(eval_seed)
         eval_metrics = run_task_eval(config, task_info, model, tokenizer)
         eval_metrics['task_name'] = task_name
         eval_metrics['finetuned'] = False
@@ -432,6 +437,7 @@ def run_eval(config: DictConfig, model_provider: ModelProvider):
         ### Run eval again ###
 
         logger.info(f"Running post-finetune eval...")
+        set_seed(eval_seed)
         eval_metrics = run_task_eval(config, task_info, model, tokenizer)
         eval_metrics['task_name'] = task_name
         eval_metrics['finetuned'] = True
@@ -522,6 +528,14 @@ def configure_logging():
     lite_llm.setLevel(logging.INFO)
 
 
+def set_seed(seed: Optional[int] = None):
+    """Set the random seed for reproducibility."""
+    if seed is not None:
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+
+
 @hydra.main(version_base=None, config_path='../src/conf', config_name='eval')
 def main(config: DictConfig):
 
@@ -535,11 +549,8 @@ def main(config: DictConfig):
     model_provider = ModelProvider.get_instance(config.model)
 
     # Set random seed
-    if config.get('seed') is not None:
-        random.seed(config.seed)
-        np.random.seed(config.seed)
-        torch.manual_seed(config.seed)
-
+    set_seed(config.get('seed'))
+
     # Run eval
     logger.info("Running eval...")
     eval_results = run_eval(config, model_provider)