fix logging

scripts/finetune.py

@@ -38,8 +38,7 @@ from axolotl.prompt_tokenizers import (
 )
 from axolotl.prompters import AlpacaPrompter, GPTeacherPrompter, ShareGPTPrompter
 
-logger = logging.getLogger(__name__)
-logger.setLevel(os.getenv("LOG_LEVEL", "INFO"))
+logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 DEFAULT_DATASET_PREPARED_PATH = "data/last_run"
 
 
@@ -171,8 +170,8 @@ def check_dataset_labels(dataset, tokenizer):
             )
             colored_tokens.append(colored_token)
 
-        logger.info(" ".join(colored_tokens))
-        logger.info("\n\n\n")
+        logging.info(" ".join(colored_tokens))
+        logging.info("\n\n\n")
 
 
 def do_inference(cfg, model, tokenizer):
@@ -349,9 +348,9 @@ def train(
         return
 
     if cfg.dataset_prepared_path and any(Path(cfg.dataset_prepared_path).glob("*")):
-        logger.info("Loading prepared dataset from disk...")
+        logging.info("Loading prepared dataset from disk...")
         dataset = load_from_disk(cfg.dataset_prepared_path)
-        logger.info("Prepared dataset loaded from disk...")
+        logging.info("Prepared dataset loaded from disk...")
     else:
         datasets = []
         for d in cfg.datasets:
@@ -391,14 +390,14 @@ def train(
         ).train_test_split(test_size=cfg.val_set_size, shuffle=True, seed=42)
 
         if cfg.local_rank == 0:
-            logger.info("Saving prepared dataset to disk...")
+            logging.info("Saving prepared dataset to disk...")
             if cfg.dataset_prepared_path:
                 dataset.save_to_disk(cfg.dataset_prepared_path)
             else:
                 dataset.save_to_disk(DEFAULT_DATASET_PREPARED_PATH)
 
         if prepare_ds_only:
-            logger.info("Finished preparing dataset. Exiting...")
+            logging.info("Finished preparing dataset. Exiting...")
             return
 
     train_dataset = dataset["train"]
@@ -415,11 +414,11 @@ def train(
     model.config.use_cache = False
 
     if torch.__version__ >= "2" and sys.platform != "win32":
-        logger.info("Compiling torch model")
+        logging.info("Compiling torch model")
         model = torch.compile(model)
 
     # go ahead and presave, so we have the adapter config available to inspect
-    logger.info(f"Pre-saving adapter config to {cfg.output_dir}")
+    logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
     lora_config.save_pretrained(cfg.output_dir)
 
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
@@ -428,11 +427,11 @@ def train(
         lambda signal, frame: (model.save_pretrained(cfg.output_dir), exit(0)),
     )
 
-    logger.info("Starting trainer...")
+    logging.info("Starting trainer...")
     trainer.train(resume_from_checkpoint=cfg.resume_from_checkpoint)
 
     # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
-    logger.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
+    logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
     model.save_pretrained(cfg.output_dir)