Save Axolotl config as WandB artifact (#716)

src/axolotl/cli/__init__.py

@@ -194,6 +194,7 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
     # load the config from the yaml file
     with open(config, encoding="utf-8") as file:
         cfg: DictDefault = DictDefault(yaml.safe_load(file))
+    cfg.axolotl_config_path = config
     # if there are any options passed in the cli, if it is something that seems valid from the yaml,
     # then overwrite the value
     cfg_keys = cfg.keys()

src/axolotl/utils/callbacks.py

@@ -514,3 +514,27 @@ def log_prediction_callback_factory(trainer: Trainer, tokenizer):
             return control
 
     return LogPredictionCallback
+
+
+class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
+    """Callback to save axolotl config to wandb"""
+
+    def __init__(self, axolotl_config_path):
+        self.axolotl_config_path = axolotl_config_path
+
+    def on_train_begin(
+        self,
+        args: AxolotlTrainingArguments,  # pylint: disable=unused-argument
+        state: TrainerState,  # pylint: disable=unused-argument
+        control: TrainerControl,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if is_main_process():
+            try:
+                artifact = wandb.Artifact(name="axolotl-config", type="config")
+                artifact.add_file(local_path=self.axolotl_config_path)
+                wandb.run.log_artifact(artifact)
+                LOG.info("Axolotl config has been saved to WandB as an artifact.")
+            except (FileNotFoundError, ConnectionError) as err:
+                LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
+        return control

src/axolotl/utils/trainer.py

@@ -30,6 +30,7 @@ from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
     EvalFirstStepCallback,
     GPUStatsCallback,
+    SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
     log_prediction_callback_factory,
@@ -775,6 +776,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         LogPredictionCallback = log_prediction_callback_factory(trainer, tokenizer)
         trainer.add_callback(LogPredictionCallback(cfg))
 
+    if cfg.use_wandb:
+        trainer.add_callback(SaveAxolotlConfigtoWandBCallback(cfg.axolotl_config_path))
+
     if cfg.do_bench_eval:
         trainer.add_callback(bench_eval_callback_factory(trainer, tokenizer))