feat: add report_to to set run name

src/axolotl/core/trainer_builder.py

@@ -323,6 +323,24 @@ class TrainerBuilderBase(abc.ABC):
             total_num_steps if self.cfg.max_steps else -1
         )
 
+        report_to = []
+        if self.cfg.use_wandb:
+            report_to.append("wandb")
+        if self.cfg.use_mlflow:
+            report_to.append("mlflow")
+        if self.cfg.use_tensorboard:
+            report_to.append("tensorboard")
+        if self.cfg.use_comet:
+            report_to.append("comet_ml")
+
+        training_args_kwargs["report_to"] = report_to
+        if self.cfg.use_wandb:
+            training_args_kwargs["run_name"] = self.cfg.wandb_name
+        elif self.cfg.use_mlflow:
+            training_args_kwargs["run_name"] = self.cfg.mlflow_run_name
+        else:
+            training_args_kwargs["run_name"] = None
+
         return training_args_kwargs
 
 
@@ -548,23 +566,29 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
-        report_to = []
-        if self.cfg.use_wandb:
-            report_to.append("wandb")
-        if self.cfg.use_mlflow:
-            report_to.append("mlflow")
-        if self.cfg.use_tensorboard:
-            report_to.append("tensorboard")
-        if self.cfg.use_comet:
-            report_to.append("comet_ml")
 
-        training_arguments_kwargs["report_to"] = report_to
+        training_arguments_kwargs["optim"] = (
-        if self.cfg.use_wandb:
+            self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
-            training_arguments_kwargs["run_name"] = self.cfg.wandb_name
+        )
-        elif self.cfg.use_mlflow:
+        if self.cfg.optim_args:
-            training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
+            if isinstance(self.cfg.optim_args, dict):
-        else:
+                optim_args = ",".join(
-            training_arguments_kwargs["run_name"] = None
+                    [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                )
+            else:
+                optim_args = self.cfg.optim_args
+            training_arguments_kwargs["optim_args"] = optim_args
+        if self.cfg.optim_target_modules:
+            training_arguments_kwargs[
+                "optim_target_modules"
+            ] = self.cfg.optim_target_modules
+        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
+        training_arguments_kwargs[
+            "loraplus_lr_embedding"
+        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
+        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
         if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"