[data] add eval_on_each_dataset arg (#7912)

src/llamafactory/data/loader.py

@@ -168,7 +168,7 @@ def _get_merged_dataset(
     data_args: "DataArguments",
     training_args: "Seq2SeqTrainingArguments",
     stage: Literal["pt", "sft", "rm", "ppo", "kto"],
-    merge: bool = True,
+    return_dict: bool = False,
 ) -> Optional[Union["Dataset", "IterableDataset", dict[str, "Dataset"]]]:
     r"""Return the merged datasets in the standard format."""
     if dataset_names is None:
@@ -181,10 +181,10 @@ def _get_merged_dataset(
 
         datasets[dataset_name] = _load_single_dataset(dataset_attr, model_args, data_args, training_args)
 
-    if merge:
-        return merge_dataset(list(datasets.values()), data_args, seed=training_args.seed)
-    else:
+    if return_dict:
         return datasets
+    else:
+        return merge_dataset(list(datasets.values()), data_args, seed=training_args.seed)
 
 
 def _get_dataset_processor(
@@ -303,7 +303,12 @@ def get_dataset(
     with training_args.main_process_first(desc="load dataset"):
         dataset = _get_merged_dataset(data_args.dataset, model_args, data_args, training_args, stage)
         eval_dataset = _get_merged_dataset(
-            data_args.eval_dataset, model_args, data_args, training_args, stage, merge=training_args.do_predict
+            data_args.eval_dataset,
+            model_args,
+            data_args,
+            training_args,
+            stage,
+            return_dict=data_args.eval_on_each_dataset,
         )
 
     with training_args.main_process_first(desc="pre-process dataset"):

src/llamafactory/hparams/data_args.py

@@ -99,6 +99,10 @@ class DataArguments:
         default=0.0,
         metadata={"help": "Size of the validation set, should be an integer or a float in range `[0,1)`."},
     )
+    eval_on_each_dataset: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to evaluate on each dataset separately."},
+    )
     packing: Optional[bool] = field(
         default=None,
         metadata={"help": "Enable sequences packing in training. Will automatically enable in pre-training."},

src/llamafactory/hparams/training_args.py

@@ -64,6 +64,7 @@ class RayArguments:
                 raise ValueError(
                     f"ray_storage_filesystem must be one of ['s3', 'gs', 'gcs'], got {self.ray_storage_filesystem}"
                 )
+
             import pyarrow.fs as fs
 
             if self.ray_storage_filesystem == "s3":