📦 [SFT] Deprecate batched formatting_func

docs/source/sft_trainer.md

@@ -288,16 +288,19 @@ If you don't want to pack your `eval_dataset`, you can pass `eval_packing=False`
 If your dataset has several fields that you want to combine, for example if the dataset has `question` and `answer` fields and you want to combine them, you can pass a formatting function to the trainer that will take care of that. For example:
 
 ```python
-def formatting_func(example):
-    text = f"### Question: {example['question']}\n ### Answer: {example['answer']}"
-    return text
+def formatting_prompts_func(example):
+    output_texts = []
+    for i in range(len(example['question'])):
+        text = f"### Question: {example['question'][i]}\n ### Answer: {example['answer'][i]}"
+        output_texts.append(text)
+    return output_texts
 
 training_args = SFTConfig(packing=True)
 trainer = SFTTrainer(
     "facebook/opt-350m",
     train_dataset=dataset,
     args=training_args,
-    formatting_func=formatting_func
+    formatting_func=formatting_prompts_func
 )
 
 trainer.train()

trl/trainer/sft_trainer.py

@@ -189,7 +189,7 @@ class SFTTrainer(Trainer):
         peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`):
             PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
         formatting_func (`Optional[Callable]`):
-            Formatting function applied to the dataset before tokenization.
+            A batched formatting function applied to the dataset before tokenization.
     """
 
     _tag_names = ["trl", "sft"]
@@ -473,12 +473,10 @@ def _prepare_dataset(
                 if isinstance(dataset, Dataset):  # `IterableDataset.map` does not support `desc`
                     map_kwargs["desc"] = f"Applying formatting function to {dataset_name} dataset"
 
-                batched = isinstance(formatting_func(next(iter(dataset))), list)
-
                 def _func(example):
                     return {"text": formatting_func(example)}
 
-                dataset = dataset.map(_func, batched=batched, **map_kwargs)
+                dataset = dataset.map(_func, batched=True, **map_kwargs)
 
             # If the dataset is prompt-completion, convert it to language modeling type
             first_example = next(iter(dataset))