fix: allow for padding free + pretraining

tuning/config/acceleration_configs/attention_and_distributed_packing.py

@@ -33,3 +33,7 @@ class AttentionAndDistributedPackingConfig:
     def __post_init__(self):
         # ensure nested dataclasses initialized
         ensure_nested_dataclasses_initialized(self)
+
+    @property
+    def is_padding_free(self):
+        return self.padding_free is not None

tuning/data/setup_dataprocessor.py

@@ -107,15 +107,21 @@ def _get_pretokenized_dataset_handlers(data_args, packing, is_eval_tokenized):
 
 
 ### Data format 2
-def _get_dataset_formatting_handlers(data_args, packing):
+def _get_dataset_formatting_handlers(data_args, packing, padding_free=None):
 
     if data_args.response_template is None:
         if packing is False:
-            raise ValueError(
-                "Since dataset_text_field or data_formatter_template \
-                is provided and packing is disabled, \
-                needs a corresponding response template for masking"
-            )
+            if padding_free:
+                logger.debug(
+                    "Assuming extended pretraining scenario because, packing is false"
+                    + ", padding_free is used and no response template was provided."
+                )
+            else:
+                raise ValueError(
+                    "Since dataset_text_field or data_formatter_template \
+                    is provided and packing is disabled, \
+                    needs a corresponding response template for masking"
+                )
 
     if data_args.response_template:
         # To use Response template, pass datasets with single sequence instances \
@@ -209,6 +215,7 @@ def _process_raw_data_args(
     packing: bool,
     max_seq_length: int,
     additional_data_handlers: Dict[str, Callable] = None,
+    **kwargs,
 ):
 
     # Create a data processor with default processor config
@@ -266,7 +273,7 @@ def _process_raw_data_args(
     elif data_args.data_formatter_template or data_args.dataset_text_field:
         # Data Format 3: Single Sequence Dataset
         handlers, dataset_text_field = _get_dataset_formatting_handlers(
-            data_args, packing
+            data_args, packing, **kwargs
         )
     else:
         # Default Data Format: Dataset with Input/Output Fields
@@ -300,6 +307,7 @@ def process_dataargs(
     tokenizer: AutoTokenizer,
     train_args: TrainingArguments,
     additional_data_handlers: Dict[str, Callable] = None,
+    **kwargs,
 ):
     """
     Args:
@@ -345,6 +353,7 @@ def process_dataargs(
             train_args.packing,
             max_seq_length,
             additional_data_handlers,
+            **kwargs,
         )
 
     # Note: This check should not be removed.

tuning/sft_trainer.py

@@ -302,6 +302,9 @@ def train(
     data_collator = None
     logger.info("Packing is set to %s ", train_args.packing)
 
+    padding_free = None
+    if attention_and_distributed_packing_config is not None:
+        padding_free = attention_and_distributed_packing_config.padding_free
     data_preprocessing_time = time.time()
     (
         formatted_train_dataset,
@@ -310,7 +313,13 @@ def train(
         data_collator,
         train_args.max_seq_length,
         dataset_kwargs,
-    ) = process_dataargs(data_args, tokenizer, train_args, additional_data_handlers)
+    ) = process_dataargs(
+        data_args,
+        tokenizer,
+        train_args,
+        additional_data_handlers,
+        padding_free=padding_free,
+    )
     additional_metrics["data_preprocessing_time"] = (
         time.time() - data_preprocessing_time
     )