move efficient_packing from data_args to model_args

Former-commit-id: 7b61659c707480bcf8c802c73e10d12ad5b9b965

src/llamafactory/data/loader.py

@@ -177,7 +177,7 @@ def get_dataset(
 
     with training_args.main_process_first(desc="pre-process dataset"):
         preprocess_func, print_function = get_preprocess_and_print_func(
-            data_args, training_args, stage, template, tokenizer, processor
+            data_args, model_args, training_args, stage, template, tokenizer, processor
         )
         column_names = list(next(iter(dataset)).keys())
         kwargs = {}

src/llamafactory/data/preprocess.py

@@ -29,12 +29,13 @@ from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsu
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments
 
-    from ..hparams import DataArguments
+    from ..hparams import DataArguments, ModelArguments
     from .template import Template
 
 
 def get_preprocess_and_print_func(
     data_args: "DataArguments",
+    model_args: "ModelArguments",
     training_args: "Seq2SeqTrainingArguments",
     stage: Literal["pt", "sft", "rm", "ppo", "kto"],
     template: "Template",
@@ -49,7 +50,7 @@ def get_preprocess_and_print_func(
         )
         print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
     elif stage == "sft" and not training_args.predict_with_generate:
-        if data_args.packing or data_args.efficient_packing:
+        if data_args.packing or model_args.efficient_packing:
             preprocess_func = partial(
                 preprocess_packed_supervised_dataset,
                 template=template,

src/llamafactory/data/processors/supervised.py

@@ -23,7 +23,7 @@ from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, gre
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer, ProcessorMixin
 
-    from ...hparams import DataArguments
+    from ...hparams import DataArguments, ModelArguments
     from ..template import Template
 
 
@@ -125,6 +125,7 @@ def preprocess_packed_supervised_dataset(
     template: "Template",
     tokenizer: "PreTrainedTokenizer",
     data_args: "DataArguments",
+    model_args: "ModelArguments"
 ) -> Dict[str, List[List[int]]]:
     # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
     # and labels with format `<ignore> ... <ignore> Y1 <eos> <ignore> ... <ignore> Y2 <eos>`
@@ -176,7 +177,7 @@ def preprocess_packed_supervised_dataset(
             raise ValueError("The length of packed example should be identical to the cutoff length.")
 
         model_inputs["input_ids"].append(packed_input_ids)
-        if data_args.efficient_packing:
+        if model_args.efficient_packing:
             model_inputs["attention_mask"].append(packed_attention_mask)
         else:
             model_inputs["attention_mask"].append([1] * data_args.cutoff_len)