move configure_packing to llamafactory.model.patcher and fix constants

Former-commit-id: 770f75dc8363bfa284a72159ff8ad25ec9abe4e0

src/llamafactory/extras/constants.py

@@ -66,7 +66,7 @@ STAGES_USE_PAIR_DATA = {"rm", "dpo"}
 
 SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
 
-SUPPORTED_CLASS_FOR_MULTIPACK = [
+SUPPORTED_CLASS_EFFECIENT_PACKING = [
     "llama",
     "mistral",
     "mixtral",

src/llamafactory/hparams/data_args.py

@@ -86,7 +86,9 @@ class DataArguments:
     )
     efficient_packing: Optional[bool] = field(
         default=None,
-        metadata={"help": "Whether or not to pack the sequences without cross-contamination attention for efficient training."},
+        metadata={
+            "help": "Whether or not to pack the sequences without cross-contamination attention for efficient training."
+        },
     )
     tokenized_path: Optional[str] = field(
         default=None,

src/llamafactory/model/loader.py

@@ -16,7 +16,7 @@ from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 
-    from ..hparams import FinetuningArguments, ModelArguments
+    from ..hparams import FinetuningArguments, ModelArguments, DataArguments
 
 
 logger = get_logger(__name__)
@@ -104,6 +104,7 @@ def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
 def load_model(
     tokenizer: "PreTrainedTokenizer",
     model_args: "ModelArguments",
+    data_args: "DataArguments",
     finetuning_args: "FinetuningArguments",
     is_trainable: bool = False,
     add_valuehead: bool = False,
@@ -113,7 +114,7 @@ def load_model(
     """
     init_kwargs = _get_init_kwargs(model_args)
     config = load_config(model_args)
-    patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
+    patch_config(config, tokenizer, model_args, data_args, finetuning_args, init_kwargs, is_trainable)
 
     model = None
     lazy_load = False

src/llamafactory/model/model_utils/packing.py

@@ -19,7 +19,7 @@ from transformers.modeling_attn_mask_utils import (
 from transformers.utils import is_torch_bf16_gpu_available
 
 from ...extras.logging import get_logger
-from ...extras.constants import SUPPORTED_CLASS_FOR_MULTIPACK
+from ...extras.constants import SUPPORTED_CLASS_EFFECIENT_PACKING
 
 if TYPE_CHECKING:
     from transformers import PretrainedConfig
@@ -303,7 +303,7 @@ def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments")
     else:
         attn_implementation = getattr(config, "_attn_implementation", "")
 
-    if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_MULTIPACK:
+    if getattr(config, "model_type", None) in SUPPORTED_CLASS_EFFECIENT_PACKING:
         patch_for_multipack(getattr(config, "model_type", None), model_args.model_name_or_path, attn_implementation)
         logger.info("Using packing sequences without cross-contamination attention for efficient training.")
     else:

src/llamafactory/model/patcher.py

@@ -19,13 +19,13 @@ from .model_utils.quantization import configure_quantization
 from .model_utils.rope import configure_rope
 from .model_utils.valuehead import prepare_valuehead_model
 from .model_utils.visual import autocast_projector_dtype, configure_visual_model
-
+from .model_utils.packing import configure_packing
 
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedTokenizer
     from trl import AutoModelForCausalLMWithValueHead
 
-    from ..hparams import ModelArguments
+    from ..hparams import ModelArguments, DataArguments, FinetuningArguments
 
 
 logger = get_logger(__name__)
@@ -40,6 +40,8 @@ def patch_config(
     config: "PretrainedConfig",
     tokenizer: "PreTrainedTokenizer",
     model_args: "ModelArguments",
+    data_args: "DataArguments",
+    finetune_args: "FinetuningArguments",
     init_kwargs: Dict[str, Any],
     is_trainable: bool,
 ) -> None:
@@ -82,6 +84,9 @@ def patch_config(
             if init_kwargs["device_map"] == "auto":
                 init_kwargs["offload_folder"] = model_args.offload_folder
     
+    if finetune_args.stage == "sft" and data_args.efficient_packing:
+        configure_packing(config, model_args)
+
 
 def patch_model(
     model: "PreTrainedModel",

src/llamafactory/train/sft/workflow.py

@@ -12,7 +12,6 @@ from ...model import load_model, load_tokenizer
 from ..trainer_utils import create_modelcard_and_push
 from .metric import ComputeMetrics
 from .trainer import CustomSeq2SeqTrainer
-from ...model.model_utils.packing import configure_packing
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -33,9 +32,6 @@ def run_sft(
     dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
     model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
 
-    if data_args.efficient_packing:
-        configure_packing(model.config, model_args)
-
     if training_args.predict_with_generate:
         tokenizer.padding_side = "left"  # use left-padding in generation