update arg name

src/llamafactory/extras/constants.py

@@ -78,22 +78,22 @@ TRAINING_STAGES = {
 
 STAGES_USE_PAIR_DATA = {"rm", "dpo"}
 
-SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
+SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN = {
-
+    "falcon",
-SUPPORTED_CLASS_EFFECIENT_PACKING = [
+    "gemma",
+    "gemma2",
+    "jamba",
     "llama",
     "mistral",
     "mixtral",
-    "qwen2",
-    "qwen2_moe",
-    "falcon",
     "phi",
     "phi3",
-    "gemma",
+    "qwen2",
-    "gemmoe",
+    "qwen2_moe",
     "starcoder2",
-    "jamba"
+}
-]
+
+SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
 
 V_HEAD_WEIGHTS_NAME = "value_head.bin"
 

src/llamafactory/model/model_utils/packing.py

@@ -283,28 +283,15 @@ def patch_for_multipack(model_type, model_name, attn_implementation):
         )
 
 
-def patch_remote(model_name, config_name, modeling_name):
-    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
-    # we need to load the model here in order for modeling_* to be available
-    with init_empty_weights():
-        AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    module_name = model_config.__class__.__module__.replace(config_name, modeling_name)
-    modeling_arch = importlib.import_module(module_name)
-    modeling_arch._get_unpad_data = get_unpad_data  # pylint: disable=protected-access
 
-    # check exist load_balancing_loss_func for moe model
+def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
-    if hasattr(modeling_arch, "load_balancing_loss_func"):
+    if not is_trainable or not model_args.block_diag_attn:
-        modeling_arch.load_balancing_loss_func = load_balancing_loss_func
+        return
 
+    model_type = getattr(config, "model_type", None)
 
-def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
+    if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
-    if getattr(config, "model_type", None) == "internlm2":  # special case for custom models
+        patch_for_block_diag_attn(model_type)
-        attn_implementation = getattr(config, "attn_implementation", "")
-    else:
-        attn_implementation = getattr(config, "_attn_implementation", "")
-
-    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:
-        raise ValueError("Current model does not support packing sequences for efficient training. Please set config `efficient_packing` is False")
+        raise ValueError("Current model does not support packing sequences for efficient training.")

src/llamafactory/model/patcher.py

@@ -29,11 +29,12 @@ from .model_utils.checkpointing import prepare_model_for_training
 from .model_utils.embedding import resize_embedding_layer
 from .model_utils.longlora import configure_longlora
 from .model_utils.moe import add_z3_leaf_module, configure_moe
+from .model_utils.packing import configure_packing
 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
@@ -73,6 +74,7 @@ def patch_config(
     configure_quantization(config, tokenizer, model_args, init_kwargs)
     configure_moe(config, model_args, is_trainable)
     configure_visual_model(config)
+    configure_packing(config, model_args, is_trainable)
 
     if model_args.use_cache and not is_trainable:
         setattr(config, "use_cache", True)
@@ -102,9 +104,6 @@ def patch_config(
             if init_kwargs.get("device_map", None) == "auto":
                 init_kwargs["offload_folder"] = model_args.offload_folder
 
-    if model_args.efficient_packing:
-        configure_packing(config, model_args)
-
 
 def patch_model(
     model: "PreTrainedModel",