fix #5542

src/llamafactory/hparams/model_args.py

@@ -15,7 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import asdict, dataclass, field, fields
+from dataclasses import dataclass, field, fields
 from typing import Any, Dict, Literal, Optional, Union
 
 import torch
@@ -308,20 +308,18 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
         if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
             raise ValueError("Quantization dataset is necessary for exporting.")
 
-    def to_dict(self) -> Dict[str, Any]:
-        return asdict(self)
-
     @classmethod
-    def copyfrom(cls, old_arg: "Self", **kwargs) -> "Self":
+    def copyfrom(cls, source: "Self", **kwargs) -> "Self":
-        arg_dict = old_arg.to_dict()
+        init_args, lazy_args = {}, {}
-        arg_dict.update(**kwargs)
+        for attr in fields(source):
-        for attr in fields(cls):
+            if attr.init:
-            if not attr.init:
+                init_args[attr.name] = getattr(source, attr.name)
-                arg_dict.pop(attr.name)
+            else:
+                lazy_args[attr.name] = getattr(source, attr.name)
 
-        new_arg = cls(**arg_dict)
+        init_args.update(kwargs)
-        new_arg.compute_dtype = old_arg.compute_dtype
+        result = cls(**init_args)
-        new_arg.device_map = old_arg.device_map
+        for name, value in lazy_args.items():
-        new_arg.model_max_length = old_arg.model_max_length
+            setattr(result, name, value)
-        new_arg.block_diag_attn = old_arg.block_diag_attn
+
-        return new_arg
+        return result

src/llamafactory/model/loader.py

@@ -21,6 +21,7 @@ from trl import AutoModelForCausalLMWithValueHead
 from ..extras.logging import get_logger
 from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_ms
 from .adapter import init_adapter
+from .model_utils.liger_kernel import apply_liger_kernel
 from .model_utils.misc import register_autoclass
 from .model_utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
 from .model_utils.unsloth import load_unsloth_pretrained_model
@@ -128,6 +129,7 @@ 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)
+    apply_liger_kernel(config, model_args, is_trainable, require_logits=(finetuning_args.stage not in ["pt", "sft"]))
 
     model = None
     lazy_load = False

src/llamafactory/model/model_utils/liger_kernel.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import inspect
 from typing import TYPE_CHECKING
 
 from ...extras.logging import get_logger
@@ -26,7 +27,12 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
+def apply_liger_kernel(
+    config: "PretrainedConfig",
+    model_args: "ModelArguments",
+    is_trainable: bool,
+    require_logits: bool,
+) -> None:
     if not is_trainable or not model_args.enable_liger_kernel:
         return
 
@@ -51,5 +57,11 @@ def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArgumen
         logger.warning("Current model does not support liger kernel.")
         return
 
-    apply_liger_kernel()
+    if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
+        logger.info("Current training stage does not support chunked cross entropy.")
+        kwargs = {"fused_linear_cross_entropy": False}
+    else:
+        kwargs = {}
+
+    apply_liger_kernel(**kwargs)
     logger.info("Liger kernel has been applied to the model.")

src/llamafactory/model/patcher.py

@@ -27,7 +27,6 @@ from ..extras.misc import infer_optim_dtype
 from .model_utils.attention import configure_attn_implementation, print_attn_implementation
 from .model_utils.checkpointing import prepare_model_for_training
 from .model_utils.embedding import resize_embedding_layer
-from .model_utils.liger_kernel import configure_liger_kernel
 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
@@ -93,7 +92,6 @@ def patch_config(
 
     configure_attn_implementation(config, model_args, is_trainable)
     configure_rope(config, model_args, is_trainable)
-    configure_liger_kernel(config, model_args, is_trainable)
     configure_longlora(config, model_args, is_trainable)
     configure_quantization(config, tokenizer, model_args, init_kwargs)
     configure_moe(config, model_args, is_trainable)