[Fix] Fix bug of clean_param_name

xtuner/v1/engine/train_engine.py

@@ -411,12 +411,13 @@ def step_optimizer(self, grad_norm):
             self.optimizer.zero_grad()
         return grad_norm
 
+    # TODO: Should be removed
     @staticmethod
     def clean_param_name(name: str) -> str:
-        if "._checkpoint_wrapped_module." in name:
-            name = name.replace("._checkpoint_wrapped_module.", ".")
-        if "._orig_mod." in name:
-            name = name.replace("._orig_mod.", ".")
+        if "_checkpoint_wrapped_module." in name:
+            name = name.replace("_checkpoint_wrapped_module.", "")
+        if "_orig_mod." in name:
+            name = name.replace("_orig_mod.", "")
         return name
 
     def save_hf(self, hf_dir: str, save_dtype: torch.dtype = torch.bfloat16):

xtuner/v1/model/base.py

@@ -85,6 +85,9 @@ class XTunerBaseModelConfig(PydanticBaseModel):
     def hf_config(self) -> PretrainedConfig | None:
         raise NotImplementedError
 
+    def build(self):
+        raise NotImplementedError
+
 
 DEFAULT_FLOAT8_CFG = {
     "xtuner.v1.float8.fsdp_utils.tensor_to_per_block_fp8_scales": TorchCompileOption(fullgraph=True),
@@ -250,7 +253,11 @@ def __init__(self, config: XTunerBaseModelConfig):
     def set_hf(self, hf_path: str | Path):
         self._hf_path = Path(hf_path)
 
-    def from_hf(self, hf_path: str | Path, strict: bool = True) -> tuple:
+    def from_hf(
+        self, hf_path: str | Path, strict: bool = True
+    ) -> tuple[
+        Annotated[set[str], "loaded keys"], Annotated[set[str], "unloaded keys"], Annotated[set[str], "missing keys"]
+    ]:
         self._hf_path = Path(hf_path)
 
         if isinstance(hf_path, Path):
@@ -348,7 +355,7 @@ def init_weights(self):
         from xtuner.v1.utils import default_init_weights
 
         initialized_params = default_init_weights(self)
-        if missing := {name for name, _ in self.named_parameters()} - initialized_params:
+        if missing := {self._clean_param_name(name) for name, _ in self.named_parameters()} - initialized_params:
             raise RuntimeError(f"{missing} is not initialized")
 
     def _init_load_spec(self) -> None:
@@ -797,11 +804,12 @@ def _get_same_hf_param(
         if buffer_tensor_list:
             yield buffer_name_list, buffer_tensor_list
 
+    # TODO: Using `xtuenr.v1.utils.misc.clean_param_name`
     def _clean_param_name(self, name: str) -> str:
-        if "._checkpoint_wrapped_module." in name:
-            name = name.replace("._checkpoint_wrapped_module.", ".")
-        if "._orig_mod." in name:
-            name = name.replace("._orig_mod.", ".")
+        if "_checkpoint_wrapped_module." in name:
+            name = name.replace("_checkpoint_wrapped_module.", "")
+        if "_orig_mod." in name:
+            name = name.replace("_orig_mod.", "")
         return name
 
     def _group_param_by_load_spec(self, load_enum: LoadEnum):

xtuner/v1/model/compose/qwen3_vl/modeling_vision.py

@@ -276,6 +276,7 @@ def init_weights(self):
 
         for layer_idx, layer in enumerate(self.blocks):
             for name, module in layer.named_modules():
+                name = self._clean_param_name(name)
                 if isinstance(module, nn.Linear):
                     init_params(module.weight,
                                 partial(torch.nn.init.normal_, mean=0.0, std=self.config.initializer_range))

xtuner/v1/train/trainer.py

@@ -33,7 +33,7 @@
 from xtuner.v1.engine import LossLog, OtherLog, TrainEngine
 from xtuner.v1.engine.vision_compose_train_engine import VisionComposeTrainEngine
 from xtuner.v1.loss import CELossConfig, CELossContext
-from xtuner.v1.model.base import ModelItem, TransformerConfig, XTunerBaseModelConfig
+from xtuner.v1.model.base import ModelItem, XTunerBaseModelConfig
 from xtuner.v1.model.compose.base import BaseComposeConfig
 from xtuner.v1.model.moe.moe import MoEConfig
 from xtuner.v1.patch import patch_default_save_plan

xtuner/v1/utils/__init__.py

@@ -11,6 +11,7 @@
     XTUNER_DETERMINISTIC,
     FunctionEnum,
     SharedMemory,
+    clean_param_name,
     get_function_type,
     get_padding_length,
     is_hf_model_path,
@@ -57,4 +58,5 @@
     "monkey_unpatch_torch_reductions",
     "ray_method",
     "profile_time",
+    "clean_param_name",
 ]

xtuner/v1/utils/init_weight.py

@@ -5,7 +5,8 @@
 import torch.nn as nn
 from torch.distributed.tensor import DTensor, distribute_tensor
 
-from xtuner.v1.utils import get_device
+from .device import get_device
+from .misc import clean_param_name
 
 
 DEVICE = get_device()
@@ -51,15 +52,15 @@ def _default_init_atom(name: str, module: nn.Module):
         if hasattr(module, "bias") and module.bias is not None:
             bias = cast(torch.Tensor, module.bias)
             init_params(bias, nn.init.zeros_)
-            initialized_params.add(f"{name}.bias")
+            initialized_params.add(clean_param_name(f"{name}.bias"))
 
         if hasattr(module, "weight") and module.weight is not None:
             weight = cast(torch.Tensor, module.weight)
             if "norm" in name:
                 init_params(weight, nn.init.ones_)
             else:
                 init_params(weight, partial(nn.init.normal_, mean=0.0, std=0.02))
-            initialized_params.add(f"{name}.weight")
+            initialized_params.add(clean_param_name(f"{name}.weight"))
 
     _init_weights_recursive("", module)
     return initialized_params

xtuner/v1/utils/misc.py

@@ -192,3 +192,11 @@ def get_function_full_qualname(function: FunctionType) -> str:
 
     full_qualname = f"{module_name}.{qualname}"
     return full_qualname
+
+
+def clean_param_name(name: str) -> str:
+    if "_checkpoint_wrapped_module." in name:
+        name = name.replace("_checkpoint_wrapped_module.", "")
+    if "_orig_mod." in name:
+        name = name.replace("_orig_mod.", "")
+    return name