[Model] Use AutoWeightsLoader for mamba2 (#18918)

Signed-off-by: iLeGend <824040212@qq.com>

vllm/model_executor/models/mamba2.py

@@ -32,7 +32,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 from vllm.utils import LayerBlockType
 
-from .utils import (is_pp_missing_parameter,
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
 
@@ -167,6 +167,27 @@ class Mamba2Model(nn.Module):
 
         return hidden_states
 
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "A_log" in name:
+                name = name.replace("A_log", "A")
+
+            # Skip loading extra bias for GPTQ models.
+            if name.endswith(".bias") and name not in params_dict:
+                continue
+            if is_pp_missing_parameter(name, self):
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
 
 class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree,
                         SupportsV0Only):
@@ -282,21 +303,5 @@ class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree,
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-        for name, loaded_weight in weights:
-            if "A_log" in name:
-                name = name.replace("A_log", "A")
-
-            # Skip loading extra bias for GPTQ models.
-            if name.endswith(".bias") and name not in params_dict:
-                continue
-            if is_pp_missing_parameter(name, self):
-                continue
-
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
-            loaded_params.add(name)
-        return loaded_params
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)