[Model][Bugfix]fix ernie45 load failed due to ernie45 eplb code

vllm/model_executor/models/ernie45_moe.py

@@ -23,7 +23,8 @@
 # limitations under the License.
 """Inference-only ErineMoE model compatible with HuggingFace weights."""
 
-from collections.abc import Iterable
+import typing
+from collections.abc import Callable, Iterable
 from itertools import islice
 from typing import Any
 
@@ -139,10 +140,10 @@ def __init__(
 
         # Load balancing settings.
         vllm_config = get_current_vllm_config()
-        parallel_config = vllm_config.parallel_config
+        eplb_config = vllm_config.parallel_config.eplb_config
         self.enable_eplb = enable_eplb
 
-        self.n_redundant_experts = parallel_config.num_redundant_experts
+        self.n_redundant_experts = eplb_config.num_redundant_experts
         self.n_logical_experts = self.n_routed_experts
         self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
         self.n_local_physical_experts = self.n_physical_experts // self.ep_size
@@ -426,8 +427,10 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.vocab_size = config.vocab_size
         self.config = config
         parallel_config = vllm_config.parallel_config
+        eplb_config = parallel_config.eplb_config
         enable_eplb = parallel_config.enable_eplb
-        self.num_redundant_experts = parallel_config.num_redundant_experts
+
+        self.num_redundant_experts = eplb_config.num_redundant_experts
 
         if get_pp_group().is_first_rank:
             self.embed_tokens = VocabParallelEmbedding(
@@ -570,20 +573,27 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
 
                     # Skip loading extra bias for GPTQ models.
                     if (
-                        name.endswith(".bias") or name.endswith("_bias")
-                    ) and name not in params_dict:
+                        name_mapped.endswith(".bias") or name_mapped.endswith("_bias")
+                    ) and name_mapped not in params_dict:
                         continue
-                    param = params_dict[name]
-
-                    weight_loader = param.weight_loader
-                    weight_loader(
+                    param = params_dict[name_mapped]
+                    # We should ask the weight loader to return success or not
+                    # here since otherwise we may skip experts with other
+                    # available replicas.
+                    weight_loader = typing.cast(
+                        Callable[..., bool], param.weight_loader
+                    )
+                    success = weight_loader(
                         param,
                         loaded_weight,
-                        name,
+                        name_mapped,
                         shard_id=shard_id,
                         expert_id=expert_id,
+                        return_success=True,
                     )
-                    break
+                    if success:
+                        name = name_mapped
+                        break
                 else:
                     if is_expert_weight:
                         # We've checked that this is an expert weight