Add get_expert_mapping to NemotronHModel (for LoRA support) (#31539)

Signed-off-by: Daniel Serebrenik <daserebrenik@nvidia.com>

vllm/model_executor/models/nemotron_h.py

@@ -632,14 +632,7 @@ class NemotronHModel(nn.Module):
         hidden_states, _ = self.norm_f(hidden_states, residual)
         return hidden_states
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-        ]
-
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
         if self.has_moe:
             # (param_name, weight_name, expert_id, shard_id)
             expert_params_mapping = FusedMoE.make_expert_params_mapping(
@@ -653,8 +646,19 @@ class NemotronHModel(nn.Module):
                 num_experts=self.config.n_routed_experts,
                 num_redundant_experts=getattr(self, "num_redundant_experts", 0),
             )
-        else:
-            expert_params_mapping = []
+            return expert_params_mapping
+
+        return []
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        expert_params_mapping = self.get_expert_mapping()
 
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()