Implement get_expert_mapping

Signed-off-by: Shane A <shanea@allenai.org>

Author: 2015aroras

vllm/model_executor/models/flex_olmo.py

@@ -359,6 +359,15 @@ def forward(
         hidden_states = self.norm(hidden_states)
         return hidden_states
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.num_experts)
+
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
@@ -370,16 +379,9 @@ def load_weights(self, weights: Iterable[tuple[str,
             ("gate_up_proj", "up_proj", 1),
         ]
 
-        # Params for weights, fp8 weight scales, fp8 activation scales
-        # (param_name, weight_name, expert_id, shard_id)
-        expert_params_mapping = FusedMoE.make_expert_params_mapping(
-            ckpt_gate_proj_name="gate_proj",
-            ckpt_down_proj_name="down_proj",
-            ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts)
-
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
+        expert_params_mapping = self.get_expert_mapping()
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
                 continue
@@ -511,3 +513,6 @@ def sample(
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)
+
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        return self.model.get_expert_mapping()