[Cleanup] Move scheduler get_routed_experts logic to separate method

vllm/v1/core/sched/scheduler.py

@@ -1197,28 +1197,7 @@ def update_from_output(
 
             routed_experts = None
             if stopped:
-                if self.vllm_config.model_config.enable_return_routed_experts:
-                    kv_blocks = self.kv_cache_manager.get_blocks(request.request_id)
-                    block_ids = kv_blocks.get_block_ids()[0]
-                    num_tokens = request.num_tokens - 1
-
-                    # compute slot mapping
-                    block_ids_array = np.array(block_ids, dtype=np.int32)
-                    num_blocks = len(block_ids)
-                    block_size = self.block_size
-
-                    # generate block offsets
-                    block_offsets = np.arange(0, block_size)
-
-                    # compute slot mapping: slot = block_id * block_size + offset
-                    slot_mapping = (
-                        block_offsets.reshape((1, block_size))
-                        + block_ids_array.reshape((num_blocks, 1)) * block_size
-                    ).flatten()[:num_tokens]
-
-                    routed_experts = self.routed_experts_reader.get_routed_experts(
-                        indices=slot_mapping
-                    )
+                routed_experts = self._get_routed_experts(request)
                 kv_transfer_params = self._free_request(request)
                 if status_before_stop == RequestStatus.RUNNING:
                     stopped_running_reqs.add(request)
@@ -1250,7 +1229,12 @@ def update_from_output(
 
             # Get prompt logprobs for this request.
             prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
-            if new_token_ids or pooler_output is not None or kv_transfer_params:
+            if (
+                new_token_ids
+                or pooler_output is not None
+                or kv_transfer_params
+                or stopped
+            ):
                 # Add EngineCoreOutput for this Request.
                 outputs[request.client_index].append(
                     EngineCoreOutput(
@@ -1351,6 +1335,30 @@ def update_from_output(
 
         return engine_core_outputs
 
+    def _get_routed_experts(self, request: Request) -> np.ndarray | None:
+        if not self.vllm_config.model_config.enable_return_routed_experts:
+            return None
+
+        kv_blocks = self.kv_cache_manager.get_blocks(request.request_id)
+        block_ids = kv_blocks.get_block_ids()[0]
+        num_tokens = request.num_tokens - 1
+
+        # compute slot mapping
+        block_ids_array = np.array(block_ids, dtype=np.int32)
+        num_blocks = len(block_ids)
+        block_size = self.block_size
+
+        # generate block offsets
+        block_offsets = np.arange(0, block_size)
+
+        # compute slot mapping: slot = block_id * block_size + offset
+        slot_mapping = (
+            block_offsets.reshape((1, block_size))
+            + block_ids_array.reshape((num_blocks, 1)) * block_size
+        ).flatten()[:num_tokens]
+
+        return self.routed_experts_reader.get_routed_experts(indices=slot_mapping)
+
     def _update_request_with_output(
         self, request: Request, new_token_ids: list[int]
     ) -> tuple[list[int], bool]: