[Core] Refactor executor classes for easier inheritance (vllm-project#7673)

[Core] Refactor executor classes to make it easier to inherit GPUExecutor (vllm-project#7673)

Author: jikunshang

vllm/executor/gpu_executor.py

@@ -62,6 +62,18 @@ def _get_worker_kwargs(
             observability_config=self.observability_config,
         )
 
+    def _get_worker_module_and_class(self) -> Tuple[str, str]:
+        if self.scheduler_config.is_multi_step:
+            worker_module_name = "vllm.worker.multi_step_worker"
+            worker_class_name = "MultiStepWorker"
+        elif self.speculative_config:
+            worker_module_name = "vllm.spec_decode.spec_decode_worker"
+            worker_class_name = "create_spec_worker"
+        else:
+            worker_module_name = "vllm.worker.worker"
+            worker_class_name = "Worker"
+        return (worker_module_name, worker_class_name)
+
     def _get_create_worker_kwargs(
             self,
             local_rank: int = 0,
@@ -70,17 +82,10 @@ def _get_create_worker_kwargs(
         worker_kwargs = self._get_worker_kwargs(local_rank, rank,
                                                 distributed_init_method)
 
-        if self.scheduler_config.is_multi_step:
-            worker_kwargs.update(
-                worker_module_name="vllm.worker.multi_step_worker",
-                worker_class_name="MultiStepWorker")
-        elif self.speculative_config:
-            worker_kwargs.update(
-                worker_module_name="vllm.spec_decode.spec_decode_worker",
-                worker_class_name="create_spec_worker")
-        else:
-            worker_kwargs.update(worker_module_name="vllm.worker.worker",
-                                 worker_class_name="Worker")
+        (worker_module_name,
+         worker_class_name) = self._get_worker_module_and_class()
+        worker_kwargs.update(worker_module_name=worker_module_name,
+                             worker_class_name=worker_class_name)
 
         return worker_kwargs
 

vllm/executor/ray_gpu_executor.py

@@ -91,22 +91,19 @@ def _configure_ray_workers_use_nsight(self,
         return ray_remote_kwargs
 
     def _get_worker_wrapper_args(self) -> Dict[str, Any]:
-        if self.speculative_config is not None:
-            worker_module_name = "vllm.spec_decode.spec_decode_worker"
-            worker_class_name = "create_spec_worker"
-        elif self.scheduler_config.is_multi_step:
-            worker_module_name = "vllm.worker.multi_step_worker"
-            worker_class_name = "MultiStepWorker"
-        else:
-            worker_module_name = "vllm.worker.worker"
-            worker_class_name = "Worker"
+        (worker_module_name,
+         worker_class_name) = self._get_worker_module_and_class()
 
         return dict(
             worker_module_name=worker_module_name,
             worker_class_name=worker_class_name,
             trust_remote_code=self.model_config.trust_remote_code,
         )
 
+    # child class could overwrite this to return actual env vars.
+    def _get_env_vars_to_be_updated(self):
+        return self._env_vars_for_all_workers
+
     def _init_workers_ray(self, placement_group: "PlacementGroup",
                           **ray_remote_kwargs):
         if (self.parallel_config.tensor_parallel_size == 1
@@ -231,8 +228,12 @@ def sort_by_driver_then_worker_ip(worker):
             "VLLM_TRACE_FUNCTION":
             str(envs.VLLM_TRACE_FUNCTION),
         }, ) for (node_id, _) in worker_node_and_gpu_ids]
+
+        self._env_vars_for_all_workers = (
+            all_args_to_update_environment_variables)
+
         self._run_workers("update_environment_variables",
-                          all_args=all_args_to_update_environment_variables)
+                          all_args=self._get_env_vars_to_be_updated())
 
         if len(node_gpus) == 1:
             # in single node case, we don't need to get the IP address.