[platforms] enable platform plugins (#11602)

Signed-off-by: youkaichao <youkaichao@gmail.com>

vllm/worker/model_runner_base.py

@@ -12,7 +12,6 @@ from torch import is_tensor
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
 
 if TYPE_CHECKING:
@@ -265,13 +264,13 @@ class ModelRunnerBase(ABC, Generic[T]):
         """
         raise NotImplementedError
 
-    @current_platform.inference_mode()
     def execute_model(
         self,
         model_input: T,
         kv_caches: Optional[List[torch.Tensor]],
-        intermediate_tensors: Optional[IntermediateTensors],
+        intermediate_tensors: Optional[IntermediateTensors] = None,
         num_steps: int = 1,
+        **kwargs,
     ) -> Optional[List[SamplerOutput]]:
         """
         Execute the model on the given input.

vllm/worker/multi_step_model_runner.py

@@ -544,6 +544,7 @@ class MultiStepModelRunner(GPUModelRunnerBase[StatefulModelInput]):
         model_input.record_step_event(current_stream)
 
         if get_pp_group().is_last_rank and self.is_driver_worker:
+            assert isinstance(output, list)
             assert len(
                 output
             ) == 1, "MultiStepModelRunner requires single-step base_models"

vllm/worker/worker_base.py

@@ -11,7 +11,6 @@ from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
-from vllm.platforms import current_platform
 from vllm.sequence import ExecuteModelRequest, IntermediateTensors
 from vllm.utils import (enable_trace_function_call_for_thread,
                         resolve_obj_by_qualname, update_environment_variables)
@@ -44,6 +43,8 @@ class WorkerBase(ABC):
         self.prompt_adapter_config = vllm_config.prompt_adapter_config
         self.observability_config = vllm_config.observability_config
         self.kv_transfer_config = vllm_config.kv_transfer_config
+        from vllm.platforms import current_platform
+        self.current_platform = current_platform
 
     @abstractmethod
     def init_device(self) -> None:
@@ -74,17 +75,17 @@ class WorkerBase(ABC):
         """
         raise NotImplementedError
 
-    @current_platform.inference_mode()
     def start_worker_execution_loop(self) -> None:
         """Execute model loop in parallel worker.
 
         You can stop the loop by executing a driver worker with an empty output.
         See `stop_remote_worker_execution_loop` for more details.
         """
-        while True:
-            output = self.execute_model(execute_model_req=None)
-            if output is None:
-                return None
+        with self.current_platform.inference_mode():
+            while True:
+                output = self.execute_model(execute_model_req=None)
+                if output is None:
+                    return None
 
     @abstractmethod
     def execute_model(
@@ -352,6 +353,7 @@ class LocalOrDistributedWorkerBase(WorkerBase):
         model_execute_time = time.perf_counter() - start_time
         if not get_pp_group().is_last_rank:
             # output is IntermediateTensors
+            assert isinstance(output, IntermediateTensors)
             if (self.observability_config is not None
                     and self.observability_config.collect_model_execute_time):
                 output.tensors["model_execute_time"] = torch.tensor(