[core] clean up executor class hierarchy between v1 and v0 (vllm-proj…

…ect#12171)

Signed-off-by: youkaichao <[email protected]>

vllm/executor/executor_base.py

@@ -79,16 +79,6 @@ def determine_num_available_blocks(self) -> Tuple[int, int]:
         b = min([r[1] for r in results])
         return a, b
 
-    def initialize(self, num_gpu_blocks: int) -> None:
-        """
-        Initialize the KV caches and begin the model execution loop of the
-        underlying workers.
-        For V1 compatibility.
-        """
-        logger.info("# GPU blocks: %d", num_gpu_blocks)
-        self.collective_rpc("initialize_cache", args=(num_gpu_blocks, ))
-        self.collective_rpc("compile_or_warm_up_model")
-
     def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:
         """Initialize the KV cache by invoking the underlying worker.
         """

vllm/v1/executor/abstract.py

@@ -1,63 +1,92 @@
-from abc import ABC, abstractmethod
 from typing import Type
 
 from vllm.config import VllmConfig
+from vllm.executor.executor_base import ExecutorBase
+from vllm.executor.ray_distributed_executor import (  # noqa
+    RayDistributedExecutor as RayDistributedExecutorV0)
+from vllm.executor.uniproc_executor import (  # noqa
+    ExecutorWithExternalLauncher as ExecutorWithExternalLauncherV0)
+from vllm.executor.uniproc_executor import (  # noqa
+    UniProcExecutor as UniProcExecutorV0)
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import ModelRunnerOutput
 
 
-class Executor(ABC):
-    """Abstract class for executors."""
+class Executor(ExecutorBase):
+    """
+    Abstract class for v1 executors, mainly define some methods for v1.
+    For methods shared by v0 and v1, define them in ExecutorBase"""
 
     @staticmethod
     def get_class(vllm_config: VllmConfig) -> Type["Executor"]:
         executor_class: Type[Executor]
+        parallel_config = vllm_config.parallel_config
         distributed_executor_backend = (
-            vllm_config.parallel_config.distributed_executor_backend)
+            parallel_config.distributed_executor_backend)
+        if distributed_executor_backend is None:
+            # If the user does not specify the distributed executor backend,
+            # we will choose the backend based on the world size.
+            if parallel_config.world_size > 1:
+                distributed_executor_backend = "mp"
+            else:
+                distributed_executor_backend = "uni"
+
         if distributed_executor_backend == "ray":
-            from vllm.executor.ray_distributed_executor import (  # noqa
-                RayDistributedExecutor)
             executor_class = RayDistributedExecutor
         elif distributed_executor_backend == "mp":
             from vllm.v1.executor.multiproc_executor import MultiprocExecutor
             executor_class = MultiprocExecutor
+        elif distributed_executor_backend == "uni":
+            executor_class = UniProcExecutor
+        elif distributed_executor_backend == "external_launcher":
+            # TODO: make v1 scheduling deterministic
+            # to support external launcher
+            executor_class = ExecutorWithExternalLauncher
         else:
-            assert (distributed_executor_backend is None)
-            from vllm.v1.executor.uniproc_executor import UniprocExecutor
-            executor_class = UniprocExecutor
+            raise ValueError("Unknown distributed executor backend: "
+                             f"{distributed_executor_backend}")
         return executor_class
 
-    @abstractmethod
-    def __init__(self, vllm_config: VllmConfig) -> None:
-        raise NotImplementedError
-
-    @abstractmethod
     def initialize(self, kv_cache_config: KVCacheConfig) -> None:
-        raise NotImplementedError
+        """
+        Initialize the KV caches and begin the model execution loop of the
+        underlying workers.
+        """
+        self.collective_rpc("initialize_cache", args=(kv_cache_config, ))
+        self.collective_rpc("compile_or_warm_up_model")
 
-    @abstractmethod
     def determine_available_memory(self) -> int:  # in bytes
-        raise NotImplementedError
+        output = self.collective_rpc("determine_available_memory")
+        # Since we use a shared centralized controller, we take the minimum
+        # memory size across all workers to make sure all the memory
+        # operators can be applied to all workers.
+        return min(output)
 
-    @abstractmethod
     def get_kv_cache_spec(self) -> KVCacheSpec:
-        raise NotImplementedError
+        output = self.collective_rpc("get_kv_cache_spec")
+        for x in output:
+            assert x == output[0]
+        return output[0]
 
-    @abstractmethod
     def execute_model(
         self,
         scheduler_output,
     ) -> ModelRunnerOutput:
-        raise NotImplementedError
+        output = self.collective_rpc("execute_model",
+                                     args=(scheduler_output, ))
+        return output[0]
 
-    @abstractmethod
     def profile(self, is_start: bool = True):
-        raise NotImplementedError
+        self.collective_rpc("profile", args=(is_start, ))
+
+
+class UniProcExecutor(UniProcExecutorV0, Executor):
+    pass
+
+
+class ExecutorWithExternalLauncher(ExecutorWithExternalLauncherV0, Executor):
+    pass
 
-    @abstractmethod
-    def shutdown(self):
-        pass
 
-    @abstractmethod
-    def check_health(self) -> None:
-        raise NotImplementedError
+class RayDistributedExecutor(RayDistributedExecutorV0, Executor):
+    pass

vllm/v1/executor/multiproc_executor.py

@@ -25,8 +25,6 @@
 from vllm.utils import (get_distributed_init_method, get_mp_context,
                         get_open_port, get_open_zmq_ipc_path, zmq_socket_ctx)
 from vllm.v1.executor.abstract import Executor
-from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
-from vllm.v1.outputs import ModelRunnerOutput
 from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
@@ -37,7 +35,7 @@
 
 class MultiprocExecutor(Executor):
 
-    def __init__(self, vllm_config: VllmConfig) -> None:
+    def _init_executor(self) -> None:
         # Call self.shutdown at exit to clean up
         # and ensure workers will be terminated.
         self._finalizer = weakref.finalize(self, self.shutdown)
@@ -55,9 +53,6 @@ def sigusr1_handler(signum, frame):
 
         signal.signal(signal.SIGUSR1, sigusr1_handler)
 
-        self.vllm_config = vllm_config
-        self.parallel_config = vllm_config.parallel_config
-
         self.world_size = self.parallel_config.world_size
         tensor_parallel_size = self.parallel_config.tensor_parallel_size
         assert self.world_size == tensor_parallel_size, (
@@ -82,7 +77,8 @@ def sigusr1_handler(signum, frame):
         # Create workers
         self.workers: List[WorkerProcHandle] = []
         for rank in range(self.world_size):
-            worker = WorkerProc.make_worker_process(vllm_config, rank, rank,
+            worker = WorkerProc.make_worker_process(self.vllm_config, rank,
+                                                    rank,
                                                     distributed_init_method,
                                                     scheduler_output_handle)
             self.workers.append(worker)
@@ -93,34 +89,6 @@ def sigusr1_handler(signum, frame):
         for w in self.workers:
             w.worker_response_mq.wait_until_ready()
 
-    def initialize(self, kv_cache_config: KVCacheConfig) -> None:
-        """
-        Initialize the KV caches and begin the model execution loop of the
-        underlying workers.
-        """
-        self.collective_rpc("initialize_cache", args=(kv_cache_config, ))
-        self.collective_rpc("compile_or_warm_up_model")
-
-    def determine_available_memory(self) -> int:
-        """
-        Determine the available memory (in bytes) for KV cache by invoking the
-        underlying worker.
-        """
-        memory_sizes = self.collective_rpc("determine_available_memory")
-
-        # Since we use a shared centralized controller, we take the minimum
-        # memory size across all workers to make sure all the memory
-        # operators can be applied to all workers.
-        return min(memory_sizes)
-
-    def get_kv_cache_spec(self) -> KVCacheSpec:
-        """
-        Get all kv cache needed by the model by invoking the underlying worker.
-        """
-        kv_cache_specs = self.collective_rpc("get_kv_cache_spec")
-        assert all(s == kv_cache_specs[0] for s in kv_cache_specs)
-        return kv_cache_specs[0]
-
     def collective_rpc(self,
                        method: Union[str, Callable],
                        timeout: Optional[float] = None,
@@ -172,18 +140,6 @@ def collective_rpc(self,
             # Re-raise any other exceptions
             raise e
 
-    def execute_model(
-        self,
-        scheduler_output,
-    ) -> ModelRunnerOutput:
-        model_output = self.collective_rpc("execute_model",
-                                           args=(scheduler_output, ))[0]
-        return model_output
-
-    def profile(self, is_start: bool = True):
-        self.collective_rpc("profile", args=(is_start, ))
-        return
-
     def _ensure_worker_termination(self):
         """Ensure that all worker processes are terminated. Assumes workers have
         received termination requests. Waits for processing, then sends