[Core] Introduce SPMD worker execution using Ray accelerated DAG (#6032)

Signed-off-by: Rui Qiao <ruisearch42@gmail.com>
Co-authored-by: Stephanie Wang <swang@cs.berkeley.edu>

.buildkite/test-pipeline.yaml

@@ -84,6 +84,8 @@ steps:
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
   - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
+  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray VLLM_USE_RAY_SPMD_WORKER=1 VLLM_USE_RAY_COMPILED_DAG=1 pytest -v -s distributed/test_basic_distributed_correctness.py
+  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray VLLM_USE_RAY_SPMD_WORKER=1 VLLM_USE_RAY_COMPILED_DAG=1 pytest -v -s distributed/test_basic_distributed_correctness.py
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
   - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
   - TEST_DIST_MODEL=llava-hf/llava-1.5-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_multimodal_broadcast.py
@@ -108,6 +110,7 @@ steps:
   # We want to test that models which use 2 GPUs work with 4 GPUs, which is why we duplicate them here.
   # See https://github.com/vllm-project/vllm/pull/5473#issuecomment-2166601837 for context.
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
+  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray VLLM_USE_RAY_SPMD_WORKER=1 VLLM_USE_RAY_COMPILED_DAG=1 pytest -v -s distributed/test_basic_distributed_correctness.py
   - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
   - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
 

vllm/engine/llm_engine.py

@@ -6,6 +6,7 @@ from typing import Set, Type, TypeVar, Union
 
 from transformers import PreTrainedTokenizer
 
+import vllm.envs as envs
 from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, LoadConfig,
                          LoRAConfig, ModelConfig, MultiModalConfig,
                          ObservabilityConfig, ParallelConfig,
@@ -414,6 +415,9 @@ class LLMEngine:
         elif distributed_executor_backend == "mp":
             from vllm.executor.multiproc_gpu_executor import (
                 MultiprocessingGPUExecutor)
+            assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
+                "multiprocessing distributed executor backend does not "
+                "support VLLM_USE_RAY_SPMD_WORKER=1")
             executor_class = MultiprocessingGPUExecutor
         else:
             from vllm.executor.gpu_executor import GPUExecutor
@@ -426,6 +430,7 @@ class LLMEngine:
             usage_context=usage_context,
             stat_loggers=stat_loggers,
         )
+
         return engine
 
     def __reduce__(self):

vllm/envs.py

@@ -34,6 +34,7 @@ if TYPE_CHECKING:
     VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS: bool = False
     VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
     VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
+    VLLM_USE_RAY_SPMD_WORKER: bool = False
     VLLM_USE_RAY_COMPILED_DAG: bool = False
     VLLM_WORKER_MULTIPROC_METHOD: str = "fork"
     VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
@@ -261,6 +262,13 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     "VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS":
     lambda: bool(os.getenv("VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS", False)),
 
+    # If the env var is set, then all workers will execute as separate
+    # processes from the engine, and we use the same mechanism to trigger
+    # execution on all workers.
+    # Run vLLM with VLLM_USE_RAY_SPMD_WORKER=1 to enable it.
+    "VLLM_USE_RAY_SPMD_WORKER":
+    lambda: bool(os.getenv("VLLM_USE_RAY_SPMD_WORKER", 0)),
+
     # If the env var is set, it uses the Ray's compiled DAG API
     # which optimizes the control plane overhead.
     # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.

vllm/executor/distributed_gpu_executor.py

@@ -64,8 +64,8 @@ class DistributedGPUExecutor(GPUExecutor):
                           num_cpu_blocks=num_cpu_blocks)
 
     def execute_model(
-        self, execute_model_req: ExecuteModelRequest
+            self,
-    ) -> Optional[List[SamplerOutput]]:
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
         if self.parallel_worker_tasks is None:
             self.parallel_worker_tasks = self._run_workers(
                 "start_worker_execution_loop",
@@ -73,7 +73,9 @@ class DistributedGPUExecutor(GPUExecutor):
                 **self.extra_execute_model_run_workers_kwargs)
 
         # Only the driver worker returns the sampling results.
-        return self._driver_execute_model(execute_model_req)
+        driver_outputs = self._driver_execute_model(execute_model_req)
+        assert driver_outputs is not None
+        return driver_outputs
 
     def stop_remote_worker_execution_loop(self) -> None:
         if self.parallel_worker_tasks is None:

vllm/executor/ray_gpu_executor.py

@@ -1,6 +1,5 @@
 import asyncio
 import os
-import pickle
 from collections import defaultdict
 from itertools import islice, repeat
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
@@ -23,12 +22,30 @@ if TYPE_CHECKING:
 
 logger = init_logger(__name__)
 
-USE_RAY_COMPILED_DAG = envs.VLLM_USE_RAY_COMPILED_DAG
-
 
 class RayGPUExecutor(DistributedGPUExecutor):
 
     def _init_executor(self) -> None:
+        # If the env var is set, it uses the Ray's compiled DAG API
+        # which optimizes the control plane overhead.
+        # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
+        # Currently, this requires USE_RAY_SPMD_WORKER=True.
+        self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
+        # If the env var is set, then we do not distinguish between the
+        # "driver worker" vs other workers. Also, the rank 0 worker will
+        # be executed in a remote Ray worker. Currently this requires
+        # USE_RAY_COMPILED_DAG=True.
+        self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
+        if self.use_ray_compiled_dag:
+            assert self.use_ray_spmd_worker, (
+                "VLLM_USE_RAY_COMPILED_DAG=1 requires "
+                "VLLM_USE_RAY_SPMD_WORKER=1")
+        if self.use_ray_spmd_worker:
+            # TODO: Support SPMD worker for non-DAG Ray executor.
+            assert self.use_ray_compiled_dag, (
+                "VLLM_USE_RAY_SPMD_WORKER=1 requires "
+                "VLLM_USE_RAY_COMPILED_DAG=1")
+
         assert self.parallel_config.distributed_executor_backend == "ray"
         placement_group = self.parallel_config.placement_group
 
@@ -40,11 +57,7 @@ class RayGPUExecutor(DistributedGPUExecutor):
         # Create the parallel GPU workers.
         self._init_workers_ray(placement_group)
 
-        self.forward_dag = None
+        self.forward_dag: Optional["ray.dag.CompiledDAG"] = None
-        if USE_RAY_COMPILED_DAG:
-            self.forward_dag = self._compiled_ray_dag()
-            self.extra_execute_model_run_workers_kwargs[
-                "use_ray_compiled_dag"] = True
 
     def _configure_ray_workers_use_nsight(self,
                                           ray_remote_kwargs) -> Dict[str, Any]:
@@ -110,21 +123,24 @@ class RayGPUExecutor(DistributedGPUExecutor):
                 trust_remote_code=self.model_config.trust_remote_code,
             )
 
-            worker_ip = ray.get(worker.get_node_ip.remote())
+            if self.use_ray_spmd_worker:
-            if worker_ip == driver_ip and self.driver_dummy_worker is None:
-                # If the worker is on the same node as the driver, we use it
-                # as the resource holder for the driver process.
-                self.driver_dummy_worker = worker
-                self.driver_worker = RayWorkerWrapper(
-                    worker_module_name=worker_module_name,
-                    worker_class_name=worker_class_name,
-                    trust_remote_code=self.model_config.trust_remote_code,
-                )
-            else:
-                # Else, added to the list of workers.
                 self.workers.append(worker)
+            else:
+                worker_ip = ray.get(worker.get_node_ip.remote())
+                if worker_ip == driver_ip and self.driver_dummy_worker is None:
+                    # If the worker is on the same node as the driver, we use it
+                    # as the resource holder for the driver process.
+                    self.driver_dummy_worker = worker
+                    self.driver_worker = RayWorkerWrapper(
+                        worker_module_name=worker_module_name,
+                        worker_class_name=worker_class_name,
+                        trust_remote_code=self.model_config.trust_remote_code,
+                    )
+                else:
+                    # Else, added to the list of workers.
+                    self.workers.append(worker)
 
-        if self.driver_dummy_worker is None:
+        if not self.use_ray_spmd_worker and self.driver_dummy_worker is None:
             raise ValueError(
                 "Ray does not allocate any GPUs on the driver node. Consider "
                 "adjusting the Ray placement group or running the driver on a "
@@ -254,9 +270,23 @@ class RayGPUExecutor(DistributedGPUExecutor):
         Passing None will cause the driver to stop the model execution
         loop running in each of the remote workers.
         """
+        assert not self.use_ray_spmd_worker, (
+            "driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
         return self.driver_worker.execute_method("execute_model",
                                                  execute_model_req)
 
+    def execute_model(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        if not self.use_ray_spmd_worker:
+            return super().execute_model(execute_model_req)
+
+        if self.forward_dag is None:
+            self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
+
+        outputs = ray.get(self.forward_dag.execute(execute_model_req))
+        return outputs[0]
+
     def _run_workers(
         self,
         method: str,
@@ -266,7 +296,6 @@ class RayGPUExecutor(DistributedGPUExecutor):
         all_kwargs: Optional[List[Dict[str, Any]]] = None,
         use_dummy_driver: bool = False,
         max_concurrent_workers: Optional[int] = None,
-        use_ray_compiled_dag: bool = False,
         **kwargs,
     ) -> Any:
         """Runs the given method on all workers. Can be used in the following
@@ -281,6 +310,10 @@ class RayGPUExecutor(DistributedGPUExecutor):
         - all_args/all_kwargs: args/kwargs for each worker are specified
           individually
         """
+        if self.use_ray_spmd_worker:
+            assert not async_run_tensor_parallel_workers_only, (
+                "async_run_tensor_parallel_workers_only is not supported for "
+                "spmd mode.")
 
         if max_concurrent_workers:
             raise NotImplementedError(
@@ -289,71 +322,69 @@ class RayGPUExecutor(DistributedGPUExecutor):
         count = len(self.workers) if not \
             async_run_tensor_parallel_workers_only \
             else len(self.non_driver_workers)
+        # If using SPMD worker, all workers are the same, so we should execute
+        # the args on all workers. Otherwise, we skip the first worker's args
+        # because those args will go to the driver worker.
+        first_worker_args_index: int = 0 if self.use_ray_spmd_worker else 1
         all_worker_args = repeat(args, count) if all_args is None \
-            else islice(all_args, 1, None)
+            else islice(all_args, first_worker_args_index, None)
         all_worker_kwargs = repeat(kwargs, count) if all_kwargs is None \
-            else islice(all_kwargs, 1, None)
+            else islice(all_kwargs, first_worker_args_index, None)
 
-        if use_ray_compiled_dag:
+        # Start the ray workers first.
-            # Right now, compiled DAG can only accept a single
+        ray_workers = self.workers
-            # input. TODO(sang): Fix it.
+        if async_run_tensor_parallel_workers_only:
-            assert self.forward_dag is not None
+            ray_workers = self.non_driver_workers
-            output_channels = self.forward_dag.execute(1)
+        ray_worker_outputs = [
-            ray_worker_outputs = []
+            worker.execute_method.remote(method, *worker_args, **worker_kwargs)
-        else:
+            for (worker, worker_args, worker_kwargs
-            # Start the ray workers first.
+                 ) in zip(ray_workers, all_worker_args, all_worker_kwargs)
-            ray_workers = self.workers
+        ]
-            if async_run_tensor_parallel_workers_only:
-                ray_workers = self.non_driver_workers
-            ray_worker_outputs = [
-                worker.execute_method.remote(method, *worker_args,
-                                             **worker_kwargs)
-                for (worker, worker_args, worker_kwargs
-                     ) in zip(ray_workers, all_worker_args, all_worker_kwargs)
-            ]
 
         if async_run_tensor_parallel_workers_only:
             # Just return futures
             return ray_worker_outputs
 
-        driver_args = args if all_args is None else all_args[0]
+        driver_worker_output = []
-        driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
+        # In SPMD mode, the driver worker is the same as any other worker,
+        # so we only explicitly execute on the driver worker if using a
+        # non-SPMD worker class.
+        if not self.use_ray_spmd_worker:
+            driver_args = args if all_args is None else all_args[0]
+            driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
+
+            # Start the driver worker after all the ray workers.
+            if not use_dummy_driver:
+                driver_worker_output = [
+                    self.driver_worker.execute_method(method, *driver_args,
+                                                      **driver_kwargs)
+                ]
+            else:
+                assert self.driver_dummy_worker is not None
+                driver_worker_output = [
+                    ray.get(
+                        self.driver_dummy_worker.execute_method.remote(
+                            method, *driver_args, **driver_kwargs))
+                ]
 
-        # Start the driver worker after all the ray workers.
-        if not use_dummy_driver:
-            driver_worker_output = self.driver_worker.execute_method(
-                method, *driver_args, **driver_kwargs)
-        else:
-            assert self.driver_dummy_worker is not None
-            driver_worker_output = ray.get(
-                self.driver_dummy_worker.execute_method.remote(
-                    method, *driver_args, **driver_kwargs))
         # Get the results of the ray workers.
         if self.workers:
-            if use_ray_compiled_dag:
+            ray_worker_outputs = ray.get(ray_worker_outputs)
-                try:
-                    ray_worker_outputs = [
-                        pickle.loads(chan.begin_read())
-                        for chan in output_channels
-                    ]
-                finally:
-                    # Has to call end_read in order to reuse the DAG.
-                    for chan in output_channels:
-                        chan.end_read()
-            else:
-                ray_worker_outputs = ray.get(ray_worker_outputs)
 
-        return [driver_worker_output] + ray_worker_outputs
+        return driver_worker_output + ray_worker_outputs
 
     def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
         """Wait for futures returned from _run_workers() with
         async_run_remote_workers_only to complete."""
         ray.get(parallel_worker_tasks)
 
-    def _compiled_ray_dag(self):
+    def _compiled_ray_dag(self, enable_asyncio: bool):
         import pkg_resources
-        required_version = "2.9"
+        from packaging import version
-        current_version = pkg_resources.get_distribution("ray").version
+
+        required_version = version.parse("2.32")
+        current_version = version.parse(
+            pkg_resources.get_distribution("ray").version)
         if current_version < required_version:
             raise ValueError(f"Ray version {required_version} or greater is "
                              f"required, but found {current_version}")
@@ -365,23 +396,47 @@ class RayGPUExecutor(DistributedGPUExecutor):
         # a dummy value for now. It will be fixed soon.
         with InputNode() as input_data:
             forward_dag = MultiOutputNode([
-                worker.execute_model_compiled_dag_remote.
+                worker.execute_model_spmd.bind(  # type: ignore[attr-defined]
-                bind(  # type: ignore[attr-defined]
                     input_data) for worker in self.workers
             ])
-        return forward_dag.experimental_compile()
+        return forward_dag.experimental_compile(enable_asyncio=enable_asyncio)
+
+    def __del__(self):
+        if self.forward_dag is not None:
+            self.forward_dag.teardown()
+            import ray
+            for worker in self.workers:
+                ray.kill(worker)
 
 
 class RayGPUExecutorAsync(RayGPUExecutor, DistributedGPUExecutorAsync):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.driver_exec_method = make_async(self.driver_worker.execute_method)
+        self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
+        if not self.use_ray_compiled_dag:
+            self.driver_exec_method = make_async(
+                self.driver_worker.execute_method)
+
+    async def execute_model_async(
+            self,
+            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        if not self.use_ray_spmd_worker:
+            return await super().execute_model_async(execute_model_req)
+
+        if self.forward_dag is None:
+            self.forward_dag = self._compiled_ray_dag(enable_asyncio=True)
+
+        dag_future = await self.forward_dag.execute_async(execute_model_req)
+        outputs = await dag_future
+        return outputs[0]
 
     async def _driver_execute_model_async(
         self,
         execute_model_req: Optional[ExecuteModelRequest] = None
     ) -> List[SamplerOutput]:
+        assert not self.use_ray_spmd_worker, (
+            "driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
         if self.pp_locks is None:
             # This locks each pipeline parallel stage so multiple virtual
             # engines can't execute on the same stage at the same time
@@ -415,8 +470,17 @@ class RayGPUExecutorAsync(RayGPUExecutor, DistributedGPUExecutorAsync):
         return results[-1]
 
     async def _start_worker_execution_loop(self):
+        assert not self.use_ray_spmd_worker, (
+            "worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1")
         coros = [
             worker.execute_method.remote("start_worker_execution_loop")
             for worker in self.non_driver_workers
         ]
         return await asyncio.gather(*coros)
+
+    def __del__(self):
+        if self.forward_dag is not None:
+            self.forward_dag.teardown()
+            import ray
+            for worker in self.workers:
+                ray.kill(worker)

vllm/executor/ray_utils.py

@@ -1,8 +1,8 @@
-import pickle
 from typing import List, Optional, Tuple
 
 from vllm.config import ParallelConfig
 from vllm.logger import init_logger
+from vllm.sequence import ExecuteModelRequest
 from vllm.utils import get_ip, is_hip, is_xpu
 from vllm.worker.worker_base import WorkerWrapperBase
 
@@ -31,16 +31,18 @@ try:
             gpu_ids = ray.get_gpu_ids()
             return node_id, gpu_ids
 
-        def execute_model_compiled_dag_remote(self, ignored):
+        def execute_model_spmd(self, execute_model_req: ExecuteModelRequest):
-            """Used only when compiled DAG is enabled."""
+            """Used only when SPMD worker and compiled DAG are both
+            enabled."""
+            # TODO(swang): This is needed right now because Ray aDAG executes
+            # on a background thread, so we need to reset torch's current
+            # device.
             import torch
             if not self.compiled_dag_cuda_device_set:
                 torch.cuda.set_device(self.worker.device)
                 self.compiled_dag_cuda_device_set = True
 
-            output = self.worker.execute_model()
+            return self.worker._execute_model_spmd(execute_model_req)
-            output = pickle.dumps(output)
-            return output
 
     ray_import_err = None
 

vllm/executor/ray_xpu_executor.py

@@ -1,11 +1,11 @@
 import asyncio
 import os
-import pickle
 from collections import defaultdict
 from itertools import islice, repeat
 from typing import (TYPE_CHECKING, Any, Awaitable, Dict, List, Optional, Set,
                     Tuple, Union)
 
+import vllm.envs as envs
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, MultiModalConfig, ParallelConfig,
                          PromptAdapterConfig, SchedulerConfig,
@@ -30,7 +30,7 @@ logger = init_logger(__name__)
 # If the env var is set, it uses the Ray's compiled DAG API
 # which optimizes the control plane overhead.
 # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
-USE_RAY_COMPILED_DAG = bool(os.getenv("VLLM_USE_RAY_COMPILED_DAG", 0))
+USE_RAY_COMPILED_DAG = envs.VLLM_USE_RAY_COMPILED_DAG
 
 
 class RayXPUExecutor(DistributedGPUExecutor):
@@ -72,10 +72,9 @@ class RayXPUExecutor(DistributedGPUExecutor):
         # Create the parallel GPU workers.
         self._init_workers_ray(placement_group)
 
-        # Profile the memory usage and initialize the cache.
         self.forward_dag = None
         if USE_RAY_COMPILED_DAG:
-            self.forward_dag = self._compiled_ray_dag()
+            self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
 
         # This is non-None when the execute model loop is running
         # in the parallel workers. It's a coroutine in the AsyncLLMEngine case.
@@ -270,7 +269,6 @@ class RayXPUExecutor(DistributedGPUExecutor):
         all_kwargs: Optional[List[Dict[str, Any]]] = None,
         use_dummy_driver: bool = False,
         max_concurrent_workers: Optional[int] = None,
-        use_ray_compiled_dag: bool = False,
         **kwargs,
     ) -> Any:
         """Runs the given method on all workers. Can be used in the following
@@ -293,26 +291,20 @@ class RayXPUExecutor(DistributedGPUExecutor):
         all_worker_kwargs = repeat(kwargs, count) if all_kwargs is None \
             else islice(all_kwargs, 1, None)
 
-        if use_ray_compiled_dag:
+        # Start the ray workers first.
-            # Right now, compiled DAG can only accept a single
+        ray_worker_outputs = [
-            # input. TODO(sang): Fix it.
+            worker.execute_method.remote(method, *worker_args, **worker_kwargs)
-            assert self.forward_dag is not None
+            for (worker, worker_args, worker_kwargs
-            output_channels = self.forward_dag.execute(1)
+                 ) in zip(self.workers, all_worker_args, all_worker_kwargs)
-        else:
+        ]
-            # Start the ray workers first.
+
-            ray_worker_outputs = [
-                worker.execute_method.remote(method, *worker_args,
-                                             **worker_kwargs)
-                for (worker, worker_args, worker_kwargs
-                     ) in zip(self.workers, all_worker_args, all_worker_kwargs)
-            ]
         if async_run_remote_workers_only:
             # Just return futures
             return ray_worker_outputs
 
+        driver_worker_output = []
         driver_args = args if all_args is None else all_args[0]
         driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
-
         # Start the driver worker after all the ray workers.
         if not use_dummy_driver:
             driver_worker_output = self.driver_worker.execute_method(
@@ -324,36 +316,28 @@ class RayXPUExecutor(DistributedGPUExecutor):
                     method, *driver_args, **driver_kwargs))
         # Get the results of the ray workers.
         if self.workers:
-            if use_ray_compiled_dag:
+            ray_worker_outputs = ray.get(ray_worker_outputs)
-                try:
-                    ray_worker_outputs = [
-                        pickle.loads(chan.begin_read())
-                        for chan in output_channels
-                    ]
-                finally:
-                    # Has to call end_read in order to reuse the DAG.
-                    for chan in output_channels:
-                        chan.end_read()
-            else:
-                ray_worker_outputs = ray.get(ray_worker_outputs)
 
-        return [driver_worker_output] + ray_worker_outputs
+        return driver_worker_output + ray_worker_outputs
 
     def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
         """Wait for futures returned from _run_workers() with
         async_run_remote_workers_only to complete."""
         ray.get(parallel_worker_tasks)
 
-    def _compiled_ray_dag(self):
+    def _compiled_ray_dag(self, enable_asyncio: bool):
         import pkg_resources
-        required_version = "2.9"
+        from packaging import version
-        current_version = pkg_resources.get_distribution("ray").version
+
+        required_version = version.parse("2.32")
+        current_version = version.parse(
+            pkg_resources.get_distribution("ray").version)
         if current_version < required_version:
             raise ValueError(f"Ray version {required_version} or greater is "
                              f"required, but found {current_version}")
 
         from ray.dag import InputNode, MultiOutputNode
-        assert self.parallel_config.worker_use_ray
+        assert self.parallel_config.distributed_executor_backend == "ray"
 
         # Right now, compiled DAG requires at least 1 arg. We send
         # a dummy value for now. It will be fixed soon.
@@ -363,7 +347,7 @@ class RayXPUExecutor(DistributedGPUExecutor):
                 bind(  # type: ignore[attr-defined]
                     input_data) for worker in self.workers
             ])
-        return forward_dag.experimental_compile()
+        return forward_dag.experimental_compile(enable_asyncio=enable_asyncio)
 
     def check_health(self) -> None:
         """Raises an error if engine is unhealthy."""

vllm/worker/worker_base.py

@@ -281,6 +281,33 @@ class LocalOrDistributedWorkerBase(WorkerBase):
         # list to conform to interface.
         return output
 
+    def _execute_model_spmd(
+        self, execute_model_req: ExecuteModelRequest
+    ) -> Optional[List[SamplerOutput]]:
+        """
+        Execute model in Single Program Multiple Data (SPMD) fashion.
+        All workers take the same request, prepare the input and
+        execute the model.
+        """
+        assert execute_model_req is not None, (
+            "_execute_model_spmd() requires each worker to take in an "
+            "ExecuteModelRequest")
+        worker_input: WorkerInput = self.prepare_worker_input(
+            execute_model_req=execute_model_req)
+        model_input: ModelRunnerInputBase = (
+            self.model_runner.prepare_model_input(
+                execute_model_req.seq_group_metadata_list))
+
+        self.execute_worker(worker_input)
+
+        # If there is no input, we don't need to execute the model.
+        if worker_input.num_seq_groups == 0:
+            return []
+
+        return self.model_runner.execute_model(
+            model_input, self.kv_cache[worker_input.virtual_engine]
+            if self.kv_cache is not None else None)
+
 
 class WorkerWrapperBase:
     """
@@ -296,7 +323,7 @@ class WorkerWrapperBase:
                  trust_remote_code: bool = False) -> None:
         self.worker_module_name = worker_module_name
         self.worker_class_name = worker_class_name
-        self.worker = None
+        self.worker: Optional[WorkerBase] = None
         if trust_remote_code:
             # note: lazy import to avoid importing torch before initializing
             from vllm.utils import init_cached_hf_modules
@@ -323,7 +350,9 @@ class WorkerWrapperBase:
 
         mod = importlib.import_module(self.worker_module_name)
         worker_class = getattr(mod, self.worker_class_name)
+
         self.worker = worker_class(*args, **kwargs)
+        assert self.worker is not None
 
     def execute_method(self, method, *args, **kwargs):
         try: