ray_xpu_executor.py

import asyncio
import os
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,
                         SpeculativeConfig)
from vllm.executor.distributed_gpu_executor import (  # yapf: disable
    DistributedGPUExecutor, DistributedGPUExecutorAsync)
from vllm.executor.ray_utils import RayWorkerWrapper, ray
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.sequence import ExecuteModelRequest, SamplerOutput
from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                        make_async)

if ray is not None:
    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy

if TYPE_CHECKING:
    from ray.util.placement_group import PlacementGroup

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 = envs.VLLM_USE_RAY_COMPILED_DAG


class RayXPUExecutor(DistributedGPUExecutor):

    def __init__(
        self,
        model_config: ModelConfig,
        cache_config: CacheConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
        device_config: DeviceConfig,
        load_config: LoadConfig,
        lora_config: Optional[LoRAConfig],
        multimodal_config: Optional[MultiModalConfig],
        prompt_adapter_config: Optional[PromptAdapterConfig],
        speculative_config: Optional[SpeculativeConfig],
    ) -> None:
        assert device_config.device_type == "xpu"
        assert (not speculative_config
                ), "Speculative decoding not yet supported for XPU backend"

        self.model_config = model_config
        self.cache_config = cache_config
        self.load_config = load_config
        self.lora_config = lora_config
        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config
        self.device_config = device_config
        self.multimodal_config = multimodal_config
        self.prompt_adapter_config = prompt_adapter_config

        placement_group = self.parallel_config.placement_group

        # Disable Ray usage stats collection.
        ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
        if ray_usage != "1":
            os.environ["RAY_USAGE_STATS_ENABLED"] = "0"

        # Create the parallel GPU workers.
        self._init_workers_ray(placement_group)

        self.forward_dag = None
        if USE_RAY_COMPILED_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.
        self.parallel_worker_tasks: Optional[Union[Any, Awaitable[Any]]] = None
        # Updated by implementations that require additional args to be passed
        # to the _run_workers execute_model call
        self.extra_execute_model_run_workers_kwargs: Dict[str, Any] = {}

    def _init_executor(self) -> None:
        pass

    def determine_num_available_blocks(self) -> Tuple[int, int]:
        """Determine the number of available KV blocks.

        This invokes `determine_num_available_blocks` on each worker and takes
        the min of the results, guaranteeing that the selected cache sizes are
        compatible with all workers.

        Returns:
            - Tuple[num_gpu_blocks, num_cpu_blocks]
        """
        # Get the maximum number of blocks that can be allocated on GPU and CPU.
        num_blocks = self._run_workers("determine_num_available_blocks", )

        # Since we use a shared centralized controller, we take the minimum
        # number of blocks across all workers to make sure all the memory
        # operators can be applied to all workers.
        num_gpu_blocks = min(b[0] for b in num_blocks)
        num_cpu_blocks = min(b[1] for b in num_blocks)

        return num_gpu_blocks, num_cpu_blocks

    def _init_workers_ray(self, placement_group: "PlacementGroup",
                          **ray_remote_kwargs):
        if self.parallel_config.tensor_parallel_size == 1:
            # For single GPU case, we use a ray worker with constrained memory.
            num_gpus = self.cache_config.gpu_memory_utilization
        else:
            # Otherwise, the ray workers are allocated with a full GPU.
            num_gpus = 1

        # The driver dummy worker does not actually use any resources.
        # It holds the resource for the driver worker.
        self.driver_dummy_worker: Optional[RayWorkerWrapper] = None
        # The remaining workers are the actual ray actors.
        self.workers: List[RayWorkerWrapper] = []

        # Create the workers.
        driver_ip = get_ip()
        for bundle_id, bundle in enumerate(placement_group.bundle_specs):
            if not bundle.get("GPU", 0):
                continue
            scheduling_strategy = PlacementGroupSchedulingStrategy(
                placement_group=placement_group,
                placement_group_capture_child_tasks=True,
                placement_group_bundle_index=bundle_id,
            )
            worker = ray.remote(
                num_cpus=0,
                num_gpus=num_gpus,
                scheduling_strategy=scheduling_strategy,
                **ray_remote_kwargs,
            )(RayWorkerWrapper).remote(
                worker_module_name="vllm.worker.xpu_worker",
                worker_class_name="XPUWorker",
                trust_remote_code=self.model_config.trust_remote_code,
            )

            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="vllm.worker.xpu_worker",
                    worker_class_name="XPUWorker",
                    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:
            raise ValueError(
                "Ray does not allocate any GPUs on the driver node. Consider "
                "adjusting the Ray placement group or running the driver on a "
                "GPU node.")

        # Get the set of GPU IDs used on each node.
        worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids",
                                                    use_dummy_driver=True)

        node_workers = defaultdict(list)
        node_gpus = defaultdict(list)

        for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
            node_workers[node_id].append(i)
            node_gpus[node_id].extend(gpu_ids)
        for node_id, gpu_ids in node_gpus.items():
            node_gpus[node_id] = sorted(gpu_ids)

        # TODO: add env var for xpu

        distributed_init_method = get_distributed_init_method(
            driver_ip, get_open_port())

        def collect_arg_helper_func(**kwargs):
            # avoid writing `{"name": value}` manually
            return kwargs

        init_worker_all_kwargs = []

        # Initialize the actual workers inside worker wrapper.
        for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids, ):
            local_rank = node_workers[node_id].index(rank)
            init_worker_all_kwargs.append(
                collect_arg_helper_func(
                    model_config=self.model_config,
                    parallel_config=self.parallel_config,
                    scheduler_config=self.scheduler_config,
                    device_config=self.device_config,
                    cache_config=self.cache_config,
                    load_config=self.load_config,
                    local_rank=local_rank,
                    rank=rank,
                    distributed_init_method=distributed_init_method,
                    lora_config=self.lora_config,
                    multimodal_config=self.multimodal_config,
                    is_driver_worker=rank == 0,
                ))
        self._run_workers("init_worker", all_kwargs=init_worker_all_kwargs)

        self._run_workers("init_device")
        self._run_workers(
            "load_model",
            max_concurrent_workers=self.parallel_config.
            max_parallel_loading_workers,
        )

    def initialize_cache(self, num_gpu_blocks: int,
                         num_cpu_blocks: int) -> None:
        """Initialize the KV cache in all workers.
        """

        # NOTE: We log here to avoid multiple logs when number of workers is
        # greater than one. We could log in the engine, but not all executors
        # have GPUs.
        logger.info("# GPU blocks: %d, "
                    "# CPU blocks: %d", num_gpu_blocks, num_cpu_blocks)

        self.cache_config.num_gpu_blocks = num_gpu_blocks
        self.cache_config.num_cpu_blocks = num_cpu_blocks

        self._run_workers("initialize_cache",
                          num_gpu_blocks=num_gpu_blocks,
                          num_cpu_blocks=num_cpu_blocks)

    def _driver_execute_model(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> List[SamplerOutput]:
        """Run execute_model in the driver worker.

        Passing None will cause the driver to stop the model execution
        loop running in each of the remote workers.
        """
        return self.driver_worker.execute_method("execute_model",
                                                 execute_model_req)

    def add_lora(self, lora_request: LoRARequest) -> bool:
        assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
        return self._run_workers(
            "add_lora",
            lora_request=lora_request,
        )

    def remove_lora(self, lora_id: int) -> bool:
        assert lora_id > 0, "lora_id must be greater than 0."
        return self._run_workers(
            "remove_lora",
            lora_id=lora_id,
        )

    def list_loras(self) -> Set[int]:
        return self._run_workers("list_loras")

    def _run_workers(
        self,
        method: str,
        *args,
        async_run_remote_workers_only: bool = False,
        all_args: Optional[List[Tuple[Any, ...]]] = None,
        all_kwargs: Optional[List[Dict[str, Any]]] = None,
        use_dummy_driver: bool = False,
        max_concurrent_workers: Optional[int] = None,
        **kwargs,
    ) -> Any:
        """Runs the given method on all workers. Can be used in the following
        ways:

        - args/kwargs: All workers share the same args/kwargs
        - args/kwargs and driver_args/driver_kwargs: Driver worker has
          different args
        - all_args/all_kwargs: args/kwargs for each worker are specified
          individually
        """

        if max_concurrent_workers:
            raise NotImplementedError(
                "max_concurrent_workers is not supported yet.")

        count = len(self.workers)
        all_worker_args = repeat(args, count) if all_args is None \
            else islice(all_args, 1, None)
        all_worker_kwargs = repeat(kwargs, count) if all_kwargs is None \
            else islice(all_kwargs, 1, None)

        # 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(
                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:
            ray_worker_outputs = ray.get(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, enable_asyncio: bool):
        import pkg_resources
        from packaging import 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.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.
        with InputNode() as input_data:
            forward_dag = MultiOutputNode([
                worker.execute_model_compiled_dag_remote.
                bind(  # type: ignore[attr-defined]
                    input_data) for worker in self.workers
            ])
        return forward_dag.experimental_compile(enable_asyncio=enable_asyncio)

    def check_health(self) -> None:
        """Raises an error if engine is unhealthy."""
        self._check_if_any_actor_is_dead()

    def _check_if_any_actor_is_dead(self):
        if not self.workers:
            return

        dead_actors = []
        for actor in self.workers:
            actor_state = ray.state.actors(actor._ray_actor_id.hex())  # pylint: disable=protected-access
            if actor_state["State"] == "DEAD":
                dead_actors.append(actor)
        if dead_actors:
            raise RuntimeError("At least one Worker is dead. "
                               f"Dead Workers: {dead_actors}. ")


class RayXPUExecutorAsync(RayXPUExecutor, DistributedGPUExecutorAsync):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.driver_exec_method = make_async(self.driver_worker.execute_method)

    async def _driver_execute_model_async(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> List[SamplerOutput]:
        return await self.driver_exec_method("execute_model",
                                             execute_model_req)

    async def _start_worker_execution_loop(self):
        coros = [
            worker.execute_method.remote("start_worker_execution_loop")
            for worker in self.workers
        ]
        return await asyncio.gather(*coros)