vllm.v1.worker.worker_base ¶

class WorkerBase:
    """Worker interface that allows vLLM to cleanly separate implementations for
    different hardware. Also abstracts control plane communication, e.g., to
    communicate request metadata to other workers.
    """

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_rank: int,
        rank: int,
        distributed_init_method: str,
        is_driver_worker: bool = False,
    ) -> None:
        """
        Initialize common worker components.

        Args:
            vllm_config: Complete vLLM configuration
            local_rank: Local device index
            rank: Global rank in distributed setup
            distributed_init_method: Distributed initialization method
            is_driver_worker: Whether this worker handles driver
                responsibilities
        """
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.device_config = vllm_config.device_config
        self.speculative_config = vllm_config.speculative_config
        self.observability_config = vllm_config.observability_config
        self.kv_transfer_config = vllm_config.kv_transfer_config
        self.compilation_config = vllm_config.compilation_config

        from vllm.platforms import current_platform

        self.current_platform = current_platform

        self.parallel_config.rank = rank
        self.local_rank = local_rank
        self.rank = rank
        self.distributed_init_method = distributed_init_method
        self.is_driver_worker = is_driver_worker

        # Device and model state
        self.device: torch.device | None = None
        self.model_runner: nn.Module | None = None

    def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
        """Get specifications for KV cache implementation."""
        raise NotImplementedError

    def compile_or_warm_up_model(self) -> None:
        """Prepare model for execution through compilation/warmup."""
        raise NotImplementedError

    def check_health(self) -> None:
        """Basic health check (override for device-specific checks)."""
        return

    def init_device(self) -> None:
        """Initialize device state, such as loading the model or other on-device
        memory allocations.
        """
        raise NotImplementedError

    def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
        """Initialize the KV cache with the given size in blocks."""
        raise NotImplementedError

    def reset_mm_cache(self) -> None:
        reset_fn = getattr(self.model_runner, "reset_mm_cache", None)
        if callable(reset_fn):
            reset_fn()

    def get_model(self) -> nn.Module:
        raise NotImplementedError

    def apply_model(self, fn: Callable[[nn.Module], _R]) -> _R:
        """Apply a function on the model inside this worker."""
        return fn(self.get_model())

    def get_model_inspection(self) -> str:
        """Return a transformers-style hierarchical view of the model."""
        from vllm.model_inspection import format_model_inspection

        return format_model_inspection(self.get_model())

    def load_model(self) -> None:
        """Load model onto target device."""
        raise NotImplementedError

    def execute_model(
        self, scheduler_output: SchedulerOutput
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | None:
        """If this method returns None, sample_tokens should be called immediately after
        to obtain the ModelRunnerOutput.

        Note that this design may be changed in future if/when structured outputs
        parallelism is re-architected.
        """
        raise NotImplementedError

    def sample_tokens(
        self, grammar_output: GrammarOutput
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        """Should be called immediately after execute_model iff it returned None."""
        raise NotImplementedError

    def get_cache_block_size_bytes(self) -> int:
        """Return the size of a single cache block, in bytes. Used in
        speculative decoding.
        """
        raise NotImplementedError

    def add_lora(self, lora_request: LoRARequest) -> bool:
        raise NotImplementedError

    def remove_lora(self, lora_id: int) -> bool:
        raise NotImplementedError

    def pin_lora(self, lora_id: int) -> bool:
        raise NotImplementedError

    def list_loras(self) -> set[int]:
        raise NotImplementedError

    @property
    def vocab_size(self) -> int:
        """Get vocabulary size from model configuration."""
        return self.model_config.get_vocab_size()

    def shutdown(self) -> None:
        """Clean up resources held by the worker."""
        return

class WorkerWrapperBase:
    """
    This class represents one process in an executor/engine. It is responsible
    for lazily initializing the worker and handling the worker's lifecycle.
    We first instantiate the WorkerWrapper, which remembers the worker module
    and class name. Then, when we call `update_environment_variables`, and the
    real initialization happens in `init_worker`.
    """

    def __init__(
        self,
        rpc_rank: int = 0,
        global_rank: int | None = None,
    ) -> None:
        """
        Initialize the worker wrapper with the given vllm_config and rpc_rank.
        Note: rpc_rank is the rank of the worker in the executor. In most cases,
        it is also the rank of the worker in the distributed group. However,
        when multiple executors work together, they can be different.
        e.g. in the case of SPMD-style offline inference with TP=2,
        users can launch 2 engines/executors, each with only 1 worker.
        All workers have rpc_rank=0, but they have different ranks in the TP
        group.
        """
        self.rpc_rank = rpc_rank
        self.global_rank = self.rpc_rank if global_rank is None else global_rank

        # Initialized after init_worker is called
        self.worker: WorkerBase
        self.vllm_config: VllmConfig

    def shutdown(self) -> None:
        if self.worker is not None:
            self.worker.shutdown()

    def adjust_rank(self, rank_mapping: dict[int, int]) -> None:
        """
        Adjust the rpc_rank based on the given mapping.
        It is only used during the initialization of the executor,
        to adjust the rpc_rank of workers after we create all workers.
        """
        if self.rpc_rank in rank_mapping:
            self.rpc_rank = rank_mapping[self.rpc_rank]

    def update_environment_variables(
        self,
        envs_list: list[dict[str, str]],
    ) -> None:
        envs = envs_list[self.rpc_rank]
        update_environment_variables(envs)

    @instrument(span_name="Worker init")
    def init_worker(self, all_kwargs: list[dict[str, Any]]) -> None:
        """
        Here we inject some common logic before initializing the worker.
        Arguments are passed to the worker class constructor.
        """
        kwargs = all_kwargs[self.rpc_rank]

        vllm_config: VllmConfig | None = kwargs.get("vllm_config")
        assert vllm_config is not None, (
            "vllm_config is required to initialize the worker"
        )
        self.vllm_config = vllm_config

        vllm_config.enable_trace_function_call_for_thread()

        from vllm.plugins import load_general_plugins

        load_general_plugins()

        parallel_config = vllm_config.parallel_config
        if isinstance(parallel_config.worker_cls, str):
            worker_class: type[WorkerBase] = resolve_obj_by_qualname(
                parallel_config.worker_cls
            )
        else:
            raise ValueError(
                "passing worker_cls is no longer supported. "
                "Please pass keep the class in a separate module "
                "and pass the qualified name of the class as a string."
            )

        if parallel_config.worker_extension_cls:
            worker_extension_cls = resolve_obj_by_qualname(
                parallel_config.worker_extension_cls
            )
            extended_calls = []
            if worker_extension_cls not in worker_class.__bases__:
                # check any conflicts between worker and worker_extension_cls
                for attr in dir(worker_extension_cls):
                    if attr.startswith("__"):
                        continue
                    assert not hasattr(worker_class, attr), (
                        f"Worker class {worker_class} already has an attribute"
                        f" {attr}, which conflicts with the worker"
                        f" extension class {worker_extension_cls}."
                    )
                    if callable(getattr(worker_extension_cls, attr)):
                        extended_calls.append(attr)
                # dynamically inherit the worker extension class
                worker_class.__bases__ = worker_class.__bases__ + (
                    worker_extension_cls,
                )
                logger.info(
                    "Injected %s into %s for extended collective_rpc calls %s",
                    worker_extension_cls,
                    worker_class,
                    extended_calls,
                )

        shared_worker_lock = kwargs.pop("shared_worker_lock", None)
        if shared_worker_lock is None:
            msg = (
                "Missing `shared_worker_lock` argument from executor. "
                "This argument is needed for mm_processor_cache_type='shm'."
            )

            mm_config = vllm_config.model_config.multimodal_config
            if mm_config and mm_config.mm_processor_cache_type == "shm":
                raise ValueError(msg)
            else:
                logger.warning_once(msg)

            self.mm_receiver_cache = None
        else:
            self.mm_receiver_cache = (
                MULTIMODAL_REGISTRY.worker_receiver_cache_from_config(
                    vllm_config,
                    shared_worker_lock,
                )
            )

        with set_current_vllm_config(self.vllm_config):
            # To make vLLM config available during worker initialization
            self.worker = worker_class(**kwargs)

    def initialize_from_config(self, kv_cache_configs: list[Any]) -> None:
        kv_cache_config = kv_cache_configs[self.global_rank]
        assert self.vllm_config is not None
        with set_current_vllm_config(self.vllm_config):
            self.worker.initialize_from_config(kv_cache_config)  # type: ignore

    def init_device(self):
        assert self.vllm_config is not None
        with set_current_vllm_config(self.vllm_config):
            # To make vLLM config available during device initialization
            self.worker.init_device()  # type: ignore

    def execute_method(self, method: str | bytes, *args, **kwargs):
        try:
            # method resolution order:
            # if a method is defined in this class, it will be called directly.
            # otherwise, since we define `__getattr__` and redirect attribute
            # query to `self.worker`, the method will be called on the worker.
            return run_method(self, method, args, kwargs)
        except Exception as e:
            # if the driver worker also execute methods,
            # exceptions in the rest worker may cause deadlock in rpc like ray
            # see https://github.com/vllm-project/vllm/issues/3455
            # print the error and inform the user to solve the error
            msg = (
                f"Error executing method {method!r}. "
                "This might cause deadlock in distributed execution."
            )
            logger.exception(msg)
            raise e

    def __getattr__(self, attr: str):
        return getattr(self.worker, attr)

    def _apply_mm_cache(self, scheduler_output: SchedulerOutput) -> None:
        mm_cache = self.mm_receiver_cache
        if mm_cache is None:
            return

        for req_data in scheduler_output.scheduled_new_reqs:
            req_data.mm_features = mm_cache.get_and_update_features(
                req_data.mm_features
            )

    def execute_model(
        self, scheduler_output: SchedulerOutput
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | None:
        self._apply_mm_cache(scheduler_output)

        return self.worker.execute_model(scheduler_output)

    def reset_mm_cache(self) -> None:
        mm_receiver_cache = self.mm_receiver_cache
        if mm_receiver_cache is not None:
            mm_receiver_cache.clear_cache()

        self.worker.reset_mm_cache()