worker_base.py 18.8 KB
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import dataclasses
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import os
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import time
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
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import torch

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from vllm.config import ObservabilityConfig, VllmConfig
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from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest, IntermediateTensors
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from vllm.utils import (enable_trace_function_call_for_thread,
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                        resolve_obj_by_qualname, update_environment_variables)
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from vllm.worker.model_runner_base import (BroadcastableModelInput,
                                           ModelRunnerBase,
                                           ModelRunnerInputBase)
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logger = init_logger(__name__)
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class WorkerBase(ABC):
    """Worker interface that allows vLLM to cleanly separate implementations for
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    different hardware. Also abstracts control plane communication, e.g., to
    communicate request metadata to other workers.
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    """

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    def __init__(
        self,
        vllm_config: VllmConfig,
    ) -> None:
        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.prompt_adapter_config = vllm_config.prompt_adapter_config
        self.observability_config = vllm_config.observability_config
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        self.kv_transfer_config = vllm_config.kv_transfer_config
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        self.compilation_config = vllm_config.compilation_config
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        from vllm.platforms import current_platform
        self.current_platform = current_platform
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    @abstractmethod
    def init_device(self) -> None:
        """Initialize device state, such as loading the model or other on-device
        memory allocations.
        """
        raise NotImplementedError

    @abstractmethod
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    def determine_num_available_blocks(self) -> Tuple[int, int]:
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        """Determine the number of available blocks for the GPU KV cache and
        swappable CPU KV cache.

        The implementation may run profiling or other heuristics to determine
        the size of caches.

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        Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
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        are blocks that are "active" on the device and can be appended to.
        num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
        appended to.
        """
        raise NotImplementedError

    @abstractmethod
    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

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    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.
        """
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        with self.current_platform.inference_mode():
            while True:
                output = self.execute_model(execute_model_req=None)
                if output is None:
                    return None
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    @abstractmethod
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    def execute_model(
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        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
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    ) -> Optional[List[SamplerOutput]]:
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        raise NotImplementedError

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

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

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

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

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    @abstractmethod
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    def list_loras(self) -> Set[int]:
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        raise NotImplementedError


class LoraNotSupportedWorkerBase(WorkerBase):
    """Partial implementation of WorkerBase that raises exceptions when LoRA
    methods are invoked.
    """

    def add_lora(self, lora_request: LoRARequest) -> bool:
        raise ValueError(f"{type(self)} does not support LoRA")

    def remove_lora(self, lora_id: int) -> bool:
        raise ValueError(f"{type(self)} does not support LoRA")

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    def pin_lora(self, lora_id: int) -> bool:
        return ValueError(
            f"{type(self)} does not support LoRA")  # type: ignore

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    def list_loras(self) -> Set[int]:
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        raise ValueError(f"{type(self)} does not support LoRA")
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@dataclasses.dataclass(frozen=True)
class WorkerInput:
    """Local inputs to each worker. May contain device-specific data. These
    fields should be broadcastable to other workers.
    """

    num_seq_groups: Optional[int] = None
    blocks_to_swap_in: Optional[torch.Tensor] = None
    blocks_to_swap_out: Optional[torch.Tensor] = None
    blocks_to_copy: Optional[torch.Tensor] = None
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    virtual_engine: int = 0
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    num_steps: int = 1
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    @classmethod
    def from_broadcasted_tensor_dict(
        cls: Type["WorkerInput"],
        tensor_dict: Dict[str, Any],
    ) -> "WorkerInput":
        """
        Pop fields from the given tensor_dict and populate a new instance of
        WorkerInput.
        """
        return cls(
            num_seq_groups=tensor_dict.pop("num_seq_groups"),
            blocks_to_swap_in=tensor_dict.pop("blocks_to_swap_in"),
            blocks_to_swap_out=tensor_dict.pop("blocks_to_swap_out"),
            blocks_to_copy=tensor_dict.pop("blocks_to_copy"),
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            virtual_engine=tensor_dict["virtual_engine"],
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            num_steps=tensor_dict.pop("num_steps"),
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        )

    def as_broadcastable_tensor_dict(
            self) -> Dict[str, Union[int, torch.Tensor]]:
        """
        Extract broadcastable fields.
        """
        tensor_dict = {
            "num_seq_groups": self.num_seq_groups,
            "blocks_to_swap_in": self.blocks_to_swap_in,
            "blocks_to_swap_out": self.blocks_to_swap_out,
            "blocks_to_copy": self.blocks_to_copy,
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            "virtual_engine": self.virtual_engine,
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            "num_steps": self.num_steps,
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        }

        return tensor_dict


class LocalOrDistributedWorkerBase(WorkerBase):
    """
    Partial implementation of WorkerBase that has a default `execute_model`
    definition to perform metadata transfer between workers when in distributed
    mode. Subclasses of this interface should use model runners that inherit
    from ModelRunnerBase, and should only need to implement worker-local logic.
    If custom control plane logic is needed to transfer metadata, or if the
    model runner cannot inherit from ModelRunnerBase, use WorkerBase instead.
    """
    is_driver_worker: bool
    model_runner: ModelRunnerBase
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    observability_config: Optional[ObservabilityConfig] = None
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    @property
    @abstractmethod
    def do_metadata_broadcast(self) -> bool:
        """
        Used by the default `execute_model` to check whether broadcast is
        needed to transfer request inputs from the driver worker to other
        workers in the TP group. If WorkerBase subclass only supports
        single-worker execution, then this method should return False.
        """
        raise NotImplementedError

    @property
    @abstractmethod
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    def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
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        """
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        Gets the list of kv caches to pass to the worker's model runner. Each
        element in the list is a kv cache corresponding to a particular virtual
        engine (PP stream). Used by the default `execute_model`. If the worker's
        model runner does not follow the ModelRunnerBase interface, then inherit
        from WorkerBase instead.
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        """
        raise NotImplementedError

    @abstractmethod
    def prepare_worker_input(
            self, execute_model_req: ExecuteModelRequest) -> WorkerInput:
        """
        Prepare the inputs to WorkerBase.execute_worker from an execution
        request. This method may move data to the worker's local device. It is
        not allowed to communicate with other workers or devices.
        """
        raise NotImplementedError

    @abstractmethod
    def execute_worker(self, worker_input: WorkerInput) -> None:
        """
        Process an execution request.
        """
        raise NotImplementedError

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    def _get_worker_input_from_broadcast(
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        self
    ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
            str, torch.Tensor]]]:
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        """ Get the worker input from the broadcasted tensor dict. """
        assert self.do_metadata_broadcast
        assert not self.is_driver_worker
        broadcast_data = broadcast_tensor_dict(src=0)
        if not broadcast_data:
            return None

        worker_input = WorkerInput.from_broadcasted_tensor_dict(broadcast_data)
        model_input = (
            self.model_runner.make_model_input_from_broadcasted_tensor_dict(
                broadcast_data))

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        kwargs = extract_previous_hidden_states(broadcast_data)

        return model_input, worker_input, kwargs
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    def _get_driver_input_and_broadcast(
        self, execute_model_req: ExecuteModelRequest
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    ) -> Tuple[BroadcastableModelInput, WorkerInput, Dict[str, torch.Tensor]]:
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        """ Get the driver input and broadcast it to other workers.  """
        assert self.is_driver_worker

        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,
                execute_model_req.virtual_engine,
                execute_model_req.finished_requests_ids))

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        kwargs = extract_previous_hidden_states(execute_model_req)

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        if self.do_metadata_broadcast:
            broadcast_data = worker_input.as_broadcastable_tensor_dict()
            broadcast_data.update(model_input.as_broadcastable_tensor_dict())
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            broadcast_data.update(kwargs)
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            broadcast_tensor_dict(broadcast_data, src=0)

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        if execute_model_req.async_callback:
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            model_input = dataclasses.replace(  # type: ignore
                model_input,
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                async_callback=execute_model_req.async_callback)
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        return model_input, worker_input, kwargs
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    def prepare_input(
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        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
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    ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
            str, torch.Tensor]]]:
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        """
        Prepare the inputs to ModelRunner and workers.
        """
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        if self.is_driver_worker:
            if execute_model_req is None:
                if self.do_metadata_broadcast:
                    # This signals that there's no more requests to process for
                    # now. All workers are running infinite loop with
                    # broadcast_tensor_dict, and it stops the loop when the
                    # driver broadcasts an empty input. Send an empty input to
                    # notify all other workers to stop their execution loop.
                    broadcast_tensor_dict({}, src=0)
                return None
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            return self._get_driver_input_and_broadcast(execute_model_req)
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        else:
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            return self._get_worker_input_from_broadcast()

    def execute_model(
        self,
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        execute_model_req: Optional[ExecuteModelRequest] = None,
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    ) -> Optional[List[SamplerOutput]]:
        """Executes at least one model step on the given sequences, unless no
        sequences are provided."""
        start_time = time.perf_counter()

        inputs = self.prepare_input(execute_model_req)
        if inputs is None:
            return None
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        model_input, worker_input, kwargs = inputs
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        num_steps = worker_input.num_steps
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        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 []

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        intermediate_tensors = None
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        orig_model_execute_time = 0.0
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        if not get_pp_group().is_first_rank:
            intermediate_tensors = IntermediateTensors(
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                get_pp_group().recv_tensor_dict(
                    all_gather_group=get_tp_group()))
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            if (self.observability_config is not None
                    and self.observability_config.collect_model_execute_time):
                orig_model_execute_time = intermediate_tensors.tensors.get(
                    "model_execute_time", torch.tensor(0)).item()
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        output = self.model_runner.execute_model(
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            model_input=model_input,
            kv_caches=self.kv_cache[worker_input.virtual_engine]
            if self.kv_cache is not None else None,
            intermediate_tensors=intermediate_tensors,
            num_steps=num_steps,
            **kwargs,
        )

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        model_execute_time = time.perf_counter() - start_time
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        if not get_pp_group().is_last_rank:
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            # output is IntermediateTensors
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            assert isinstance(output, IntermediateTensors)
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            if (self.observability_config is not None
                    and self.observability_config.collect_model_execute_time):
                output.tensors["model_execute_time"] = torch.tensor(
                    model_execute_time + orig_model_execute_time)
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            get_pp_group().send_tensor_dict(output.tensors,
                                            all_gather_group=get_tp_group())
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            return [None]
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        if (self.observability_config is not None
                and self.observability_config.collect_model_execute_time
                and output is not None):
            for o in output:
                o.model_execute_time = (orig_model_execute_time +
                                        model_execute_time)
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        # output is List[SamplerOutput]
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        return output
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    def _execute_model_spmd(
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        self,
        execute_model_req: ExecuteModelRequest,
        intermediate_tensors: Optional[IntermediateTensors] = None
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    ) -> 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 []

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        kwargs = extract_previous_hidden_states(execute_model_req)

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        return self.model_runner.execute_model(
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            model_input=model_input,
            kv_caches=self.kv_cache[worker_input.virtual_engine]
            if self.kv_cache is not None else None,
            intermediate_tensors=intermediate_tensors,
            **kwargs,
        )
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class WorkerWrapperBase:
    """
    The whole point of this class is to lazily initialize the worker.
    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`.
    """

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    def __init__(
        self,
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        vllm_config: VllmConfig,
    ) -> None:
        self.vllm_config = vllm_config
        trust_remote_code = vllm_config.model_config.trust_remote_code
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        self.worker: Optional[WorkerBase] = None
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        if trust_remote_code:
            # note: lazy import to avoid importing torch before initializing
            from vllm.utils import init_cached_hf_modules
            init_cached_hf_modules()
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    @staticmethod
    def update_environment_variables(envs: Dict[str, str]) -> None:
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        key = 'CUDA_VISIBLE_DEVICES'
        if key in envs and key in os.environ:
            # overwriting CUDA_VISIBLE_DEVICES is desired behavior
            # suppress the warning in `update_environment_variables`
            del os.environ[key]
        update_environment_variables(envs)

    def init_worker(self, *args, **kwargs):
        """
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        Here we inject some common logic before initializing the worker.
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        Arguments are passed to the worker class constructor.
        """
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        enable_trace_function_call_for_thread(self.vllm_config)
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        # see https://github.com/NVIDIA/nccl/issues/1234
        os.environ['NCCL_CUMEM_ENABLE'] = '0'

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        from vllm.plugins import load_general_plugins
        load_general_plugins()

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        worker_class = resolve_obj_by_qualname(
            self.vllm_config.parallel_config.worker_cls)
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        self.worker = worker_class(*args, **kwargs)
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        assert self.worker is not None
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    def execute_method(self, method: str, *args, **kwargs):
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        try:
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            target = self if self.worker is None else self.worker
            executor = getattr(target, method)
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            return executor(*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}. "
                   "This might cause deadlock in distributed execution.")
            logger.exception(msg)
            raise e
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    def __getattr__(self, attr):
        return getattr(self.worker, attr)

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def extract_previous_hidden_states(
        data: Union[ExecuteModelRequest, Dict[str, torch.Tensor]]) -> \
            Dict[str, torch.Tensor]:
    """If data contains previous_hidden_states, extract it. This returns a dict
    which can be used directly as additional kwargs in any following 
    execute_model calls. This is used in draft models like EAGLE."""
    output = {}

    # When called from non-driver worker, data is dict but when called from
    # driver worker, data is ExecuteModelRequest.
    if isinstance(data, dict):
        if "previous_hidden_states" in data:
            output["previous_hidden_states"] = data["previous_hidden_states"]
    elif data.previous_hidden_states is not None:
        output["previous_hidden_states"] = data.previous_hidden_states\
            .hidden_states

    return output