worker_base.py

# SPDX-License-Identifier: Apache-2.0

import dataclasses
import os
import time
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union

import cloudpickle
import torch
import torch.nn as nn

from vllm.config import (ObservabilityConfig, VllmConfig,
                         set_current_vllm_config)
from vllm.distributed import broadcast_tensor_dict, get_pp_group, get_tp_group
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.sequence import ExecuteModelRequest, IntermediateTensors
from vllm.utils import (enable_trace_function_call_for_thread,
                        resolve_obj_by_qualname, run_method,
                        update_environment_variables,
                        warn_for_unimplemented_methods)
from vllm.worker.cache_engine import CacheEngine
from vllm.worker.model_runner_base import (BroadcastableModelInput,
                                           ModelRunnerBase,
                                           ModelRunnerInputBase)

logger = init_logger(__name__)


@warn_for_unimplemented_methods
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.
    """

    model_input: Optional[ModelRunnerInputBase] = None
    tree_decoding = (os.environ.get('VLLM_TREE_DECODING') == '1')

    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
        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


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

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

    def execute_model(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> Optional[List[SamplerOutput]]:
        raise NotImplementedError

    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.
        """
        with self.current_platform.inference_mode():
            while True:
                output = self.execute_model(execute_model_req=None)
                if output is None:
                    return None

    def determine_num_available_blocks(self) -> Tuple[int, int]:
        """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.

        Returns a Tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
        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

    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
    # @abstractmethod
    # def cache_engines(self) -> Optional[List[CacheEngine]]:
    #     raise NotImplementedError

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


class DelegateWorkerBase(WorkerBase):
    """
    A class that delegates all methods to another WorkerBase instance. This is
    useful for creating a WorkerBase that wraps another WorkerBase instance,
    e.g. speculative decoding.
    """
    worker: WorkerBase

    def __init__(
        self,
        *args,
        **kwargs,
    ) -> None:
        vllm_config: VllmConfig = kwargs.get("vllm_config")
        cls = resolve_obj_by_qualname(vllm_config.parallel_config.worker_cls)
        self.worker = cls(*args, **kwargs)

    def init_device(self) -> None:
        self.worker.init_device()

    def determine_num_available_blocks(self) -> Tuple[int, int]:
        return self.worker.determine_num_available_blocks()

    def initialize_cache(self, num_gpu_blocks: int,
                         num_cpu_blocks: int) -> None:
        self.worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)

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

    def get_model(self) -> nn.Module:
        return self.worker.get_model()

    def execute_model(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> Optional[List[SamplerOutput]]:
        return self.worker.execute_model(execute_model_req)

    def get_cache_block_size_bytes(self) -> int:
        return self.worker.get_cache_block_size_bytes()

    def add_lora(self, lora_request: LoRARequest) -> bool:
        return self.worker.add_lora(lora_request)

    def remove_lora(self, lora_id: int) -> bool:
        return self.worker.remove_lora(lora_id)

    def pin_lora(self, lora_id: int) -> bool:
        return self.worker.pin_lora(lora_id)

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

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


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")

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

    def list_loras(self) -> Set[int]:
        raise ValueError(f"{type(self)} does not support LoRA")

    @property
    def cache_engines(self) -> Optional[List[CacheEngine]]:
        return None


@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
    virtual_engine: int = 0
    num_steps: int = 1

    # Optional slot mapping of kvcache that pending to be moved generated from draft model.
    kvcache_slot_to_be_moved: Optional[torch.Tensor] = None

    @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"),
            virtual_engine=tensor_dict["virtual_engine"],
            num_steps=tensor_dict.pop("num_steps"),
            kvcache_slot_to_be_moved=tensor_dict.pop("kvcache_slot_to_be_moved"),
        )

    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,
            "virtual_engine": self.virtual_engine,
            "num_steps": self.num_steps,
            "kvcache_slot_to_be_moved": self.kvcache_slot_to_be_moved
        }

        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
    observability_config: Optional[ObservabilityConfig] = None

    @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
    def kv_cache(self) -> Optional[List[List[torch.Tensor]]]:
        """
        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.
        """
        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

    def _get_worker_input_from_broadcast(
        self
    ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
            str, torch.Tensor]]]:
        """ 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))

        kwargs = extract_previous_hidden_states(broadcast_data)

        return model_input, worker_input, kwargs

    def _get_driver_input_and_broadcast(
        self, execute_model_req: ExecuteModelRequest
    ) -> Tuple[BroadcastableModelInput, WorkerInput, Dict[str, torch.Tensor]]:
        """ 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))

        if self.tree_decoding and execute_model_req.tree_position_ids is not None and \
            execute_model_req.tree_attn_masks is not None:
            if hasattr(model_input, "input_positions") and \
                hasattr(model_input, "attn_metadata") and \
                    hasattr(model_input.attn_metadata, "tree_attention_masks_tensor"):
                attn_metadata = model_input.attn_metadata
                attn_metadata.tree_attention_masks_tensor = execute_model_req.tree_attn_masks.contiguous()
                model_input = dataclasses.replace(model_input,
                                    input_positions=execute_model_req.tree_position_ids.contiguous(),
                                    attn_metadata=attn_metadata)

        kwargs = extract_previous_hidden_states(execute_model_req)

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

        if execute_model_req.async_callback:
            model_input = dataclasses.replace(  # type: ignore
                model_input,
                async_callback=execute_model_req.async_callback)

        return model_input, worker_input, kwargs

    def prepare_input(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None
    ) -> Optional[Tuple[BroadcastableModelInput, WorkerInput, Dict[
            str, torch.Tensor]]]:
        """
        Prepare the inputs to ModelRunner and workers.
        """
        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
            return self._get_driver_input_and_broadcast(execute_model_req)
        else:
            return self._get_worker_input_from_broadcast()

    def get_model(self) -> nn.Module:
        return self.model_runner.get_model()

    def execute_model(
        self,
        execute_model_req: Optional[ExecuteModelRequest] = None,
    ) -> 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

        model_input, worker_input, kwargs = inputs
        num_steps = worker_input.num_steps
        if (execute_model_req is not None and execute_model_req.spec_step_idx):
            kwargs["spec_step_idx"] = execute_model_req.spec_step_idx

        self.model_input = model_input

        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 []

        intermediate_tensors = None
        orig_model_execute_time = 0.0
        if not get_pp_group().is_first_rank:
            intermediate_tensors = IntermediateTensors(
                get_pp_group().recv_tensor_dict(
                    all_gather_group=get_tp_group()))
            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()

        output = self.model_runner.execute_model(
            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,
        )

        model_execute_time = time.perf_counter() - start_time
        if not get_pp_group().is_last_rank:
            # output is IntermediateTensors
            assert isinstance(output, IntermediateTensors)
            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)
            get_pp_group().send_tensor_dict(output.tensors,
                                            all_gather_group=get_tp_group())
            return [None]
        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)

        # output is List[SamplerOutput]
        return output

    def _execute_model_spmd(
        self,
        execute_model_req: ExecuteModelRequest,
        intermediate_tensors: Optional[IntermediateTensors] = None
    ) -> 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 []

        kwargs = extract_previous_hidden_states(execute_model_req)

        return self.model_runner.execute_model(
            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,
        )


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,
        vllm_config: VllmConfig,
        rpc_rank: int = 0,
    ) -> 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.worker: Optional[WorkerBase] = None
        # do not store this `vllm_config`, `init_worker` will set the final
        # one. TODO: investigate if we can remove this field in
        # `WorkerWrapperBase`, `init_cached_hf_modules` should be
        # unnecessary now.
        if vllm_config.model_config is not None:
            # it can be None in tests
            trust_remote_code = vllm_config.model_config.trust_remote_code
            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()

    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]
        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, 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]
        self.vllm_config = kwargs.get("vllm_config", None)
        assert self.vllm_config is not None, (
            "vllm_config is required to initialize the worker")
        enable_trace_function_call_for_thread(self.vllm_config)

        from vllm.plugins import load_general_plugins
        load_general_plugins()

        if isinstance(self.vllm_config.parallel_config.worker_cls, str):
            worker_class = resolve_obj_by_qualname(
                self.vllm_config.parallel_config.worker_cls)
        else:
            logger.warning(
                "passing worker_cls as a class object is strongly deprecated,"
                " as the serialization of class objects can be tricky and"
                " error-prone. To be safe, please keep the class in a separate"
                " module and pass the qualified name of the class as a string."
            )
            assert isinstance(self.vllm_config.parallel_config.worker_cls,
                              bytes)
            worker_class = cloudpickle.loads(
                self.vllm_config.parallel_config.worker_cls)
        if self.vllm_config.parallel_config.worker_extension_cls:
            worker_extension_cls = resolve_obj_by_qualname(
                self.vllm_config.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)
        with set_current_vllm_config(self.vllm_config):
            # To make vLLM config available during worker initialization
            self.worker = worker_class(**kwargs)
            assert self.worker is not None

    def initialize_from_config(self, kv_cache_configs: List[Any]) -> None:
        kv_cache_config = kv_cache_configs[self.rpc_rank]
        self.worker.initialize_from_config(kv_cache_config)  # type: ignore

    def init_device(self):
        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: Union[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):
        return getattr(self.worker, attr)


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