abstract.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import time
from abc import ABC, abstractmethod
from collections.abc import Callable
from concurrent.futures import Future
from functools import cached_property
from typing import TYPE_CHECKING, Literal, TypeVar, overload

import vllm.envs as envs
from vllm.config import VllmConfig
from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
from vllm.distributed.kv_transfer.kv_connector.v1.base import (
    KVConnectorHandshakeMetadata,
)
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.tasks import SupportedTask
from vllm.tracing import instrument
from vllm.utils.import_utils import resolve_obj_by_qualname
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
from vllm.v1.engine import ReconfigureDistributedRequest
from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
from vllm.v1.worker.worker_base import WorkerBase

if TYPE_CHECKING:
    from vllm.distributed.kv_transfer.kv_connector.base import KVConnectorBase

logger = init_logger(__name__)

_R = TypeVar("_R")

FailureCallback = Callable[[], None]


class Executor(ABC):
    """Abstract base class for vLLM executors."

    An executor is responsible for executing the model on one device,
    or it can be a distributed executor that can execute the model on multiple devices.
    """

    uses_ray: bool = False  # whether the executor uses Ray for orchestration.
    supports_pp: bool = False  # whether the executor supports PP

    @staticmethod
    def get_class(vllm_config: VllmConfig) -> type["Executor"]:
        executor_class: type[Executor]
        parallel_config = vllm_config.parallel_config
        distributed_executor_backend = parallel_config.distributed_executor_backend
        # distributed_executor_backend must be set in VllmConfig.__post_init__
        if isinstance(distributed_executor_backend, type):
            if not issubclass(distributed_executor_backend, Executor):
                raise TypeError(
                    "distributed_executor_backend must be a subclass of "
                    f"Executor. Got {distributed_executor_backend}."
                )
            executor_class = distributed_executor_backend
        elif distributed_executor_backend == "ray":
            if envs.VLLM_USE_RAY_V2_EXECUTOR_BACKEND:
                from vllm.v1.executor.ray_executor_v2 import RayExecutorV2

                executor_class = RayExecutorV2
            else:
                from vllm.v1.executor.ray_executor import RayDistributedExecutor

                executor_class = RayDistributedExecutor
        elif distributed_executor_backend == "mp":
            from vllm.v1.executor.multiproc_executor import MultiprocExecutor

            executor_class = MultiprocExecutor
        elif distributed_executor_backend == "uni":
            from vllm.v1.executor.uniproc_executor import UniProcExecutor

            executor_class = UniProcExecutor
        elif distributed_executor_backend == "external_launcher":
            # TODO: make v1 scheduling deterministic
            # to support external launcher
            executor_class = ExecutorWithExternalLauncher
        elif isinstance(distributed_executor_backend, str):
            executor_class = resolve_obj_by_qualname(distributed_executor_backend)
            if not issubclass(executor_class, Executor):
                raise TypeError(
                    "distributed_executor_backend must be a subclass of "
                    f"Executor. Got {executor_class}."
                )
        else:
            raise ValueError(
                f"Unknown distributed executor backend: {distributed_executor_backend}"
            )
        return executor_class

    @instrument(span_name="Executor init")
    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.observability_config = vllm_config.observability_config
        self._init_executor()
        self.is_sleeping = False
        self.sleeping_tags: set[str] = set()
        self.kv_output_aggregator: KVOutputAggregator | None = None

    @abstractmethod
    def _init_executor(self) -> None:
        raise NotImplementedError

    def initialize_from_config(self, kv_cache_configs: list[KVCacheConfig]) -> None:
        """
        Initialize the KV caches and begin the model execution loop of the
        underlying workers.
        """
        self.collective_rpc("initialize_from_config", args=(kv_cache_configs,))
        compilation_times: list[float] = self.collective_rpc("compile_or_warm_up_model")
        # Propagate compilation time from workers back to the main process.
        # With TP>1, compilation happens in worker processes, so the main
        # process config is never updated. Use max across workers since they
        # compile in parallel.
        if compilation_times:
            self.vllm_config.compilation_config.compilation_time = max(
                compilation_times
            )

    def register_failure_callback(self, callback: FailureCallback):  # noqa: B027
        """
        Register a function to be called if the executor enters a permanent
        failed state.
        """
        pass

    def determine_available_memory(self) -> list[int]:  # in bytes
        return self.collective_rpc("determine_available_memory")

    def get_kv_cache_specs(self) -> list[dict[str, KVCacheSpec]]:
        return self.collective_rpc("get_kv_cache_spec")

    @overload
    def collective_rpc(
        self,
        method: str | Callable[[WorkerBase], _R],
        timeout: float | None = None,
        args: tuple = (),
        kwargs: dict | None = None,
        non_block: Literal[False] = False,
    ) -> list[_R]:
        """
        Execute an RPC call on all workers.

        Args:
            method: Name of the worker method to execute, or a callable that
                is serialized and sent to all workers to execute.

                If the method is a callable, it should accept an additional
                `self` argument, in addition to the arguments passed in `args`
                and `kwargs`. The `self` argument will be the worker object.
            timeout: Maximum time in seconds to wait for execution. Raises a
                [`TimeoutError`][] on timeout. `None` means wait indefinitely.
            args: Positional arguments to pass to the worker method.
            kwargs: Keyword arguments to pass to the worker method.
            non_block: If `True`, returns a list of Futures instead of waiting
                for the results.

        Returns:
            A list containing the results from each worker.

        Note:
            It is recommended to use this API to only pass control messages,
            and set up data-plane communication to pass data.
        """
        pass

    @overload
    def collective_rpc(
        self,
        method: str | Callable[[WorkerBase], _R],
        timeout: float | None = None,
        args: tuple = (),
        kwargs: dict | None = None,
        non_block: Literal[True] = True,
    ) -> Future[list[_R]]:
        pass

    @abstractmethod
    def collective_rpc(
        self, method, timeout=None, args=(), kwargs=None, non_block: bool = False
    ):
        raise NotImplementedError

    def get_kv_connector_handshake_metadata(
        self,
    ) -> list[dict[int, KVConnectorHandshakeMetadata]]:
        return self.collective_rpc("get_kv_connector_handshake_metadata")

    @overload
    def execute_model(
        self, scheduler_output: SchedulerOutput, non_block: Literal[False] = False
    ) -> ModelRunnerOutput | None:
        pass

    @overload
    def execute_model(
        self, scheduler_output: SchedulerOutput, non_block: Literal[True] = True
    ) -> Future[ModelRunnerOutput | None]:
        pass

    def execute_model(
        self, scheduler_output: SchedulerOutput, non_block: bool = False
    ) -> ModelRunnerOutput | None | Future[ModelRunnerOutput | None]:
        output = self.collective_rpc(  # type: ignore[call-overload]
            "execute_model", args=(scheduler_output,), non_block=non_block
        )
        return output[0]

    @overload
    def sample_tokens(
        self, grammar_output: GrammarOutput | None, non_block: Literal[False] = False
    ) -> ModelRunnerOutput:
        pass

    @overload
    def sample_tokens(
        self, grammar_output: GrammarOutput | None, non_block: Literal[True] = True
    ) -> Future[ModelRunnerOutput]:
        pass

    def sample_tokens(
        self, grammar_output: GrammarOutput | None, non_block: bool = False
    ) -> ModelRunnerOutput | Future[ModelRunnerOutput]:
        output = self.collective_rpc(  # type: ignore[call-overload]
            "sample_tokens", args=(grammar_output,), non_block=non_block
        )
        return output[0]

    def execute_dummy_batch(self) -> None:
        self.collective_rpc("execute_dummy_batch")

    def take_draft_token_ids(self) -> DraftTokenIds | None:
        output: list[DraftTokenIds] = self.collective_rpc("take_draft_token_ids")
        return output[0]

    @property
    def max_concurrent_batches(self) -> int:
        return 1

    def profile(self, is_start: bool = True, profile_prefix: str | None = None):
        self.collective_rpc("profile", args=(is_start, profile_prefix))

    def save_sharded_state(
        self,
        path: str,
        pattern: str | None = None,
        max_size: int | None = None,
    ) -> None:
        self.collective_rpc(
            "save_sharded_state",
            kwargs=dict(path=path, pattern=pattern, max_size=max_size),
        )

    @abstractmethod
    def check_health(self) -> None:
        """Checks if the executor is healthy. If not, it should raise an
        exception."""
        raise NotImplementedError

    def shutdown(self) -> None:
        """Shutdown the executor."""
        self.collective_rpc("shutdown")

    def init_kv_output_aggregator(self, connector: "KVConnectorBase") -> None:
        """Init KVOutputAggregator"""
        self.kv_output_aggregator = KVOutputAggregator.from_connector(
            connector, self.parallel_config.world_size
        )

    @cached_property  # Avoid unnecessary RPC calls
    def supported_tasks(self) -> tuple[SupportedTask, ...]:
        output: list[tuple[SupportedTask, ...]]
        output = self.collective_rpc("get_supported_tasks")
        return output[0]

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

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

    def pin_lora(self, lora_id: int) -> bool:
        assert lora_id > 0, "lora_id must be greater than 0."
        return all(self.collective_rpc("pin_lora", args=(lora_id,)))

    def list_loras(self) -> set[int]:
        sets: list[set[int]] = self.collective_rpc("list_loras")
        for s in sets:
            assert s == sets[0], "All workers should have the same LORAs."
        return sets[0]

    def reset_mm_cache(self) -> None:
        """Reset the multi-modal cache in each worker."""
        self.collective_rpc("reset_mm_cache")

    def reset_encoder_cache(self) -> None:
        """Reset the encoder cache in each worker to clear cached encoder outputs."""
        self.collective_rpc("reset_encoder_cache")

    def sleep(self, level: int = 1):
        if self.is_sleeping:
            logger.warning("Executor is already sleeping.")
            return
        time_before_sleep = time.perf_counter()
        self.collective_rpc("sleep", kwargs=dict(level=level))
        time_after_sleep = time.perf_counter()
        self.sleeping_tags = {"weights", "kv_cache"}
        self.is_sleeping = True
        logger.info(
            "It took %.6f seconds to fall asleep.", time_after_sleep - time_before_sleep
        )

    def wake_up(self, tags: list[str] | None = None):
        if not self.is_sleeping:
            logger.warning("Executor is not sleeping.")
            return
        if tags:
            for tag in tags:
                if tag not in self.sleeping_tags:
                    logger.warning(
                        "Tag %s is not in sleeping tags %s", tag, self.sleeping_tags
                    )
                    return
        time_before_wakeup = time.perf_counter()
        self.collective_rpc("wake_up", kwargs=dict(tags=tags))
        time_after_wakeup = time.perf_counter()
        logger.info(
            "It took %.6f seconds to wake up tags %s.",
            time_after_wakeup - time_before_wakeup,
            tags if tags is not None else self.sleeping_tags,
        )
        if tags:
            for tag in tags:
                self.sleeping_tags.remove(tag)
        else:
            self.sleeping_tags.clear()
        if not self.sleeping_tags:
            self.is_sleeping = False

    def reinitialize_distributed(
        self, reconfig_request: ReconfigureDistributedRequest
    ) -> None:
        raise NotImplementedError

    @classmethod
    def supports_async_scheduling(cls) -> bool:
        """
        Whether the executor supports async scheduling.
        """
        return False


from vllm.v1.executor.uniproc_executor import (  # noqa: E402
    ExecutorWithExternalLauncher as _ExecutorWithExternalLauncher,
)
from vllm.v1.executor.uniproc_executor import (  # noqa: E402
    UniProcExecutor as _UniProcExecutor,
)

# For backwards compatibility.
UniProcExecutor = _UniProcExecutor
ExecutorWithExternalLauncher = _ExecutorWithExternalLauncher