serve/gpu_executor.py

from typing import Dict, List, Set, Tuple, Optional, Set
import argparse

from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                         ModelConfig, ParallelConfig, SchedulerConfig,
                         SpeculativeConfig, VisionLanguageConfig)
from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.sequence import SamplerOutput, SequenceGroupMetadata
from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
                        make_async)

logger = init_logger(__name__)


class GPUExecutor(ExecutorBase):
    def __init__(
        self,
        args: argparse.ArgumentParser,
        model_config: ModelConfig,
        cache_config: CacheConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
        device_config: DeviceConfig,
        load_config: LoadConfig,
        lora_config: Optional[LoRAConfig],
        vision_language_config: Optional[VisionLanguageConfig],
        speculative_config: Optional[SpeculativeConfig],
    ) -> None:
        self.args = args
        self.model_config = model_config
        self.cache_config = cache_config
        self.lora_config = lora_config
        self.load_config = load_config
        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config
        self.device_config = device_config
        self.vision_language_config = vision_language_config
        self.speculative_config = speculative_config

        self._init_executor()

    def _init_executor(self) -> None:
        """Initialize the worker and load the model.

        If speculative decoding is enabled, we instead create the speculative
        worker.
        """
        if self.speculative_config is None:
            self._init_non_spec_worker()
        else:
            self._init_spec_worker()

    def _init_non_spec_worker(self):
        # Lazy import the Worker to avoid importing torch.cuda/xformers
        # before CUDA_VISIBLE_DEVICES is set in the Worker
        # from vllm.worker.worker import Worker
        from serve.worker import Worker

        assert self.parallel_config.world_size == 1, (
            "GPUExecutor only supports single GPU.")

        distributed_init_method = get_distributed_init_method(
            get_ip(), get_open_port())
        self.driver_worker = Worker(
            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=0,
            rank=0,
            distributed_init_method=distributed_init_method,
            lora_config=self.lora_config,
            vision_language_config=self.vision_language_config,
            is_driver_worker=True,
        )
        self.driver_worker.init_device()
        self.driver_worker.load_model(self.args)

    def _init_spec_worker(self):
        """Initialize a SpecDecodeWorker, using a draft model for proposals.
        """
        assert self.speculative_config is not None

        from vllm.spec_decode.multi_step_worker import MultiStepWorker
        from vllm.spec_decode.spec_decode_worker import SpecDecodeWorker
        from vllm.worker.worker import Worker

        distributed_init_method = get_distributed_init_method(
            get_ip(), get_open_port())

        target_worker = Worker(
            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=0,
            rank=0,
            distributed_init_method=distributed_init_method,
            lora_config=self.lora_config,
            vision_language_config=self.vision_language_config,
            is_driver_worker=True,
        )

        draft_worker = MultiStepWorker(
            model_config=self.speculative_config.draft_model_config,
            parallel_config=self.speculative_config.draft_parallel_config,
            scheduler_config=self.scheduler_config,
            device_config=self.device_config,
            cache_config=self.cache_config,
            load_config=self.load_config,
            local_rank=0,
            rank=0,
            distributed_init_method=distributed_init_method,
            lora_config=self.lora_config,
            vision_language_config=self.vision_language_config,
            is_driver_worker=True,
        )

        spec_decode_worker = SpecDecodeWorker.from_workers(
            proposer_worker=draft_worker, scorer_worker=target_worker)

        assert self.parallel_config.world_size == 1, (
            "GPUExecutor only supports single GPU.")

        self.driver_worker = spec_decode_worker

        # Load model handled in spec decode worker.
        self.driver_worker.init_device()

    def determine_num_available_blocks(self) -> Tuple[int, int]:
        """Determine the number of available KV blocks by invoking the
        underlying worker.
        """
        return self.driver_worker.determine_num_available_blocks()

    def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:
        """Initialize the KV cache by invoking the underlying worker.
        """
        # NOTE: This is logged in the executor because there can be >1 worker
        # with other executors. We could log in the engine level, but work
        # remains to abstract away the device for non-GPU configurations.
        logger.info(f"# GPU blocks: {num_gpu_blocks}, "
                    f"# CPU blocks: {num_cpu_blocks}")

        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)

    def execute_model(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        blocks_to_swap_in: Dict[int, int],
        blocks_to_swap_out: Dict[int, int],
        blocks_to_copy: Dict[int, List[int]],
        num_lookahead_slots: int,
    ) -> List[SamplerOutput]:
        output = self.driver_worker.execute_model(
            seq_group_metadata_list=seq_group_metadata_list,
            blocks_to_swap_in=blocks_to_swap_in,
            blocks_to_swap_out=blocks_to_swap_out,
            blocks_to_copy=blocks_to_copy,
            num_lookahead_slots=num_lookahead_slots,
        )
        return output

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

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

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

    def check_health(self) -> None:
        # GPUExecutor will always be healthy as long as
        # it's running.
        return


class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):

    async def execute_model_async(
        self,
        seq_group_metadata_list: List[SequenceGroupMetadata],
        blocks_to_swap_in: Dict[int, int],
        blocks_to_swap_out: Dict[int, int],
        blocks_to_copy: Dict[int, List[int]],
    ) -> SamplerOutput:
        output = await make_async(self.driver_worker.execute_model)(
            seq_group_metadata_list=seq_group_metadata_list,
            blocks_to_swap_in=blocks_to_swap_in,
            blocks_to_swap_out=blocks_to_swap_out,
            blocks_to_copy=blocks_to_copy)
        return output