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https://git.datalinker.icu/vllm-project/vllm.git
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111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
from typing import Dict, List, Set, Tuple
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from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
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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.sequence import SamplerOutput, SequenceGroupMetadata
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from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
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make_async)
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logger = init_logger(__name__)
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class GPUExecutor(ExecutorBase):
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def _init_executor(self) -> None:
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assert (not self.speculative_config
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), "Speculative decoding not yet supported for GPU backend"
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# Instantiate the worker and load the model to GPU.
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self._init_worker()
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def _init_worker(self):
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# Lazy import the Worker to avoid importing torch.cuda/xformers
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# before CUDA_VISIBLE_DEVICES is set in the Worker
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from vllm.worker.worker import Worker
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assert self.parallel_config.world_size == 1, (
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"GPUExecutor only supports single GPU.")
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distributed_init_method = get_distributed_init_method(
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get_ip(), get_open_port())
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self.driver_worker = Worker(
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model_config=self.model_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config,
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device_config=self.device_config,
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cache_config=self.cache_config,
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local_rank=0,
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rank=0,
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distributed_init_method=distributed_init_method,
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lora_config=self.lora_config,
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vision_language_config=self.vision_language_config,
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tensorizer_config=self.tensorizer_config,
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is_driver_worker=True,
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)
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self.driver_worker.init_device()
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self.driver_worker.load_model()
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def determine_num_available_blocks(self) -> Tuple[int, int]:
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"""Determine the number of available KV blocks by invoking the
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underlying worker.
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"""
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return self.driver_worker.determine_num_available_blocks()
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def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:
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"""Initialize the KV cache by invoking the underlying worker.
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"""
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# NOTE: This is logged in the executor because there can be >1 worker
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# with other executors. We could log in the engine level, but work
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# remains to abstract away the device for non-GPU configurations.
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logger.info(f"# GPU blocks: {num_gpu_blocks}, "
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f"# CPU blocks: {num_cpu_blocks}")
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self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
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def execute_model(self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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blocks_to_swap_in: Dict[int, int],
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blocks_to_swap_out: Dict[int, int],
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blocks_to_copy: Dict[int, List[int]]) -> SamplerOutput:
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output = self.driver_worker.execute_model(
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seq_group_metadata_list=seq_group_metadata_list,
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blocks_to_swap_in=blocks_to_swap_in,
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blocks_to_swap_out=blocks_to_swap_out,
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blocks_to_copy=blocks_to_copy,
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)
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return output
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def add_lora(self, lora_request: LoRARequest) -> bool:
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assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
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return self.driver_worker.add_lora(lora_request)
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def remove_lora(self, lora_id: int) -> bool:
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assert lora_id > 0, "lora_id must be greater than 0."
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return self.driver_worker.remove_lora(lora_id)
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def list_loras(self) -> Set[int]:
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return self.driver_worker.list_loras()
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def check_health(self) -> None:
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# GPUExecutor will always be healthy as long as
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# it's running.
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return
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class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):
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async def execute_model_async(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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blocks_to_swap_in: Dict[int, int],
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blocks_to_swap_out: Dict[int, int],
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blocks_to_copy: Dict[int, List[int]],
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) -> SamplerOutput:
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output = await make_async(self.driver_worker.execute_model)(
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seq_group_metadata_list=seq_group_metadata_list,
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blocks_to_swap_in=blocks_to_swap_in,
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blocks_to_swap_out=blocks_to_swap_out,
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blocks_to_copy=blocks_to_copy)
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return output
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