90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
from typing import Dict, List, Optional, Tuple
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from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
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ParallelConfig, SchedulerConfig, SpeculativeConfig,
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VisionLanguageConfig)
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from vllm.executor.executor_base import 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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logger = init_logger(__name__)
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class NeuronExecutor(ExecutorBase):
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def __init__(
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self,
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model_config: ModelConfig,
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cache_config: CacheConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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device_config: DeviceConfig,
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lora_config: Optional[LoRAConfig],
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vision_language_config: Optional[VisionLanguageConfig],
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speculative_config: Optional[SpeculativeConfig],
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) -> None:
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self.model_config = model_config
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self.cache_config = cache_config
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assert lora_config is None, "LoRA is not supported for Neuron backend."
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self.parallel_config = parallel_config
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self.scheduler_config = scheduler_config
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self.device_config = device_config
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assert (not speculative_config
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), "Speculative decoding not yet supported for Neuron backend."
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# Instantiate the worker and load the model to the device.
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self._init_worker()
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def _init_worker(self):
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from vllm.worker.neuron_worker import NeuronWorker
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self.driver_worker = NeuronWorker(
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self.model_config,
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self.parallel_config,
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self.scheduler_config,
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self.device_config,
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self.cache_config,
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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,
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num_cpu_blocks: int) -> None:
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"""Initialize the KV cache by invoking the underlying worker.
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"""
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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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assert (blocks_to_swap_in == {} and blocks_to_swap_out == {}
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and blocks_to_copy == {}), (
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"Cache operations are not supported for Neuron backend.")
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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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return output
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def add_lora(self, lora_request: LoRARequest) -> bool:
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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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return self.driver_worker.remove_lora(lora_id)
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def list_loras(self) -> List[int]:
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return self.driver_worker.list_loras()
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def check_health(self) -> None:
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# NeuronExecutor will always be healthy as long as
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# it's running.
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return
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