102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
from abc import ABC, abstractmethod
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from typing import Dict, List, Optional
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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.lora.request import LoRARequest
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from vllm.sequence import SamplerOutput, SequenceGroupMetadata
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class ExecutorBase(ABC):
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"""Base class for all executors.
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An executor is responsible for executing the model on a specific device
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type (e.g., CPU, GPU, Neuron, etc.). Or it can be a distributed executor
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that can execute the model on multiple devices.
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"""
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@abstractmethod
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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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raise NotImplementedError
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@abstractmethod
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def determine_num_available_blocks(self) -> tuple[int, int]:
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"""Determine the number of available blocks for the GPU KV cache and
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swappable CPU KV cache.
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Normally, this should simply delegate to the underlying Worker. Some
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ExecutorBase may require modification of the result, e.g. to ensure the
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selected cache sizes are compatible with all workers.
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Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
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are blocks that are "active" on the device and can be appended to.
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num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
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appended to.
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"""
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raise NotImplementedError
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@abstractmethod
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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 with the given size in blocks.
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"""
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raise NotImplementedError
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@abstractmethod
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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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"""Executes one model step on the given sequences."""
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raise NotImplementedError
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@abstractmethod
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def add_lora(self, lora_request: LoRARequest) -> bool:
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raise NotImplementedError
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@abstractmethod
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def remove_lora(self, lora_id: int) -> bool:
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raise NotImplementedError
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@abstractmethod
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def list_loras(self) -> List[int]:
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raise NotImplementedError
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@abstractmethod
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def check_health(self) -> None:
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"""Checks if the executor is healthy. If not, it should raise an
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exception."""
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raise NotImplementedError
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class ExecutorAsyncBase(ExecutorBase):
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@abstractmethod
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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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"""Executes one model step on the given sequences."""
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raise NotImplementedError
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@abstractmethod
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async def check_health_async(self) -> None:
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"""Checks if the executor is healthy. If not, it should raise an
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exception."""
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raise NotImplementedError
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