116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
from abc import ABC, abstractmethod
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from typing import List, Optional, Set, Tuple
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from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
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ModelConfig, ParallelConfig, SchedulerConfig,
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SpeculativeConfig, VisionLanguageConfig)
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from vllm.lora.request import LoRARequest
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from vllm.sequence import ExecuteModelRequest, SamplerOutput
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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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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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load_config: LoadConfig,
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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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self.lora_config = lora_config
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self.load_config = load_config
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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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self.vision_language_config = vision_language_config
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self.speculative_config = speculative_config
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self._init_executor()
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@abstractmethod
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def _init_executor(self) -> None:
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pass
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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(
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self,
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execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
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"""Executes at least 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) -> Set[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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def shutdown(self) -> None:
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"""Shutdown the executor."""
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return
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def __del__(self):
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self.shutdown()
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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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execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
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"""Executes one model step on the given sequences."""
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raise NotImplementedError
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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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self.check_health()
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