92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
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from typing import 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 ExecuteModelRequest, SamplerOutput
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from vllm.utils import make_async
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logger = init_logger(__name__)
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class NeuronExecutor(ExecutorBase):
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def _init_executor(self) -> None:
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assert (self.lora_config is
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None), "LoRA is not supported for Neuron backend."
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assert (not self.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(
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self,
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execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
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assert (execute_model_req.blocks_to_swap_in == {}
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and execute_model_req.blocks_to_swap_out == {}
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and execute_model_req.blocks_to_copy == {}), (
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"Cache operations are not supported for Neuron backend.")
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assert execute_model_req.num_lookahead_slots == 0, (
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"lookahead not supported for Neuron backend.")
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output = self.driver_worker.execute_model(
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execute_model_req.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) -> 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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# NeuronExecutor will always be healthy as long as
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# it's running.
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return
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class NeuronExecutorAsync(NeuronExecutor, ExecutorAsyncBase):
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async def execute_model_async(
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self,
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execute_model_req: ExecuteModelRequest,
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) -> List[SamplerOutput]:
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output = await make_async(
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self.driver_worker.execute_model
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)(seq_group_metadata_list=execute_model_req.seq_group_metadata_list, )
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return output
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async def check_health_async(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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