102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import os
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from typing import Optional
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import torch
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from vllm import envs
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from vllm.config import VllmConfig
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from vllm.distributed.parallel_state import get_pp_group, get_tp_group
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from vllm.logger import init_logger
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from vllm.model_executor.utils import set_random_seed
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from vllm.sequence import IntermediateTensors
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.outputs import ModelRunnerOutput
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from vllm.v1.worker.cpu_model_runner import CPUModelRunner
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from vllm.v1.worker.gpu_worker import (Worker,
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init_worker_distributed_environment)
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logger = init_logger(__name__)
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class CPUWorker(Worker):
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def __init__(self,
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vllm_config: VllmConfig,
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local_rank: int,
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rank: int,
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distributed_init_method: str,
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is_driver_worker: bool = False):
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super().__init__(vllm_config,
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local_rank,
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rank,
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distributed_init_method,
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is_driver_worker=is_driver_worker)
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self.parallel_config.disable_custom_all_reduce = True
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def init_device(self):
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# Setup OpenMP threads affinity.
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omp_cpuids = envs.VLLM_CPU_OMP_THREADS_BIND
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if omp_cpuids == "all":
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self.local_omp_cpuid = "all"
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else:
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self.local_omp_cpuid = omp_cpuids.split("|")[self.rank]
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ret = torch.ops._C_utils.init_cpu_threads_env(self.local_omp_cpuid)
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if ret:
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logger.info(ret)
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# Note: unique identifier for creating allreduce shared memory
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os.environ["VLLM_DIST_IDENT"] = self.distributed_init_method.split(
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":")[-1]
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# Initialize the distributed environment.
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init_worker_distributed_environment(self.vllm_config, self.rank,
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self.distributed_init_method,
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self.local_rank, "gloo")
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# Set random seed.
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set_random_seed(self.model_config.seed)
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# Construct the model runner
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self.model_runner: CPUModelRunner = CPUModelRunner(
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self.vllm_config, torch.device("cpu"))
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def sleep(self, level: int = 1) -> None:
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logger.warning("sleep mode is not supported on CPU, ignore it.")
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pass
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def wake_up(self, tags: Optional[list[str]] = None) -> None:
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logger.warning("sleep mode is not supported on CPU, ignore it.")
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pass
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def determine_available_memory(self) -> int:
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return self.cache_config.cpu_kvcache_space_bytes # type: ignore
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def compile_or_warm_up_model(self) -> None:
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# Reset the seed to ensure that the random state is not affected by
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# the model initialization and profiling.
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set_random_seed(self.model_config.seed)
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self.model_runner.warming_up_model()
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@torch.inference_mode()
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def execute_model(
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self,
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scheduler_output: "SchedulerOutput",
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) -> Optional[ModelRunnerOutput]:
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intermediate_tensors = None
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if not get_pp_group().is_first_rank:
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intermediate_tensors = IntermediateTensors(
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get_pp_group().recv_tensor_dict(
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all_gather_group=get_tp_group()))
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output = self.model_runner.execute_model(scheduler_output,
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intermediate_tensors)
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if not get_pp_group().is_last_rank:
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assert isinstance(output, IntermediateTensors)
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get_pp_group().send_tensor_dict(output.tensors,
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all_gather_group=get_tp_group())
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return None
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assert isinstance(output, ModelRunnerOutput)
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return output if self.is_driver_worker else None
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