76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
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################################################################################
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# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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################################################################################
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from concurrent.futures import Future
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from typing import Optional, Union
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from vllm.executor.ray_utils import RayWorkerWrapper, ray
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.executor.ray_distributed_executor import RayDistributedExecutor
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from vllm.v1.outputs import ModelRunnerOutput
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class FutureWrapper(Future):
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"""A wrapper around a Ray output reference to meet the interface
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of .execute_model().
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"""
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def __init__(self, ref):
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super().__init__()
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self.ref = ref
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def result(self, timeout=None):
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if timeout is not None:
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raise NotImplementedError("timeout is not supported")
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return ray.get(self.ref)
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def execute_model(
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self,
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scheduler_output,
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non_block: bool = False,
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) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
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# TODO: current only support non_block is True, need to apdapt new non_block param
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assert self.parallel_config.use_ray
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refs = []
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for pp_rank, tp_group in enumerate(self.pp_tp_workers):
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task_refs = [
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worker.execute_model_ray.remote(scheduler_output)
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for worker in tp_group
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]
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last_pp_rank = len(self.pp_tp_workers) - 1
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if pp_rank == last_pp_rank:
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refs.extend(task_refs)
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# When PP is not used, we block here until the result is available.
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if self.max_concurrent_batches == 1:
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return ray.get(refs[0])
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# When PP is used, we return a FutureWrapper immediately so that
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# the scheduler can yield to the next batch.
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return FutureWrapper(refs[0])
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def execute_model_ray(
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self,
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scheduler_output: SchedulerOutput) -> Optional[ModelRunnerOutput]:
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return self.worker.execute_model(scheduler_output)
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RayDistributedExecutor.execute_model = execute_model # type: ignore[attr-defined]
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RayWorkerWrapper.execute_model_ray = execute_model_ray # type: ignore[attr-defined]
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