122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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a simple demonstration to show how to control
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the placement of the vLLM workers with Ray.
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The key is to set VLLM_RAY_PER_WORKER_GPUS and
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VLLM_RAY_BUNDLE_INDICES properly.
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"""
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import os
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import ray
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from ray.util.placement_group import placement_group
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from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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from vllm import LLM
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from vllm.worker.worker import Worker
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class MyWorker(Worker):
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def report_device_id(self) -> str:
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from vllm.platforms import current_platform
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return current_platform.get_device_uuid(self.device.index)
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class MyLLM(LLM):
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def __init__(self, *args, bundle_indices: list, **kwargs):
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# a hack to make the script work.
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# stop ray from manipulating CUDA_VISIBLE_DEVICES
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# at the top-level
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del os.environ["CUDA_VISIBLE_DEVICES"]
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# every worker will use 0.4 GPU, so that we can schedule
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# 2 instances on the same GPUs.
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os.environ["VLLM_RAY_PER_WORKER_GPUS"] = "0.4"
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os.environ["VLLM_RAY_BUNDLE_INDICES"] = ",".join(
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map(str, bundle_indices))
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print(f"creating LLM with bundle_indices={bundle_indices}")
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super().__init__(*args, **kwargs)
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class RayTrainingActor:
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def report_device_id(self) -> str:
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# the argument for get_device_uuid is the index
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# of the GPU in the visible devices.
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# ray will set CUDA_VISIBLE_DEVICES to the assigned GPUs
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from vllm.platforms import current_platform
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return current_platform.get_device_uuid(0)
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# ray manages 4 GPUs
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os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
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ray.init()
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# we want to co-locate vLLM instance and the training actor
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# on the same set of GPUs.
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# the placement plan is as follows:
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# GPU 0 and 1: training actor 0, 1, and vLLM instance 0 (with TP=2)
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# GPU 2 and 3: training actor 2, 3, and vLLM instance 1 (with TP=2)
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pg = placement_group([{"GPU": 1, "CPU": 0}] * 4)
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ray.get(pg.ready())
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print(f"placement group has bundles {pg.bundle_specs=}")
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training_actors = []
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training_actor_device_ids = []
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inference_engines = []
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inference_engine_device_ids = []
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for bundle_index in [0, 1, 2, 3]:
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training_actor = ray.remote(
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num_cpus=0,
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num_gpus=0.4,
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scheduling_strategy=PlacementGroupSchedulingStrategy(
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placement_group=pg,
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placement_group_capture_child_tasks=True,
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placement_group_bundle_index=bundle_index,
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),
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)(RayTrainingActor).remote()
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training_actors.append(training_actor)
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device_id = ray.get(training_actor.report_device_id.remote())
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print(f"training actor {bundle_index} is on {device_id}")
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training_actor_device_ids.append(device_id)
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for (i, bundle_indices) in enumerate([[0, 1], [2, 3]]):
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# IMPORTANT: when creating vLLM instances, we need to
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# make sure there are no GPU activities on the target GPUs,
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# otherwise, they will interfere with the vLLM memory profiling,
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# and cause unexpected behaviors.
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llm = ray.remote(
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num_cpus=0,
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num_gpus=0,
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scheduling_strategy=PlacementGroupSchedulingStrategy(
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placement_group=pg,
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placement_group_capture_child_tasks=True,
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),
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)(MyLLM).remote(
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model="facebook/opt-125m",
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enforce_eager=True,
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worker_cls=MyWorker,
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tensor_parallel_size=2,
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distributed_executor_backend="ray",
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gpu_memory_utilization=0.4,
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bundle_indices=bundle_indices,
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)
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inference_engines.append(llm)
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# don't call any method on the inference engine here,
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# otherwise it will block until the vLLM instance is created.
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for i, llm in enumerate(inference_engines):
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inference_engine_device_ids.append(
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ray.get(llm.collective_rpc.remote("report_device_id", args=tuple())))
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print(f"inference engine {i} is on {inference_engine_device_ids[-1]}")
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# check the placement
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# the first two training actors should be
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# on the same GPUs as the first inference engine
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assert training_actor_device_ids[:2] == inference_engine_device_ids[0]
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# the last two training actors should be
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# on the same GPUs as the second inference engine
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assert training_actor_device_ids[2:] == inference_engine_device_ids[1]
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