363 lines
14 KiB
Python
363 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
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import os
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from collections import defaultdict
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from typing import TYPE_CHECKING, Any
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import vllm.envs as envs
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.ray.ray_env import get_env_vars_to_copy
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from vllm.v1.executor.ray_executor import RayDistributedExecutor, RayWorkerMetaData
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from vllm.v1.executor.ray_utils import (
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RayWorkerWrapper,
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initialize_ray_cluster,
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ray,
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)
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from vllm.utils.network_utils import (
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get_distributed_init_method,
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get_ip,
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get_open_port,
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)
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from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
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if TYPE_CHECKING:
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from ray.util.placement_group import PlacementGroup
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from vllm_mlu.mlu_hijack_utils import MluHijackObject
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logger = init_logger(__name__)
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class RayDistributedExecutor_MluHijack(RayDistributedExecutor):
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def _init_executor(self) -> None:
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self.forward_dag: ray.dag.CompiledDAG | None = None
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'''
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=============================
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Modify by vllm_mlu
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=============================
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@brief: For MLU, avoid compiling NVIDIA's NCCL
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'''
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# For TPU or XPU, avoid compiling NVIDIA's NCCL
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if current_platform.is_tpu() or current_platform.is_xpu() or \
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current_platform.is_out_of_tree():
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os.environ["VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE"] = "shm"
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'''
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==================
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End of MLU Hijack
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==================
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'''
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assert self.uses_ray
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initialize_ray_cluster(self.parallel_config)
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placement_group = self.parallel_config.placement_group
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# Disable Ray usage stats collection.
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ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
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if ray_usage != "1":
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os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
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# Create the parallel GPU workers.
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self._init_workers_ray(placement_group)
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# KV connector setup
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self.has_connector = self.vllm_config.kv_transfer_config is not None
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self.uses_sampler = self.vllm_config.model_config.runner_type != "pooling" and (
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self.vllm_config.ec_transfer_config is None
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or not self.vllm_config.ec_transfer_config.is_ec_producer
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)
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self.scheduler_output: SchedulerOutput | None = None
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def _configure_ray_workers_use_nsight(self, ray_remote_kwargs) -> dict[str, Any]:
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# If nsight profiling is enabled, we need to set the profiling
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# configuration for the ray workers as runtime env.
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runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
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'''
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=============================
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Modify by vllm_mlu
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=============================
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@brief: use default cnperf config.
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'''
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runtime_env.update({
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# use default cnperf config
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"nsight": "default"
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})
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'''
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==================
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End of MLU Hijack
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==================
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'''
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return ray_remote_kwargs
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def _init_workers_ray(self, placement_group: "PlacementGroup", **ray_remote_kwargs):
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num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
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# The driver dummy worker does not actually use any resources.
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# It holds the resource for the driver worker.
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self.driver_dummy_worker: RayWorkerWrapper | None = None
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# The remaining workers are the actual ray actors.
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self.workers: list[RayWorkerWrapper] = []
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# Used in ray compiled DAG: indexed first by PP rank,
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# and then TP rank. In other words, the inner list is
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# the TP group of workers for a PP rank.
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self.pp_tp_workers: list[list[RayWorkerWrapper]] = []
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if self.parallel_config.ray_workers_use_nsight:
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ray_remote_kwargs = self._configure_ray_workers_use_nsight(
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ray_remote_kwargs
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)
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# Create the workers.
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bundle_indices: list[int]
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if envs.VLLM_RAY_BUNDLE_INDICES:
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# Use the bundle indices specified by the user.
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bundle_indices = list(map(int, envs.VLLM_RAY_BUNDLE_INDICES.split(",")))
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assert len(bundle_indices) == self.parallel_config.world_size, (
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"VLLM_RAY_BUNDLE_INDICES must have the same size"
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f" as the world size, but got {bundle_indices=} "
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f"and {self.parallel_config.world_size=}"
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)
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assert len(set(bundle_indices)) == len(bundle_indices), (
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"VLLM_RAY_BUNDLE_INDICES cannot have duplicate values,"
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f" but got {bundle_indices=}"
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)
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else:
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# use the first N bundles that have GPU resources.
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bundle_indices = []
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for bundle_id, bundle in enumerate(placement_group.bundle_specs):
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if bundle.get(current_platform.ray_device_key, 0):
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bundle_indices.append(bundle_id)
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bundle_indices = bundle_indices[: self.parallel_config.world_size]
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worker_metadata: list[RayWorkerMetaData] = []
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driver_ip = get_ip()
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for rank, bundle_id in enumerate(bundle_indices):
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'''
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=============================
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Modify by vllm_mlu
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=============================
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@brief: support ray + cnperf-cli
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'''
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if self.parallel_config.ray_workers_use_nsight:
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ray_remote_kwargs['runtime_env'].update({
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"nsight": {
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"o": f"cnperf_rank_{rank}",
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"force_overwrite": "true"
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}
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})
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if rank == 0:
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ray_remote_kwargs['runtime_env'].update({
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"nsight": {}
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})
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'''
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==================
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End of MLU Hijack
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==================
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'''
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scheduling_strategy = PlacementGroupSchedulingStrategy(
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placement_group=placement_group,
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placement_group_capture_child_tasks=True,
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placement_group_bundle_index=bundle_id,
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)
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if current_platform.ray_device_key == "GPU":
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# NV+AMD GPUs, and Intel XPUs
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worker = ray.remote(
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num_cpus=0,
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num_gpus=num_gpus,
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scheduling_strategy=scheduling_strategy,
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**ray_remote_kwargs,
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)(RayWorkerWrapper).remote( # type: ignore[attr-defined]
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vllm_config=self.vllm_config, rpc_rank=rank
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)
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else:
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worker = ray.remote(
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num_cpus=0,
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num_gpus=0,
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resources={current_platform.ray_device_key: num_gpus},
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scheduling_strategy=scheduling_strategy,
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**ray_remote_kwargs,
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)(RayWorkerWrapper).remote( # type: ignore[attr-defined]
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vllm_config=self.vllm_config, rpc_rank=rank
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)
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worker_metadata.append(RayWorkerMetaData(worker=worker, created_rank=rank))
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worker_ips = ray.get(
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[
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each.worker.get_node_ip.remote() # type: ignore[attr-defined]
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for each in worker_metadata
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]
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)
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for each, ip in zip(worker_metadata, worker_ips):
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each.ip = ip
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logger.debug("workers: %s", worker_metadata)
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logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
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ip_counts: dict[str, int] = {}
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for ip in worker_ips:
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ip_counts[ip] = ip_counts.get(ip, 0) + 1
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def sort_by_driver_then_worker_ip(item: RayWorkerMetaData):
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"""
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Sort the workers based on 3 properties:
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1. If the worker is on the same node as the driver (vllm engine),
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it should be placed first.
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2. Then, if the worker is on a node with fewer workers, it should
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be placed first.
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3. Finally, if the work is on a node with smaller IP address, it
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should be placed first.
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"""
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ip = item.ip
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return 0 if ip == driver_ip else 1, ip_counts[ip], ip
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# After sorting, the workers on the same node will be
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# close to each other, and the workers on the driver
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# node will be placed first.
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sorted_worker_metadata = sorted(
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worker_metadata, key=sort_by_driver_then_worker_ip
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)
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for i, item in enumerate(sorted_worker_metadata):
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item.adjusted_rank = i
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self.workers = [item.worker for item in sorted_worker_metadata]
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rerank_mapping = {
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item.created_rank: item.adjusted_rank for item in sorted_worker_metadata
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}
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self.collective_rpc("adjust_rank", args=(rerank_mapping,))
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# Get the set of GPU IDs used on each node.
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worker_node_and_gpu_ids = []
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for worker in [self.driver_dummy_worker] + self.workers:
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if worker is None:
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# driver_dummy_worker can be None when using ray spmd worker.
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continue
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worker_node_and_gpu_ids.append(
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ray.get(worker.get_node_and_gpu_ids.remote())
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) # type: ignore[attr-defined]
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node_workers = defaultdict(list) # node id -> list of worker ranks
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node_gpus = defaultdict(list) # node id -> list of gpu ids
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for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
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node_workers[node_id].append(i)
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# `gpu_ids` can be a list of strings or integers.
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# convert them to integers for consistency.
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# NOTE: gpu_ids can be larger than 9 (e.g. 16 GPUs),
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# string sorting is not sufficient.
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# see https://github.com/vllm-project/vllm/issues/5590
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gpu_ids = [int(x) for x in gpu_ids]
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node_gpus[node_id].extend(gpu_ids)
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for node_id, gpu_ids in node_gpus.items():
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node_gpus[node_id] = sorted(gpu_ids)
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all_ips = set(worker_ips + [driver_ip])
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n_ips = len(all_ips)
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n_nodes = len(node_workers)
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if n_nodes != n_ips:
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raise RuntimeError(
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f"Every node should have a unique IP address. Got {n_nodes}"
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f" nodes with node ids {list(node_workers.keys())} and "
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f"{n_ips} unique IP addresses {all_ips}. Please check your"
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" network configuration. If you set `VLLM_HOST_IP`"
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" environment variable, make sure it is unique for"
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" each node."
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)
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# Set environment variables for the driver and workers.
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all_args_to_update_environment_variables = [
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{
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current_platform.device_control_env_var: ",".join(
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map(str, node_gpus[node_id])
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),
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}
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for (node_id, _) in worker_node_and_gpu_ids
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]
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# Environment variables to copy from driver to workers
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env_vars_to_copy = get_env_vars_to_copy(
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exclude_vars=self.WORKER_SPECIFIC_ENV_VARS,
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additional_vars=set(current_platform.additional_env_vars).union(
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self.ADDITIONAL_ENV_VARS
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),
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destination="workers",
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)
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# Copy existing env vars to each worker's args
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for args in all_args_to_update_environment_variables:
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# TODO: refactor platform-specific env vars
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for name in env_vars_to_copy:
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if name in os.environ:
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args[name] = os.environ[name]
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self._env_vars_for_all_workers = all_args_to_update_environment_variables
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self.collective_rpc(
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"update_environment_variables", args=(self._get_env_vars_to_be_updated(),)
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)
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if len(node_gpus) == 1:
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# in single node case, we don't need to get the IP address.
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# the loopback address is sufficient
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# NOTE: a node may have several IP addresses, one for each
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# network interface. `get_ip()` might return any of them,
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# while they might not work for communication inside the node
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# if the network setup is complicated. Using the loopback address
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# solves this issue, as it always works for communication inside
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# the node.
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driver_ip = "127.0.0.1"
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distributed_init_method = get_distributed_init_method(
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driver_ip, get_open_port()
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)
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# Initialize the actual workers inside worker wrapper.
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all_kwargs = []
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for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):
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local_rank = node_workers[node_id].index(rank)
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kwargs = dict(
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vllm_config=self.vllm_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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is_driver_worker=(not self.parallel_config)
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or (rank % self.parallel_config.tensor_parallel_size == 0),
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)
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all_kwargs.append(kwargs)
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self.collective_rpc("init_worker", args=(all_kwargs,))
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self.collective_rpc("init_device")
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self.collective_rpc("load_model")
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for pp_rank in range(self.parallel_config.pipeline_parallel_size):
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self.pp_tp_workers.append([])
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for tp_rank in range(self.parallel_config.tensor_parallel_size):
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# PP=2, TP=4
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# pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]]
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rank = (pp_rank * self.parallel_config.tensor_parallel_size) + tp_rank
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assert len(self.pp_tp_workers[pp_rank]) == tp_rank
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assert pp_rank < len(self.pp_tp_workers)
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self.pp_tp_workers[pp_rank].append(self.workers[rank])
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MluHijackObject.apply_hijack(
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RayDistributedExecutor,
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RayDistributedExecutor._configure_ray_workers_use_nsight,
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RayDistributedExecutor_MluHijack._configure_ray_workers_use_nsight
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)
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MluHijackObject.apply_hijack(
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RayDistributedExecutor,
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RayDistributedExecutor._init_workers_ray,
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RayDistributedExecutor_MluHijack._init_workers_ray
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)
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MluHijackObject.apply_hijack(
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RayDistributedExecutor,
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RayDistributedExecutor._init_executor,
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RayDistributedExecutor_MluHijack._init_executor
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) |