mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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72ff3a9686
Signed-off-by: Rui Qiao <ubuntu@ip-172-31-15-128.us-west-2.compute.internal> Signed-off-by: Rui Qiao <ruisearch42@gmail.com> Co-authored-by: Rui Qiao <ubuntu@ip-172-31-15-128.us-west-2.compute.internal>
564 lines
24 KiB
Python
564 lines
24 KiB
Python
import asyncio
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import os
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from collections import defaultdict
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from itertools import islice, repeat
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import msgspec
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import vllm.envs as envs
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from vllm.executor.distributed_gpu_executor import ( # yapf: disable
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DistributedGPUExecutor, DistributedGPUExecutorAsync)
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from vllm.executor.msgspec_utils import encode_hook
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from vllm.executor.ray_utils import RayWorkerWrapper, ray
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from vllm.logger import init_logger
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.sequence import ExecuteModelRequest
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from vllm.utils import (_run_task_with_lock, get_distributed_init_method,
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get_ip, get_open_port, make_async)
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if ray is not None:
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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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logger = init_logger(__name__)
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class RayGPUExecutor(DistributedGPUExecutor):
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uses_ray: bool = True
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def _init_executor(self) -> None:
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self.forward_dag: Optional[ray.dag.CompiledDAG] = None
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# If the env var is set, it uses the Ray's compiled DAG API
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# which optimizes the control plane overhead.
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# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
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# Currently, this requires USE_RAY_SPMD_WORKER=True.
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self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
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# If the env var is set, then we do not distinguish between the
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# "driver worker" vs other workers. Also, the rank 0 worker will
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# be executed in a remote Ray worker. Currently this requires
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# USE_RAY_COMPILED_DAG=True.
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self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
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if self.use_ray_compiled_dag:
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assert self.use_ray_spmd_worker, (
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"VLLM_USE_RAY_COMPILED_DAG=1 requires "
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"VLLM_USE_RAY_SPMD_WORKER=1")
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if self.use_ray_spmd_worker:
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# TODO: Support SPMD worker for non-DAG Ray executor.
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assert self.use_ray_compiled_dag, (
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"VLLM_USE_RAY_SPMD_WORKER=1 requires "
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"VLLM_USE_RAY_COMPILED_DAG=1")
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assert self.uses_ray
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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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self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
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self.output_decoder = msgspec.msgpack.Decoder(
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Optional[List[SamplerOutput]])
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def shutdown(self) -> None:
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if hasattr(self, "forward_dag") and self.forward_dag is not None:
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self.forward_dag.teardown()
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import ray
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for worker in self.workers:
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ray.kill(worker)
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self.forward_dag = None
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def _configure_ray_workers_use_nsight(self,
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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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runtime_env.update({
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"nsight": {
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"t": "cuda,cudnn,cublas",
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"o": "'worker_process_%p'",
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"cuda-graph-trace": "node",
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}
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})
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return ray_remote_kwargs
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# child class could overwrite this to return actual env vars.
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def _get_env_vars_to_be_updated(self):
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return self._env_vars_for_all_workers
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def _init_workers_ray(self, placement_group: "PlacementGroup",
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**ray_remote_kwargs):
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if (self.parallel_config.tensor_parallel_size == 1
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and self.parallel_config.pipeline_parallel_size == 1):
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# For single GPU case, we use a ray worker with constrained memory.
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num_gpus = self.cache_config.gpu_memory_utilization
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else:
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# Otherwise, the ray workers are allocated with a full GPU.
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num_gpus = 1
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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: Optional[RayWorkerWrapper] = 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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logger.info("use_ray_spmd_worker: %s", self.use_ray_spmd_worker)
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# Create the workers.
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driver_ip = get_ip()
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for bundle_id, bundle in enumerate(placement_group.bundle_specs):
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if not bundle.get("GPU", 0):
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continue
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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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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(vllm_config=self.vllm_config)
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if self.use_ray_spmd_worker:
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self.workers.append(worker)
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else:
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worker_ip = ray.get(worker.get_node_ip.remote())
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if worker_ip == driver_ip and self.driver_dummy_worker is None:
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# If the worker is on the same node as the driver, we use it
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# as the resource holder for the driver process.
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self.driver_dummy_worker = worker
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self.driver_worker = RayWorkerWrapper(
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vllm_config=self.vllm_config)
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else:
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# Else, added to the list of workers.
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self.workers.append(worker)
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logger.debug("workers: %s", self.workers)
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logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
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if not self.use_ray_spmd_worker and self.driver_dummy_worker is None:
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raise ValueError(
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"Ray does not allocate any GPUs on the driver node. Consider "
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"adjusting the Ray placement group or running the driver on a "
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"GPU node.")
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worker_ips = [
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ray.get(worker.get_node_ip.remote()) # type: ignore[attr-defined]
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for worker in self.workers
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]
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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(worker):
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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 = ray.get(worker.get_node_ip.remote())
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return (ip != driver_ip, 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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self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip)
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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
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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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# Set environment variables for the driver and workers.
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all_args_to_update_environment_variables = [({
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"CUDA_VISIBLE_DEVICES":
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",".join(map(str, node_gpus[node_id])),
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"VLLM_TRACE_FUNCTION":
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str(envs.VLLM_TRACE_FUNCTION),
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**({
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"VLLM_ATTENTION_BACKEND": envs.VLLM_ATTENTION_BACKEND
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} if envs.VLLM_ATTENTION_BACKEND is not None else {})
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}, ) for (node_id, _) in worker_node_and_gpu_ids]
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self._env_vars_for_all_workers = (
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all_args_to_update_environment_variables)
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self._run_workers("update_environment_variables",
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all_args=self._get_env_vars_to_be_updated())
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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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# Initialize the actual workers inside worker wrapper.
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init_worker_all_kwargs = [
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self._get_worker_kwargs(
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local_rank=node_workers[node_id].index(rank),
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rank=rank,
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distributed_init_method=distributed_init_method,
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) for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids)
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]
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self._run_workers("init_worker", all_kwargs=init_worker_all_kwargs)
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self._run_workers("init_device")
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self._run_workers("load_model",
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max_concurrent_workers=self.parallel_config.
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max_parallel_loading_workers)
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if self.use_ray_spmd_worker:
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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(
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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
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) + 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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# This is the list of workers that are rank 0 of each TP group EXCEPT
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# global rank 0. These are the workers that will broadcast to the
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# rest of the workers.
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self.tp_driver_workers: List[RayWorkerWrapper] = []
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# This is the list of workers that are not drivers and not the first
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# worker in a TP group. These are the workers that will be
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# broadcasted to.
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self.non_driver_workers: List[RayWorkerWrapper] = []
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# Enforce rank order for correct rank to return final output.
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for index, worker in enumerate(self.workers):
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# The driver worker is rank 0 and not in self.workers.
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rank = index + 1
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if rank % self.parallel_config.tensor_parallel_size == 0:
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self.tp_driver_workers.append(worker)
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else:
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self.non_driver_workers.append(worker)
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def _driver_execute_model(
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self, execute_model_req: Optional[ExecuteModelRequest]
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) -> Optional[List[SamplerOutput]]:
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"""Run execute_model in the driver worker.
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Passing None will cause the driver to stop the model execution
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loop running in each of the remote workers.
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"""
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assert not self.use_ray_spmd_worker, (
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"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
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return self.driver_worker.execute_method("execute_model",
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execute_model_req)
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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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if not self.use_ray_spmd_worker:
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return super().execute_model(execute_model_req)
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if self.forward_dag is None:
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self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
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serialized_data = self.input_encoder.encode(execute_model_req)
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outputs = ray.get(self.forward_dag.execute(serialized_data))
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output = self.output_decoder.decode(outputs[0])
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return output
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def _run_workers(
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self,
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method: str,
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*args,
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async_run_tensor_parallel_workers_only: bool = False,
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all_args: Optional[List[Tuple[Any, ...]]] = None,
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all_kwargs: Optional[List[Dict[str, Any]]] = None,
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max_concurrent_workers: Optional[int] = None,
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**kwargs,
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) -> Any:
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"""Runs the given method on all workers. Can be used in the following
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ways:
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Args:
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- async_run_tensor_parallel_workers_only: If True the method will be
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run only in the remote TP workers, not the driver worker.
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It will also be run asynchronously and return a list of futures
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rather than blocking on the results.
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- args/kwargs: All workers share the same args/kwargs
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- all_args/all_kwargs: args/kwargs for each worker are specified
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individually
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"""
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if self.use_ray_spmd_worker:
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assert not async_run_tensor_parallel_workers_only, (
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"async_run_tensor_parallel_workers_only is not supported for "
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"spmd mode.")
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if max_concurrent_workers:
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raise NotImplementedError(
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"max_concurrent_workers is not supported yet.")
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count = len(self.workers) if not \
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async_run_tensor_parallel_workers_only \
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else len(self.non_driver_workers)
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# If using SPMD worker, all workers are the same, so we should execute
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# the args on all workers. Otherwise, we skip the first worker's args
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# because those args will go to the driver worker.
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first_worker_args_index: int = 0 if self.use_ray_spmd_worker else 1
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all_worker_args = repeat(args, count) if all_args is None \
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else islice(all_args, first_worker_args_index, None)
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all_worker_kwargs = repeat(kwargs, count) if all_kwargs is None \
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else islice(all_kwargs, first_worker_args_index, None)
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# Start the ray workers first.
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ray_workers = self.workers
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if async_run_tensor_parallel_workers_only:
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ray_workers = self.non_driver_workers
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ray_worker_outputs = [
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worker.execute_method.remote(method, *worker_args, **worker_kwargs)
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for (worker, worker_args, worker_kwargs
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) in zip(ray_workers, all_worker_args, all_worker_kwargs)
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]
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if async_run_tensor_parallel_workers_only:
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# Just return futures
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return ray_worker_outputs
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driver_worker_output = []
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# In SPMD mode, the driver worker is the same as any other worker,
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# so we only explicitly execute on the driver worker if using a
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# non-SPMD worker class.
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if not self.use_ray_spmd_worker:
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driver_args = args if all_args is None else all_args[0]
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driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
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# Start the driver worker after all the ray workers.
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driver_worker_output = [
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self.driver_worker.execute_method(method, *driver_args,
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**driver_kwargs)
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]
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# Get the results of the ray workers.
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if self.workers:
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ray_worker_outputs = ray.get(ray_worker_outputs)
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return driver_worker_output + ray_worker_outputs
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def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
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"""Wait for futures returned from _run_workers() with
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async_run_remote_workers_only to complete."""
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ray.get(parallel_worker_tasks)
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def _check_ray_adag_installation(self):
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import pkg_resources
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from packaging import version
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required_version = version.parse("2.40")
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current_version = version.parse(
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pkg_resources.get_distribution("ray").version)
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if current_version < required_version:
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raise ValueError(f"Ray version {required_version} is "
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f"required, but found {current_version}")
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import importlib.util
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adag_spec = importlib.util.find_spec(
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"ray.experimental.compiled_dag_ref")
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if adag_spec is None:
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raise ValueError("Ray accelerated DAG is not installed. "
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"Run `pip install ray[adag]` to install it.")
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cupy_spec = importlib.util.find_spec("cupy")
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if cupy_spec is None and envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL:
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raise ValueError(
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"cupy is not installed but required since "
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"VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL is set."
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"Run `pip install ray[adag]` and check cupy installation.")
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def _compiled_ray_dag(self, enable_asyncio: bool):
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assert self.parallel_config.use_ray
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self._check_ray_adag_installation()
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from ray.dag import InputNode, MultiOutputNode
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from ray.experimental.channel.torch_tensor_type import TorchTensorType
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logger.info("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL = %s",
|
|
envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL)
|
|
logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s",
|
|
envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
|
|
with InputNode() as input_data:
|
|
# Example DAG: PP=2, TP=4
|
|
# (ExecuteModelReq, None) -> 0 -> (ExecuteModelReq, IntermediateOutput) -> 4 -> SamplerOutput # noqa: E501
|
|
# -> 1 -> (ExecuteModelReq, IntermediateOutput) -> 5 -> SamplerOutput # noqa: E501
|
|
# -> 2 -> (ExecuteModelReq, IntermediateOutput) -> 6 -> SamplerOutput # noqa: E501
|
|
# -> 3 -> (ExecuteModelReq, IntermediateOutput) -> 7 -> SamplerOutput # noqa: E501
|
|
|
|
# All workers in the first TP group will take in the
|
|
# ExecuteModelRequest as input.
|
|
outputs = [input_data for _ in self.pp_tp_workers[0]]
|
|
for pp_rank, tp_group in enumerate(self.pp_tp_workers):
|
|
# Each PP worker takes in the output of the previous PP worker,
|
|
# and the TP group executes in SPMD fashion.
|
|
outputs = [
|
|
worker.execute_model_spmd.
|
|
bind( # type: ignore[attr-defined]
|
|
outputs[i]) for i, worker in enumerate(tp_group)
|
|
]
|
|
|
|
last_pp_rank = len(self.pp_tp_workers) - 1
|
|
if pp_rank < last_pp_rank:
|
|
# Specify how intermediate tensors should be passed
|
|
# between pp stages, no need to specify for the last
|
|
# pp stage.
|
|
transport = "nccl" \
|
|
if envs.VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL \
|
|
else "auto"
|
|
outputs = [
|
|
output.with_type_hint(
|
|
TorchTensorType(transport=transport))
|
|
for output in outputs
|
|
]
|
|
|
|
forward_dag = MultiOutputNode(outputs)
|
|
|
|
return forward_dag.experimental_compile(
|
|
enable_asyncio=enable_asyncio,
|
|
_overlap_gpu_communication=envs.
|
|
VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
|
|
|
|
def __del__(self):
|
|
self.shutdown()
|
|
|
|
|
|
class RayGPUExecutorAsync(RayGPUExecutor, DistributedGPUExecutorAsync):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.pp_locks: Optional[List[asyncio.Lock]] = None
|
|
self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
|
|
if not self.use_ray_compiled_dag:
|
|
self.driver_exec_method = make_async(
|
|
self.driver_worker.execute_method)
|
|
|
|
async def execute_model_async(
|
|
self,
|
|
execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
|
|
if not self.use_ray_spmd_worker:
|
|
return await super().execute_model_async(execute_model_req)
|
|
|
|
if self.forward_dag is None:
|
|
self.forward_dag = self._compiled_ray_dag(enable_asyncio=True)
|
|
|
|
serialized_data = self.input_encoder.encode(execute_model_req)
|
|
dag_future = await self.forward_dag.execute_async(serialized_data)
|
|
output = await dag_future[0]
|
|
return self.output_decoder.decode(output)
|
|
|
|
async def _driver_execute_model_async(
|
|
self,
|
|
execute_model_req: Optional[ExecuteModelRequest] = None
|
|
) -> List[SamplerOutput]:
|
|
assert not self.use_ray_spmd_worker, (
|
|
"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
|
|
if not self.tp_driver_workers:
|
|
return await self.driver_exec_method("execute_model",
|
|
execute_model_req)
|
|
if self.pp_locks is None:
|
|
# This locks each pipeline parallel stage so multiple virtual
|
|
# engines can't execute on the same stage at the same time
|
|
# We create the locks here to avoid creating them in the constructor
|
|
# which uses a different asyncio loop.
|
|
self.pp_locks = [
|
|
asyncio.Lock()
|
|
for _ in range(self.parallel_config.pipeline_parallel_size)
|
|
]
|
|
|
|
tasks = [
|
|
asyncio.create_task(
|
|
_run_task_with_lock(self.driver_exec_method, self.pp_locks[0],
|
|
"execute_model", execute_model_req))
|
|
]
|
|
for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
|
|
start=1):
|
|
tasks.append(
|
|
asyncio.create_task(
|
|
_run_task_with_lock(driver_worker.execute_method.remote,
|
|
self.pp_locks[pp_rank],
|
|
"execute_model", execute_model_req)))
|
|
|
|
results = await asyncio.gather(*tasks)
|
|
|
|
# Only the last PP stage has the final results.
|
|
return results[-1]
|
|
|
|
async def _start_worker_execution_loop(self):
|
|
assert not self.use_ray_spmd_worker, (
|
|
"worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1")
|
|
coros = [
|
|
worker.execute_method.remote("start_worker_execution_loop")
|
|
for worker in self.non_driver_workers
|
|
]
|
|
return await asyncio.gather(*coros)
|
|
|
|
def __del__(self):
|
|
self.shutdown()
|