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74b3a2014a
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> Signed-off-by: yurekami <yurekami@users.noreply.github.com>
475 lines
19 KiB
Python
475 lines
19 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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import time
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from collections import defaultdict
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from concurrent.futures import Future
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from typing import TYPE_CHECKING, Union
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import vllm.platforms
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from vllm.config import ParallelConfig
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from vllm.distributed import get_pp_group
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from vllm.distributed.kv_transfer.kv_connector.utils import KVOutputAggregator
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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.sequence import IntermediateTensors
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from vllm.utils.network_utils import get_ip
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from vllm.v1.outputs import AsyncModelRunnerOutput
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from vllm.v1.worker.worker_base import WorkerWrapperBase
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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from vllm.v1.outputs import ModelRunnerOutput
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logger = init_logger(__name__)
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PG_WAIT_TIMEOUT = 1800
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try:
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import ray
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from ray.util import placement_group_table
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from ray.util.placement_group import PlacementGroup
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try:
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from ray._private.state import available_resources_per_node
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except ImportError:
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# Ray 2.9.x doesn't expose `available_resources_per_node`
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from ray._private.state import state as _state
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available_resources_per_node = _state._available_resources_per_node
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class RayWorkerWrapper(WorkerWrapperBase):
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"""Ray wrapper for vllm.worker.Worker, allowing Worker to be
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lazily initialized after Ray sets CUDA_VISIBLE_DEVICES."""
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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# Since the compiled DAG runs a main execution
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# in a different thread that calls cuda.set_device.
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# The flag indicates is set_device is called on
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# that thread.
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self.compiled_dag_cuda_device_set = False
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def get_node_ip(self) -> str:
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return get_ip()
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def get_node_and_gpu_ids(self) -> tuple[str, list[int]]:
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node_id = ray.get_runtime_context().get_node_id()
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device_key = vllm.platforms.current_platform.ray_device_key
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if not device_key:
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raise RuntimeError(
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"current platform %s does not support ray.",
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vllm.platforms.current_platform.device_name,
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)
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gpu_ids = ray.get_runtime_context().get_accelerator_ids()[device_key]
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return node_id, gpu_ids
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def setup_device_if_necessary(self):
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# TODO(swang): This is needed right now because Ray CG executes
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# on a background thread, so we need to reset torch's current
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# device.
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# We can remove this API after it is fixed in compiled graph.
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assert self.worker is not None, "Worker is not initialized"
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if not self.compiled_dag_cuda_device_set:
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if current_platform.is_tpu():
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# Not needed
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pass
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else:
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assert self.worker.device is not None
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current_platform.set_device(self.worker.device)
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self.compiled_dag_cuda_device_set = True
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def execute_model_ray(
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self,
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execute_model_input: tuple["SchedulerOutput", "GrammarOutput"]
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| tuple["SchedulerOutput", "GrammarOutput", "IntermediateTensors"],
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) -> Union[
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"ModelRunnerOutput",
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tuple["SchedulerOutput", "GrammarOutput", "IntermediateTensors"],
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]:
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# This method is used by Ray Compiled Graph to execute the model,
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# and it needs a special logic of self.setup_device_if_necessary()
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self.setup_device_if_necessary()
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assert self.worker is not None, "Worker is not initialized"
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if len(execute_model_input) == 3:
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scheduler_output, grammar_output, intermediate_tensors = (
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execute_model_input
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)
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else:
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scheduler_output, grammar_output = execute_model_input
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intermediate_tensors = None
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assert self.worker.model_runner is not None
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output = self.worker.model_runner.execute_model(
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scheduler_output, intermediate_tensors
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)
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if isinstance(output, IntermediateTensors):
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output = scheduler_output, grammar_output, output
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elif not get_pp_group().is_last_rank:
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# Case where there are no scheduled requests
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# but may still be finished requests.
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assert not output or not output.req_ids
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output = scheduler_output, grammar_output, None
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elif output is None:
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output = self.worker.model_runner.sample_tokens(grammar_output)
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# Ensure outputs crossing Ray compiled DAG are serializable.
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# AsyncModelRunnerOutput holds CUDA events and cannot be
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# pickled.
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if isinstance(output, AsyncModelRunnerOutput):
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output = output.get_output()
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return output
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def override_env_vars(self, vars: dict[str, str]):
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os.environ.update(vars)
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ray_import_err = None
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except ImportError as e:
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ray = None # type: ignore
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# only capture string to avoid variable references in the traceback that can
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# prevent garbage collection in some cases
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ray_import_err = str(e)
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RayWorkerWrapper = None # type: ignore
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class FutureWrapper(Future):
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"""A wrapper around Ray output reference to meet the interface
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of .execute_model(): The top level (core busy loop) expects .result() api
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to block and return a single output.
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If aggregator is provided, the outputs from all workers are aggregated upon
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the result() call. If not only the first worker's output is returned.
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"""
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def __init__(self, ref_or_refs, aggregator: KVOutputAggregator | None = None):
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super().__init__()
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self.ref_or_refs = ref_or_refs
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self.aggregator = aggregator
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def result(self, timeout=None):
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outputs = ray.get(self.ref_or_refs, timeout=timeout)
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if self.aggregator is None:
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return outputs
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return self.aggregator.aggregate(outputs, output_rank=0)
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def ray_is_available() -> bool:
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"""Returns True if Ray is available."""
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return ray is not None
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def assert_ray_available():
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"""Raise an exception if Ray is not available."""
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if ray is None:
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raise ValueError(
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f"Failed to import Ray: {ray_import_err}."
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"Please install Ray with `pip install ray`."
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)
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def _verify_bundles(
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placement_group: "PlacementGroup", parallel_config: ParallelConfig, device_str: str
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):
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"""Verify a given placement group has bundles located in the right place.
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There are 2 rules.
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- Warn if all tensor parallel workers cannot fit in a single node.
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- Fail if driver node is not included in a placement group.
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"""
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assert ray.is_initialized(), (
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"Ray is not initialized although distributed-executor-backend is ray."
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)
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pg_data = placement_group_table(placement_group)
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# bundle_idx -> node_id
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bundle_to_node_ids = pg_data["bundles_to_node_id"]
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# bundle_idx -> bundle (e.g., {"GPU": 1})
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bundles = pg_data["bundles"]
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# node_id -> List of bundle (e.g., {"GPU": 1})
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node_id_to_bundle: dict[str, list[dict[str, float]]] = defaultdict(list)
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for bundle_idx, node_id in bundle_to_node_ids.items():
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node_id_to_bundle[node_id].append(bundles[bundle_idx])
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driver_node_id = ray.get_runtime_context().get_node_id()
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if driver_node_id not in node_id_to_bundle:
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raise RuntimeError(
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f"driver node id {driver_node_id} is not included in a placement "
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f"group {placement_group.id}. Node id -> bundles "
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f"{node_id_to_bundle}. "
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"You don't have enough GPUs available in a current node. Check "
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"`ray status` and `ray list nodes` to see if you have available "
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"GPUs in a node `{driver_node_id}` before starting an vLLM engine."
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)
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for node_id, bundles in node_id_to_bundle.items():
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if len(bundles) < parallel_config.tensor_parallel_size:
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logger.warning(
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"tensor_parallel_size=%d "
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"is bigger than a reserved number of %ss (%d "
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"%ss) in a node %s. Tensor parallel workers can be "
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"spread out to 2+ nodes which can degrade the performance "
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"unless you have fast interconnect across nodes, like "
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"Infiniband. To resolve this issue, make sure you have more "
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"than %d GPUs available at each node.",
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parallel_config.tensor_parallel_size,
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device_str,
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len(bundles),
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device_str,
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node_id,
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parallel_config.tensor_parallel_size,
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)
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def _wait_until_pg_ready(current_placement_group: "PlacementGroup"):
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"""Wait until a placement group is ready.
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It prints the informative log messages if the placement group is
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not created within time.
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"""
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# Wait until PG is ready - this will block until all
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# requested resources are available, and will time out
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# if they cannot be provisioned.
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placement_group_specs = current_placement_group.bundle_specs
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s = time.time()
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pg_ready_ref = current_placement_group.ready()
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wait_interval = 10
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while time.time() - s < PG_WAIT_TIMEOUT:
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ready, _ = ray.wait([pg_ready_ref], timeout=wait_interval)
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if len(ready) > 0:
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break
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# Exponential backoff for warning print.
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wait_interval *= 2
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logger.info(
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"Waiting for creating a placement group of specs for "
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"%d seconds. specs=%s. Check `ray status` and "
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"`ray list nodes` to see if you have enough resources,"
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" and make sure the IP addresses used by ray cluster"
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" are the same as VLLM_HOST_IP environment variable"
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" specified in each node if you are running on a multi-node.",
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int(time.time() - s),
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placement_group_specs,
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)
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try:
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ray.get(pg_ready_ref, timeout=0)
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except ray.exceptions.GetTimeoutError:
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# Provide more helpful error message when GPU count is exceeded
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total_gpu_required = sum(spec.get("GPU", 0) for spec in placement_group_specs)
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# If more than one GPU is required for the placement group, provide a
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# more specific error message.
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# We use >1 here because multi-GPU (tensor parallel) jobs are more
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# likely to fail due to insufficient cluster resources, and users may
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# need to adjust tensor_parallel_size to fit available GPUs.
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if total_gpu_required > 1:
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raise ValueError(
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f"Cannot provide a placement group requiring "
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f"{total_gpu_required} GPUs "
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f"(placement_group_specs={placement_group_specs}) within "
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f"{PG_WAIT_TIMEOUT} seconds.\n"
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f"Tensor parallel size may exceed available GPUs in your "
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f"cluster. Check resources with `ray status` and "
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f"`ray list nodes`.\n"
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f"If running on K8s with limited GPUs, consider reducing "
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f"--tensor-parallel-size to match available GPU resources."
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) from None
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else:
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raise ValueError(
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"Cannot provide a placement group of "
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f"{placement_group_specs=} within "
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f"{PG_WAIT_TIMEOUT} seconds. See "
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"`ray status` and `ray list nodes` to make sure the cluster "
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"has enough resources."
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) from None
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def _wait_until_pg_removed(current_placement_group: "PlacementGroup"):
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ray.util.remove_placement_group(current_placement_group)
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s = time.time()
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wait_interval = 10
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while time.time() - s < PG_WAIT_TIMEOUT:
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pg = ray.util.get_current_placement_group()
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if pg is None:
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break
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# Exponential backoff for warning print.
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wait_interval *= 2
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logger.info(
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"Waiting for removing a placement group of specs for %d seconds.",
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int(time.time() - s),
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)
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time.sleep(wait_interval)
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def initialize_ray_cluster(
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parallel_config: ParallelConfig,
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ray_address: str | None = None,
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):
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"""Initialize the distributed cluster with Ray.
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it will connect to the Ray cluster and create a placement group
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for the workers, which includes the specification of the resources
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for each distributed worker.
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Args:
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parallel_config: The configurations for parallel execution.
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ray_address: The address of the Ray cluster. If None, uses
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the default Ray cluster address.
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"""
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assert_ray_available()
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from vllm.platforms import current_platform
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# Prevalidate GPU requirements before Ray processing
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if current_platform.is_cuda() and parallel_config.world_size > 1:
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from vllm.utils.torch_utils import cuda_device_count_stateless
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available_gpus = cuda_device_count_stateless()
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if parallel_config.world_size > available_gpus:
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logger.warning(
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"World size (%d) exceeds locally visible GPUs (%d). "
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"For single-node deployments, this may result in Ray "
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"placement group allocation failures. For multi-node Ray "
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"clusters, ensure your cluster has %d GPUs available across "
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"all nodes. (world_size = tensor_parallel_size=%d × "
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"pipeline_parallel_size=%d%s)",
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parallel_config.world_size,
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available_gpus,
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parallel_config.world_size,
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parallel_config.tensor_parallel_size,
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parallel_config.pipeline_parallel_size,
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(
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f" × prefill_context_parallel_size="
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f"{parallel_config.prefill_context_parallel_size}"
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if parallel_config.prefill_context_parallel_size > 1
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else ""
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),
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)
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if ray.is_initialized():
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logger.info("Ray is already initialized. Skipping Ray initialization.")
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elif current_platform.is_rocm() or current_platform.is_xpu():
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# Try to connect existing ray instance and create a new one if not found
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try:
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ray.init("auto")
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except ConnectionError:
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logger.warning(
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"No existing RAY instance detected. "
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"A new instance will be launched with current node resources."
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)
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ray.init(
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address=ray_address,
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num_gpus=parallel_config.world_size,
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runtime_env=parallel_config.ray_runtime_env,
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)
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else:
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ray.init(address=ray_address, runtime_env=parallel_config.ray_runtime_env)
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device_str = current_platform.ray_device_key
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if not device_str:
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raise ValueError(
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f"current platform {current_platform.device_name} does not support ray."
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)
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# Create or get the placement group for worker processes
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if parallel_config.placement_group:
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current_placement_group = parallel_config.placement_group
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else:
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current_placement_group = ray.util.get_current_placement_group()
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if current_placement_group:
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logger.info("Using the existing placement group")
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# We are in a placement group
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bundles = current_placement_group.bundle_specs
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# Verify that we can use the placement group.
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device_bundles = 0
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for bundle in bundles:
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bundle_devices = bundle.get(device_str, 0)
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if bundle_devices > 1:
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raise ValueError(
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f"Placement group bundle cannot have more than 1 {device_str}."
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)
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if bundle_devices:
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device_bundles += 1
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if parallel_config.world_size > device_bundles:
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raise ValueError(
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f"The number of required {device_str}s exceeds the total "
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f"number of available {device_str}s in the placement group. "
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f"Required number of devices: {parallel_config.world_size}. "
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f"Total number of devices: {device_bundles}."
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)
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else:
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logger.info("No current placement group found. Creating a new placement group.")
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num_devices_in_cluster = ray.cluster_resources().get(device_str, 0)
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# Log a warning message and delay resource allocation failure response.
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# Avoid immediate rejection to allow user-initiated placement group
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# created and wait cluster to be ready
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if parallel_config.world_size > num_devices_in_cluster:
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logger.warning(
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"The number of required %ss exceeds the total "
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"number of available %ss in the placement group.",
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device_str,
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device_str,
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)
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# Create a new placement group
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placement_group_specs: list[dict[str, float]] = [
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{device_str: 1.0} for _ in range(parallel_config.world_size)
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]
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# vLLM engine is also a worker to execute model with an accelerator,
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# so it requires to have the device in a current node. Check if
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# the current node has at least one device.
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current_ip = get_ip()
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current_node_id = ray.get_runtime_context().get_node_id()
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current_node_resource = available_resources_per_node()[current_node_id]
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if current_node_resource.get(device_str, 0) < 1:
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raise ValueError(
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f"Current node has no {device_str} available. "
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f"{current_node_resource=}. vLLM engine cannot start without "
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f"{device_str}. Make sure you have at least 1 {device_str} "
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f"available in a node {current_node_id=} {current_ip=}."
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)
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# This way, at least bundle is required to be created in a current
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# node.
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placement_group_specs[0][f"node:{current_ip}"] = 0.001
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# By default, Ray packs resources as much as possible.
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current_placement_group = ray.util.placement_group(
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placement_group_specs, strategy="PACK"
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)
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_wait_until_pg_ready(current_placement_group)
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assert current_placement_group is not None
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_verify_bundles(current_placement_group, parallel_config, device_str)
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# Set the placement group in the parallel config
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parallel_config.placement_group = current_placement_group
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def get_num_tpu_nodes() -> int:
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from ray._private.accelerators import TPUAcceleratorManager
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cluster_resources = ray.cluster_resources()
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total_tpus = int(cluster_resources["TPU"])
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tpus_per_node = TPUAcceleratorManager.get_current_node_num_accelerators()
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assert total_tpus % tpus_per_node == 0
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return total_tpus // tpus_per_node
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def get_num_nodes_in_placement_group() -> int:
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pg_table = ray.util.placement_group_table()
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current_pg = ray.util.get_current_placement_group()
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num_nodes = 0
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if current_pg:
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nodes_in_pg = set()
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for pg_key, pg in pg_table.items():
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if pg_key == current_pg.id.hex():
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for _, node in pg["bundles_to_node_id"].items():
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nodes_in_pg.add(node)
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num_nodes = len(nodes_in_pg)
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return num_nodes
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