248 lines
11 KiB
Python
248 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapt from https://github.com/vllm-project/vllm/blob/releases/v0.11.0/vllm/executor/ray_utils.py
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from collections import defaultdict
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import os
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import time
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from fastvideo.utils import get_ip
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from fastvideo.fastvideo_args import FastVideoArgs
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from fastvideo.worker.worker_base import WorkerWrapperBase
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from fastvideo.logger import init_logger
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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 fastvideo.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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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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from fastvideo.platforms import current_platform
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node_id = ray.get_runtime_context().get_node_id()
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device_key = current_platform.ray_device_key
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if not device_key:
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raise RuntimeError("current platform %s does not support ray.", current_platform.device_name)
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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 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
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ray_import_err = str(e)
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def assert_ray_available() -> None:
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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(f"Failed to import Ray: {ray_import_err}."
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"Please install Ray with `uv pip install ray`.")
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def _verify_bundles(placement_group: "PlacementGroup", fastvideo_args: FastVideoArgs, device_str: str):
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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(), ("Ray is not initialized although distributed-executor-backend is ray.")
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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(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 FastVideo engine.")
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for node_id, bundles in node_id_to_bundle.items():
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if len(bundles) < fastvideo_args.tp_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.", fastvideo_args.tp_size, device_str, len(bundles), device_str,
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node_id, fastvideo_args.tp_size)
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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 timeout
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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 FASTVIDEO_HOST_IP environment variable"
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" specified in each node if you are running on a multi-node.", int(time.time() - s), placement_group_specs)
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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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raise ValueError("Cannot provide a placement group of "
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f"{placement_group_specs=} within {PG_WAIT_TIMEOUT} seconds. See "
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"`ray status` and `ray list nodes` to make sure the cluster has "
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"enough resources.") from None
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def initialize_ray_cluster(
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fastvideo_args: FastVideoArgs,
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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 fastvideo.platforms import current_platform
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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("No existing RAY instance detected. "
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"A new instance will be launched with current node resources.")
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ray.init(address=ray_address, num_gpus=fastvideo_args.num_gpus, runtime_env=fastvideo_args.ray_runtime_env)
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else:
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ray.init(address=ray_address, runtime_env=fastvideo_args.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(f"current platform {current_platform.device_name} does not "
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"support ray.")
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# Create or get the placement group for worker processes
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current_placement_group = fastvideo_args.ray_placement_group or 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("Placement group bundle cannot have more than 1 "
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f"{device_str}.")
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if bundle_devices:
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device_bundles += 1
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if fastvideo_args.num_gpus > device_bundles:
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raise ValueError(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: {fastvideo_args.num_gpus}. "
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f"Total number of devices: {device_bundles}.")
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else:
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logger.info("No current placement group found. "
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"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 fastvideo_args.num_gpus > 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.", device_str, device_str)
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# Create a new placement group
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placement_group_specs: list[dict[str, float]] = ([{device_str: 1.0} for _ in range(fastvideo_args.num_gpus)])
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# FastVideo 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(f"Current node has no {device_str} available. "
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f"{current_node_resource=}. FastVideo 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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# 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(placement_group_specs, strategy="PACK")
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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, fastvideo_args, device_str)
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# Set the placement group in the fastvideo args
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fastvideo_args.ray_placement_group = current_placement_group
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def is_in_ray_actor():
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"""Check if we are in a Ray actor."""
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try:
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import ray
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return (ray.is_initialized() and ray.get_runtime_context().get_actor_id() is not None)
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except ImportError:
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return False
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