# SPDX-License-Identifier: Apache-2.0 # Adapt from https://github.com/vllm-project/vllm/blob/releases/v0.11.0/vllm/executor/ray_utils.py from collections import defaultdict import os import time from fastvideo.utils import get_ip from fastvideo.fastvideo_args import FastVideoArgs from fastvideo.worker.worker_base import WorkerWrapperBase from fastvideo.logger import init_logger logger = init_logger(__name__) PG_WAIT_TIMEOUT = 1800 try: import ray from ray.util import placement_group_table from ray.util.placement_group import PlacementGroup try: from ray._private.state import available_resources_per_node except ImportError: # Ray 2.9.x doesn't expose `available_resources_per_node` from ray._private.state import state as _state available_resources_per_node = _state._available_resources_per_node class RayWorkerWrapper(WorkerWrapperBase): """Ray wrapper for fastvideo.worker.Worker, allowing Worker to be lazily initialized after Ray sets CUDA_VISIBLE_DEVICES.""" def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) def get_node_ip(self) -> str: return get_ip() def get_node_and_gpu_ids(self) -> tuple[str, list[int]]: from fastvideo.platforms import current_platform node_id = ray.get_runtime_context().get_node_id() device_key = current_platform.ray_device_key if not device_key: raise RuntimeError("current platform %s does not support ray.", current_platform.device_name) gpu_ids = ray.get_runtime_context().get_accelerator_ids()[device_key] return node_id, gpu_ids def override_env_vars(self, vars: dict[str, str]): os.environ.update(vars) ray_import_err = None except ImportError as e: ray = None ray_import_err = str(e) def assert_ray_available() -> None: """Raise an exception if Ray is not available.""" if ray is None: raise ValueError(f"Failed to import Ray: {ray_import_err}." "Please install Ray with `uv pip install ray`.") def _verify_bundles(placement_group: "PlacementGroup", fastvideo_args: FastVideoArgs, device_str: str): """Verify a given placement group has bundles located in the right place. There are 2 rules. - Warn if all tensor parallel workers cannot fit in a single node. - Fail if driver node is not included in a placement group. """ assert ray.is_initialized(), ("Ray is not initialized although distributed-executor-backend is ray.") pg_data = placement_group_table(placement_group) # bundle_idx -> node_id bundle_to_node_ids = pg_data["bundles_to_node_id"] # bundle_idx -> bundle (e.g., {"GPU": 1}) bundles = pg_data["bundles"] # node_id -> List of bundle (e.g., {"GPU": 1}) node_id_to_bundle: dict[str, list[dict[str, float]]] = defaultdict(list) for bundle_idx, node_id in bundle_to_node_ids.items(): node_id_to_bundle[node_id].append(bundles[bundle_idx]) driver_node_id = ray.get_runtime_context().get_node_id() if driver_node_id not in node_id_to_bundle: raise RuntimeError(f"driver node id {driver_node_id} is not included in a placement " f"group {placement_group.id}. Node id -> bundles " f"{node_id_to_bundle}. " "You don't have enough GPUs available in a current node. Check " "`ray status` and `ray list nodes` to see if you have available " "GPUs in a node `{driver_node_id}` before starting an FastVideo engine.") for node_id, bundles in node_id_to_bundle.items(): if len(bundles) < fastvideo_args.tp_size: logger.warning( "tensor_parallel_size=%d " "is bigger than a reserved number of %ss (%d " "%ss) in a node %s. Tensor parallel workers can be " "spread out to 2+ nodes which can degrade the performance " "unless you have fast interconnect across nodes, like " "Infiniband. To resolve this issue, make sure you have more " "than %d GPUs available at each node.", fastvideo_args.tp_size, device_str, len(bundles), device_str, node_id, fastvideo_args.tp_size) def _wait_until_pg_ready(current_placement_group: "PlacementGroup"): """Wait until a placement group is ready. It prints the informative log messages if the placement group is not created within time. """ # Wait until PG is ready - this will block until all # requested resources are available, and will timeout # if they cannot be provisioned. placement_group_specs = current_placement_group.bundle_specs s = time.time() pg_ready_ref = current_placement_group.ready() wait_interval = 10 while time.time() - s < PG_WAIT_TIMEOUT: ready, _ = ray.wait([pg_ready_ref], timeout=wait_interval) if len(ready) > 0: break # Exponential backoff for warning print. wait_interval *= 2 logger.info( "Waiting for creating a placement group of specs for " "%d seconds. specs=%s. Check `ray status` and " "`ray list nodes` to see if you have enough resources," " and make sure the IP addresses used by ray cluster" " are the same as FASTVIDEO_HOST_IP environment variable" " specified in each node if you are running on a multi-node.", int(time.time() - s), placement_group_specs) try: ray.get(pg_ready_ref, timeout=0) except ray.exceptions.GetTimeoutError: raise ValueError("Cannot provide a placement group of " f"{placement_group_specs=} within {PG_WAIT_TIMEOUT} seconds. See " "`ray status` and `ray list nodes` to make sure the cluster has " "enough resources.") from None def initialize_ray_cluster( fastvideo_args: FastVideoArgs, ray_address: str | None = None, ): """Initialize the distributed cluster with Ray. it will connect to the Ray cluster and create a placement group for the workers, which includes the specification of the resources for each distributed worker. Args: parallel_config: The configurations for parallel execution. ray_address: The address of the Ray cluster. If None, uses the default Ray cluster address. """ assert_ray_available() from fastvideo.platforms import current_platform if ray.is_initialized(): logger.info("Ray is already initialized. Skipping Ray initialization.") elif current_platform.is_rocm() or current_platform.is_xpu(): # Try to connect existing ray instance and create a new one if not found try: ray.init("auto") except ConnectionError: logger.warning("No existing RAY instance detected. " "A new instance will be launched with current node resources.") ray.init(address=ray_address, num_gpus=fastvideo_args.num_gpus, runtime_env=fastvideo_args.ray_runtime_env) else: ray.init(address=ray_address, runtime_env=fastvideo_args.ray_runtime_env) device_str = current_platform.ray_device_key if not device_str: raise ValueError(f"current platform {current_platform.device_name} does not " "support ray.") # Create or get the placement group for worker processes current_placement_group = fastvideo_args.ray_placement_group or ray.util.get_current_placement_group() if current_placement_group: logger.info("Using the existing placement group") # We are in a placement group bundles = current_placement_group.bundle_specs # Verify that we can use the placement group. device_bundles = 0 for bundle in bundles: bundle_devices = bundle.get(device_str, 0) if bundle_devices > 1: raise ValueError("Placement group bundle cannot have more than 1 " f"{device_str}.") if bundle_devices: device_bundles += 1 if fastvideo_args.num_gpus > device_bundles: raise ValueError(f"The number of required {device_str}s exceeds the total " f"number of available {device_str}s in the placement group. " f"Required number of devices: {fastvideo_args.num_gpus}. " f"Total number of devices: {device_bundles}.") else: logger.info("No current placement group found. " "Creating a new placement group.") num_devices_in_cluster = ray.cluster_resources().get(device_str, 0) # Log a warning message and delay resource allocation failure response. # Avoid immediate rejection to allow user-initiated placement group # created and wait cluster to be ready if fastvideo_args.num_gpus > num_devices_in_cluster: logger.warning( "The number of required %ss exceeds the total " "number of available %ss in the placement group.", device_str, device_str) # Create a new placement group placement_group_specs: list[dict[str, float]] = ([{device_str: 1.0} for _ in range(fastvideo_args.num_gpus)]) # FastVideo engine is also a worker to execute model with an accelerator, # so it requires to have the device in a current node. Check if # the current node has at least one device. current_ip = get_ip() current_node_id = ray.get_runtime_context().get_node_id() current_node_resource = available_resources_per_node()[current_node_id] if current_node_resource.get(device_str, 0) < 1: raise ValueError(f"Current node has no {device_str} available. " f"{current_node_resource=}. FastVideo engine cannot start without " f"{device_str}. Make sure you have at least 1 {device_str} " f"available in a node {current_node_id=} {current_ip=}.") # This way, at least bundle is required to be created in a current # node. placement_group_specs[0][f"node:{current_ip}"] = 0.001 # By default, Ray packs resources as much as possible. current_placement_group = ray.util.placement_group(placement_group_specs, strategy="PACK") _wait_until_pg_ready(current_placement_group) assert current_placement_group is not None _verify_bundles(current_placement_group, fastvideo_args, device_str) # Set the placement group in the fastvideo args fastvideo_args.ray_placement_group = current_placement_group def is_in_ray_actor(): """Check if we are in a Ray actor.""" try: import ray return (ray.is_initialized() and ray.get_runtime_context().get_actor_id() is not None) except ImportError: return False