vllm.v1.executor.ray_utils ¶

def _verify_bundles(
    placement_group: "PlacementGroup", parallel_config: ParallelConfig, 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 vLLM engine."
        )

    for node_id, bundles in node_id_to_bundle.items():
        if len(bundles) < parallel_config.tensor_parallel_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.",
                parallel_config.tensor_parallel_size,
                device_str,
                len(bundles),
                device_str,
                node_id,
                parallel_config.tensor_parallel_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 time out
    # 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 VLLM_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:
        # Provide more helpful error message when GPU count is exceeded
        total_gpu_required = sum(spec.get("GPU", 0) for spec in placement_group_specs)
        # If more than one GPU is required for the placement group, provide a
        # more specific error message.
        # We use >1 here because multi-GPU (tensor parallel) jobs are more
        # likely to fail due to insufficient cluster resources, and users may
        # need to adjust tensor_parallel_size to fit available GPUs.
        if total_gpu_required > 1:
            raise ValueError(
                f"Cannot provide a placement group requiring "
                f"{total_gpu_required} GPUs "
                f"(placement_group_specs={placement_group_specs}) within "
                f"{PG_WAIT_TIMEOUT} seconds.\n"
                f"Tensor parallel size may exceed available GPUs in your "
                f"cluster. Check resources with `ray status` and "
                f"`ray list nodes`.\n"
                f"If running on K8s with limited GPUs, consider reducing "
                f"--tensor-parallel-size to match available GPU resources."
            ) from None
        else:
            raise ValueError(
                "Cannot provide a placement group of "
                f"{placement_group_specs=} within "
                f"{PG_WAIT_TIMEOUT} seconds. See "
                "`ray status` and `ray list nodes` to make sure the cluster "
                "has enough resources."
            ) from None

def _wait_until_pg_removed(current_placement_group: "PlacementGroup"):
    ray.util.remove_placement_group(current_placement_group)
    s = time.time()
    wait_interval = 10
    while time.time() - s < PG_WAIT_TIMEOUT:
        pg = ray.util.get_current_placement_group()
        if pg is None:
            break

        # Exponential backoff for warning print.
        wait_interval *= 2
        logger.info(
            "Waiting for removing a placement group of specs for %d seconds.",
            int(time.time() - s),
        )
        time.sleep(wait_interval)

def assert_ray_available():
    """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 `pip install ray`."
        )

def get_num_nodes_in_placement_group() -> int:
    pg_table = ray.util.placement_group_table()
    current_pg = ray.util.get_current_placement_group()
    num_nodes = 0

    if current_pg:
        nodes_in_pg = set()
        for pg_key, pg in pg_table.items():
            if pg_key == current_pg.id.hex():
                for _, node in pg["bundles_to_node_id"].items():
                    nodes_in_pg.add(node)
        num_nodes = len(nodes_in_pg)

    return num_nodes

def get_num_tpu_nodes() -> int:
    from ray._private.accelerators import TPUAcceleratorManager

    cluster_resources = ray.cluster_resources()
    total_tpus = int(cluster_resources["TPU"])
    tpus_per_node = TPUAcceleratorManager.get_current_node_num_accelerators()
    assert total_tpus % tpus_per_node == 0
    return total_tpus // tpus_per_node

def initialize_ray_cluster(
    parallel_config: ParallelConfig,
    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 vllm.platforms import current_platform

    # Prevalidate GPU requirements before Ray processing
    if current_platform.is_cuda() and parallel_config.world_size > 1:
        from vllm.utils.torch_utils import cuda_device_count_stateless

        available_gpus = cuda_device_count_stateless()
        if parallel_config.world_size > available_gpus:
            logger.warning(
                "Tensor parallel size (%d) exceeds available GPUs (%d). "
                "This may result in Ray placement group allocation failures. "
                "Consider reducing tensor_parallel_size to %d or less, "
                "or ensure your Ray cluster has %d GPUs available.",
                parallel_config.world_size,
                available_gpus,
                available_gpus,
                parallel_config.world_size,
            )

    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=parallel_config.world_size,
                runtime_env=parallel_config.ray_runtime_env,
            )
    else:
        ray.init(address=ray_address, runtime_env=parallel_config.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
    if parallel_config.placement_group:
        current_placement_group = parallel_config.placement_group
    else:
        current_placement_group = 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(
                    f"Placement group bundle cannot have more than 1 {device_str}."
                )
            if bundle_devices:
                device_bundles += 1
        if parallel_config.world_size > 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: {parallel_config.world_size}. "
                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 parallel_config.world_size > 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(parallel_config.world_size)
        ]

        # vLLM 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=}. vLLM 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, parallel_config, device_str)
    # Set the placement group in the parallel config
    parallel_config.placement_group = current_placement_group

def ray_is_available() -> bool:
    """Returns True if Ray is available."""
    return ray is not None