init

executor/ray_utils.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+import time
+from collections import defaultdict
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+
+import msgspec
+
+import vllm.platforms
+from vllm.config import ParallelConfig
+from vllm.executor.msgspec_utils import decode_hook, encode_hook
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.sequence import ExecuteModelRequest, IntermediateTensors
+from vllm.utils import get_ip
+from vllm.worker.worker_base import WorkerWrapperBase
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.outputs import ModelRunnerOutput
+
+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 vllm.worker.Worker, allowing Worker to be
+        lazily initialized after Ray sets CUDA_VISIBLE_DEVICES."""
+
+        def __init__(self, *args, **kwargs) -> None:
+            super().__init__(*args, **kwargs)
+            # Since the compiled DAG runs a main execution
+            # in a different thread that calls cuda.set_device.
+            # The flag indicates is set_device is called on
+            # that thread.
+            self.compiled_dag_cuda_device_set = False
+
+            self.input_decoder = msgspec.msgpack.Decoder(ExecuteModelRequest,
+                                                         dec_hook=decode_hook)
+            self.output_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
+
+        def get_node_ip(self) -> str:
+            return get_ip()
+
+        def get_node_and_gpu_ids(self) -> Tuple[str, List[int]]:
+            node_id = ray.get_runtime_context().get_node_id()
+            device_key = vllm.platforms.current_platform.ray_device_key
+            if not device_key:
+                raise RuntimeError("current platform %s does not support ray.",
+                                   vllm.platforms.current_platform.device_name)
+            gpu_ids = ray.get_runtime_context().get_accelerator_ids(
+            )[device_key]
+            return node_id, gpu_ids
+
+        def execute_model_spmd(
+            self, req_or_tuple: Union[bytes,
+                                      Tuple[bytes,
+                                            Optional[IntermediateTensors]]]
+        ) -> bytes:
+            """Execute model in SPMD fashion: used only when SPMD worker and
+            compiled DAG are both enabled.
+
+            Args:
+                req_or_tuple: A request or a tuple containing the
+                    request and intermediate tensors. Intermediate tensors are
+                    None unless if it is provided because it is > 0 pipeline
+                    stage. The request is serialized by msgspec.
+            """
+            if isinstance(req_or_tuple, bytes):
+                serialized_req, intermediate_tensors = req_or_tuple, None
+            else:
+                serialized_req, intermediate_tensors = req_or_tuple
+
+            execute_model_req = self.input_decoder.decode(serialized_req)
+
+            # TODO(swang): This is needed right now because Ray Compiled Graph
+            # executes on a background thread, so we need to reset torch's
+            # current device.
+            if not self.compiled_dag_cuda_device_set:
+                current_platform.set_device(self.worker.device)
+                self.compiled_dag_cuda_device_set = True
+
+            output = self.worker._execute_model_spmd(execute_model_req,
+                                                     intermediate_tensors)
+            # Pipeline model request and output to the next pipeline stage.
+            if isinstance(output, IntermediateTensors):
+                output = serialized_req, output
+            else:
+                output = self.output_encoder.encode(output)
+
+            return output
+
+        def setup_device_if_necessary(self):
+            # TODO(swang): This is needed right now because Ray CG executes
+            # on a background thread, so we need to reset torch's current
+            # device.
+            # We can remove this API after it is fixed in compiled graph.
+            assert self.worker is not None, "Worker is not initialized"
+            if not self.compiled_dag_cuda_device_set:
+                if current_platform.is_tpu():
+                    # Not needed
+                    pass
+                else:
+                    current_platform.set_device(self.worker.device)
+
+                self.compiled_dag_cuda_device_set = True
+
+        def execute_model_ray(
+            self,
+            scheduler_output: Union["SchedulerOutput",
+                                    Tuple["SchedulerOutput",
+                                          "IntermediateTensors"]],
+        ) -> Union["ModelRunnerOutput", Tuple["SchedulerOutput",
+                                              "IntermediateTensors"]]:
+            # This method is used by Ray Compiled Graph to execute the model,
+            # and it needs a special logic of self.setup_device_if_necessary()
+            self.setup_device_if_necessary()
+            assert self.worker is not None, "Worker is not initialized"
+            if isinstance(scheduler_output, tuple):
+                scheduler_output, intermediate_tensors = scheduler_output
+            else:
+                scheduler_output, intermediate_tensors = scheduler_output, None
+            output = self.worker.model_runner.execute_model(
+                scheduler_output, intermediate_tensors)
+            if isinstance(output, IntermediateTensors):
+                output = scheduler_output, output
+            return output
+
+        def override_env_vars(self, vars: Dict[str, str]):
+            os.environ.update(vars)
+
+    ray_import_err = None
+
+except ImportError as e:
+    ray = None  # type: ignore
+    ray_import_err = e
+    RayWorkerWrapper = None  # type: ignore
+
+
+def ray_is_available() -> bool:
+    """Returns True if Ray is available."""
+    return ray is not None
+
+
+def assert_ray_available():
+    """Raise an exception if Ray is not available."""
+    if ray is None:
+        raise ValueError("Failed to import Ray, please install Ray with "
+                         "`pip install ray`.") from ray_import_err
+
+
+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 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 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:
+        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 _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 initialize_ray_cluster(
+    parallel_config: ParallelConfig,
+    ray_address: Optional[str] = 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
+
+    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)
+    else:
+        ray.init(address=ray_address)
+
+    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(
+                    "Placement group bundle cannot have more than 1 "
+                    f"{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 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 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