frozenleaves/vllm
1
0
Fork 0
mirror of https://github.com/vllm-project/vllm.git synced 2025-10-20 23:03:52 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
8dd41d6bcc6680b69819cd6518c44ff3242b73f1
vllm/vllm/executor/ray_distributed_executor.py
Rui Qiao 8dd41d6bcc [Misc] Use envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE (#15831) 
Signed-off-by: Rui Qiao <ruisearch42@gmail.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
2025-04-01 06:07:53 -07:00

701 lines
30 KiB
Python
Raw Blame History

# SPDX-License-Identifier: Apache-2.0
import asyncio
import json
import os
from collections import defaultdict
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
import cloudpickle
import msgspec
import vllm.envs as envs
from vllm.executor.executor_base import (
DistributedExecutorBase) # yapf: disable
from vllm.executor.msgspec_utils import encode_hook
from vllm.executor.ray_utils import (RayWorkerWrapper, initialize_ray_cluster,
ray)
from vllm.logger import init_logger
from vllm.model_executor.layers.sampler import SamplerOutput
from vllm.platforms import current_platform
from vllm.sequence import ExecuteModelRequest
from vllm.utils import (_run_task_with_lock, get_distributed_init_method,
get_ip, get_open_port, make_async)
if ray is not None:
from ray.actor import ActorHandle
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
else:
ActorHandle = None
if TYPE_CHECKING:
from ray.util.placement_group import PlacementGroup
logger = init_logger(__name__)
@dataclass
class RayWorkerMetaData:
"""
Metadata for a Ray worker.
The order of ray worker creation can be random,
and we need to reset the rank after creating all workers.
"""
worker: ActorHandle
created_rank: int
adjusted_rank: int = -1
ip: str = ""
class RayDistributedExecutor(DistributedExecutorBase):
"""Ray-based distributed executor"""
# These env vars are worker-specific, therefore are NOT copied
# from the driver to the workers
WORKER_SPECIFIC_ENV_VARS = {
"VLLM_HOST_IP", "VLLM_HOST_PORT", "LOCAL_RANK", "CUDA_VISIBLE_DEVICES"
}
config_home = envs.VLLM_CONFIG_ROOT
# This file contains a list of env vars that should not be copied
# from the driver to the Ray workers.
non_carry_over_env_vars_file = os.path.join(
config_home, "ray_non_carry_over_env_vars.json")
if os.path.exists(non_carry_over_env_vars_file):
with open(non_carry_over_env_vars_file) as f:
non_carry_over_env_vars = set(json.load(f))
else:
non_carry_over_env_vars = set()
uses_ray: bool = True
def _init_executor(self) -> None:
self.forward_dag: Optional[ray.dag.CompiledDAG] = None
if envs.VLLM_USE_V1:
# V1 uses SPMD worker and compiled DAG
os.environ["VLLM_USE_RAY_SPMD_WORKER"] = "1"
os.environ["VLLM_USE_RAY_COMPILED_DAG"] = "1"
# For TPU, avoid compiling NVIDIA's NCCL
if current_platform.is_tpu():
os.environ["VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE"] = "shm"
# If the env var is set, it uses the Ray's compiled DAG API
# which optimizes the control plane overhead.
# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
# Currently, this requires USE_RAY_SPMD_WORKER=True.
self.use_ray_compiled_dag = envs.VLLM_USE_RAY_COMPILED_DAG
# If the env var is set, then we do not distinguish between the
# "driver worker" vs other workers. Also, the rank 0 worker will
# be executed in a remote Ray worker. Currently this requires
# USE_RAY_COMPILED_DAG=True.
self.use_ray_spmd_worker = envs.VLLM_USE_RAY_SPMD_WORKER
if self.use_ray_compiled_dag:
assert self.use_ray_spmd_worker, (
"VLLM_USE_RAY_COMPILED_DAG=1 requires "
"VLLM_USE_RAY_SPMD_WORKER=1")
if self.use_ray_spmd_worker:
# TODO: Support SPMD worker for non-DAG Ray executor.
assert self.use_ray_compiled_dag, (
"VLLM_USE_RAY_SPMD_WORKER=1 requires "
"VLLM_USE_RAY_COMPILED_DAG=1")
assert self.uses_ray
initialize_ray_cluster(self.parallel_config)
placement_group = self.parallel_config.placement_group
# Disable Ray usage stats collection.
ray_usage = os.environ.get("RAY_USAGE_STATS_ENABLED", "0")
if ray_usage != "1":
os.environ["RAY_USAGE_STATS_ENABLED"] = "0"
# Create the parallel GPU workers.
self._init_workers_ray(placement_group)
self.input_encoder = msgspec.msgpack.Encoder(enc_hook=encode_hook)
self.output_decoder = msgspec.msgpack.Decoder(
Optional[List[SamplerOutput]])
self.use_v1 = envs.VLLM_USE_V1
self.pp_locks: Optional[List[asyncio.Lock]] = None
if not self.use_ray_compiled_dag:
self.driver_exec_method = make_async(
self.driver_worker.execute_method)
def shutdown(self) -> None:
logger.info(
"Shutting down Ray distributed executor. If you see error log "
"from logging.cc regarding SIGTERM received, please ignore because "
"this is the expected termination process in Ray.")
if hasattr(self, "forward_dag") and self.forward_dag is not None:
self.forward_dag.teardown()
import ray
for worker in self.workers:
ray.kill(worker)
self.forward_dag = None
def _configure_ray_workers_use_nsight(self,
ray_remote_kwargs) -> Dict[str, Any]:
# If nsight profiling is enabled, we need to set the profiling
# configuration for the ray workers as runtime env.
runtime_env = ray_remote_kwargs.setdefault("runtime_env", {})
runtime_env.update({
"nsight": {
"t": "cuda,cudnn,cublas",
"o": "'worker_process_%p'",
"cuda-graph-trace": "node",
}
})
return ray_remote_kwargs
# child class could overwrite this to return actual env vars.
def _get_env_vars_to_be_updated(self):
return self._env_vars_for_all_workers
def _init_workers_ray(self, placement_group: "PlacementGroup",
**ray_remote_kwargs):
num_gpus = envs.VLLM_RAY_PER_WORKER_GPUS
# The driver dummy worker does not actually use any resources.
# It holds the resource for the driver worker.
self.driver_dummy_worker: Optional[RayWorkerWrapper] = None
# The remaining workers are the actual ray actors.
self.workers: List[RayWorkerWrapper] = []
# Used in ray compiled DAG: indexed first by PP rank,
# and then TP rank. In other words, the inner list is
# the TP group of workers for a PP rank.
self.pp_tp_workers: List[List[RayWorkerWrapper]] = []
if self.parallel_config.ray_workers_use_nsight:
ray_remote_kwargs = self._configure_ray_workers_use_nsight(
ray_remote_kwargs)
logger.info("use_ray_spmd_worker: %s", self.use_ray_spmd_worker)
# Create the workers.
bundle_indices: List[int]
if envs.VLLM_RAY_BUNDLE_INDICES:
# Use the bundle indices specified by the user.
bundle_indices = list(
map(int, envs.VLLM_RAY_BUNDLE_INDICES.split(",")))
assert len(bundle_indices) == self.parallel_config.world_size, \
("VLLM_RAY_BUNDLE_INDICES must have the same size"
f" as the world size, but got {bundle_indices=} "
f"and {self.parallel_config.world_size=}")
assert len(set(bundle_indices)) == len(bundle_indices), \
("VLLM_RAY_BUNDLE_INDICES cannot have duplicate values,"
f" but got {bundle_indices=}")
else:
# use the first N bundles that have GPU resources.
bundle_indices = []
for bundle_id, bundle in enumerate(placement_group.bundle_specs):
if bundle.get(current_platform.ray_device_key, 0):
bundle_indices.append(bundle_id)
bundle_indices = bundle_indices[:self.parallel_config.world_size]
worker_metadata: List[RayWorkerMetaData] = []
driver_ip = get_ip()
for rank, bundle_id in enumerate(bundle_indices):
scheduling_strategy = PlacementGroupSchedulingStrategy(
placement_group=placement_group,
placement_group_capture_child_tasks=True,
placement_group_bundle_index=bundle_id,
)
if current_platform.ray_device_key == "GPU":
# NV+AMD GPUs, and Intel XPUs
worker = ray.remote(
num_cpus=0,
num_gpus=num_gpus,
scheduling_strategy=scheduling_strategy,
**ray_remote_kwargs,
)(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
rpc_rank=rank)
else:
worker = ray.remote(
num_cpus=0,
num_gpus=0,
resources={current_platform.ray_device_key: num_gpus},
scheduling_strategy=scheduling_strategy,
**ray_remote_kwargs,
)(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
rpc_rank=rank)
worker_metadata.append(
RayWorkerMetaData(worker=worker, created_rank=rank))
worker_ips = ray.get([
each.worker.get_node_ip.remote() # type: ignore[attr-defined]
for each in worker_metadata
])
for each, ip in zip(worker_metadata, worker_ips):
each.ip = ip
if not self.use_ray_spmd_worker:
for i, each in enumerate(worker_metadata):
# find and remove the dummy worker from the list
worker = each.worker
worker_ip = each.ip
if self.driver_dummy_worker is None and worker_ip == driver_ip:
# If the worker is on the same node as the driver, we use it
# as the resource holder for the driver process.
self.driver_dummy_worker = worker
self.driver_worker = RayWorkerWrapper(
vllm_config=self.vllm_config, rpc_rank=0)
worker_metadata.pop(i)
break
logger.debug("workers: %s", worker_metadata)
logger.debug("driver_dummy_worker: %s", self.driver_dummy_worker)
if not self.use_ray_spmd_worker and self.driver_dummy_worker is None:
raise ValueError(
"Ray does not allocate any GPUs on the driver node."
f"Driver IP: {driver_ip}, worker IPs: {worker_ips}."
"Consider adjusting the Ray placement group or running "
"the driver on a GPU node.")
ip_counts: Dict[str, int] = {}
for ip in worker_ips:
ip_counts[ip] = ip_counts.get(ip, 0) + 1
def sort_by_driver_then_worker_ip(item: RayWorkerMetaData):
"""
Sort the workers based on 3 properties:
1. If the worker is on the same node as the driver (vllm engine),
it should be placed first.
2. Then, if the worker is on a node with fewer workers, it should
be placed first.
3. Finally, if the work is on a node with smaller IP address, it
should be placed first.
"""
ip = item.ip
return (0 if ip == driver_ip else 1, ip_counts[ip], ip)
# After sorting, the workers on the same node will be
# close to each other, and the workers on the driver
# node will be placed first.
sorted_worker_metadata = sorted(worker_metadata,
key=sort_by_driver_then_worker_ip)
start_rank = 0 if self.use_ray_spmd_worker else 1
for i, item in enumerate(sorted_worker_metadata):
item.adjusted_rank = i + start_rank
self.workers = [item.worker for item in sorted_worker_metadata]
rerank_mapping = {
item.created_rank: item.adjusted_rank
for item in sorted_worker_metadata
}
self._run_workers("adjust_rank", rerank_mapping)
# Get the set of GPU IDs used on each node.
worker_node_and_gpu_ids = []
for worker in [self.driver_dummy_worker] + self.workers:
if worker is None:
# driver_dummy_worker can be None when using ray spmd worker.
continue
worker_node_and_gpu_ids.append(
ray.get(worker.get_node_and_gpu_ids.remote()) \
) # type: ignore
node_workers = defaultdict(list) # node id -> list of worker ranks
node_gpus = defaultdict(list) # node id -> list of gpu ids
for i, (node_id, gpu_ids) in enumerate(worker_node_and_gpu_ids):
node_workers[node_id].append(i)
# `gpu_ids` can be a list of strings or integers.
# convert them to integers for consistency.
# NOTE: gpu_ids can be larger than 9 (e.g. 16 GPUs),
# string sorting is not sufficient.
# see https://github.com/vllm-project/vllm/issues/5590
gpu_ids = [int(x) for x in gpu_ids]
node_gpus[node_id].extend(gpu_ids)
for node_id, gpu_ids in node_gpus.items():
node_gpus[node_id] = sorted(gpu_ids)
all_ips = set(worker_ips + [driver_ip])
n_ips = len(all_ips)
n_nodes = len(node_workers)
if n_nodes != n_ips:
raise RuntimeError(
f"Every node should have a unique IP address. Got {n_nodes}"
f" nodes with node ids {list(node_workers.keys())} and "
f"{n_ips} unique IP addresses {all_ips}. Please check your"
" network configuration. If you set `VLLM_HOST_IP`"
" environment variable, make sure it is unique for"
" each node.")
# Set environment variables for the driver and workers.
all_args_to_update_environment_variables = [{
current_platform.device_control_env_var:
",".join(map(str, node_gpus[node_id])),
} for (node_id, _) in worker_node_and_gpu_ids]
# Environment variables to copy from driver to workers
env_vars_to_copy = [
v for v in envs.environment_variables
if v not in self.WORKER_SPECIFIC_ENV_VARS
and v not in self.non_carry_over_env_vars
]
env_vars_to_copy.extend(current_platform.additional_env_vars)
# Copy existing env vars to each worker's args
for args in all_args_to_update_environment_variables:
# TODO: refactor platform-specific env vars
for name in env_vars_to_copy:
if name in os.environ:
args[name] = os.environ[name]
logger.info("non_carry_over_env_vars from config: %s",
self.non_carry_over_env_vars)
logger.info(
"Copying the following environment variables to workers: %s",
[v for v in env_vars_to_copy if v in os.environ])
logger.info(
"If certain env vars should NOT be copied to workers, add them to "
"%s file", self.non_carry_over_env_vars_file)
self._env_vars_for_all_workers = (
all_args_to_update_environment_variables)
self._run_workers("update_environment_variables",
self._get_env_vars_to_be_updated())
if len(node_gpus) == 1:
# in single node case, we don't need to get the IP address.
# the loopback address is sufficient
# NOTE: a node may have several IP addresses, one for each
# network interface. `get_ip()` might return any of them,
# while they might not work for communication inside the node
# if the network setup is complicated. Using the loopback address
# solves this issue, as it always works for communication inside
# the node.
driver_ip = "127.0.0.1"
distributed_init_method = get_distributed_init_method(
driver_ip, get_open_port())
# Initialize the actual workers inside worker wrapper.
all_kwargs = []
for rank, (node_id, _) in enumerate(worker_node_and_gpu_ids):
local_rank = node_workers[node_id].index(rank)
kwargs = dict(
vllm_config=self.vllm_config,
local_rank=local_rank,
rank=rank,
distributed_init_method=distributed_init_method,
is_driver_worker=(not self.parallel_config)
or (rank % self.parallel_config.tensor_parallel_size == 0),
)
all_kwargs.append(kwargs)
self._run_workers("init_worker", all_kwargs)
self._run_workers("init_device")
self._run_workers("load_model",
max_concurrent_workers=self.parallel_config.
max_parallel_loading_workers)
if self.use_ray_spmd_worker:
for pp_rank in range(self.parallel_config.pipeline_parallel_size):
self.pp_tp_workers.append([])
for tp_rank in range(
self.parallel_config.tensor_parallel_size):
# PP=2, TP=4
# pp_tp_workers = [[0, 1, 2, 3], [4, 5, 6, 7]]
rank = (pp_rank * self.parallel_config.tensor_parallel_size
) + tp_rank
assert len(self.pp_tp_workers[pp_rank]) == tp_rank
assert pp_rank < len(self.pp_tp_workers)
self.pp_tp_workers[pp_rank].append(self.workers[rank])
# This is the list of workers that are rank 0 of each TP group EXCEPT
# global rank 0. These are the workers that will broadcast to the
# rest of the workers.
self.tp_driver_workers: List[RayWorkerWrapper] = []
# This is the list of workers that are not drivers and not the first
# worker in a TP group. These are the workers that will be
# broadcasted to.
self.non_driver_workers: List[RayWorkerWrapper] = []
# Enforce rank order for correct rank to return final output.
for index, worker in enumerate(self.workers):
# The driver worker is rank 0 and not in self.workers.
rank = index + 1
if rank % self.parallel_config.tensor_parallel_size == 0:
self.tp_driver_workers.append(worker)
else:
self.non_driver_workers.append(worker)
def _driver_execute_model(
self, execute_model_req: Optional[ExecuteModelRequest]
) -> Optional[List[SamplerOutput]]:
"""Run execute_model in the driver worker.
Passing None will cause the driver to stop the model execution
loop running in each of the remote workers.
"""
assert not self.use_ray_spmd_worker, (
"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
return self.driver_worker.execute_method("execute_model",
execute_model_req)
def execute_model(
self,
execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
if not self.use_ray_spmd_worker:
return super().execute_model(execute_model_req)
if self.forward_dag is None:
self.forward_dag = self._compiled_ray_dag(enable_asyncio=False)
if self.use_v1:
serialized_data = execute_model_req
else:
serialized_data = self.input_encoder.encode(execute_model_req)
outputs = ray.get(self.forward_dag.execute(serialized_data))
if self.use_v1:
output = outputs[0]
else:
output = self.output_decoder.decode(outputs[0])
return output
def _run_workers(
self,
method: Union[str, Callable],
*args,
async_run_tensor_parallel_workers_only: bool = False,
max_concurrent_workers: Optional[int] = None,
**kwargs,
) -> Any:
"""Runs the given method on all workers. Can be used in the following
ways:
Args:
- async_run_tensor_parallel_workers_only: If True the method will be
run only in the remote TP workers, not the driver worker.
It will also be run asynchronously and return a list of futures
rather than blocking on the results.
- args/kwargs: All workers share the same args/kwargs
"""
if isinstance(method, str):
sent_method = method
else:
sent_method = cloudpickle.dumps(method)
del method
if self.use_ray_spmd_worker:
assert not async_run_tensor_parallel_workers_only, (
"async_run_tensor_parallel_workers_only is not supported for "
"spmd mode.")
if max_concurrent_workers:
raise NotImplementedError(
"max_concurrent_workers is not supported yet.")
# Start the ray workers first.
ray_workers = self.workers
if async_run_tensor_parallel_workers_only:
ray_workers = self.non_driver_workers
ray_worker_outputs = [
worker.execute_method.remote(sent_method, *args, **kwargs)
for worker in ray_workers
]
if async_run_tensor_parallel_workers_only:
# Just return futures
return ray_worker_outputs
driver_worker_output = []
# In SPMD mode, the driver worker is the same as any other worker,
# so we only explicitly execute on the driver worker if using a
# non-SPMD worker class.
if not self.use_ray_spmd_worker:
# Start the driver worker after all the ray workers.
driver_worker_output = [
self.driver_worker.execute_method(sent_method, *args, **kwargs)
]
# Get the results of the ray workers.
if self.workers:
ray_worker_outputs = ray.get(ray_worker_outputs)
return driver_worker_output + ray_worker_outputs
def _wait_for_tasks_completion(self, parallel_worker_tasks: Any) -> None:
"""Wait for futures returned from _run_workers() with
async_run_remote_workers_only to complete."""
ray.get(parallel_worker_tasks)
def _check_ray_cgraph_installation(self):
import pkg_resources
from packaging import version
required_version = version.parse("2.43.0")
current_version = version.parse(
pkg_resources.get_distribution("ray").version)
if current_version < required_version:
raise ValueError(f"Ray version {required_version} is "
f"required, but found {current_version}")
import importlib.util
cgraph_spec = importlib.util.find_spec(
"ray.experimental.compiled_dag_ref")
if cgraph_spec is None:
raise ValueError("Ray Compiled Graph is not installed. "
"Run `pip install ray[cgraph]` to install it.")
cupy_spec = importlib.util.find_spec("cupy")
if (cupy_spec is None
and envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE == "nccl"):
raise ValueError(
"cupy is not installed but required since "
"VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE is set to 'nccl'. "
"Run `pip install ray[cgraph]` and check cupy installation.")
def _compiled_ray_dag(self, enable_asyncio: bool):
assert self.parallel_config.use_ray
self._check_ray_cgraph_installation()
from ray.dag import InputNode, MultiOutputNode
logger.info("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE = %s",
envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE)
logger.info("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM = %s",
envs.VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
channel_type = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
if channel_type not in ("auto", "nccl", "shm"):
raise ValueError(
"Invalid value for VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: "
f"{channel_type}. Valid values are: 'auto', 'nccl', or 'shm'.")
# Enlarge the default value of "RAY_CGRAPH_get_timeout" to 300 seconds
# (it is 10 seconds by default). This is a Ray environment variable to
# control the timeout of getting result from a compiled graph execution,
# i.e., the distributed execution that includes model forward runs and
# intermediate tensor communications, in the case of vllm.
os.environ.setdefault("RAY_CGRAPH_get_timeout", "300") # noqa: SIM112
logger.info("RAY_CGRAPH_get_timeout is set to %s",
os.environ["RAY_CGRAPH_get_timeout"]) # noqa: SIM112
with InputNode() as input_data:
# Example DAG: PP=2, TP=4
#
# For V0:
# ExecuteModelRequest -> 0 -> (ExecuteModelReq, IntermediateTensors) -> 4 -> SamplerOutput # noqa: E501
# ExecuteModelRequest -> 1 -> (ExecuteModelReq, IntermediateTensors) -> 5 -> SamplerOutput # noqa: E501
# ExecuteModelRequest -> 2 -> (ExecuteModelReq, IntermediateTensors) -> 6 -> SamplerOutput # noqa: E501
# ExecuteModelRequest -> 3 -> (ExecuteModelReq, IntermediateTensors) -> 7 -> SamplerOutput # noqa: E501
#
# For V1:
# SchedulerOutput -> 0 -> (SchedulerOutput, IntermediateTensors) -> 4 -> ModelRunnerOutput # noqa: E501
# SchedulerOutput -> 1 -> (SchedulerOutput, IntermediateTensors) -> 5 -> ModelRunnerOutput # noqa: E501
# SchedulerOutput -> 2 -> (SchedulerOutput, IntermediateTensors) -> 6 -> ModelRunnerOutput # noqa: E501
# SchedulerOutput -> 3 -> (SchedulerOutput, IntermediateTensors) -> 7 -> ModelRunnerOutput # noqa: E501
# All workers in the first TP group will take in the
# ExecuteModelRequest as input.
outputs = [input_data for _ in self.pp_tp_workers[0]]
for pp_rank, tp_group in enumerate(self.pp_tp_workers):
# Each PP worker takes in the output of the previous PP worker,
# and the TP group executes in SPMD fashion.
if self.use_v1:
outputs = [
worker.execute_model_ray.
bind( # type: ignore[attr-defined]
outputs[i]) for i, worker in enumerate(tp_group)
]
else:
outputs = [
worker.execute_model_spmd.
bind( # type: ignore[attr-defined]
outputs[i]) for i, worker in enumerate(tp_group)
]
last_pp_rank = len(self.pp_tp_workers) - 1
if (pp_rank < last_pp_rank and
envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE != "shm"):
# Specify how intermediate tensors should be passed
# between pp stages, no need to specify for the last
# pp stage or when using shared memory (the default).
transport = envs.VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE
outputs = [
output.with_tensor_transport(transport=transport)
for output in outputs
]
forward_dag = MultiOutputNode(outputs)
return forward_dag.experimental_compile(
enable_asyncio=enable_asyncio,
_overlap_gpu_communication=envs.
VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM)
def __del__(self):
self.shutdown()
async def execute_model_async(
self,
execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
if not self.use_ray_spmd_worker:
return await super().execute_model_async(execute_model_req)
if self.forward_dag is None:
self.forward_dag = self._compiled_ray_dag(enable_asyncio=True)
serialized_data = self.input_encoder.encode(execute_model_req)
dag_future = await self.forward_dag.execute_async(serialized_data)
output = await dag_future[0]
return self.output_decoder.decode(output)
async def _driver_execute_model_async(
self,
execute_model_req: Optional[ExecuteModelRequest] = None
) -> List[SamplerOutput]:
assert not self.use_ray_spmd_worker, (
"driver_worker does not exist for VLLM_USE_RAY_SPMD_WORKER=1")
if not self.tp_driver_workers:
return await self.driver_exec_method("execute_model",
execute_model_req)
if self.pp_locks is None:
# This locks each pipeline parallel stage so multiple virtual
# engines can't execute on the same stage at the same time
# We create the locks here to avoid creating them in the constructor
# which uses a different asyncio loop.
self.pp_locks = [
asyncio.Lock()
for _ in range(self.parallel_config.pipeline_parallel_size)
]
tasks = [
asyncio.create_task(
_run_task_with_lock(self.driver_exec_method, self.pp_locks[0],
"execute_model", execute_model_req))
]
for pp_rank, driver_worker in enumerate(self.tp_driver_workers,
start=1):
tasks.append(
asyncio.create_task(
_run_task_with_lock(driver_worker.execute_method.remote,
self.pp_locks[pp_rank],
"execute_model", execute_model_req)))
results = await asyncio.gather(*tasks)
# Only the last PP stage has the final results.
return results[-1]
async def _start_worker_execution_loop(self):
assert not self.use_ray_spmd_worker, (
"worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1")
coros = [
worker.execute_method.remote("start_worker_execution_loop")
for worker in self.non_driver_workers
]
return await asyncio.gather(*coros)
def check_health(self) -> None:
# Assume that the Ray workers are healthy.
# TODO: check the health of the Ray workers
return
Reference in New Issue View Git Blame Copy Permalink