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[core][misc] remove use_dummy driver for _run_workers (#10920)

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Signed-off-by: youkaichao <youkaichao@gmail.com>
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youkaichao 2024-12-07 12:06:08 -08:00 committed by GitHub
parent 1b62745b1d
commit 7be15d9356
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4 changed files with 43 additions and 44 deletions
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27
vllm/executor/ray_gpu_executor.py
View File

@ -188,8 +188,14 @@ class RayGPUExecutor(DistributedGPUExecutor):
self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip) self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip)
# Get the set of GPU IDs used on each node. # Get the set of GPU IDs used on each node.
worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids", worker_node_and_gpu_ids = []
use_dummy_driver=True) 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_workers = defaultdict(list) # node id -> list of worker ranks
node_gpus = defaultdict(list) # node id -> list of gpu ids node_gpus = defaultdict(list) # node id -> list of gpu ids
@ -329,7 +335,6 @@ class RayGPUExecutor(DistributedGPUExecutor):
async_run_tensor_parallel_workers_only: bool = False, async_run_tensor_parallel_workers_only: bool = False,
all_args: Optional[List[Tuple[Any, ...]]] = None, all_args: Optional[List[Tuple[Any, ...]]] = None,
all_kwargs: Optional[List[Dict[str, Any]]] = None, all_kwargs: Optional[List[Dict[str, Any]]] = None,
use_dummy_driver: bool = False,
max_concurrent_workers: Optional[int] = None, max_concurrent_workers: Optional[int] = None,
**kwargs, **kwargs,
) -> Any: ) -> Any:
@ -389,18 +394,10 @@ class RayGPUExecutor(DistributedGPUExecutor):
driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0] driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
# Start the driver worker after all the ray workers. # Start the driver worker after all the ray workers.
if not use_dummy_driver: driver_worker_output = [
driver_worker_output = [ self.driver_worker.execute_method(method, *driver_args,
self.driver_worker.execute_method(method, *driver_args, **driver_kwargs)
**driver_kwargs) ]
]
else:
assert self.driver_dummy_worker is not None
driver_worker_output = [
ray.get(
self.driver_dummy_worker.execute_method.remote(
method, *driver_args, **driver_kwargs))
]
# Get the results of the ray workers. # Get the results of the ray workers.
if self.workers: if self.workers:

28
vllm/executor/ray_hpu_executor.py
View File

@ -163,9 +163,14 @@ class RayHPUExecutor(DistributedGPUExecutor):
# node will be placed first. # node will be placed first.
self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip) self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip)
# Get the set of GPU IDs used on each node. worker_node_and_gpu_ids = []
worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids", for worker in [self.driver_dummy_worker] + self.workers:
use_dummy_driver=True) 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_workers = defaultdict(list) # node id -> list of worker ranks
node_gpus = defaultdict(list) # node id -> list of gpu ids node_gpus = defaultdict(list) # node id -> list of gpu ids
@ -296,7 +301,6 @@ class RayHPUExecutor(DistributedGPUExecutor):
async_run_tensor_parallel_workers_only: bool = False, async_run_tensor_parallel_workers_only: bool = False,
all_args: Optional[List[Tuple[Any, ...]]] = None, all_args: Optional[List[Tuple[Any, ...]]] = None,
all_kwargs: Optional[List[Dict[str, Any]]] = None, all_kwargs: Optional[List[Dict[str, Any]]] = None,
use_dummy_driver: bool = False,
max_concurrent_workers: Optional[int] = None, max_concurrent_workers: Optional[int] = None,
**kwargs, **kwargs,
) -> Any: ) -> Any:
@ -356,18 +360,10 @@ class RayHPUExecutor(DistributedGPUExecutor):
driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0] driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
# Start the driver worker after all the ray workers. # Start the driver worker after all the ray workers.
if not use_dummy_driver: driver_worker_output = [
driver_worker_output = [ self.driver_worker.execute_method(method, *driver_args,
self.driver_worker.execute_method(method, *driver_args, **driver_kwargs)
**driver_kwargs) ]
]
else:
assert self.driver_dummy_worker is not None
driver_worker_output = [
ray.get(
self.driver_dummy_worker.execute_method.remote(
method, *driver_args, **driver_kwargs))
]
# Get the results of the ray workers. # Get the results of the ray workers.
if self.workers: if self.workers:

21
vllm/executor/ray_tpu_executor.py
View File

@ -137,8 +137,14 @@ class RayTPUExecutor(TPUExecutor):
self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip) self.workers = sorted(self.workers, key=sort_by_driver_then_worker_ip)
# Get the set of TPU IDs used on each node. # Get the set of TPU IDs used on each node.
worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids", worker_node_and_gpu_ids = []
use_dummy_driver=True) 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_workers = defaultdict(list)
for i, (node_id, _) in enumerate(worker_node_and_gpu_ids): for i, (node_id, _) in enumerate(worker_node_and_gpu_ids):
@ -199,7 +205,6 @@ class RayTPUExecutor(TPUExecutor):
async_run_remote_workers_only: bool = False, async_run_remote_workers_only: bool = False,
all_args: Optional[List[Tuple[Any, ...]]] = None, all_args: Optional[List[Tuple[Any, ...]]] = None,
all_kwargs: Optional[List[Dict[str, Any]]] = None, all_kwargs: Optional[List[Dict[str, Any]]] = None,
use_dummy_driver: bool = False,
max_concurrent_workers: Optional[int] = None, max_concurrent_workers: Optional[int] = None,
use_ray_compiled_dag: bool = False, use_ray_compiled_dag: bool = False,
**kwargs, **kwargs,
@ -241,14 +246,8 @@ class RayTPUExecutor(TPUExecutor):
driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0] driver_kwargs = kwargs if all_kwargs is None else all_kwargs[0]
# Start the driver worker after all the ray workers. # Start the driver worker after all the ray workers.
if not use_dummy_driver: driver_worker_output = self.driver_worker.execute_method(
driver_worker_output = self.driver_worker.execute_method( method, *driver_args, **driver_kwargs)
method, *driver_args, **driver_kwargs)
else:
assert self.driver_dummy_worker is not None
driver_worker_output = ray.get(
self.driver_dummy_worker.execute_method.remote(
method, *driver_args, **driver_kwargs))
# Get the results of the ray workers. # Get the results of the ray workers.
if self.workers: if self.workers:
ray_worker_outputs = ray.get(ray_worker_outputs) ray_worker_outputs = ray.get(ray_worker_outputs)

11
vllm/executor/ray_xpu_executor.py
View File

@ -1,6 +1,8 @@
import asyncio import asyncio
from typing import List, Optional from typing import List, Optional
import ray
import vllm.envs as envs import vllm.envs as envs
from vllm.executor.ray_gpu_executor import RayGPUExecutor, RayGPUExecutorAsync from vllm.executor.ray_gpu_executor import RayGPUExecutor, RayGPUExecutorAsync
from vllm.executor.xpu_executor import XPUExecutor from vllm.executor.xpu_executor import XPUExecutor
@ -14,8 +16,13 @@ class RayXPUExecutor(RayGPUExecutor, XPUExecutor):
def _get_env_vars_to_be_updated(self): def _get_env_vars_to_be_updated(self):
# Get the set of GPU IDs used on each node. # Get the set of GPU IDs used on each node.
worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids", worker_node_and_gpu_ids = []
use_dummy_driver=True) 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
# Set environment variables for the driver and workers. # Set environment variables for the driver and workers.
all_args_to_update_environment_variables = [({ all_args_to_update_environment_variables = [({