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[Core] Drop the Ray driver-node GPU pin left over from the legacy executor (#61713)

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Signed-off-by: Woosuk Kwon <woosuk@inferact.ai>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
Woosuk Kwon
2026-10-15 16:00:18 -07:00
committed by GitHub
co-authored by Claude Opus 5.5
parent 3f1e57225a
commit 4a89316efa
11 changed files with 7 additions and 160 deletions
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.buildkite/test_areas/distributed.yaml
+1 -1
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@@ -640,7 +640,7 @@ steps:
- tests/distributed/
- examples/features/data_parallel/data_parallel_offline.py
commands:
- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
- ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
- label: ":nvidia: (L4) Pipeline + Context Parallelism"
key: pipeline-context-parallelism-4-gpus
requirements/test/cpu.txt
-5
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@@ -157,8 +157,6 @@ cryptography==50.0.1
# azure-storage-blob
# msal
# pyjwt
cupy-cuda12x==13.6.0
# via ray
cycler==0.12.1
# via matplotlib
datamodel-code-generator==0.79.0
@@ -239,8 +237,6 @@ fastavro==1.12.2
# via cohere
fastparquet==2024.11.0
# via genai-perf
fastrlock==0.8.2
# via cupy-cuda12x
fastsafetensors==0.3.3
# via -r requirements/test/cuda.in
filelock==4.0.7
@@ -569,7 +565,6 @@ numpy==2.2.6
# albumentations
# bm25s
# contourpy
# cupy-cuda12x
# datasets
# decord
# einx
requirements/test/cuda.in
+1 -1
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@@ -22,7 +22,7 @@ vector_quantize_pytorch # required for minicpmo_26 test
vocos # required for minicpmo_26 test
peft>=0.20.0 # required for phi-4-mm test
pqdm
ray[cgraph,default]>=2.56.0,<2.57 # Ray Compiled Graph, required by pipeline parallelism tests; sharded-RDT weight transfer needs Ray Direct Transport (>=2.56)
ray[default]>=2.56.0,<2.57 # sharded-RDT weight transfer needs Ray Direct Transport (>=2.56)
sentence-transformers>=5.2.0 # required for embedding tests
soundfile # required for audio tests
jiwer # required for audio tests
requirements/test/cuda.txt
-5
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@@ -170,8 +170,6 @@ cuda-pathfinder==1.3.3
# via cuda-bindings
cuda-toolkit==13.0.3.0
# via torch
cupy-cuda12x==13.6.0
# via ray
cycler==0.12.1
# via matplotlib
datamodel-code-generator==0.79.0
@@ -255,8 +253,6 @@ fastavro==1.12.2
# via cohere
fastparquet==2024.11.0
# via genai-perf
fastrlock==0.8.2
# via cupy-cuda12x
fastsafetensors==0.3.3
# via
# -c requirements/cuda.txt
@@ -604,7 +600,6 @@ numpy==2.2.6
# albumentations
# bm25s
# contourpy
# cupy-cuda12x
# datasets
# decord
# einx
requirements/test/nightly-torch.txt
+1 -1
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@@ -16,7 +16,7 @@ librosa # required for audio tests
vocos # required for minicpmo_26 test
peft
pqdm
ray[cgraph,default]>=2.56.0,<2.57 # Ray Compiled Graph, required by pipeline parallelism tests; sharded-RDT weight transfer needs Ray Direct Transport (>=2.56)
ray[default]>=2.56.0,<2.57 # sharded-RDT weight transfer needs Ray Direct Transport (>=2.56)
sentence-transformers>=5.2.0 # required for embedding tests
soundfile # required for audio tests
jiwer # required for audio tests
requirements/test/rocm.in
+1 -1
View File
@@ -21,7 +21,7 @@ vector_quantize_pytorch # required for minicpmo_26 test
vocos # required for minicpmo_26 test
peft>=0.20.0 # required for phi-4-mm test
pqdm
ray[cgraph,default]==2.55.1 # Includes worker startup getenv/setenv race fix
ray[default]==2.55.1 # Includes worker startup getenv/setenv race fix
sentence-transformers>=5.2.0 # required for embedding tests
soundfile # required for audio tests
jiwer # required for audio tests
requirements/test/rocm.txt
-2
View File
@@ -606,7 +606,6 @@ numpy==2.2.6
# bitsandbytes
# bm25s
# contourpy
# cupy-cuda12x
# datasets
# decord
# einx
@@ -1439,7 +1438,6 @@ zipp==3.23.0
# cuda-bindings
# cuda-pathfinder
# cuda-toolkit
# cupy-cuda12x
# nvidia-cublas
# nvidia-cuda-cupti
# nvidia-cuda-nvrtc
tests/basic_correctness/models/test_basic_correctness.py
-9
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@@ -216,15 +216,6 @@ def test_models_distributed(
pytest.skip(f"Skip test for {TARGET_TEST_SUITE}")
with monkeypatch.context() as monkeypatch_context:
if (
model == "meta-llama/Llama-3.2-1B-Instruct"
and distributed_executor_backend == "ray"
and attention_backend == ""
and TARGET_TEST_SUITE == "L4"
and enable_prompt_embeds
): # noqa
pytest.skip("enable_prompt_embeds does not work with ray compiled dag.")
for k, v in extra_env.items():
monkeypatch_context.setenv(k, v)
tests/distributed/test_multi_node_assignment.py
-79
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@@ -1,79 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Make sure ray assigns GPU workers to the correct node.
Run:
```sh
cd $VLLM_PATH/tests
pytest distributed/test_multi_node_assignment.py
```
"""
import os
import time
import pytest
import ray
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from vllm import initialize_ray_cluster
from vllm.config import ParallelConfig
from vllm.utils.network_utils import get_ip
from vllm.v1.executor.ray_utils import available_resources_per_node
VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1"
@pytest.mark.skipif(
not VLLM_MULTI_NODE, reason="Need at least 2 nodes to run the test."
)
def test_multi_node_assignment() -> None:
# NOTE: important to keep this class definition here
# to let ray use cloudpickle to serialize it.
class Actor:
def get_ip(self):
return get_ip()
for _ in range(10):
config = ParallelConfig(1, 2)
initialize_ray_cluster(config)
current_ip = get_ip()
workers = []
for bundle_id, bundle in enumerate(config.placement_group.bundle_specs):
if not bundle.get("GPU", 0):
continue
scheduling_strategy = PlacementGroupSchedulingStrategy(
placement_group=config.placement_group,
placement_group_capture_child_tasks=True,
placement_group_bundle_index=bundle_id,
)
worker = ray.remote(
num_cpus=0,
num_gpus=1,
scheduling_strategy=scheduling_strategy,
)(Actor).remote()
worker_ip = ray.get(worker.get_ip.remote())
assert worker_ip == current_ip
workers.append(worker)
for worker in workers:
ray.kill(worker)
ray.util.remove_placement_group(config.placement_group)
gpus_needed = len(workers)
deadline = time.monotonic() + 60
node_id = ray.get_runtime_context().get_node_id()
while True:
free_gpus = available_resources_per_node().get(node_id, {}).get("GPU", 0)
if free_gpus >= gpus_needed:
break
remaining = deadline - time.monotonic()
if remaining <= 0:
raise TimeoutError("placement group resources were not released")
time.sleep(min(1, remaining))
vllm/v1/executor/ray_executor_v2.py
+1 -1
View File
@@ -292,7 +292,7 @@ class RayExecutorV2(MultiprocExecutor):
# Step 1: Initialize Ray cluster and retrieve placement group
if ray is None:
raise ImportError("Using Ray backend requires installation of ray.")
initialize_ray_cluster(self.parallel_config, require_gpu_on_driver=False)
initialize_ray_cluster(self.parallel_config)
placement_group = self.parallel_config.placement_group
tp_size, pp_size, pcp_size = self._get_parallel_sizes()
vllm/v1/executor/ray_utils.py
+2 -55
View File
@@ -8,7 +8,6 @@ from collections import defaultdict
from vllm.config import ParallelConfig
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils.network_utils import get_ip
logger = init_logger(__name__)
PG_WAIT_TIMEOUT = 1800
@@ -30,14 +29,6 @@ try:
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
ray_import_err = None
except ImportError as e:
@@ -65,15 +56,8 @@ def _verify_bundles(
placement_group: "PlacementGroup",
parallel_config: ParallelConfig,
device_str: str,
require_gpu_on_driver: bool = True,
):
"""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
(only when require_gpu_on_driver is True).
"""
"""Warn if all tensor parallel workers cannot fit in a single node."""
assert ray.is_initialized(), (
"Ray is not initialized although distributed-executor-backend is ray."
)
@@ -87,17 +71,6 @@ def _verify_bundles(
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 require_gpu_on_driver and 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:
@@ -293,7 +266,6 @@ def _wait_until_pg_ready(current_placement_group: "PlacementGroup"):
def initialize_ray_cluster(
parallel_config: ParallelConfig,
ray_address: str | None = None,
require_gpu_on_driver: bool = True,
):
"""Initialize the distributed cluster with Ray.
@@ -305,10 +277,6 @@ def initialize_ray_cluster(
parallel_config: The configurations for parallel execution.
ray_address: The address of the Ray cluster. If None, uses
the default Ray cluster address.
require_gpu_on_driver: If True (default), require at least one GPU
on the current (driver) node and pin the first PG bundle to it.
Set to False for executors like RayExecutorV2 where all GPU work
is delegated to remote Ray actors.
"""
assert_ray_available()
@@ -404,25 +372,6 @@ def initialize_ray_cluster(
{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 require_gpu_on_driver:
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 "
f"without {device_str}. Make sure you have at least 1 "
f"{device_str} available in a node "
f"{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"
@@ -430,8 +379,6 @@ def initialize_ray_cluster(
_wait_until_pg_ready(current_placement_group)
assert current_placement_group is not None
_verify_bundles(
current_placement_group, parallel_config, device_str, require_gpu_on_driver
)
_verify_bundles(current_placement_group, parallel_config, device_str)
# Set the placement group in the parallel config
parallel_config.placement_group = current_placement_group