Skip to content

[None][chore] Add placement test for ray executor #9122

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
hchings merged 1 commit into from
Nov 15, 2025
Merged

[None][chore] Add placement test for ray executor #9122

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
61 changes: 58 additions & 3 deletions tests/unittest/_torch/ray_orchestrator/multi_gpu/test_executor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,10 +1,14 @@
import os

import pytest
import ray
from ray.util.placement_group import (PlacementGroupSchedulingStrategy,
placement_group, remove_placement_group)
from utils.llm_data import llm_models_root

from tensorrt_llm import LLM
from tensorrt_llm._torch.utils import get_device_uuid
from tensorrt_llm.llmapi import KvCacheConfig


class DummyWorkerExtension:
Expand All @@ -22,17 +26,68 @@ def test_worker_extension():
assert result[0] == "SUCCESS"


@pytest.mark.gpu4
def test_bundle_indices(monkeypatch):
"""Placement via bundle indices"""

monkeypatch.setenv("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES", "1")
monkeypatch.setenv("TLLM_RAY_USE_RPC", "1")

pg = None
try:
ray.init()
pg = placement_group([{"GPU": 1, "CPU": 1}] * 4)
ray.get(pg.ready())
print(f"Placement group ready with bundles {pg.bundle_specs}")

bundle_indices = [2, 3]
runtime_env = {
"env_vars": {
"TRTLLM_RAY_PER_WORKER_GPUS": "0.8",
"TRTLLM_RAY_BUNDLE_INDICES": ",".join(map(str, bundle_indices))
}
}

llm = ray.remote(
num_cpus=0,
num_gpus=0,
runtime_env=runtime_env,
scheduling_strategy=PlacementGroupSchedulingStrategy(
placement_group=pg,
placement_group_capture_child_tasks=True,
),
)(LLM).remote(
model=os.path.join(llm_models_root(), "llama-models-v2",
"TinyLlama-1.1B-Chat-v1.0"),
kv_cache_config=KvCacheConfig(free_gpu_memory_fraction=0.1),
tensor_parallel_size=2,
orchestrator_type="ray",
)

inference_actor_uuids = ray.get(
llm._collective_rpc.remote("report_device_id"))

expected_uuids = [get_device_uuid(idx) for idx in bundle_indices]

assert sorted(inference_actor_uuids) == sorted(expected_uuids), \
f"Workers not placed on expected GPUs. Expected UUIDs: {expected_uuids}, Got: {inference_actor_uuids}"

finally:
if pg is not None:
remove_placement_group(pg)
ray.shutdown()


@pytest.mark.gpu2
def test_cuda_visible_device():
def test_cuda_visible_device(monkeypatch):
"""Placement via cuda_visible_device"""
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "1")

llm = LLM(model=llm_models_root() /
"llama-models-v2/TinyLlama-1.1B-Chat-v1.0",
orchestrator_type="ray")

infer_actor_uuids = llm._collective_rpc("report_device_id")

del os.environ["CUDA_VISIBLE_DEVICES"]
assert infer_actor_uuids[0] == get_device_uuid(1)
print(f"{infer_actor_uuids=}")
Loading