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[misc] Rename Ray ADAG to Compiled Graph (#13928)

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Rui Qiao 2025-02-26 20:03:28 -08:00 committed by GitHub
parent ca377cf1b9
commit c9944acbf9
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6 changed files with 22 additions and 20 deletions
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2
tests/basic_correctness/test_basic_correctness.py
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@ -117,7 +117,7 @@ def test_models_distributed(
pytest.skip(f"Skip test for {test_suite}")
if model == "meta-llama/Llama-3.2-1B-Instruct" and distributed_executor_backend == "ray" and attention_backend == "" and test_suite == "L4": # noqa
# test ray adag
# test Ray Compiled Graph
os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"

2
tests/basic_correctness/test_chunked_prefill.py
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@ -93,7 +93,7 @@ def test_models_distributed(
if (model == "meta-llama/Llama-3.2-1B-Instruct"
and distributed_executor_backend == "ray"):
# test ray adag
# test Ray Compiled Graph
os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"

11
tests/distributed/test_pipeline_parallel.py
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@ -324,8 +324,8 @@ def _compare_tp(
specific_case = tp_size == 2 and pp_size == 2 and chunked_prefill
if distributed_backend == "ray" and (vllm_major_version == "1"
or specific_case):
# For V1, test Ray ADAG for all the tests
# For V0, test Ray ADAG for a subset of the tests
# For V1, test Ray Compiled Graph for all the tests
# For V0, test Ray Compiled Graph for a subset of the tests
pp_env = {
"VLLM_USE_V1": vllm_major_version,
"VLLM_USE_RAY_COMPILED_DAG": "1",
@ -333,7 +333,7 @@ def _compare_tp(
"VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": "1",
}
# Temporary. Currently when zeromq + SPMD is used, it does not properly
# terminate because of aDAG issue.
# terminate because of a Ray Compiled Graph issue.
common_args.append("--disable-frontend-multiprocessing")
else:
pp_env = None
@ -367,8 +367,9 @@ def _compare_tp(
if pp_env is None:
raise
else:
# Ray ADAG tests are flaky, so we don't want to fail the test
logger.exception("Ray ADAG tests failed")
# Ray Compiled Graph tests are flaky,
# so we don't want to fail the test
logger.exception("Ray Compiled Graph tests failed")
@pytest.mark.parametrize(

9
vllm/envs.py
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@ -371,21 +371,22 @@ environment_variables: Dict[str, Callable[[], Any]] = {
"VLLM_USE_RAY_SPMD_WORKER":
lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
# If the env var is set, it uses the Ray's compiled DAG API
# which optimizes the control plane overhead.
# If the env var is set, it uses the Ray's Compiled Graph
# (previously known as ADAG) API which optimizes the
# control plane overhead.
# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
"VLLM_USE_RAY_COMPILED_DAG":
lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),
# If the env var is set, it uses NCCL for communication in
# Ray's compiled DAG. This flag is ignored if
# Ray's Compiled Graph. This flag is ignored if
# VLLM_USE_RAY_COMPILED_DAG is not set.
"VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL":
lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL", "1"))
),
# If the env var is set, it enables GPU communication overlap
# (experimental feature) in Ray's compiled DAG. This flag is ignored if
# (experimental feature) in Ray's Compiled Graph. This flag is ignored if
# VLLM_USE_RAY_COMPILED_DAG is not set.
"VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))

10
vllm/executor/ray_distributed_executor.py
View File

@ -491,7 +491,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
async_run_remote_workers_only to complete."""
ray.get(parallel_worker_tasks)
def _check_ray_adag_installation(self):
def _check_ray_cgraph_installation(self):
import pkg_resources
from packaging import version
@ -503,10 +503,10 @@ class RayDistributedExecutor(DistributedExecutorBase):
f"required, but found {current_version}")
import importlib.util
adag_spec = importlib.util.find_spec(
cgraph_spec = importlib.util.find_spec(
"ray.experimental.compiled_dag_ref")
if adag_spec is None:
raise ValueError("Ray accelerated DAG is not installed. "
if cgraph_spec is None:
raise ValueError("Ray Compiled Graph is not installed. "
"Run `pip install ray[adag]` to install it.")
cupy_spec = importlib.util.find_spec("cupy")
@ -518,7 +518,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
def _compiled_ray_dag(self, enable_asyncio: bool):
assert self.parallel_config.use_ray
self._check_ray_adag_installation()
self._check_ray_cgraph_installation()
from ray.dag import InputNode, MultiOutputNode
from ray.experimental.channel.torch_tensor_type import TorchTensorType

8
vllm/executor/ray_utils.py
View File

@ -83,9 +83,9 @@ try:
execute_model_req = self.input_decoder.decode(serialized_req)
# TODO(swang): This is needed right now because Ray aDAG executes
# on a background thread, so we need to reset torch's current
# device.
# 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.
import torch
if not self.compiled_dag_cuda_device_set:
torch.cuda.set_device(self.worker.device)
@ -119,7 +119,7 @@ try:
"IntermediateTensors"]],
) -> Union["ModelRunnerOutput", Tuple["SchedulerOutput",
"IntermediateTensors"]]:
# this method is used to compile ray CG,
# 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"