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[CI] Fix mypy for vllm/executor (#26845)

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2025-10-15 04:23:33 -04:00
committed by GitHub
parent f3c378ffa7
commit 71557a5f7c
4 changed files with 23 additions and 11 deletions
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2
tools/pre_commit/mypy.py
View File

@@ -28,6 +28,7 @@ FILES = [
"vllm/assets",
"vllm/distributed",
"vllm/entrypoints",
"vllm/executor",
"vllm/inputs",
"vllm/logging_utils",
"vllm/multimodal",
@@ -44,7 +45,6 @@ SEPARATE_GROUPS = [
"vllm/attention",
"vllm/compilation",
"vllm/engine",
"vllm/executor",
"vllm/inputs",
"vllm/lora",
"vllm/model_executor",

7
vllm/executor/executor_base.py
View File

@@ -18,7 +18,7 @@ from vllm.lora.request import LoRARequest
from vllm.sequence import ExecuteModelRequest
from vllm.tasks import SupportedTask
from vllm.utils import make_async
from vllm.v1.outputs import PoolerOutput, SamplerOutput
from vllm.v1.outputs import SamplerOutput
from vllm.v1.worker.worker_base import WorkerBase
logger = init_logger(__name__)
@@ -54,7 +54,7 @@ class ExecutorBase(ABC):
self._init_executor()
self.is_sleeping = False
self.sleeping_tags: set[str] = set()
self.kv_output_aggregator = None
self.kv_output_aggregator: KVOutputAggregator | None = None
@abstractmethod
def _init_executor(self) -> None:
@@ -143,8 +143,9 @@ class ExecutorBase(ABC):
def execute_model(
self, execute_model_req: ExecuteModelRequest
) -> list[SamplerOutput | PoolerOutput] | None:
) -> list[SamplerOutput]:
output = self.collective_rpc("execute_model", args=(execute_model_req,))
assert output[0] is not None
return output[0]
def stop_remote_worker_execution_loop(self) -> None:

18
vllm/executor/ray_distributed_executor.py
View File

@@ -217,7 +217,9 @@ class RayDistributedExecutor(DistributedExecutorBase):
num_gpus=num_gpus,
scheduling_strategy=scheduling_strategy,
**ray_remote_kwargs,
)(RayWorkerWrapper).remote(vllm_config=self.vllm_config, rpc_rank=rank)
)(RayWorkerWrapper).remote( # type: ignore[attr-defined]
vllm_config=self.vllm_config, rpc_rank=rank
)
else:
worker = ray.remote(
num_cpus=0,
@@ -225,7 +227,9 @@ class RayDistributedExecutor(DistributedExecutorBase):
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)
)(RayWorkerWrapper).remote( # type: ignore[attr-defined]
vllm_config=self.vllm_config, rpc_rank=rank
)
worker_metadata.append(RayWorkerMetaData(worker=worker, created_rank=rank))
worker_ips = ray.get(
@@ -303,7 +307,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
continue
worker_node_and_gpu_ids.append(
ray.get(worker.get_node_and_gpu_ids.remote())
) # type: ignore
) # type: ignore[attr-defined]
node_workers = defaultdict(list) # node id -> list of worker ranks
node_gpus = defaultdict(list) # node id -> list of gpu ids
@@ -495,7 +499,9 @@ class RayDistributedExecutor(DistributedExecutorBase):
if async_run_tensor_parallel_workers_only:
ray_workers = self.non_driver_workers
ray_worker_outputs = [
worker.execute_method.remote(sent_method, *args, **kwargs)
worker.execute_method.remote( # type: ignore[attr-defined]
sent_method, *args, **kwargs
)
for worker in ray_workers
]
@@ -715,7 +721,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
tasks.append(
asyncio.create_task(
_run_task_with_lock(
driver_worker.execute_method.remote,
driver_worker.execute_method.remote, # type: ignore[attr-defined]
self.pp_locks[pp_rank],
"execute_model",
execute_model_req,
@@ -733,7 +739,7 @@ class RayDistributedExecutor(DistributedExecutorBase):
"worker loop is disabled for VLLM_USE_RAY_SPMD_WORKER=1"
)
coros = [
worker.execute_method.remote("start_worker_execution_loop")
worker.execute_method.remote("start_worker_execution_loop") # type: ignore[attr-defined]
for worker in self.non_driver_workers
]
return await asyncio.gather(*coros)

7
vllm/executor/ray_utils.py
View File

@@ -90,14 +90,17 @@ try:
execute_model_req = self.input_decoder.decode(serialized_req)
assert self.worker is not None, "Worker is not initialized"
# 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.
if not self.compiled_dag_cuda_device_set:
assert self.worker.device is not None
current_platform.set_device(self.worker.device)
self.compiled_dag_cuda_device_set = True
output = self.worker._execute_model_spmd(
output = self.worker._execute_model_spmd( # type: ignore[attr-defined]
execute_model_req, intermediate_tensors
)
# Pipeline model request and output to the next pipeline stage.
@@ -119,6 +122,7 @@ try:
# Not needed
pass
else:
assert self.worker.device is not None
current_platform.set_device(self.worker.device)
self.compiled_dag_cuda_device_set = True
@@ -139,6 +143,7 @@ try:
scheduler_output, intermediate_tensors = scheduler_output
else:
scheduler_output, intermediate_tensors = scheduler_output, None
assert self.worker.model_runner is not None
output = self.worker.model_runner.execute_model(
scheduler_output, intermediate_tensors
)