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[BugFix] Async scheduling: handle model forward errors more cleanly #31611

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Merged
vllm-bot merged 1 commit into from
Jan 4, 2026
Merged

[BugFix] Async scheduling: handle model forward errors more cleanly #31611

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25 changes: 9 additions & 16 deletions vllm/v1/engine/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -178,7 +178,7 @@ def __init__(
# to eliminate pipeline bubbles.
self.batch_queue_size = self.model_executor.max_concurrent_batches
self.batch_queue: (
deque[tuple[Future[ModelRunnerOutput], SchedulerOutput]] | None
deque[tuple[Future[ModelRunnerOutput], SchedulerOutput, Future[Any]]] | None
) = None
if self.batch_queue_size > 1:
logger.info("Batch queue is enabled with size %d", self.batch_queue_size)
Expand Down Expand Up @@ -336,16 +336,6 @@ def log_error_detail(self, scheduler_output: SchedulerOutput):
)
raise err

def _log_err_callback(self, scheduler_output: SchedulerOutput):
"""Log error details of a future that's not expected to return a result."""

def callback(f, sched_output=scheduler_output):
with self.log_error_detail(sched_output):
result = f.result()
assert result is None

return callback

def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]:
"""Schedule, execute, and make output.

Expand Down Expand Up @@ -422,8 +412,6 @@ def step_with_batch_queue(
# No sampling required (no requests scheduled).
future = cast(Future[ModelRunnerOutput], exec_future)
else:
exec_future.add_done_callback(self._log_err_callback(scheduler_output))

if not scheduler_output.pending_structured_output_tokens:
# We aren't waiting for any tokens, get any grammar output
# and sample immediately.
Expand All @@ -440,7 +428,7 @@ def step_with_batch_queue(

if not deferred_scheduler_output:
# Add this step's future to the queue.
batch_queue.appendleft((future, scheduler_output))
batch_queue.appendleft((future, scheduler_output, exec_future))
if (
model_executed
and len(batch_queue) < self.batch_queue_size
Expand All @@ -457,9 +445,14 @@ def step_with_batch_queue(
return None, False

# Block until the next result is available.
future, scheduler_output = batch_queue.pop()
future, scheduler_output, exec_model_fut = batch_queue.pop()
with self.log_error_detail(scheduler_output):
model_output = future.result()
if model_output is None:
# None from sample_tokens() implies that the original execute_model()
# call failed - raise that exception.
exec_model_fut.result()
raise RuntimeError("unexpected error")

# Before processing the model output, process any aborts that happened
# during the model execution.
Expand All @@ -478,7 +471,7 @@ def step_with_batch_queue(
deferred_scheduler_output
)
future = self.model_executor.sample_tokens(grammar_output, non_block=True)
batch_queue.appendleft((future, deferred_scheduler_output))
batch_queue.appendleft((future, deferred_scheduler_output, exec_future))

return engine_core_outputs, model_executed

Expand Down