[Core] Restructure core loop for async input preparation

vllm/v1/core/sched/interface.py

@@ -6,7 +6,7 @@
 
 if TYPE_CHECKING:
     from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
-    from vllm.v1.core.sched.output import SchedulerOutput
+    from vllm.v1.core.sched.output import GrammarBitmask, SchedulerOutput
     from vllm.v1.engine import EngineCoreOutputs
     from vllm.v1.metrics.stats import SchedulerStats
     from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
@@ -41,6 +41,14 @@ def schedule(self) -> "SchedulerOutput":
         """
         raise NotImplementedError
 
+    @abstractmethod
+    def get_grammar_bitmask(
+        self,
+        scheduler_output: "SchedulerOutput",
+    ) -> Optional["GrammarBitmask"]:
+        """Get the grammar bitmask for the scheduled requests."""
+        raise NotImplementedError
+
     @abstractmethod
     def update_from_output(
         self,

vllm/v1/core/sched/output.py

@@ -147,11 +147,15 @@ class SchedulerOutput:
     # Used to free the encoder cache.
     free_encoder_input_ids: list[tuple[str, int]]
 
+    # KV Cache Connector metadata.
+    kv_connector_metadata: Optional[KVConnectorMetadata] = None
+
+
+@dataclass
+class GrammarBitmask:
+
     # Dict of request ids to their index within the batch
     # for filling the next token bitmask
     structured_output_request_ids: dict[str, int]
     # the bitmask for the whole batch
     grammar_bitmask: Optional[npt.NDArray[np.int32]]
-
-    # KV Cache Connector metadata.
-    kv_connector_metadata: Optional[KVConnectorMetadata] = None

vllm/v1/core/sched/scheduler.py

@@ -21,8 +21,8 @@
                                                 compute_encoder_budget)
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager
 from vllm.v1.core.sched.interface import SchedulerInterface
-from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData,
-                                       SchedulerOutput)
+from vllm.v1.core.sched.output import (CachedRequestData, GrammarBitmask,
+                                       NewRequestData, SchedulerOutput)
 from vllm.v1.core.sched.request_queue import (SchedulingPolicy,
                                               create_request_queue)
 from vllm.v1.core.sched.utils import check_stop, remove_all
@@ -534,9 +534,6 @@ def schedule(self) -> SchedulerOutput:
             scheduled_spec_decode_tokens,
             req_to_new_blocks,
         )
-        structured_output_request_ids, grammar_bitmask = (
-            self.get_grammar_bitmask(self.running,
-                                     scheduled_spec_decode_tokens))
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=new_reqs_data,
             scheduled_cached_reqs=cached_reqs_data,
@@ -551,8 +548,6 @@ def schedule(self) -> SchedulerOutput:
             # the previous and the current steps.
             finished_req_ids=self.finished_req_ids,
             free_encoder_input_ids=self.encoder_cache_manager.get_freed_ids(),
-            structured_output_request_ids=structured_output_request_ids,
-            grammar_bitmask=grammar_bitmask,
         )
 
         # NOTE(Kuntai): this function is designed for multiple purposes:
@@ -736,33 +731,33 @@ def _try_schedule_encoder_inputs(
 
     def get_grammar_bitmask(
         self,
-        requests: list[Request],
-        scheduled_spec_decode_tokens: dict[str, list[int]],
-    ):
+        scheduler_output: SchedulerOutput,
+    ) -> Optional[GrammarBitmask]:
         # NOTE: structured_output_request_ids maps
         # a request's (request that uses structured output)
         # request_id to its index in the batch.
         # This will helps us determine to slice the grammar bitmask
         # and only applies valid mask for requests that
         # uses structured decoding.
         structured_output_request_ids: dict[str, int] = {}
-        for i, req in enumerate(requests):
-            if req.use_structured_output:
+        req_ids = scheduler_output.num_scheduled_tokens.keys()
+        for i, req_id in enumerate(req_ids):
+            req = self.requests.get(req_id)
+            if req is not None and req.use_structured_output:
                 # PERF: in case of chunked prefill,
                 # request might not include any new tokens.
                 # Therefore, we might introduce some additional
                 # cycle to fill in the bitmask, which could be a big no-op.
                 structured_output_request_ids[req.request_id] = i
 
         if not structured_output_request_ids:
-            bitmask = None
-        else:
-            bitmask = self.structured_output_manager.grammar_bitmask(
-                self.requests,
-                structured_output_request_ids,
-                scheduled_spec_decode_tokens,
-            )
-        return structured_output_request_ids, bitmask
+            return None
+        bitmask = self.structured_output_manager.grammar_bitmask(
+            self.requests,
+            structured_output_request_ids,
+            scheduler_output.scheduled_spec_decode_tokens,
+        )
+        return GrammarBitmask(structured_output_request_ids, bitmask)
 
     def update_from_output(
         self,

vllm/v1/engine/core.py

@@ -286,12 +286,12 @@
         if not self.scheduler.has_requests():
             return {}, False
         scheduler_output = self.scheduler.schedule()
-        model_output = self.execute_model_with_error_logging(
-            self.model_executor.execute_model,  # type: ignore
-            scheduler_output)
+        self.model_executor.prepare_inputs(scheduler_output)
+        self.model_executor.execute_model()
+        bitmask = self.scheduler.get_grammar_bitmask(scheduler_output)
+        model_output = self.model_executor.sample(bitmask, non_block=False)
         engine_core_outputs = self.scheduler.update_from_output(
             scheduler_output, model_output)  # type: ignore
-
         return (engine_core_outputs,
                 scheduler_output.total_num_scheduled_tokens > 0)
 
@@ -327,7 +327,10 @@
         if not self.batch_queue.full():
             scheduler_output = self.scheduler.schedule()
             if scheduler_output.total_num_scheduled_tokens > 0:
-                future = self.model_executor.execute_model(scheduler_output)
+                self.model_executor.prepare_inputs(scheduler_output)
+                self.model_executor.execute_model()
+                bitmask = self.scheduler.get_grammar_bitmask(scheduler_output)
+                future = self.model_executor.sample(bitmask)
                 self.batch_queue.put_nowait(
                     (future, scheduler_output))  # type: ignore
 
@@ -353,6 +356,32 @@
 
         return engine_core_outputs, scheduled_batch
 
+    def step_async(
+            self) -> tuple[Optional[dict[int, EngineCoreOutputs]], bool]:
+        model_output = None
+        engine_core_outputs = None
+        bitmask = None
+
+        scheduler_output = self.scheduler.schedule()
+        is_scheduled = scheduler_output.total_num_scheduled_tokens > 0
+        if is_scheduled:
+            self.model_executor.prepare_inputs(scheduler_output)
+            if self.inflight_batch:
+                model_output = self.model_executor.sample(self.prev_bitmask)
+            self.model_executor.execute_model()
+            bitmask = self.scheduler.get_grammar_bitmask(scheduler_output)
+        elif self.inflight_batch:
+            model_output = self.model_executor.sample(self.prev_bitmask)
+
+        if model_output is not None:
+            engine_core_outputs = self.scheduler.update_from_output(
+                self.prev_scheduler_output, model_output.result())
+
+        self.inflight_batch = is_scheduled
+        self.prev_scheduler_output = scheduler_output
+        self.prev_bitmask = bitmask
+        return engine_core_outputs, is_scheduled
+
     def shutdown(self):
         self.structured_output_manager.clear_backend()
         if self.model_executor:
@@ -529,8 +558,14 @@
                 assert addresses.coordinator_input is not None
                 logger.info("Waiting for READY message from DP Coordinator...")
 
-        self.step_fn = (self.step if self.batch_queue is None else
-                        self.step_with_batch_queue)
+        if self.batch_queue is None:
+            if self.vllm_config.scheduler_config.async_scheduling:
+                self.step_fn = self.step_async
+                self.inflight_batch = False
+            else:
+                self.step_fn = self.step
+        else:
+            self.step_fn = self.step_with_batch_queue
 
     @contextmanager
     def _perform_handshakes(

vllm/v1/executor/abstract.py

@@ -80,12 +80,19 @@ def get_kv_cache_specs(self) -> list[dict[str, KVCacheSpec]]:
         output = self.collective_rpc("get_kv_cache_spec")
         return output
 
-    def execute_model(
+    def prepare_inputs(self, scheduler_output) -> None:
+        self.collective_rpc("prepare_inputs", args=(scheduler_output, ))
+
+    def execute_model(self) -> None:
+        self.collective_rpc("execute_model")
+
+    def sample(
         self,
-        scheduler_output,
+        grammar_bitmask,
+        non_block: bool = True,
     ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
-        output = self.collective_rpc("execute_model",
-                                     args=(scheduler_output, ))
+        del non_block
+        output = self.collective_rpc("sample", args=(grammar_bitmask, ))
         return output[0]
 
     def take_draft_token_ids(self) -> Optional[DraftTokenIds]:

vllm/v1/executor/multiproc_executor.py

@@ -115,12 +115,11 @@ def _init_executor(self) -> None:
 
         # For pipeline parallel, we use a thread pool for asynchronous
         # execute_model.
-        if self.max_concurrent_batches > 1:
-            # Note: must use only 1 IO thread to keep dequeue sequence
-            # from the response queue
-            # _async_aggregate_workers_output also assumes a single IO thread
-            self.io_thread_pool = ThreadPoolExecutor(
-                max_workers=1, thread_name_prefix="mp_exec_io")
+        # Note: must use only 1 IO thread to keep dequeue sequence
+        # from the response queue
+        # _async_aggregate_workers_output also assumes a single IO thread
+        self.io_thread_pool = ThreadPoolExecutor(
+            max_workers=1, thread_name_prefix="mp_exec_io")
 
         self.output_rank = self._get_output_rank()
         self.has_connector = self.vllm_config.kv_transfer_config is not None
@@ -162,26 +161,42 @@ def register_failure_callback(self, callback: FailureCallback):
         else:
             self.failure_callback = callback
 
-    def execute_model(
+    def prepare_inputs(self, scheduler_output) -> None:
+        self.collective_rpc(
+            "prepare_inputs",
+            args=(scheduler_output, ),
+            non_block=True,
+            skip_response=True,
+            timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS,
+        )
+
+    def execute_model(self) -> None:
+        self.collective_rpc(
+            "execute_model",
+            non_block=True,
+            skip_response=True,
+            timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS,
+        )
+
+    def sample(
         self,
-        scheduler_output,
+        grammar_bitmask,
+        non_block: bool = True,
     ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]:
-        non_block = self.max_concurrent_batches > 1
-
         if not self.has_connector:
             # get output only from a single worker (output_rank)
             (output, ) = self.collective_rpc(
-                "execute_model",
-                args=(scheduler_output, ),
+                "sample",
+                args=(grammar_bitmask, ),
                 unique_reply_rank=self.output_rank,
                 non_block=non_block,
                 timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS)
             return output
 
         # get output from all workers
         outputs = self.collective_rpc(
-            "execute_model",
-            args=(scheduler_output, ),
+            "sample",
+            args=(grammar_bitmask, ),
             non_block=non_block,
             timeout=envs.VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS)
 
@@ -203,6 +218,7 @@ def collective_rpc(self,
                        args: tuple = (),
                        kwargs: Optional[dict] = None,
                        non_block: bool = False,
+                       skip_response: bool = False,
                        unique_reply_rank: Optional[int] = None) -> list[Any]:
         if self.is_failed:
             raise RuntimeError("Executor failed.")
@@ -219,6 +235,15 @@ def collective_rpc(self,
             else:
                 send_method = cloudpickle.dumps(
                     method, protocol=pickle.HIGHEST_PROTOCOL)
+
+            if skip_response:
+                if unique_reply_rank is not None:
+                    raise ValueError(
+                        "unique_reply_rank must be None "
+                        f"when skip_response is True. got {unique_reply_rank}")
+                self.rpc_broadcast_mq.enqueue((send_method, args, kwargs, -1))
+                return []
+
             self.rpc_broadcast_mq.enqueue(
                 (send_method, args, kwargs, unique_reply_rank))
 
@@ -309,8 +334,6 @@ def check_health(self) -> None:
 
     @property
     def max_concurrent_batches(self) -> int:
-        if self.scheduler_config.async_scheduling:
-            return 2
         return self.parallel_config.pipeline_parallel_size
 
     def _get_output_rank(self) -> int:

vllm/v1/executor/ray_distributed_executor.py

@@ -58,11 +58,9 @@
         """Ray distributed executor supports pipeline parallelism,
         meaning that it allows PP size batches to be executed concurrently.
         """
-        if self.scheduler_config.async_scheduling:
-            return 2
         return self.parallel_config.pipeline_parallel_size
 
     def execute_model(
         self,
         scheduler_output,
     ) -> Union[ModelRunnerOutput, Future[ModelRunnerOutput]]: