# mypy: ignore-errors import signal import time import torch import torch.distributed as dist import vllm from vllm.config import ParallelConfig from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata from vllm.logger import logger from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.transformers_utils.config import maybe_register_config_serialize_by_value from vllm.utils.system_utils import decorate_logs, set_process_title from vllm.v1.core.kv_cache_manager import KVCacheBlocks from vllm.v1.core.sched.interface import PauseState from vllm.v1.core.sched.output import NewRequestData, SchedulerOutput from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue from vllm.v1.core.sched.scheduler import Scheduler from vllm.v1.engine import EngineCoreEventType, EngineCoreOutputs from vllm.v1.engine.core import DPEngineCoreProc, EngineCoreProc from vllm.v1.kv_cache_interface import KVCacheConfig from vllm.v1.request import Request, RequestStatus from vllm.v1.structured_output import StructuredOutputManager from vllm.v1.utils import record_function_or_nullcontext class BalanceScheduler(Scheduler): def __init__( self, vllm_config, kv_cache_config: KVCacheConfig, structured_output_manager: StructuredOutputManager, block_size: int, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, include_finished_set: bool = False, log_stats: bool = False, ) -> None: super().__init__( vllm_config, kv_cache_config, structured_output_manager, block_size, mm_registry, include_finished_set, log_stats, ) # Balance scheduling. self.balance_queue = [ torch.tensor([0], dtype=torch.int, device="cpu") for _ in range(self.vllm_config.parallel_config.data_parallel_size) ] def balance_gather(self, dp_group): running_tensor = torch.tensor([len(self.running)], dtype=torch.int, device="cpu") dist.all_gather(self.balance_queue, running_tensor, group=dp_group) def schedule(self) -> SchedulerOutput: # NOTE(woosuk) on the scheduling algorithm: # There's no "decoding phase" nor "prefill phase" in the scheduler. # Each request just has the num_computed_tokens and # num_tokens_with_spec. num_tokens_with_spec = # len(prompt_token_ids) + len(output_token_ids) + len(spec_token_ids). # At each step, the scheduler tries to assign tokens to the requests # so that each request's num_computed_tokens can catch up its # num_tokens_with_spec. This is general enough to cover # chunked prefills, prefix caching, speculative decoding, # and the "jump decoding" optimization in the future. scheduled_new_reqs: list[Request] = [] scheduled_resumed_reqs: list[Request] = [] scheduled_running_reqs: list[Request] = [] preempted_reqs: list[Request] = [] req_to_new_blocks: dict[str, KVCacheBlocks] = {} num_scheduled_tokens: dict[str, int] = {} token_budget = self.max_num_scheduled_tokens if self._pause_state == PauseState.PAUSED_ALL: # Do not schedule any requests when paused. token_budget = 0 # Encoder-related. scheduled_encoder_inputs: dict[str, list[int]] = {} encoder_compute_budget = self.max_num_encoder_input_tokens # Spec decode-related. scheduled_spec_decode_tokens: dict[str, list[int]] = {} # For logging. scheduled_timestamp = time.monotonic() self.kv_cache_manager.new_step_starts() # First, schedule the RUNNING requests. req_index = 0 while req_index < len(self.running) and token_budget > 0: request = self.running[req_index] if ( request.num_output_placeholders > 0 # This is (num_computed_tokens + 1) - (num_output_placeholders - 1). # Since output placeholders are also included in the computed tokens # count, we subtract (num_output_placeholders - 1) to remove any draft # tokens, so that we can be sure no further steps are needed even if # they are all rejected. and request.num_computed_tokens + 2 - request.num_output_placeholders >= request.num_prompt_tokens + request.max_tokens ): # Async scheduling: Avoid scheduling an extra step when we are sure that # the previous step has reached request.max_tokens. We don't schedule # partial draft tokens since this prevents uniform decode optimizations. req_index += 1 continue num_new_tokens = ( request.num_tokens_with_spec + request.num_output_placeholders - request.num_computed_tokens ) if 0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens: num_new_tokens = self.scheduler_config.long_prefill_token_threshold num_new_tokens = min(num_new_tokens, token_budget) # Make sure the input position does not exceed the max model len. # This is necessary when using spec decoding. num_new_tokens = min(num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens) # Schedule encoder inputs. encoder_inputs_to_schedule = None external_load_encoder_input: list[int] = [] new_encoder_compute_budget = encoder_compute_budget if request.has_encoder_inputs: ( encoder_inputs_to_schedule, num_new_tokens, new_encoder_compute_budget, external_load_encoder_input, ) = self._try_schedule_encoder_inputs( request, request.num_computed_tokens, num_new_tokens, encoder_compute_budget, shift_computed_tokens=1 if self.use_eagle else 0, ) if self.need_mamba_block_aligned_split: num_new_tokens = self._mamba_block_aligned_split(request, num_new_tokens) if num_new_tokens == 0: # The request cannot be scheduled because one of the following # reasons: # 1. No new tokens to schedule. This may happen when # (1) PP>1 and we have already scheduled all prompt tokens # but they are not finished yet. # (2) Async scheduling and the request has reached to either # its max_total_tokens or max_model_len. # 2. The encoder budget is exhausted. # 3. The encoder cache is exhausted. # 4. Insufficient budget for a block-aligned chunk in hybrid # models with mamba cache mode \"align\". # NOTE(woosuk): Here, by doing `continue` instead of `break`, # we do not strictly follow the FCFS scheduling policy and # allow the lower-priority requests to be scheduled. req_index += 1 continue # Schedule newly needed KV blocks for the request. with record_function_or_nullcontext("schedule: allocate_slots"): while True: new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens, num_lookahead_tokens=self.num_lookahead_tokens, ) if new_blocks is not None: # The request can be scheduled. break # The request cannot be scheduled. # Preempt the lowest-priority request. if self.policy == SchedulingPolicy.PRIORITY: preempted_req = max( self.running, key=lambda r: (r.priority, r.arrival_time), ) self.running.remove(preempted_req) if preempted_req in scheduled_running_reqs: preempted_req_id = preempted_req.request_id scheduled_running_reqs.remove(preempted_req) token_budget += num_scheduled_tokens.pop(preempted_req_id) req_to_new_blocks.pop(preempted_req_id) scheduled_spec_decode_tokens.pop(preempted_req_id, None) preempted_encoder_inputs = scheduled_encoder_inputs.pop(preempted_req_id, None) if preempted_encoder_inputs: # Restore encoder compute budget if the preempted # request had encoder inputs scheduled in this step. num_embeds_to_restore = sum( preempted_req.get_num_encoder_embeds(i) for i in preempted_encoder_inputs ) encoder_compute_budget += num_embeds_to_restore req_index -= 1 else: preempted_req = self.running.pop() self._preempt_request(preempted_req, scheduled_timestamp) preempted_reqs.append(preempted_req) if preempted_req == request: # No more request to preempt. Cannot schedule this request. break if new_blocks is None: # Cannot schedule this request. break # Schedule the request. scheduled_running_reqs.append(request) request_id = request.request_id req_to_new_blocks[request_id] = new_blocks num_scheduled_tokens[request_id] = num_new_tokens token_budget -= num_new_tokens req_index += 1 # Speculative decode related. if request.spec_token_ids: num_scheduled_spec_tokens = ( num_new_tokens + request.num_computed_tokens - request.num_tokens - request.num_output_placeholders ) if num_scheduled_spec_tokens > 0: spec_token_ids = request.spec_token_ids if len(spec_token_ids) > num_scheduled_spec_tokens: spec_token_ids = spec_token_ids[:num_scheduled_spec_tokens] scheduled_spec_decode_tokens[request.request_id] = spec_token_ids # New spec tokens will be set in `update_draft_token_ids` before the # next step when applicable. request.spec_token_ids = [] # Encoder-related. if encoder_inputs_to_schedule: scheduled_encoder_inputs[request_id] = encoder_inputs_to_schedule # Allocate the encoder cache. for i in encoder_inputs_to_schedule: self.encoder_cache_manager.allocate(request, i) encoder_compute_budget = new_encoder_compute_budget if external_load_encoder_input: for i in external_load_encoder_input: self.encoder_cache_manager.allocate(request, i) if self.ec_connector is not None: self.ec_connector.update_state_after_alloc(request, i) # Record the LoRAs in scheduled_running_reqs scheduled_loras: set[int] = set() if self.lora_config: scheduled_loras = set( req.lora_request.lora_int_id for req in scheduled_running_reqs if req.lora_request and req.lora_request.lora_int_id > 0 ) assert len(scheduled_loras) <= self.lora_config.max_loras # Next, schedule the WAITING requests. if not preempted_reqs and self._pause_state == PauseState.UNPAUSED: # Use a temporary RequestQueue to collect requests that need to be # skipped and put back at the head of the waiting queue later skipped_waiting_requests = create_request_queue(self.policy) while self.waiting and token_budget > 0: if len(self.running) == self.max_num_running_reqs: break balance_flag = max(t.item() for t in self.balance_queue) >= self.max_num_running_reqs - 1 if balance_flag: break request = self.waiting.peek_request() request_id = request.request_id # KVTransfer: skip request if still waiting for remote kvs. if request.status == RequestStatus.WAITING_FOR_REMOTE_KVS: is_ready = self._update_waiting_for_remote_kv(request) if is_ready: if request.num_preemptions: # We must be loading for a resumed preemption # rather than a new request. request.status = RequestStatus.PREEMPTED else: request.status = RequestStatus.WAITING else: logger.debug( "%s is still in WAITING_FOR_REMOTE_KVS state.", request_id, ) self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue # Skip request if the structured output request is still waiting # for FSM compilation. if request.status == RequestStatus.WAITING_FOR_FSM: structured_output_req = request.structured_output_request if structured_output_req and structured_output_req.grammar: request.status = RequestStatus.WAITING else: self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue # Streaming: skip request if still waiting for next streaming req. if request.status == RequestStatus.WAITING_FOR_STREAMING_REQ: assert not request.streaming_queue self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue # Check that adding the request still respects the max_loras # constraint. if ( self.lora_config and request.lora_request and ( len(scheduled_loras) == self.lora_config.max_loras and request.lora_request.lora_int_id not in scheduled_loras ) ): # Scheduling would exceed max_loras, skip. self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue num_external_computed_tokens = 0 load_kv_async = False connector_prefix_cache_queries, connector_prefix_cache_hits = 0, 0 # Get already-cached tokens. if request.num_computed_tokens == 0: # Get locally-cached tokens. new_computed_blocks, num_new_local_computed_tokens = self.kv_cache_manager.get_computed_blocks( request ) # Get externally-cached tokens if using a KVConnector. if self.connector is not None: ext_tokens, load_kv_async = self.connector.get_num_new_matched_tokens( request, num_new_local_computed_tokens ) if ext_tokens is None: # The request cannot be scheduled because # the KVConnector couldn't determine # the number of matched tokens. self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue request.num_external_computed_tokens = ext_tokens num_external_computed_tokens = ext_tokens connector_prefix_cache_queries = request.num_tokens - num_new_local_computed_tokens connector_prefix_cache_hits = num_external_computed_tokens # Total computed tokens (local + external). num_computed_tokens = num_new_local_computed_tokens + num_external_computed_tokens else: # KVTransfer: WAITING reqs have num_computed_tokens > 0 # after async KV recvs are completed. new_computed_blocks = self.kv_cache_manager.empty_kv_cache_blocks num_new_local_computed_tokens = 0 num_computed_tokens = request.num_computed_tokens encoder_inputs_to_schedule = None external_load_encoder_input = [] new_encoder_compute_budget = encoder_compute_budget if load_kv_async: # KVTransfer: loading remote KV, do not allocate for new work. assert num_external_computed_tokens > 0 num_new_tokens = 0 else: # Number of tokens to be scheduled. # We use `request.num_tokens` instead of # `request.num_prompt_tokens` to consider the resumed # requests, which have output tokens. num_new_tokens = request.num_tokens - num_computed_tokens threshold = self.scheduler_config.long_prefill_token_threshold if 0 < threshold < num_new_tokens: num_new_tokens = threshold # chunked prefill has to be enabled explicitly to allow # pooling requests to be chunked if not self.scheduler_config.enable_chunked_prefill and num_new_tokens > token_budget: # If chunked_prefill is disabled, # we can stop the scheduling here. break num_new_tokens = min(num_new_tokens, token_budget) assert num_new_tokens > 0 # Schedule encoder inputs. if request.has_encoder_inputs: ( encoder_inputs_to_schedule, num_new_tokens, new_encoder_compute_budget, external_load_encoder_input, ) = self._try_schedule_encoder_inputs( request, num_computed_tokens, num_new_tokens, encoder_compute_budget, shift_computed_tokens=1 if self.use_eagle else 0, ) if num_new_tokens == 0: # The request cannot be scheduled. break if self.need_mamba_block_aligned_split: num_new_tokens = self._mamba_block_aligned_split( request, num_new_tokens, num_new_local_computed_tokens, num_external_computed_tokens, ) if num_new_tokens == 0: break # Handles an edge case when P/D Disaggregation # is used with Spec Decoding where an # extra block gets allocated which # creates a mismatch between the number # of local and remote blocks. effective_lookahead_tokens = 0 if request.num_computed_tokens == 0 else self.num_lookahead_tokens # Determine if we need to allocate cross-attention blocks. num_encoder_tokens = 0 if self.is_encoder_decoder and request.has_encoder_inputs and encoder_inputs_to_schedule: num_encoder_tokens = sum(request.get_num_encoder_embeds(i) for i in encoder_inputs_to_schedule) new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens, num_new_computed_tokens=num_new_local_computed_tokens, new_computed_blocks=new_computed_blocks, num_lookahead_tokens=effective_lookahead_tokens, num_external_computed_tokens=num_external_computed_tokens, delay_cache_blocks=load_kv_async, num_encoder_tokens=num_encoder_tokens, ) if new_blocks is None: # The request cannot be scheduled. # NOTE: we need to untouch the request from the encode cache # manager if request.has_encoder_inputs: self.encoder_cache_manager.free(request) break # KVTransfer: the connector uses this info to determine # if a load is needed. Note that # This information is used to determine if a load is # needed for this request. if self.connector is not None: self.connector.update_state_after_alloc( request, self.kv_cache_manager.get_blocks(request_id), num_external_computed_tokens, ) if self.connector_prefix_cache_stats is not None and connector_prefix_cache_queries != 0: self.connector_prefix_cache_stats.record( num_tokens=connector_prefix_cache_queries, num_hits=connector_prefix_cache_hits, preempted=request.num_preemptions > 0, ) # Request was already popped from self.waiting # unless it was re-added above due to new_blocks being None. request = self.waiting.pop_request() if load_kv_async: # If loading async, allocate memory and put request # into the WAITING_FOR_REMOTE_KV state. skipped_waiting_requests.prepend_request(request) request.status = RequestStatus.WAITING_FOR_REMOTE_KVS continue self.running.append(request) if self.log_stats: request.record_event(EngineCoreEventType.SCHEDULED, scheduled_timestamp) if request.status == RequestStatus.WAITING: scheduled_new_reqs.append(request) elif request.status == RequestStatus.PREEMPTED: scheduled_resumed_reqs.append(request) else: raise RuntimeError(f"Invalid request status: {request.status}") if self.lora_config and request.lora_request: scheduled_loras.add(request.lora_request.lora_int_id) req_to_new_blocks[request_id] = self.kv_cache_manager.get_blocks(request_id) num_scheduled_tokens[request_id] = num_new_tokens token_budget -= num_new_tokens request.status = RequestStatus.RUNNING request.num_computed_tokens = num_computed_tokens # Count the number of prefix cached tokens. if request.num_cached_tokens < 0: request.num_cached_tokens = num_computed_tokens # Encoder-related. if encoder_inputs_to_schedule: scheduled_encoder_inputs[request_id] = encoder_inputs_to_schedule # Allocate the encoder cache. for i in encoder_inputs_to_schedule: self.encoder_cache_manager.allocate(request, i) encoder_compute_budget = new_encoder_compute_budget # Allocate for external load encoder cache if external_load_encoder_input: for i in external_load_encoder_input: self.encoder_cache_manager.allocate(request, i) if self.ec_connector is not None: self.ec_connector.update_state_after_alloc(request, i) # Put back any skipped requests at the head of the waiting queue if skipped_waiting_requests: self.waiting.prepend_requests(skipped_waiting_requests) # Check if the scheduling constraints are satisfied. total_num_scheduled_tokens = sum(num_scheduled_tokens.values()) assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens assert token_budget >= 0 assert len(self.running) <= self.max_num_running_reqs # Since some requests in the RUNNING queue may not be scheduled in # this step, the total number of scheduled requests can be smaller than # len(self.running). assert len(scheduled_new_reqs) + len(scheduled_resumed_reqs) + len(scheduled_running_reqs) <= len(self.running) # Get the longest common prefix among all requests in the running queue. # This can be potentially used for cascade attention. num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups) with record_function_or_nullcontext("schedule: get_num_common_prefix_blocks"): if self.running: any_request_id = self.running[0].request_id num_common_prefix_blocks = self.kv_cache_manager.get_num_common_prefix_blocks(any_request_id) # Construct the scheduler output. if self.use_v2_model_runner: scheduled_new_reqs = scheduled_new_reqs + scheduled_resumed_reqs scheduled_resumed_reqs = [] new_reqs_data = [ NewRequestData.from_request( req, req_to_new_blocks[req.request_id].get_block_ids(), req._all_token_ids, ) for req in scheduled_new_reqs ] else: new_reqs_data = [ NewRequestData.from_request(req, req_to_new_blocks[req.request_id].get_block_ids()) for req in scheduled_new_reqs ] with record_function_or_nullcontext("schedule: make_cached_request_data"): cached_reqs_data = self._make_cached_request_data( scheduled_running_reqs, scheduled_resumed_reqs, num_scheduled_tokens, scheduled_spec_decode_tokens, req_to_new_blocks, ) # Record the request ids that were scheduled in this step. self.prev_step_scheduled_req_ids.clear() self.prev_step_scheduled_req_ids.update(num_scheduled_tokens.keys()) scheduler_output = SchedulerOutput( scheduled_new_reqs=new_reqs_data, scheduled_cached_reqs=cached_reqs_data, num_scheduled_tokens=num_scheduled_tokens, total_num_scheduled_tokens=total_num_scheduled_tokens, scheduled_spec_decode_tokens=scheduled_spec_decode_tokens, scheduled_encoder_inputs=scheduled_encoder_inputs, num_common_prefix_blocks=num_common_prefix_blocks, preempted_req_ids={req.request_id for req in preempted_reqs}, # finished_req_ids is an existing state in the scheduler, # instead of being newly scheduled in this step. # It contains the request IDs that are finished in between # the previous and the current steps. finished_req_ids=self.finished_req_ids, free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(), ) # NOTE(Kuntai): this function is designed for multiple purposes: # 1. Plan the KV cache store # 2. Wrap up all the KV cache load / save ops into an opaque object # 3. Clear the internal states of the connector if self.connector is not None: meta: KVConnectorMetadata = self.connector.build_connector_meta(scheduler_output) scheduler_output.kv_connector_metadata = meta # Build the connector meta for ECConnector if self.ec_connector is not None: ec_meta: ECConnectorMetadata = self.ec_connector.build_connector_meta(scheduler_output) scheduler_output.ec_connector_metadata = ec_meta with record_function_or_nullcontext("schedule: update_after_schedule"): self._update_after_schedule(scheduler_output) return scheduler_output class BalanceDPEngineCoreProc(DPEngineCoreProc): def run_busy_loop(self): """Core busy loop of the EngineCore for data parallel case.""" # Loop until process is sent a SIGINT or SIGTERM while True: # 1) Poll the input queue until there is work to do. self._process_input_queue() # 2) Step the engine core. executed = self._process_engine_step() self._maybe_publish_request_counts() local_unfinished_reqs = self.scheduler.has_unfinished_requests() if not executed: if not local_unfinished_reqs and not self.engines_running: # All engines are idle. continue # We are in a running state and so must execute a dummy pass # if the model didn't execute any ready requests. self.execute_dummy_batch() # 3) All-reduce operation to determine global unfinished reqs. self.engines_running = self._has_global_unfinished_reqs(local_unfinished_reqs) self.scheduler.balance_gather(self.dp_group) if not self.engines_running: if self.dp_rank == 0 or not self.has_coordinator: # Notify client that we are pausing the loop. logger.debug("Wave %d finished, pausing engine loop.", self.current_wave) # In the coordinator case, dp rank 0 sends updates to the # coordinator. Otherwise (offline spmd case), each rank # sends the update to its colocated front-end process. client_index = -1 if self.has_coordinator else 0 self.output_queue.put_nowait( ( client_index, EngineCoreOutputs(wave_complete=self.current_wave), ) ) # Increment wave count and reset step counter. self.current_wave += 1 self.step_counter = 0 def run_engine_core(*args, dp_rank: int = 0, local_dp_rank: int = 0, **kwargs): """Launch EngineCore busy loop in background process.""" # Signal handler used for graceful termination. # SystemExit exception is only raised once to allow this and worker # processes to terminate without error shutdown_requested = False # Ensure we can serialize transformer config after spawning maybe_register_config_serialize_by_value() def signal_handler(signum, frame): nonlocal shutdown_requested if not shutdown_requested: shutdown_requested = True raise SystemExit() # Either SIGTERM or SIGINT will terminate the engine_core signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) engine_core: EngineCoreProc | None = None try: parallel_config: ParallelConfig = kwargs["vllm_config"].parallel_config if parallel_config.data_parallel_size > 1 or dp_rank > 0: set_process_title("EngineCore", f"DP{dp_rank}") decorate_logs() # Set data parallel rank for this engine process. parallel_config.data_parallel_rank = dp_rank parallel_config.data_parallel_rank_local = local_dp_rank engine_core = BalanceDPEngineCoreProc(*args, **kwargs) else: set_process_title("EngineCore") decorate_logs() engine_core = EngineCoreProc(*args, **kwargs) engine_core.run_busy_loop() except SystemExit: logger.debug("EngineCore exiting.") raise except Exception as e: if engine_core is None: logger.exception("EngineCore failed to start.") else: logger.exception("EngineCore encountered a fatal error.") engine_core._send_engine_dead() raise e finally: if engine_core is not None: engine_core.shutdown() EngineCoreProc.run_engine_core = run_engine_core vllm.v1.core.sched.scheduler.Scheduler = BalanceScheduler