# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from __future__ import annotations import itertools import time from collections import defaultdict from collections.abc import Iterable from typing import Any, Optional, Union from vllm.config import VllmConfig from vllm.distributed.kv_events import EventPublisherFactory, KVEventBatch from vllm.distributed.kv_transfer.kv_connector.factory import ( KVConnectorFactory) from vllm.distributed.kv_transfer.kv_connector.v1 import (KVConnectorBase_V1, KVConnectorRole) from vllm.logger import init_logger from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.v1.core.encoder_cache_manager import (EncoderCacheManager, compute_encoder_budget) from vllm.v1.core.kv_cache_manager import KVCacheManager from vllm.v1.core.sched.interface import SchedulerInterface from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData, SchedulerOutput) from vllm.v1.core.sched.request_queue import (SchedulingPolicy, create_request_queue) from vllm.v1.core.sched.utils import check_stop from vllm.v1.engine import (EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs) from vllm.v1.kv_cache_interface import KVCacheConfig from vllm.v1.metrics.stats import SchedulerStats from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.request import Request, RequestStatus from vllm.v1.spec_decode.metrics import SpecDecodingStats from vllm.v1.structured_output import StructuredOutputManager logger = init_logger(__name__) class Scheduler(SchedulerInterface): def __init__( self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig, structured_output_manager: StructuredOutputManager, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, include_finished_set: bool = False, log_stats: bool = False, ) -> None: self.vllm_config = vllm_config self.scheduler_config = vllm_config.scheduler_config self.cache_config = vllm_config.cache_config self.lora_config = vllm_config.lora_config self.kv_cache_config = kv_cache_config self.kv_events_config = vllm_config.kv_events_config self.parallel_config = vllm_config.parallel_config self.log_stats = log_stats self.structured_output_manager = structured_output_manager # include_finished_set controls whether a separate set of finished # request ids should be included in the EngineCoreOutputs returned # by update_from_outputs(). This is currently used in the multi-engine # case to track request lifetimes efficiently. self.finished_req_ids_dict: Optional[dict[int, set[str]]] = ( defaultdict(set) if include_finished_set else None) # Scheduling constraints. self.max_num_running_reqs = self.scheduler_config.max_num_seqs self.max_num_scheduled_tokens = \ self.scheduler_config.max_num_batched_tokens self.max_model_len = self.scheduler_config.max_model_len self.enable_kv_cache_events = ( self.kv_events_config is not None and self.kv_events_config.enable_kv_cache_events) # Create KVConnector for the Scheduler. Note that each Worker # will have a corresponding KVConnector with Role=WORKER. # KV Connector pushes/pull of remote KVs for P/D and offloading. self.connector = None if self.vllm_config.kv_transfer_config is not None: assert len(self.kv_cache_config.kv_cache_groups) == 1, ( "Multiple KV cache groups are not currently supported " "with KV connectors") self.connector = KVConnectorFactory.create_connector_v1( config=self.vllm_config, role=KVConnectorRole.SCHEDULER) self.kv_event_publisher = EventPublisherFactory.create( self.kv_events_config, self.parallel_config.data_parallel_rank, ) num_gpu_blocks = self.cache_config.num_gpu_blocks assert num_gpu_blocks is not None and num_gpu_blocks > 0 self.block_size = self.cache_config.block_size # req_id -> Request self.requests: dict[str, Request] = {} # Scheduling policy if self.scheduler_config.policy == "priority": self.policy = SchedulingPolicy.PRIORITY elif self.scheduler_config.policy == "fcfs": self.policy = SchedulingPolicy.FCFS else: raise ValueError( f"Unknown scheduling policy: {self.scheduler_config.policy}") # Priority queues for requests. self.waiting = create_request_queue(self.policy) self.running: list[Request] = [] # The request IDs that are finished in between the previous and the # current steps. This is used to notify the workers about the finished # requests so that they can free the cached states for those requests. # This is flushed at the end of each scheduling step. self.finished_req_ids: set[str] = set() # KV Connector: requests in process of async KV loading or recving self.finished_recving_kv_req_ids: set[str] = set() # Encoder-related. # Calculate encoder cache size if applicable # NOTE: For now we use the same budget for both compute and space. # This can be changed when we make encoder cache for embedding caching # across requests. encoder_compute_budget, encoder_cache_size = compute_encoder_budget( model_config=vllm_config.model_config, scheduler_config=vllm_config.scheduler_config, mm_registry=mm_registry, ) # NOTE(woosuk): Here, "encoder" includes the vision encoder (and # projector if needed). Currently, we assume that the encoder also # has the Transformer architecture (e.g., ViT). self.max_num_encoder_input_tokens = encoder_compute_budget # NOTE: For the models without encoder (e.g., text-only models), # the encoder cache will not be initialized because cache size is 0 # for these models. self.encoder_cache_manager = EncoderCacheManager( cache_size=encoder_cache_size) speculative_config = vllm_config.speculative_config self.speculative_config = speculative_config self.use_eagle = False self.num_spec_tokens = self.num_lookahead_tokens = 0 if speculative_config: self.num_spec_tokens = speculative_config.num_speculative_tokens if speculative_config.use_eagle(): self.use_eagle = True self.num_lookahead_tokens = self.num_spec_tokens # Create the KV cache manager. self.kv_cache_manager = KVCacheManager( kv_cache_config=kv_cache_config, max_model_len=self.max_model_len, enable_caching=self.cache_config.enable_prefix_caching, caching_hash_algo=self.cache_config.prefix_caching_hash_algo, use_eagle=self.use_eagle, log_stats=self.log_stats, enable_kv_cache_events=self.enable_kv_cache_events, ) self.use_pp = self.parallel_config.pipeline_parallel_size > 1 def schedule_default(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] = [] # NOTE: structured_output_request_ids maps # a request's (request that uses structured output) # request_id to the running request index. # 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] = {} req_to_new_block_ids: dict[str, tuple[list[int], ...]] = {} num_scheduled_tokens: dict[str, int] = {} token_budget = self.max_num_scheduled_tokens # Encoder-related. scheduled_encoder_inputs: dict[str, list[int]] = {} encoder_budget = self.max_num_encoder_input_tokens # Spec decode-related. scheduled_spec_decode_tokens: dict[str, list[int]] = {} # For logging. scheduled_timestamp = time.monotonic() # First, schedule the RUNNING requests. req_index = 0 while req_index < len(self.running) and token_budget > 0: request = self.running[req_index] num_new_tokens = (request.num_tokens_with_spec - 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 new_encoder_budget = encoder_budget if request.has_encoder_inputs: (encoder_inputs_to_schedule, num_new_tokens, new_encoder_budget) = self._try_schedule_encoder_inputs( request, request.num_computed_tokens, num_new_tokens, encoder_budget) 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 PP>1 and # we have already scheduled all prompt tokens but they are # not finished yet. # 2. The encoder budget is exhausted. # 3. The encoder cache is exhausted. # 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 num_draft_tokens = max( num_new_tokens + request.num_computed_tokens - request.num_tokens, 0) while True: new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens, num_draft_tokens=num_draft_tokens, num_lookahead_tokens=self.num_lookahead_tokens) if new_blocks is None: # 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) else: preempted_req = self.running.pop() self.kv_cache_manager.free(preempted_req) preempted_req.status = RequestStatus.PREEMPTED preempted_req.num_computed_tokens = 0 if self.log_stats: preempted_req.record_event( EngineCoreEventType.PREEMPTED, scheduled_timestamp) self.waiting.prepend_request(preempted_req) preempted_reqs.append(preempted_req) if preempted_req == request: # No more request to preempt. can_schedule = False break else: # The request can be scheduled. can_schedule = True break if not can_schedule: break assert new_blocks is not None # Schedule the request. scheduled_running_reqs.append(request) if request.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[request.request_id] = req_index req_to_new_block_ids[request.request_id] = ( new_blocks.get_block_ids()) num_scheduled_tokens[request.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) if num_scheduled_spec_tokens > 0: # Trim spec_token_ids list to num_scheduled_spec_tokens. del request.spec_token_ids[num_scheduled_spec_tokens:] scheduled_spec_decode_tokens[request.request_id] = ( request.spec_token_ids) # Encoder-related. if encoder_inputs_to_schedule: scheduled_encoder_inputs[request.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_budget = new_encoder_budget # 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 # 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) # Next, schedule the WAITING requests. if not preempted_reqs: while self.waiting and token_budget > 0: if len(self.running) == self.max_num_running_reqs: break request = self.waiting.peek_request() # 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: request.status = RequestStatus.WAITING else: logger.debug( "%s is still in WAITING_FOR_REMOTE_KVS state.", request.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 # 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 # 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: num_external_computed_tokens, load_kv_async = ( self.connector.get_num_new_matched_tokens( request, num_new_local_computed_tokens)) # Total computed tokens (local + external). num_computed_tokens = (num_new_local_computed_tokens + num_external_computed_tokens) # KVTransfer: WAITING reqs have num_computed_tokens > 0 # after async KV recvs are completed. else: new_computed_blocks = ( self.kv_cache_manager.create_empty_block_list()) num_new_local_computed_tokens = 0 num_computed_tokens = request.num_computed_tokens encoder_inputs_to_schedule = None new_encoder_budget = encoder_budget # KVTransfer: loading remote KV, do not allocate for new work. if load_kv_async: assert num_external_computed_tokens > 0 num_new_tokens = 0 # Number of tokens to be scheduled. else: # 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 if (0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens): num_new_tokens = ( self.scheduler_config.long_prefill_token_threshold) # chunked prefill has to be enabled explicitly to allow # pooling requests to be chunked if not self.scheduler_config.chunked_prefill_enabled and \ num_new_tokens > token_budget: self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue 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_budget ) = self._try_schedule_encoder_inputs( request, num_computed_tokens, num_new_tokens, encoder_budget) if num_new_tokens == 0: # The request cannot be scheduled. break new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens + num_external_computed_tokens, num_new_local_computed_tokens, new_computed_blocks, num_lookahead_tokens=self.num_lookahead_tokens, delay_cache_blocks=load_kv_async, ) if new_blocks is None: # The request cannot be scheduled. 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, new_computed_blocks + new_blocks, num_external_computed_tokens, ) # 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 if request.use_structured_output: structured_output_request_ids[request.request_id] = ( req_index) req_index += 1 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_block_ids[request.request_id] = ( self.kv_cache_manager.get_block_ids(request.request_id)) num_scheduled_tokens[request.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.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_budget = new_encoder_budget # 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) if self.running: any_request = self.running[0] num_common_prefix_blocks = ( self.kv_cache_manager.get_num_common_prefix_blocks( any_request, len(self.running))) grammar_bitmask = self.structured_output_manager.grammar_bitmask( self.requests, structured_output_request_ids, scheduled_spec_decode_tokens, ) # Construct the scheduler output. new_reqs_data = [ NewRequestData.from_request(req, req_to_new_block_ids[req.request_id]) for req in scheduled_new_reqs ] cached_reqs_data = self._make_cached_request_data( scheduled_running_reqs, scheduled_resumed_reqs, num_scheduled_tokens, scheduled_spec_decode_tokens, req_to_new_block_ids, ) 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, # 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_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: # 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 = self.connector.build_connector_meta(scheduler_output) scheduler_output.kv_connector_metadata = meta events = self.kv_cache_manager.take_events() if events: batch = KVEventBatch(ts=time.time(), events=events) self.kv_event_publisher.publish(batch) self._update_after_schedule(scheduler_output) return scheduler_output def schedule_split_pd(self) -> SchedulerOutput: # Give priority to scheduling waiting requests scheduled_new_reqs: list[Request] = [] scheduled_resumed_reqs: list[Request] = [] scheduled_running_reqs: list[Request] = [] preempted_reqs: list[Request] = [] # NOTE: structured_output_request_ids maps # a request's (request that uses structured output) # request_id to the running request index. # 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] = {} req_to_new_block_ids: dict[str, tuple[list[int], ...]] = {} num_scheduled_tokens: dict[str, int] = {} token_budget = self.max_num_scheduled_tokens # Encoder-related. scheduled_encoder_inputs: dict[str, list[int]] = {} encoder_budget = self.max_num_encoder_input_tokens # Spec decode-related. scheduled_spec_decode_tokens: dict[str, list[int]] = {} # For logging. scheduled_timestamp = time.monotonic() # 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) req_index = len(self.running) # First, schedule the WAITING requests. while self.waiting and token_budget > 0: if len(self.running) == self.max_num_running_reqs: break request = self.waiting.peek_request() # 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: request.status = RequestStatus.WAITING else: logger.debug( "%s is still in WAITING_FOR_REMOTE_KVS state.", request.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 # 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 # 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: num_external_computed_tokens, load_kv_async = ( self.connector.get_num_new_matched_tokens( request, num_new_local_computed_tokens)) # Total computed tokens (local + external). num_computed_tokens = (num_new_local_computed_tokens + num_external_computed_tokens) # KVTransfer: WAITING reqs have num_computed_tokens > 0 # after async KV recvs are completed. else: new_computed_blocks = ( self.kv_cache_manager.create_empty_block_list()) num_new_local_computed_tokens = 0 num_computed_tokens = request.num_computed_tokens encoder_inputs_to_schedule = None new_encoder_budget = encoder_budget # KVTransfer: loading remote KV, do not allocate for new work. if load_kv_async: assert num_external_computed_tokens > 0 num_new_tokens = 0 # Number of tokens to be scheduled. else: # 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 if (0 < self.scheduler_config.long_prefill_token_threshold < num_new_tokens): num_new_tokens = ( self.scheduler_config.long_prefill_token_threshold) # chunked prefill has to be enabled explicitly to allow # pooling requests to be chunked if not self.scheduler_config.chunked_prefill_enabled and \ num_new_tokens > token_budget: self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue 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_budget ) = self._try_schedule_encoder_inputs( request, num_computed_tokens, num_new_tokens, encoder_budget) if num_new_tokens == 0: # The request cannot be scheduled. break new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens + num_external_computed_tokens, num_new_local_computed_tokens, new_computed_blocks, num_lookahead_tokens=self.num_lookahead_tokens, delay_cache_blocks=load_kv_async, ) if new_blocks is None: # The request cannot be scheduled. 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, new_computed_blocks + new_blocks, num_external_computed_tokens, ) # 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 if request.use_structured_output: structured_output_request_ids[request.request_id] = ( req_index) req_index += 1 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_block_ids[request.request_id] = ( self.kv_cache_manager.get_block_ids(request.request_id)) num_scheduled_tokens[request.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.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_budget = new_encoder_budget # Put back any skipped requests at the head of the waiting queue if skipped_waiting_requests: self.waiting.prepend_requests(skipped_waiting_requests) # Next, schedule the RUNNING requests. if not scheduled_new_reqs and not scheduled_resumed_reqs: req_index = 0 while req_index < len(self.running) and token_budget > 0: request = self.running[req_index] num_new_tokens = (request.num_tokens_with_spec - 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 new_encoder_budget = encoder_budget if request.has_encoder_inputs: (encoder_inputs_to_schedule, num_new_tokens, new_encoder_budget) = self._try_schedule_encoder_inputs( request, request.num_computed_tokens, num_new_tokens, encoder_budget) 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 PP>1 and # we have already scheduled all prompt tokens but they are # not finished yet. # 2. The encoder budget is exhausted. # 3. The encoder cache is exhausted. # 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 num_draft_tokens = max( num_new_tokens + request.num_computed_tokens - request.num_tokens, 0) while True: new_blocks = self.kv_cache_manager.allocate_slots( request, num_new_tokens, num_draft_tokens=num_draft_tokens, num_lookahead_tokens=self.num_lookahead_tokens) if new_blocks is None: # 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) else: preempted_req = self.running.pop() self.kv_cache_manager.free(preempted_req) preempted_req.status = RequestStatus.PREEMPTED preempted_req.num_computed_tokens = 0 if self.log_stats: preempted_req.record_event( EngineCoreEventType.PREEMPTED, scheduled_timestamp) self.waiting.prepend_request(preempted_req) preempted_reqs.append(preempted_req) if preempted_req == request: # No more request to preempt. can_schedule = False break else: # The request can be scheduled. can_schedule = True break if not can_schedule: break assert new_blocks is not None # Schedule the request. scheduled_running_reqs.append(request) if request.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[request.request_id] = req_index req_to_new_block_ids[request.request_id] = ( new_blocks.get_block_ids()) num_scheduled_tokens[request.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) if num_scheduled_spec_tokens > 0: # Trim spec_token_ids list to num_scheduled_spec_tokens. del request.spec_token_ids[num_scheduled_spec_tokens:] scheduled_spec_decode_tokens[request.request_id] = ( request.spec_token_ids) # Encoder-related. if encoder_inputs_to_schedule: scheduled_encoder_inputs[request.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_budget = new_encoder_budget # 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 # 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) if self.running: any_request = self.running[0] num_common_prefix_blocks = ( self.kv_cache_manager.get_num_common_prefix_blocks( any_request, len(self.running))) grammar_bitmask = self.structured_output_manager.grammar_bitmask( self.requests, structured_output_request_ids, scheduled_spec_decode_tokens, ) # Construct the scheduler output. new_reqs_data = [ NewRequestData.from_request(req, req_to_new_block_ids[req.request_id]) for req in scheduled_new_reqs ] cached_reqs_data = self._make_cached_request_data( scheduled_running_reqs, scheduled_resumed_reqs, num_scheduled_tokens, scheduled_spec_decode_tokens, req_to_new_block_ids, ) 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, # 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_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: # 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 = self.connector.build_connector_meta(scheduler_output) scheduler_output.kv_connector_metadata = meta events = self.kv_cache_manager.take_events() if events: batch = KVEventBatch(ts=time.time(), events=events) self.kv_event_publisher.publish(batch) self._update_after_schedule(scheduler_output) return scheduler_output def schedule(self) -> SchedulerOutput: if self.num_spec_tokens > 0: return self.schedule_split_pd() else: return self.schedule_default() def _update_after_schedule( self, scheduler_output: SchedulerOutput, ) -> None: # Advance the number of computed tokens for the request AFTER # the request is scheduled. # 1. The scheduler_output of the current step has to include the # original number of scheduled tokens to determine input IDs. # 2. Advance the number of computed tokens here allowing us to # schedule the prefill request again immediately in the next # scheduling step. # 3. If some tokens (e.g. spec tokens) are rejected later, the number of # computed tokens will be adjusted in update_from_output. num_scheduled_tokens = scheduler_output.num_scheduled_tokens for req_id, num_scheduled_token in num_scheduled_tokens.items(): request = self.requests[req_id] request.num_computed_tokens += num_scheduled_token # Clear the finished request IDs. # NOTE: We shouldn't do self.finished_req_ids.clear() here because # it will also affect the scheduler output. self.finished_req_ids = set() def _make_cached_request_data( self, running_reqs: list[Request], resumed_reqs: list[Request], num_scheduled_tokens: dict[str, int], spec_decode_tokens: dict[str, list[int]], req_to_new_block_ids: dict[str, tuple[list[int], ...]], ) -> CachedRequestData: req_ids: list[str] = [] new_token_ids: list[list[int]] = [] new_block_ids: list[tuple[list[int], ...]] = [] num_computed_tokens: list[int] = [] for req in itertools.chain(running_reqs, resumed_reqs): req_id = req.request_id req_ids.append(req_id) num_tokens = (num_scheduled_tokens[req_id] - len(spec_decode_tokens.get(req_id, ()))) if self.use_pp: # When using PP, the scheduler sends the sampled tokens back, # because there's no direct communication between the first- # stage worker and the last-stage worker. Otherwise, we don't # need to send the sampled tokens back because the model runner # will cache them. token_ids = req.all_token_ids[req.num_computed_tokens:req. num_computed_tokens + num_tokens] new_token_ids.append(token_ids) new_block_ids.append(req_to_new_block_ids[req_id]) num_computed_tokens.append(req.num_computed_tokens) # Because resumed_reqs is usually empty, it is more efficient to do # in-place appending so that we don't need to allocate a new list. resumed_from_preemption = [False] * len(running_reqs) resumed_from_preemption += [True] * len(resumed_reqs) return CachedRequestData( req_ids=req_ids, resumed_from_preemption=resumed_from_preemption, new_token_ids=new_token_ids, new_block_ids=new_block_ids, num_computed_tokens=num_computed_tokens, ) def _try_schedule_encoder_inputs( self, request: Request, num_computed_tokens: int, num_new_tokens: int, encoder_budget: int, ) -> tuple[list[int], int, int]: """ Determine which encoder inputs need to be scheduled in the current step, and update `num_new_tokens` and encoder token budget accordingly. An encoder input will be scheduled if: - Its output tokens overlap with the range of tokens being computed in this step, i.e., [num_computed_tokens, num_computed_tokens + num_new_tokens). - It is not already computed and stored in the encoder cache. - There is sufficient encoder token budget to process it. - The encoder cache has space to store it. If an encoder input cannot be scheduled due to cache or budget limitations, the method adjusts `num_new_tokens` to schedule only the decoder tokens up to just before the unschedulable encoder input. Note that num_computed_tokens includes both locally cached blocks and externally cached blocks (via KVConnector). """ if num_new_tokens == 0 or not request.has_encoder_inputs: return [], num_new_tokens, encoder_budget encoder_inputs_to_schedule: list[int] = [] mm_positions = request.mm_positions assert mm_positions is not None assert len(mm_positions) > 0 for i, pos_info in enumerate(mm_positions): start_pos = pos_info.offset num_encoder_tokens = pos_info.length # The encoder output is needed if the two ranges overlap: # [num_computed_tokens, num_computed_tokens + num_new_tokens) and # [start_pos, start_pos + num_encoder_tokens) if start_pos >= num_computed_tokens + num_new_tokens: # The encoder input is not needed in this step. break if start_pos + num_encoder_tokens <= num_computed_tokens: # The encoder input is already computed and stored # in the decoder's KV cache. continue if self.encoder_cache_manager.has_cache(request, i): # The encoder input is already computed and cached. continue # If no encoder input chunking is allowed, we do not want to # partially schedule a multimodal item. If the scheduled range would # only cover part of the mm input, roll back to before the mm item. if (self.scheduler_config.disable_chunked_mm_input and num_computed_tokens < start_pos and (num_computed_tokens + num_new_tokens) < (start_pos + num_encoder_tokens)): num_new_tokens = start_pos - num_computed_tokens break if (not self.encoder_cache_manager.can_allocate(request, i) or num_encoder_tokens > encoder_budget): # The encoder cache is full or the encoder budget is exhausted. # NOTE(woosuk): We assume that the encoder input tokens should # be processed altogether, as the encoder usually uses # bidirectional attention. if num_computed_tokens < start_pos: # We only schedule the decoder tokens just before the # encoder input. num_new_tokens = start_pos - num_computed_tokens else: # Because of prefix caching, num_computed_tokens is greater # than start_pos even though its encoder input is not # available. In this case, we can't schedule any token for # the request in this step. num_new_tokens = 0 break encoder_budget -= num_encoder_tokens encoder_inputs_to_schedule.append(i) return encoder_inputs_to_schedule, num_new_tokens, encoder_budget def update_from_output( self, scheduler_output: SchedulerOutput, model_runner_output: ModelRunnerOutput, ) -> dict[int, EngineCoreOutputs]: sampled_token_ids = model_runner_output.sampled_token_ids spec_token_ids = model_runner_output.spec_token_ids logprobs = model_runner_output.logprobs prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict num_scheduled_tokens = scheduler_output.num_scheduled_tokens pooler_outputs = model_runner_output.pooler_output num_nans_in_logits = model_runner_output.num_nans_in_logits new_running: list[Request] = [] outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list) spec_decoding_stats: Optional[SpecDecodingStats] = None # NOTE(woosuk): As len(self.running) can be up to 1K or more, the below # loop can be a performance bottleneck. We should do our best to avoid # expensive operations inside the loop. for request in self.running: req_id = request.request_id num_tokens_scheduled = num_scheduled_tokens.get(req_id, 0) if num_tokens_scheduled == 0: # The request was not scheduled in this step. new_running.append(request) continue req_index = model_runner_output.req_id_to_index[req_id] generated_token_ids = sampled_token_ids[ req_index] if sampled_token_ids else [] scheduled_spec_token_ids = ( scheduler_output.scheduled_spec_decode_tokens.get(req_id)) if scheduled_spec_token_ids: # num_computed_tokens represents the number of tokens # processed in the current step, considering scheduled # tokens and rejections. If some tokens are rejected, # num_computed_tokens is decreased by the number of rejected # tokens, where is given by: # len(scheduled_spec_token_ids) + 1 - len(generated_token_ids). num_tokens_rejected = (len(scheduled_spec_token_ids) + 1 - len(generated_token_ids)) request.num_computed_tokens -= num_tokens_rejected spec_decoding_stats = self.make_spec_decoding_stats( spec_decoding_stats, num_draft_tokens=len(scheduled_spec_token_ids), num_accepted_tokens=len(generated_token_ids) - 1) # NOTE(woosuk): This has to be executed after updating # `request.num_computed_tokens`. if request.has_encoder_inputs: self._free_encoder_inputs(request) stopped = False new_logprobs = None new_token_ids = generated_token_ids kv_transfer_params = None # Append generated tokens and check for stop. Note that if # a request is still being prefilled, we expect the model runner # to return empty token ids for the request. for num_new, output_token_id in enumerate(new_token_ids, 1): request.append_output_token_ids(output_token_id) # Check for stop and update request state. # This must be called before we make the EngineCoreOutput. stopped = check_stop(request, self.max_model_len) if stopped: kv_transfer_params = self._free_request(request) del new_token_ids[num_new:] # Trim new tokens if needed. break pooler_output = None if pooler_outputs: pooler_output = pooler_outputs[req_index] stopped = check_stop(request, self.max_model_len, pooler_output) if stopped: kv_transfer_params = self._free_request(request) # Extract sample logprobs if needed. if request.sampling_params is not None \ and request.sampling_params.logprobs is not None and logprobs: # NOTE: once we support N tokens per step (spec decode), # the outer lists can be of length > 1. new_logprobs = logprobs.slice(req_index, req_index + 1) if new_token_ids and self.structured_output_manager.should_advance( request): # NOTE: structured_output_request # should not be None if use_structured_output, we have # check above, so safe to ignore type warning request.structured_output_request.grammar.accept_tokens( # type: ignore[union-attr] req_id, new_token_ids) # spec_token_ids comes from the model runner output if num_nans_in_logits is not None and req_id in num_nans_in_logits: request.num_nans_in_logits = num_nans_in_logits[req_id] # Add newly generated spec token ids to the request. if spec_token_ids is not None: if self.structured_output_manager.should_advance(request): metadata = request.structured_output_request # Needs to happen after new_token_ids are accepted. request.spec_token_ids = metadata.grammar.validate_tokens( # type: ignore[union-attr] spec_token_ids[req_index]) else: request.spec_token_ids = spec_token_ids[req_index] # Get prompt logprobs for this request. prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id) if new_token_ids or pooler_output is not None \ or kv_transfer_params: # Add EngineCoreOutput for this Request. outputs[request.client_index].append( EngineCoreOutput( request_id=req_id, new_token_ids=new_token_ids, finish_reason=request.get_finished_reason(), new_logprobs=new_logprobs, new_prompt_logprobs_tensors=prompt_logprobs_tensors, pooling_output=pooler_output, stop_reason=request.stop_reason, events=request.take_events(), kv_transfer_params=kv_transfer_params, num_cached_tokens=request.num_cached_tokens, )) else: # Invariant: EngineCore returns no partial prefill outputs. assert not prompt_logprobs_tensors if not stopped: new_running.append(request) self.running = new_running # KV Connector: update state for finished KV Transfers. self._update_from_kv_xfer_finished(model_runner_output) # Create EngineCoreOutputs for all clients that have requests with # outputs in this step. engine_core_outputs = { client_index: EngineCoreOutputs(outputs=outs) for client_index, outs in outputs.items() } finished_req_ids = self.finished_req_ids_dict if finished_req_ids: # Include ids of requests that finished since last outputs # were sent. for client_index, finished_set in finished_req_ids.items(): # Set finished request set in EngineCoreOutputs for this client. if (eco := engine_core_outputs.get(client_index)) is not None: eco.finished_requests = finished_set else: engine_core_outputs[client_index] = EngineCoreOutputs( finished_requests=finished_set) finished_req_ids.clear() if engine_core_outputs: # Return stats to only one of the front-ends. next(iter(engine_core_outputs.values())).scheduler_stats = ( self.make_stats(spec_decoding_stats)) return engine_core_outputs def _free_encoder_inputs(self, request: Request) -> None: cached_encoder_input_ids = ( self.encoder_cache_manager.get_cached_input_ids(request)) # OPTIMIZATION: Avoid list(set) if the set is empty. if not cached_encoder_input_ids: return # Here, we use list(set) to avoid modifying the set while iterating # over it. for input_id in list(cached_encoder_input_ids): mm_positions = request.mm_positions[input_id] start_pos = mm_positions.offset num_tokens = mm_positions.length if start_pos + num_tokens <= request.num_computed_tokens: # The encoder output is already processed and stored # in the decoder's KV cache. self.encoder_cache_manager.free_encoder_input( request, input_id) def get_request_counts(self) -> tuple[int, int]: """Returns (num_running_reqs, num_waiting_reqs).""" return len(self.running), len(self.waiting) def add_request(self, request: Request) -> None: self.waiting.add_request(request) self.requests[request.request_id] = request if self.log_stats: request.record_event(EngineCoreEventType.QUEUED) def finish_requests( self, request_ids: Union[str, Iterable[str]], finished_status: RequestStatus, ) -> None: """Handles the finish signal from outside the scheduler. For example, the API server can abort a request when the client disconnects. """ assert RequestStatus.is_finished(finished_status) if isinstance(request_ids, str): request_ids = (request_ids, ) else: request_ids = set(request_ids) running_requests_to_remove = [] waiting_requests_to_remove = [] valid_requests = [] # First pass: collect requests to remove from queues for req_id in request_ids: request = self.requests.get(req_id) if request is None: # Invalid request ID. continue valid_requests.append(request) if request.status == RequestStatus.RUNNING: running_requests_to_remove.append(request) else: waiting_requests_to_remove.append(request) # Remove all requests from queues at once for better efficiency for request in running_requests_to_remove: self.running.remove(request) if waiting_requests_to_remove: self.waiting.remove_requests(waiting_requests_to_remove) # Second pass: set status and free requests for request in valid_requests: request.status = finished_status self._free_request(request) def _free_request(self, request: Request) -> Optional[dict[str, Any]]: assert request.is_finished() delay_free_blocks, kv_xfer_params = self._connector_finished(request) self.encoder_cache_manager.free(request) request_id = request.request_id self.finished_req_ids.add(request_id) if self.finished_req_ids_dict is not None: self.finished_req_ids_dict[request.client_index].add(request_id) if not delay_free_blocks: self._free_blocks(request) return kv_xfer_params def _free_blocks(self, request: Request): assert request.is_finished() self.kv_cache_manager.free(request) self.kv_cache_manager.free_block_hashes(request) del self.requests[request.request_id] def get_num_unfinished_requests(self) -> int: return len(self.waiting) + len(self.running) def has_finished_requests(self) -> bool: return len(self.finished_req_ids) > 0 def reset_prefix_cache(self) -> bool: return self.kv_cache_manager.reset_prefix_cache() def make_stats( self, spec_decoding_stats: Optional[SpecDecodingStats] = None, ) -> Optional[SchedulerStats]: if not self.log_stats: return None prefix_cache_stats = self.kv_cache_manager.make_prefix_cache_stats() assert prefix_cache_stats is not None return SchedulerStats( num_running_reqs=len(self.running), num_waiting_reqs=len(self.waiting), kv_cache_usage=self.kv_cache_manager.usage, prefix_cache_stats=prefix_cache_stats, spec_decoding_stats=spec_decoding_stats, num_corrupted_reqs=sum(req.is_output_corrupted for req in self.running), ) def make_spec_decoding_stats( self, spec_decoding_stats: Optional[SpecDecodingStats], num_draft_tokens: int, num_accepted_tokens: int, ) -> Optional[SpecDecodingStats]: if not self.log_stats: return None if spec_decoding_stats is None: spec_decoding_stats = SpecDecodingStats.new(self.num_spec_tokens) spec_decoding_stats.observe_draft( num_draft_tokens=num_draft_tokens, num_accepted_tokens=num_accepted_tokens) return spec_decoding_stats def shutdown(self) -> None: if self.kv_event_publisher: self.kv_event_publisher.shutdown() ######################################################################## # KV Connector Related Methods ######################################################################## def get_kv_connector(self) -> Optional[KVConnectorBase_V1]: return self.connector def _connector_finished( self, request: Request) -> tuple[bool, Optional[dict[str, Any]]]: """ Invoke the KV connector request_finished() method if applicable. Returns optional kv transfer parameters to be included with the request outputs. """ if self.connector is None: return False, None (block_ids, ) = self.kv_cache_manager.get_block_ids(request.request_id) return self.connector.request_finished(request, block_ids) def _update_waiting_for_remote_kv(self, request: Request) -> bool: """ KV Connector: check if the request_id is finished_recving. The finished_recving_kv_req_ids list is populated on the previous steps()'s update_from_output based on the worker side connector. When the kv transfer is ready, we cache the blocks and the request state will be moved back to WAITING from WAITING_FOR_REMOTE_KV. """ assert self.connector is not None if request.request_id not in self.finished_recving_kv_req_ids: return False # Now that the blocks are ready, actually cache them. (block_ids, ) = self.kv_cache_manager.get_block_ids(request.request_id) num_computed_tokens = len(block_ids) * self.block_size # Handle the case where num request tokens less then one block. num_computed_tokens = min(num_computed_tokens, request.num_tokens) if num_computed_tokens == request.num_tokens: num_computed_tokens -= 1 # This will cache the blocks iff caching is enabled. self.kv_cache_manager.cache_blocks(request, num_computed_tokens) # Update the request state for scheduling. request.num_computed_tokens = num_computed_tokens # Return that we are ready. self.finished_recving_kv_req_ids.remove(request.request_id) return True def _update_from_kv_xfer_finished(self, model_runner_output: ModelRunnerOutput): """ KV Connector: update the scheduler state based on the output. The Worker side connectors add finished_recving and finished_sending reqs to the output. * if finished_sending: free the blocks # if finished_recving: add to state so we can scheduler the request during the next step. """ # KV Connector:: update recv and send status from last step. for req_id in (model_runner_output.finished_recving or ()): logger.debug("Finished recving KV transfer for request %s", req_id) self.finished_recving_kv_req_ids.add(req_id) for req_id in (model_runner_output.finished_sending or ()): logger.debug("Finished sending KV transfer for request %s", req_id) self._free_blocks(self.requests[req_id])