################################################################################ # Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ################################################################################ from __future__ import annotations import itertools import time from typing import Optional from fastcore.basics import patch_to from vllm.distributed.kv_events import KVEventBatch from vllm.logger import init_logger from vllm.v1.core.kv_cache_manager import KVCacheBlocks 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.scheduler import Scheduler from vllm.v1.engine import EngineCoreEventType from vllm.v1.request import Request, RequestStatus logger = init_logger(__name__) @patch_to(Scheduler) 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 # 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() # 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_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 new_encoder_compute_budget = encoder_compute_budget if request.has_encoder_inputs: (encoder_inputs_to_schedule, num_new_tokens, new_encoder_compute_budget) = self._try_schedule_encoder_inputs( request, request.num_computed_tokens, num_new_tokens, encoder_compute_budget) if self.scheduler_config.chunked_prefill_enabled and request.num_output_tokens == 0: # shortest chunked prefill length is num_spec_tokens + 1 prefill_schedul_threshold = self.num_spec_tokens + 1 # Calculate remaining prompt tokens when request is in prefill phase remaining_prompt_tokens = request.num_tokens - request.num_computed_tokens - num_new_tokens if num_new_tokens > prefill_schedul_threshold: # Boundary condition: when remaining tokens equal or less than threshold, # reduce current round's token count to prevent phase misclassification # in reorder batch later in next round if 0 < remaining_prompt_tokens <= prefill_schedul_threshold: num_new_tokens -= (prefill_schedul_threshold - remaining_prompt_tokens + 1) num_new_tokens = 0 if num_new_tokens < prefill_schedul_threshold else num_new_tokens elif remaining_prompt_tokens > 0: # cannot schedule less than threshold tokens in chunked prefill num_new_tokens = 0 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. # 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 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 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) if preempted_req in scheduled_running_reqs: scheduled_running_reqs.remove(preempted_req) else: preempted_req = self.running.pop() self.kv_cache_manager.free(preempted_req) self.encoder_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) req_to_new_blocks[request.request_id] = new_blocks 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_compute_budget = new_encoder_compute_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)) if num_external_computed_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 # 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_compute_budget = encoder_compute_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_compute_budget ) = self._try_schedule_encoder_inputs( request, num_computed_tokens, num_new_tokens, encoder_compute_budget) if num_new_tokens == 0: # The request cannot be scheduled. break if num_new_tokens <= self.num_spec_tokens + 1: # Too short waiting requests can not be scheduled. self.waiting.pop_request() skipped_waiting_requests.prepend_request(request) continue # 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. if self.is_encoder_decoder and request.has_encoder_inputs: # TODO(russellb): For Whisper, we know that the input is # always padded to the maximum length. If we support other # encoder-decoder models, this will need to be updated if we # want to only allocate what is needed. num_encoder_tokens =\ self.scheduler_config.max_num_encoder_input_tokens else: num_encoder_tokens = 0 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=effective_lookahead_tokens, delay_cache_blocks=load_kv_async, num_encoder_tokens=num_encoder_tokens, ) 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 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_blocks[request.request_id] = ( self.kv_cache_manager.get_blocks(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_compute_budget = new_encoder_compute_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))) # Construct the scheduler output. new_reqs_data = [ NewRequestData.from_request( req, req_to_new_blocks[req.request_id].get_block_ids()) 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_blocks, ) scheduled_requests = (scheduled_new_reqs + scheduled_running_reqs + scheduled_resumed_reqs) structured_output_request_ids, grammar_bitmask = (self.get_grammar_bitmask( scheduled_requests, scheduled_spec_decode_tokens)) 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_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes( ), 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 # collect KV cache events from KV cache manager events = self.kv_cache_manager.take_events() # collect KV cache events from connector if self.connector is not None: connector_events = self.connector.take_events() if connector_events: if events is None: events = list(connector_events) else: events.extend(connector_events) # publish collected KV cache 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 @patch_to(Scheduler) 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_blocks: dict[str, KVCacheBlocks], ) -> CachedRequestData: req_ids: list[str] = [] new_token_ids: list[list[int]] = [] new_block_ids: list[Optional[tuple[list[int], ...]]] = [] num_computed_tokens: list[int] = [] use_connector = self.connector is not None 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: if not use_connector: # 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) elif use_connector: # When using a KVConnector, we add a placeholder to avoid index # out of bounds errors. TODO: Remove this once the KVConnector # is updated to handle token IDs properly. new_token_ids.append([]) new_block_ids.append( req_to_new_blocks[req_id].get_block_ids(allow_none=True)) 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, )