import enum import time from collections import deque from dataclasses import dataclass, field from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple, Union from loguru import logger from aphrodite.common.config import CacheConfig, LoRAConfig, SchedulerConfig from aphrodite.common.sequence import (Sequence, SequenceData, SequenceGroup, SequenceGroupMetadata, SequenceStatus) from aphrodite.common.utils import merge_dicts from aphrodite.lora.request import LoRARequest from aphrodite.processing.interfaces import AllocStatus, BlockSpaceManager from aphrodite.processing.policy import Policy, PolicyFactory class PreemptionMode(enum.Enum): """Preemption modes. 1. Swapping: Swap out the blocks of the preempted sequences to CPU memory and swap them back in when the sequences are resumed. 2. Recomputation: Discard the blocks of the preempted sequences and recompute them when the sequences are resumed, treating the sequences as new prompts. """ SWAP = enum.auto() RECOMPUTE = enum.auto() @dataclass class SchedulingBudget: """The available slots for scheduling. TODO: Right now, the budget is request_id-aware meaning it can ignore budget update from the same request_id. It is because in normal scheduling path, we update RUNNING num_seqs ahead of time, meaning it could be updated more than once when scheduling RUNNING requests. Since this won't happen if we only have chunked prefill scheduling, we can remove this feature from the API when chunked prefill is enabled by default. """ token_budget: int max_num_seqs: int _requeset_ids_num_batched_tokens: Set[str] = field(default_factory=set) _requeset_ids_num_curr_seqs: Set[str] = field(default_factory=set) _num_batched_tokens: int = 0 _num_curr_seqs: int = 0 def can_schedule(self, *, num_new_tokens: int, num_new_seqs: int): assert num_new_tokens != 0 assert num_new_seqs != 0 return (self.num_batched_tokens + num_new_tokens <= self.token_budget and self.num_curr_seqs + num_new_seqs <= self.max_num_seqs) def remaining_token_budget(self): return self.token_budget - self.num_batched_tokens def add_num_batched_tokens(self, req_id: str, num_batched_tokens: int): if req_id in self._requeset_ids_num_batched_tokens: return self._requeset_ids_num_batched_tokens.add(req_id) self._num_batched_tokens += num_batched_tokens def subtract_num_batched_tokens(self, req_id: str, num_batched_tokens: int): if req_id in self._requeset_ids_num_batched_tokens: self._requeset_ids_num_batched_tokens.remove(req_id) self._num_batched_tokens -= num_batched_tokens def add_num_seqs(self, req_id: str, num_curr_seqs: int): if req_id in self._requeset_ids_num_curr_seqs: return self._requeset_ids_num_curr_seqs.add(req_id) self._num_curr_seqs += num_curr_seqs def subtract_num_seqs(self, req_id: str, num_curr_seqs: int): if req_id in self._requeset_ids_num_curr_seqs: self._requeset_ids_num_curr_seqs.remove(req_id) self._num_curr_seqs -= num_curr_seqs @property def num_batched_tokens(self): return self._num_batched_tokens @property def num_curr_seqs(self): return self._num_curr_seqs @dataclass class ScheduledSequenceGroup: # A sequence group that's scheduled. seq_group: SequenceGroup # The total chunk size (number of tokens) to process for next iteration. # 1 for decoding. Same as prompt tokens for prefill, but if prefill is # chunked, it can be smaller than that. token_chunk_size: int @dataclass class SchedulerOutputs: """The scheduling decision made from a scheduler.""" # Scheduled sequence groups. scheduled_seq_groups: Iterable[ScheduledSequenceGroup] # Number of prefill groups scheduled. num_prefill_groups: int # Total number of batched tokens. num_batched_tokens: int # Blocks to swap in. Dict of CPU -> GPU block number. blocks_to_swap_in: Dict[int, int] # Blocks to swap out. Dict of GPU -> CPU block number. blocks_to_swap_out: Dict[int, int] # Blocks to copy. Source to a list of dest blocks. blocks_to_copy: Dict[int, List[int]] # Sequence groups that are going to be ignored. ignored_seq_groups: List[SequenceGroup] # The number of slots for lookahead decoding. num_lookahead_slots: int def __post_init__(self): # Swap in and swap out should never happen at the same time. assert not (self.blocks_to_swap_in and self.blocks_to_swap_out) self.num_loras: int = len(self.lora_requests) if self.num_loras > 0: self._sort_by_lora_ids() def is_empty(self) -> bool: # NOTE: We do not consider the ignored sequence groups. return (not self.scheduled_seq_groups and not self.blocks_to_swap_in and not self.blocks_to_swap_out and not self.blocks_to_copy) def _sort_by_lora_ids(self): self.scheduled_seq_groups = sorted( self.scheduled_seq_groups, key=lambda g: (g.seq_group.lora_int_id, g.seq_group.request_id)) @property def lora_requests(self) -> Set[LoRARequest]: return { g.seq_group.lora_request for g in self.scheduled_seq_groups if g.seq_group.lora_request is not None } @dataclass class SchedulerRunningOutputs: """The requests that are scheduled from a running queue. Could contain prefill (prefill that's chunked) or decodes. If there's not enough memory, it can be preempted (for recompute) or swapped out. """ # Selected sequences that are running and in a decoding phase. decode_seq_groups: List[SequenceGroup] # Selected sequences that are running and in a prefill phase. # I.e., it means the prefill has been chunked. prefill_seq_groups: List[SequenceGroup] # The preempted sequences. preempted: List[SequenceGroup] # Sequences that are swapped out. swapped_out: List[SequenceGroup] # The blocks to swap out. blocks_to_swap_out: Dict[int, int] # The blocks to copy. blocks_to_copy: Dict[int, List[int]] # The number of slots for lookahead decoding. num_lookahead_slots: int @classmethod def create_empty(cls) -> "SchedulerRunningOutputs": return SchedulerRunningOutputs( decode_seq_groups=[], prefill_seq_groups=[], preempted=[], swapped_out=[], blocks_to_swap_out={}, blocks_to_copy={}, num_lookahead_slots=0, ) @dataclass class SchedulerSwappedInOutputs: """The requests that are scheduled from a swap queue. Could contain prefill (prefill that's chunked) or decodes. """ # Selected sequences that are going to be swapped in and is in a # decoding phase. decode_seq_groups: List[SequenceGroup] # Selected sequences that are going to be swapped in and in a prefill # phase. I.e., it means the prefill has been chunked. prefill_seq_groups: List[SequenceGroup] # The blocks to swap in. blocks_to_swap_in: Dict[int, int] # The blocks to copy. blocks_to_copy: Dict[int, List[int]] # The number of slots for lookahead decoding. num_lookahead_slots: int @classmethod def create_empty(cls) -> "SchedulerSwappedInOutputs": return SchedulerSwappedInOutputs( decode_seq_groups=[], prefill_seq_groups=[], blocks_to_swap_in={}, blocks_to_copy={}, num_lookahead_slots=0, ) @dataclass class SchedulerPrefillOutputs: """The requests that are scheduled from a waiting queue. Could contain a fresh prefill requests or preempted requests that need to be recomputed from scratch. """ # Selected sequences for prefill. seq_groups: List[SequenceGroup] # Ignored sequence groups. ignored_seq_groups: List[SequenceGroup] num_lookahead_slots: int @classmethod def create_empty(cls) -> "SchedulerPrefillOutputs": return SchedulerPrefillOutputs( seq_groups=[], ignored_seq_groups=[], num_lookahead_slots=0, ) class Scheduler: def __init__( self, scheduler_config: SchedulerConfig, cache_config: CacheConfig, lora_config: Optional[LoRAConfig], ) -> None: self.scheduler_config = scheduler_config self.cache_config = cache_config # Note for LoRA scheduling: the current policy is extremely # simple and NOT fair. It can lead to starvation of some # LoRAs. This should be improved in the future. self.lora_config = lora_config if self.scheduler_config.chunked_prefill_enabled: self.prompt_limit = self.scheduler_config.max_model_len else: self.prompt_limit = min( self.scheduler_config.max_model_len, self.scheduler_config.max_num_batched_tokens) BlockSpaceManagerImpl = BlockSpaceManager.get_block_space_manager_class( version="v2" if self.scheduler_config. use_v2_block_manager else "v1") # Create the block space manager. self.block_manager = BlockSpaceManagerImpl( block_size=self.cache_config.block_size, num_gpu_blocks=self.cache_config.num_gpu_blocks, num_cpu_blocks=self.cache_config.num_cpu_blocks, sliding_window=self.cache_config.sliding_window, enable_caching=self.cache_config.enable_prefix_caching) # Sequence groups in the WAITING state. # Contain new prefill or preempted requests. self.waiting: Deque[SequenceGroup] = deque() # Sequence groups in the RUNNING state. # Contain decode requests. self.running: Deque[SequenceGroup] = deque() # Sequence groups in the SWAPPED state. # Contain decode requests that are swapped out. self.swapped: Deque[SequenceGroup] = deque() # Time at previous scheduling step self.prev_time = 0.0 # Did we schedule a prompt at previous step? self.prev_prompt = False # Latency of the last prompt step self.last_prompt_latency = 0.0 @property def lora_enabled(self) -> bool: return bool(self.lora_config) @property def num_decoding_tokens_per_seq(self) -> int: """The number of new tokens.""" return 1 def add_seq_group(self, seq_group: SequenceGroup) -> None: # Add sequence groups to the waiting queue. self.waiting.append(seq_group) def abort_seq_group(self, request_id: Union[str, Iterable[str]]) -> None: """Aborts a sequence group with the given ID. Check if the sequence group with the given ID is present in any of the state queue. If present, remove the sequence group from the state queue. Also, if any of the sequences in the sequence group is not finished, free the sequence with status `FINISHED_ABORTED`. Otherwise, do nothing. Args: request_id: The ID(s) of the sequence group to abort. """ if isinstance(request_id, str): request_id = (request_id, ) request_ids = set(request_id) for state_queue in [self.waiting, self.running, self.swapped]: aborted_groups: List[SequenceGroup] = [] for seq_group in state_queue: if not request_ids: # Using 'break' here may add two extra iterations, # but is acceptable to reduce complexity . break if seq_group.request_id in request_ids: # Appending aborted group into pending list. aborted_groups.append(seq_group) request_ids.remove(seq_group.request_id) for aborted_group in aborted_groups: # Remove the sequence group from the state queue. state_queue.remove(aborted_group) for seq in aborted_group.get_seqs(): if seq.is_finished(): continue seq.status = SequenceStatus.FINISHED_ABORTED self.free_seq(seq) def has_unfinished_seqs(self) -> bool: return len(self.waiting) != 0 or len(self.running) != 0 or len( self.swapped) != 0 def get_num_unfinished_seq_groups(self) -> int: return len(self.waiting) + len(self.running) + len(self.swapped) def _schedule_running( self, running_queue: deque, budget: SchedulingBudget, curr_loras: Optional[Set[int]], policy: Policy, enable_chunking: bool = False, ) -> Tuple[deque, SchedulerRunningOutputs]: """Schedule sequence groups that are running. Running queue should include decode and chunked prefill requests. Args: running_queue: The queue that contains running requests (i.e., decodes). The given arguments are NOT in-place modified. budget: The scheduling budget. The argument is in-place updated when any decodes are preempted. curr_loras: Currently batched lora request ids. The argument is in-place updated when any decodes are preempted. policy: The sorting policy to sort running_queue. enable_chunking: If True, seq group can be chunked and only a chunked number of tokens are scheduled if `budget.num_batched_tokens` has not enough capacity to schedule all tokens. Returns: A tuple of remaining running queue (should be always 0) after scheduling and SchedulerRunningOutputs. """ # Blocks that need to be swapped or copied before model execution. blocks_to_swap_out: Dict[int, int] = {} blocks_to_copy: Dict[int, List[int]] = {} decode_seq_groups: List[ScheduledSequenceGroup] = [] prefill_seq_groups: List[ScheduledSequenceGroup] = [] preempted: List[SequenceGroup] = [] swapped_out: List[SequenceGroup] = [] # NOTE: Preemption happens only when there is no available slot # to keep all the sequence groups in the RUNNING state. # In this case, the policy is responsible for deciding which sequence # groups to preempt. now = time.time() running_queue = policy.sort_by_priority(now, running_queue) while running_queue: seq_group = running_queue[0] num_running_tokens = self._get_num_new_tokens( seq_group, SequenceStatus.RUNNING, enable_chunking, budget) # We can have up to 1 running prefill at any given time in running # queue, which means we can guarantee chunk size is at least 1. assert num_running_tokens != 0 running_queue.popleft() while not self._can_append_slots(seq_group): budget.subtract_num_batched_tokens(seq_group.request_id, num_running_tokens) num_running_seqs = seq_group.get_max_num_running_seqs() budget.subtract_num_seqs(seq_group.request_id, num_running_seqs) if curr_loras is not None and seq_group.lora_int_id > 0: curr_loras.remove(seq_group.lora_int_id) if running_queue: # Preempt the lowest-priority sequence groups. victim_seq_group = running_queue.pop() preempted_mode = self._preempt(victim_seq_group, blocks_to_swap_out) if preempted_mode == PreemptionMode.RECOMPUTE: preempted.append(victim_seq_group) else: swapped_out.append(victim_seq_group) else: # No other sequence groups can be preempted. # Preempt the current sequence group. preempted_mode = self._preempt(seq_group, blocks_to_swap_out) if preempted_mode == PreemptionMode.RECOMPUTE: preempted.append(seq_group) else: swapped_out.append(seq_group) break else: self._append_slots(seq_group, blocks_to_copy) is_prefill = seq_group.is_prefill() if is_prefill: prefill_seq_groups.append( ScheduledSequenceGroup( seq_group=seq_group, token_chunk_size=num_running_tokens)) else: decode_seq_groups.append( ScheduledSequenceGroup(seq_group=seq_group, token_chunk_size=1)) budget.add_num_batched_tokens(seq_group.request_id, num_running_tokens) # OPTIMIZATION: Note that get_max_num_running_seqs is # expensive. For the default scheduling chase where # enable_chunking is False, num_seqs are updated before running # this method, so we don't have to update it again here. if enable_chunking: num_running_seqs = seq_group.get_max_num_running_seqs() budget.add_num_seqs(seq_group.request_id, num_running_seqs) if curr_loras is not None and seq_group.lora_int_id > 0: curr_loras.add(seq_group.lora_int_id) # Make sure all queues are updated. assert len(running_queue) == 0 return running_queue, SchedulerRunningOutputs( decode_seq_groups=decode_seq_groups, prefill_seq_groups=prefill_seq_groups, preempted=preempted, swapped_out=swapped_out, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, num_lookahead_slots=self._get_num_lookahead_slots( is_prefill=False)) def _schedule_swapped( self, swapped_queue: deque, budget: SchedulingBudget, curr_loras: Optional[Set[int]], policy: Policy, enable_chunking: bool = False, ) -> Tuple[deque, SchedulerSwappedInOutputs]: """Schedule sequence groups that are swapped out. It schedules swapped requests as long as it fits `budget` and curr_loras <= max_lora from the scheduling config. The input arguments `budget` and `curr_loras` are updated based on scheduled seq_groups. Args: swapped_queue: The queue that contains swapped out requests. The given arguments are NOT in-place modified. budget: The scheduling budget. The argument is in-place updated when any requests are swapped in. curr_loras: Currently batched lora request ids. The argument is in-place updated when any requests are swapped in. policy: The sorting policy to sort swapped_queue. enable_chunking: If True, seq group can be chunked and only a chunked number of tokens are scheduled if `budget.num_batched_tokens` has not enough capacity to schedule all tokens. Returns: A tuple of remaining swapped_queue after scheduling and SchedulerSwappedInOutputs. """ # Blocks that need to be swapped or copied before model execution. blocks_to_swap_in: Dict[int, int] = {} blocks_to_copy: Dict[int, List[int]] = {} decode_seq_groups: List[ScheduledSequenceGroup] = [] prefill_seq_groups: List[ScheduledSequenceGroup] = [] now = time.time() swapped_queue = policy.sort_by_priority(now, swapped_queue) leftover_swapped: Deque[SequenceGroup] = deque() while swapped_queue: seq_group = swapped_queue[0] # If the sequence group cannot be swapped in, stop. if not self.block_manager.can_swap_in(seq_group): break lora_int_id = 0 if self.lora_enabled: lora_int_id = seq_group.lora_int_id assert curr_loras is not None assert self.lora_config is not None if (lora_int_id > 0 and (lora_int_id not in curr_loras) and len(curr_loras) >= self.lora_config.max_loras): # We don't have a space for another LoRA, so # we ignore this request for now. leftover_swapped.appendleft(seq_group) swapped_queue.popleft() continue # The total number of sequences in the RUNNING state should not # exceed the maximum number of sequences. num_new_seqs = seq_group.get_max_num_running_seqs() num_new_tokens = self._get_num_new_tokens(seq_group, SequenceStatus.SWAPPED, enable_chunking, budget) if (num_new_tokens == 0 or not budget.can_schedule(num_new_tokens=num_new_tokens, num_new_seqs=num_new_seqs)): break if lora_int_id > 0 and curr_loras is not None: curr_loras.add(lora_int_id) swapped_queue.popleft() self._swap_in(seq_group, blocks_to_swap_in) self._append_slots(seq_group, blocks_to_copy) is_prefill = seq_group.is_prefill() if is_prefill: prefill_seq_groups.append( ScheduledSequenceGroup(seq_group, token_chunk_size=num_new_tokens)) else: assert num_new_tokens == 1 decode_seq_groups.append( ScheduledSequenceGroup(seq_group, token_chunk_size=1)) budget.add_num_batched_tokens(seq_group.request_id, num_new_tokens) budget.add_num_seqs(seq_group.request_id, num_new_seqs) swapped_queue.extendleft(leftover_swapped) return swapped_queue, SchedulerSwappedInOutputs( decode_seq_groups=decode_seq_groups, prefill_seq_groups=prefill_seq_groups, blocks_to_swap_in=blocks_to_swap_in, blocks_to_copy=blocks_to_copy, num_lookahead_slots=self._get_num_lookahead_slots( is_prefill=False)) def _schedule_prefills( self, waiting_queue: deque, budget: SchedulingBudget, curr_loras: Optional[Set[int]], enable_chunking: bool = False, ) -> Tuple[deque, SchedulerPrefillOutputs]: """Schedule sequence groups that are in prefill stage. Note that the current scheduler treats PREEMPTED_FOR_RECOMPUTE as a new prefill (that starts from beginning -> most recently generated tokens). It schedules waiting requests as long as it fits `budget` and curr_loras <= max_lora from the scheduling config. The input arguments `budget` and `curr_loras` are updated based on scheduled seq_groups. Args: waiting_queue: The queue that contains prefill requests. The given arguments are NOT in-place modified. budget: The scheduling budget. The argument is in-place updated when any requests are scheduled. curr_loras: Currently batched lora request ids. The argument is in-place updated when any requests are scheduled. enable_chunking: If True, seq group can be chunked and only a chunked number of tokens are scheduled if `budget.num_batched_tokens` has not enough capacity to schedule all tokens. Returns: A tuple of remaining waiting_queue after scheduling and SchedulerSwappedInOutputs. """ ignored_seq_groups: List[SequenceGroup] = [] seq_groups: List[SequenceGroup] = [] # We don't sort waiting queue because we assume it is sorted. # Copy the queue so that the input queue is not modified. waiting_queue = deque([s for s in waiting_queue]) leftover_waiting_sequences: Deque[SequenceGroup] = deque() while self._passed_delay(time.time()) and waiting_queue: seq_group = waiting_queue[0] waiting_seqs = seq_group.get_seqs(status=SequenceStatus.WAITING) assert len(waiting_seqs) == 1, ( "Waiting sequence group should have only one prompt " "sequence.") num_new_tokens = self._get_num_new_tokens(seq_group, SequenceStatus.WAITING, enable_chunking, budget) if not enable_chunking: num_prompt_tokens = waiting_seqs[0].get_len() assert num_new_tokens == num_prompt_tokens if num_new_tokens > self.prompt_limit: logger.warning( "Input prompt (%d tokens) is too long" " and exceeds limit of %d", num_new_tokens, self.prompt_limit) for seq in waiting_seqs: seq.status = SequenceStatus.FINISHED_IGNORED ignored_seq_groups.append(seq_group) waiting_queue.popleft() continue # If the sequence group cannot be allocated, stop. can_allocate = self.block_manager.can_allocate(seq_group) if can_allocate == AllocStatus.LATER: break elif can_allocate == AllocStatus.NEVER: logger.warning( "Input prompt (%d tokens) is too long" " and exceeds the capacity of block_manager", num_new_tokens) for seq in waiting_seqs: seq.status = SequenceStatus.FINISHED_IGNORED ignored_seq_groups.append(seq_group) waiting_queue.popleft() continue lora_int_id = 0 if self.lora_enabled: lora_int_id = seq_group.lora_int_id assert curr_loras is not None assert self.lora_config is not None if (self.lora_enabled and lora_int_id > 0 and lora_int_id not in curr_loras and len(curr_loras) >= self.lora_config.max_loras): # We don't have a space for another LoRA, so # we ignore this request for now. leftover_waiting_sequences.appendleft(seq_group) waiting_queue.popleft() continue num_new_seqs = seq_group.get_max_num_running_seqs() if (num_new_tokens == 0 or not budget.can_schedule(num_new_tokens=num_new_tokens, num_new_seqs=num_new_seqs)): break # Can schedule this request. if curr_loras is not None and lora_int_id > 0: curr_loras.add(lora_int_id) waiting_queue.popleft() self._allocate_and_set_running(seq_group) seq_groups.append( ScheduledSequenceGroup(seq_group=seq_group, token_chunk_size=num_new_tokens)) budget.add_num_batched_tokens(seq_group.request_id, num_new_tokens) budget.add_num_seqs(seq_group.request_id, num_new_seqs) # Queue requests that couldn't be scheduled. waiting_queue.extendleft(leftover_waiting_sequences) if len(seq_groups) > 0: self.prev_prompt = True return waiting_queue, SchedulerPrefillOutputs( seq_groups=seq_groups, ignored_seq_groups=ignored_seq_groups, num_lookahead_slots=self._get_num_lookahead_slots(is_prefill=True)) def _schedule_default(self) -> SchedulerOutputs: """Schedule queued requests. The current policy is designed to optimize the throughput. First, it batches as many prefill requests as possible. And it schedules decodes. If there's a pressure on GPU memory, decode requests can be swapped or preempted. """ # Include running requests to the budget. budget = SchedulingBudget( token_budget=self.scheduler_config.max_num_batched_tokens, max_num_seqs=self.scheduler_config.max_num_seqs, ) # Make sure we include num running seqs before scheduling prefill, # so that we don't schedule beyond max_num_seqs for prefill. for seq_group in self.running: budget.add_num_seqs(seq_group.request_id, seq_group.get_max_num_running_seqs()) curr_loras = set( seq_group.lora_int_id for seq_group in self.running) if self.lora_enabled else None remaining_waiting, prefills = (self.waiting, SchedulerPrefillOutputs.create_empty()) remaining_running, running_scheduled = ( self.running, SchedulerRunningOutputs.create_empty()) remaining_swapped, swapped_in = ( self.swapped, SchedulerSwappedInOutputs.create_empty()) # If any requests are swapped, prioritized swapped requests. if not self.swapped: remaining_waiting, prefills = self._schedule_prefills( self.waiting, budget, curr_loras, enable_chunking=False) fcfs_policy = PolicyFactory.get_policy(policy_name="fcfs") # Don't schedule decodes if prefills are scheduled. # NOTE: If `_schedule_prefills` doesn't enable chunking, self.running # only contains decode requests, not chunked prefills. if len(prefills.seq_groups) == 0: remaining_running, running_scheduled = self._schedule_running( self.running, budget, curr_loras, fcfs_policy, enable_chunking=False) # If any sequence group is preempted, do not swap in any sequence # group. because it means there's no slot for new running requests. if len(running_scheduled.preempted) + len( running_scheduled.swapped_out) == 0: remaining_swapped, swapped_in = self._schedule_swapped( self.swapped, budget, curr_loras, fcfs_policy) assert (budget.num_batched_tokens <= self.scheduler_config.max_num_batched_tokens) assert budget.num_curr_seqs <= self.scheduler_config.max_num_seqs # Update waiting requests. self.waiting = remaining_waiting self.waiting.extendleft(running_scheduled.preempted) # Update new running requests. self.running = remaining_running self.running.extend([s.seq_group for s in prefills.seq_groups]) self.running.extend( [s.seq_group for s in running_scheduled.decode_seq_groups]) self.running.extend( [s.seq_group for s in swapped_in.decode_seq_groups]) # Update swapped requests. self.swapped = remaining_swapped self.swapped.extend(running_scheduled.swapped_out) # There should be no prefill from running queue because this policy # doesn't allow chunked prefills. assert len(running_scheduled.prefill_seq_groups) == 0 assert len(swapped_in.prefill_seq_groups) == 0 return SchedulerOutputs( scheduled_seq_groups=(prefills.seq_groups + running_scheduled.decode_seq_groups + swapped_in.decode_seq_groups), num_prefill_groups=len(prefills.seq_groups), num_batched_tokens=budget.num_batched_tokens, blocks_to_swap_in=swapped_in.blocks_to_swap_in, blocks_to_swap_out=running_scheduled.blocks_to_swap_out, blocks_to_copy=merge_dicts(running_scheduled.blocks_to_copy, swapped_in.blocks_to_copy), ignored_seq_groups=prefills.ignored_seq_groups, num_lookahead_slots=running_scheduled.num_lookahead_slots, ) def _schedule_chunked_prefill(self): """Schedule queued requests. Chunked prefill allows to chunk prefill requests, batch them together with decode requests. This policy 1. schedule as many decoding requests as possible. 2. schedule chunked prefill requests that are not finished. 3. schedule swapped request. 4. schedule new prefill requests. The policy can sustain the high GPU utilization because it can put prefill and decodes requests to the same batch, while it improves inter token latency because decodes requests don't need to blocked by prefill requests. """ budget = SchedulingBudget( token_budget=self.scheduler_config.max_num_batched_tokens, max_num_seqs=self.scheduler_config.max_num_seqs, ) curr_loras: Set[int] = set() remaining_waiting, prefills = (self.waiting, SchedulerPrefillOutputs.create_empty()) remaining_running, running_scheduled = ( self.running, SchedulerRunningOutputs.create_empty()) remaining_swapped, swapped_in = ( self.swapped, SchedulerSwappedInOutputs.create_empty()) # Decoding should be always scheduled first by fcfs. fcfs_policy = PolicyFactory.get_policy(policy_name="fcfs") remaining_running, running_scheduled = self._schedule_running( self.running, budget, curr_loras, fcfs_policy, enable_chunking=True) # Schedule swapped out requests. # If preemption happens, it means we don't have space for swap-in. if len(running_scheduled.preempted) + len( running_scheduled.swapped_out) == 0: remaining_swapped, swapped_in = self._schedule_swapped( self.swapped, budget, curr_loras, fcfs_policy) # Schedule new prefills. remaining_waiting, prefills = self._schedule_prefills( self.waiting, budget, curr_loras, enable_chunking=True) assert (budget.num_batched_tokens <= self.scheduler_config.max_num_batched_tokens) assert budget.num_curr_seqs <= self.scheduler_config.max_num_seqs # Update waiting requests. self.waiting = remaining_waiting self.waiting.extendleft(running_scheduled.preempted) # Update new running requests. self.running = remaining_running self.running.extend([s.seq_group for s in prefills.seq_groups]) self.running.extend( [s.seq_group for s in running_scheduled.decode_seq_groups]) self.running.extend( [s.seq_group for s in running_scheduled.prefill_seq_groups]) self.running.extend( [s.seq_group for s in swapped_in.decode_seq_groups]) self.running.extend( [s.seq_group for s in swapped_in.prefill_seq_groups]) # Update swapped requests. self.swapped = remaining_swapped self.swapped.extend(running_scheduled.swapped_out) return SchedulerOutputs( scheduled_seq_groups=(prefills.seq_groups + running_scheduled.prefill_seq_groups + swapped_in.prefill_seq_groups + running_scheduled.decode_seq_groups + swapped_in.decode_seq_groups), num_prefill_groups=(len(prefills.seq_groups) + len(swapped_in.prefill_seq_groups) + len(running_scheduled.prefill_seq_groups)), num_batched_tokens=budget.num_batched_tokens, blocks_to_swap_in=swapped_in.blocks_to_swap_in, blocks_to_swap_out=running_scheduled.blocks_to_swap_out, blocks_to_copy=merge_dicts(running_scheduled.blocks_to_copy, swapped_in.blocks_to_copy), ignored_seq_groups=prefills.ignored_seq_groups, num_lookahead_slots=running_scheduled.num_lookahead_slots, ) def _schedule(self) -> SchedulerOutputs: """Schedule queued requests.""" if self.scheduler_config.chunked_prefill_enabled: return self._schedule_chunked_prefill() else: return self._schedule_default() def _can_append_slots(self, seq_group: SequenceGroup) -> bool: """Determine whether or not we have enough space in the KV cache to continue generation of the sequence group. """ # Appending slots only occurs in decoding. is_prefill = False return self.block_manager.can_append_slots( seq_group=seq_group, num_lookahead_slots=self._get_num_lookahead_slots(is_prefill), ) def _can_swap_in(self, seq_group: SequenceGroup) -> bool: # Swapping in is considered decode. is_prefill = False return self.block_manager.can_swap_in( seq_group=seq_group, num_lookahead_slots=self._get_num_lookahead_slots(is_prefill), ) def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]: # Schedule sequence groups. # This function call changes the internal states of the scheduler # such as self.running, self.swapped, and self.waiting. scheduler_outputs = self._schedule() now = time.time() # Create input data structures. seq_group_metadata_list: List[SequenceGroupMetadata] = [] for i, scheduled_seq_group in enumerate( scheduler_outputs.scheduled_seq_groups): seq_group = scheduled_seq_group.seq_group token_chunk_size = scheduled_seq_group.token_chunk_size seq_group.maybe_set_first_scheduled_time(now) # seq_id -> SequenceData seq_data: Dict[int, SequenceData] = {} # seq_id -> physical block numbers block_tables: Dict[int, List[int]] = {} for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): seq_id = seq.seq_id seq_data[seq_id] = seq.data block_tables[seq_id] = self.block_manager.get_block_table(seq) self.block_manager.access_all_blocks_in_seq(seq, now) common_computed_block_nums = ( self.block_manager.get_common_computed_block_ids( seq_group.get_seqs(status=SequenceStatus.RUNNING))) do_sample = True if seq_group.is_prefill(): seqs = seq_group.get_seqs() # Prefill has only 1 sequence. assert len(seqs) == 1 # In the next iteration, all prompt tokens are not computed. # It means the prefill is chunked, and we don't need sampling. # NOTE: We use get_len instead of get_prompt_len because when # a sequence is preempted, prefill includes previous generated # output tokens. if (token_chunk_size + seqs[0].data.get_num_computed_tokens() < seqs[0].data.get_len()): do_sample = False # It assumes the scheduled_seq_groups is ordered by # prefill < decoding. is_prompt = seq_group.is_prefill() seq_group_metadata = SequenceGroupMetadata( request_id=seq_group.request_id, is_prompt=is_prompt, seq_data=seq_data, sampling_params=seq_group.sampling_params, block_tables=block_tables, do_sample=do_sample, token_chunk_size=token_chunk_size, lora_request=seq_group.lora_request, computed_block_nums=common_computed_block_nums, state=seq_group.state, # `multi_modal_data` will only be present for the 1st comm # between engine and worker. # the subsequent comms can still use delta, but # `multi_modal_data` will be None. multi_modal_data=seq_group.multi_modal_data if scheduler_outputs.num_prefill_groups > 0 else None, ) seq_group_metadata_list.append(seq_group_metadata) # Now that the batch has been created, we can assume all blocks in the # batch will have been computed before the next scheduling invocation. # This is because the engine assumes that a failure in model execution # will crash the Aphrodite instance / will not retry. for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups: self.block_manager.mark_blocks_as_computed( scheduled_seq_group.seq_group) return seq_group_metadata_list, scheduler_outputs def fork_seq(self, parent_seq: Sequence, child_seq: Sequence) -> None: self.block_manager.fork(parent_seq, child_seq) def free_seq(self, seq: Sequence) -> None: """Free a sequence from a block table.""" self.block_manager.free(seq) def free_finished_seq_groups(self) -> None: self.running = deque(seq_group for seq_group in self.running if not seq_group.is_finished()) def _allocate_and_set_running(self, seq_group: SequenceGroup) -> None: self.block_manager.allocate(seq_group) for seq in seq_group.get_seqs(status=SequenceStatus.WAITING): seq.status = SequenceStatus.RUNNING def _append_slots( self, seq_group: SequenceGroup, blocks_to_copy: Dict[int, List[int]], ) -> None: """Appends new slots to the sequences in the given sequence group. Args: seq_group (SequenceGroup): The sequence group containing the sequences to append slots to. blocks_to_copy (Dict[int, List[int]]): A dictionary mapping source block indices to lists of destination block indices. This dictionary is updated with the new source and destination block indices for the appended slots. """ num_lookahead_slots = self._get_num_lookahead_slots(is_prefill=False) for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): cows = self.block_manager.append_slots(seq, num_lookahead_slots) for src, dests in cows.items(): if src not in blocks_to_copy: blocks_to_copy[src] = [] blocks_to_copy[src].extend(dests) def _preempt( self, seq_group: SequenceGroup, blocks_to_swap_out: Dict[int, int], preemption_mode: Optional[PreemptionMode] = None, ) -> PreemptionMode: # If preemption mode is not specified, we determine the mode as follows: # We use recomputation by default since it incurs lower overhead than # swapping. However, when the sequence group has multiple sequences # (e.g., beam search), recomputation is not currently supported. In # such a case, we use swapping instead. # FIXME: This makes our scheduling policy a bit bizarre. # As swapped sequences are prioritized over waiting sequences, # sequence groups with multiple sequences are implicitly prioritized # over sequence groups with a single sequence. # TODO: Support recomputation for sequence groups with multiple # sequences. This may require a more sophisticated CUDA kernel. if preemption_mode is None: if seq_group.get_max_num_running_seqs() == 1: preemption_mode = PreemptionMode.RECOMPUTE else: preemption_mode = PreemptionMode.SWAP if preemption_mode == PreemptionMode.RECOMPUTE: self._preempt_by_recompute(seq_group) elif preemption_mode == PreemptionMode.SWAP: self._preempt_by_swap(seq_group, blocks_to_swap_out) else: raise AssertionError("Invalid preemption mode.") return preemption_mode def _preempt_by_recompute( self, seq_group: SequenceGroup, ) -> None: seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING) assert len(seqs) == 1 for seq in seqs: seq.status = SequenceStatus.WAITING self.free_seq(seq) seq.reset_state_for_recompute() def _preempt_by_swap( self, seq_group: SequenceGroup, blocks_to_swap_out: Dict[int, int], ) -> None: self._swap_out(seq_group, blocks_to_swap_out) def _swap_in( self, seq_group: SequenceGroup, blocks_to_swap_in: Dict[int, int], ) -> None: mapping = self.block_manager.swap_in(seq_group) blocks_to_swap_in.update(mapping) for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED): seq.status = SequenceStatus.RUNNING def _swap_out( self, seq_group: SequenceGroup, blocks_to_swap_out: Dict[int, int], ) -> None: if not self.block_manager.can_swap_out(seq_group): # FIXME: Abort the sequence group instead of aborting the # entire engine. raise RuntimeError( "Aborted due to the lack of CPU swap space. Please increase " "the swap space to avoid this error.") mapping = self.block_manager.swap_out(seq_group) blocks_to_swap_out.update(mapping) for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): seq.status = SequenceStatus.SWAPPED def _passed_delay(self, now: float) -> bool: if self.prev_prompt: self.last_prompt_latency = now - self.prev_time self.prev_time, self.prev_prompt = now, False # Delay scheduling prompts to let waiting queue fill up if self.scheduler_config.delay_factor > 0 and self.waiting: earliest_arrival_time = min( [e.metrics.arrival_time for e in self.waiting]) passed_delay = ( (now - earliest_arrival_time) > (self.scheduler_config.delay_factor * self.last_prompt_latency) or not self.running) else: passed_delay = True return passed_delay def _get_num_lookahead_slots(self, is_prefill: bool) -> int: """The number of slots to allocate per sequence per step, beyond known token ids. Speculative decoding uses these slots to store KV activations of tokens which may or may not be accepted. Speculative decoding does not yet support prefill, so we do not perform lookahead allocation for prefill. """ if is_prefill: return 0 return self.scheduler_config.num_lookahead_slots def _get_num_new_tokens(self, seq_group: SequenceGroup, status: SequenceStatus, enable_chunking: bool, budget: SchedulingBudget) -> int: """Get the next new tokens to compute for a given sequence group that's in a given `status`. The API could chunk the number of tokens to compute based on `budget` if `enable_chunking` is True. If a sequence group has multiple sequences (e.g., running beam search), it means it is in decoding phase, so chunking doesn't happen. """ num_new_tokens = 0 seqs = seq_group.get_seqs(status=status) for seq in seqs: num_new_tokens += seq.get_num_new_tokens() # Chunk if a running request cannot fit in. # If number of seq > 1, it means it is doing beam search in a # decode phase. Do not chunk in that case. if enable_chunking and len(seqs) == 1: num_new_tokens = min(num_new_tokens, budget.remaining_token_budget()) return num_new_tokens