from collections import deque import enum import time from typing import Deque, Dict, Iterable, List, Optional, Tuple, Union, Set from loguru import logger from aphrodite.common.config import CacheConfig, LoRAConfig, SchedulerConfig from aphrodite.processing.block_manager import AllocStatus, BlockSpaceManager from aphrodite.processing.policy import PolicyFactory from aphrodite.lora.request import LoRARequest from aphrodite.common.sequence import (Sequence, SequenceData, SequenceGroup, SequenceGroupMetadata, SequenceStatus) 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() class SchedulerOutputs: def __init__( self, scheduled_seq_groups: Iterable[SequenceGroup], prompt_run: bool, num_batched_tokens: int, blocks_to_swap_in: Dict[int, int], blocks_to_swap_out: Dict[int, int], blocks_to_copy: Dict[int, List[int]], ignored_seq_groups: List[SequenceGroup], ) -> None: self.scheduled_seq_groups = scheduled_seq_groups self.prompt_run = prompt_run self.num_batched_tokens = num_batched_tokens self.blocks_to_swap_in = blocks_to_swap_in self.blocks_to_swap_out = blocks_to_swap_out self.blocks_to_copy = blocks_to_copy # Swap in and swap out should never happen at the same time. assert not (blocks_to_swap_in and blocks_to_swap_out) self.ignored_seq_groups = ignored_seq_groups self.num_loras = 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) -> bool: self.scheduled_seq_groups = sorted( self.scheduled_seq_groups, key=lambda g: (g.lora_request.lora_int_id if g.lora_request else 0, g.request_id)) @property def lora_requests(self) -> Set[LoRARequest]: return {g.lora_request for g in self.scheduled_seq_groups} 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 self.prompt_limit = min(self.scheduler_config.max_model_len, self.scheduler_config.max_num_batched_tokens) # Instantiate the scheduling policy. self.policy = PolicyFactory.get_policy(policy_name="fcfs") # Create the block space manager. self.block_manager = BlockSpaceManager( 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.context_shift) # Sequence groups in the WAITING state. self.waiting: Deque[SequenceGroup] = deque() # Sequence groups in the RUNNING state. self.running: Deque[SequenceGroup] = deque() # Sequence groups in the SWAPPED state. self.swapped: Deque[SequenceGroup] = deque() @property def lora_enabled(self) -> bool: return bool(self.lora_config) 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 self.waiting or self.running or self.swapped def get_num_unfinished_seq_groups(self) -> int: return len(self.waiting) + len(self.running) + len(self.swapped) def _schedule(self) -> SchedulerOutputs: # Blocks that need to be swapped or copied before model execution. blocks_to_swap_in: Dict[int, int] = {} blocks_to_swap_out: Dict[int, int] = {} blocks_to_copy: Dict[int, List[int]] = {} # Fix the current time. now = time.monotonic() # Join waiting sequences if possible. if not self.swapped: ignored_seq_groups: List[SequenceGroup] = [] scheduled: List[SequenceGroup] = [] # The total number of sequences on the fly, including the # requests in the generation phase. num_curr_seqs = sum(seq_group.get_max_num_running_seqs() for seq_group in self.running) curr_loras = set( seq_group.lora_int_id for seq_group in self.running) if self.lora_enabled else None seq_lens: List[int] = [] # Optimization: We do not sort the waiting queue since the preempted # sequence groups are added to the front and the new sequence groups # are added to the back. leftover_waiting_sequences = deque() while self.waiting: seq_group = self.waiting[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_prompt_tokens = waiting_seqs[0].get_len() if num_prompt_tokens > self.prompt_limit: logger.warning( f"Input prompt ({num_prompt_tokens} tokens) is too long" f" and exceeds limit of {self.prompt_limit}") for seq in waiting_seqs: seq.status = SequenceStatus.FINISHED_IGNORED ignored_seq_groups.append(seq_group) self.waiting.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( f"Input prompt ({num_prompt_tokens} tokens) is too long" f" and exceeds the capacity of block_manager") for seq in waiting_seqs: seq.status = SequenceStatus.FINISHED_IGNORED ignored_seq_groups.append(seq_group) self.waiting.popleft() continue lora_int_id = 0 if self.lora_enabled: lora_int_id = seq_group.lora_int_id 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_waiting_sequences.appendleft(seq_group) self.waiting.popleft() continue # If the number of batched tokens exceeds the limit, stop. new_seq_lens = seq_lens + [num_prompt_tokens] num_batched_tokens = len(new_seq_lens) * max(new_seq_lens) if (num_batched_tokens > self.scheduler_config.max_num_batched_tokens): break # 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() if (num_curr_seqs + num_new_seqs > self.scheduler_config.max_num_seqs): break num_paddings = num_batched_tokens - sum(new_seq_lens) if num_paddings > self.scheduler_config.max_paddings: break seq_lens = new_seq_lens if lora_int_id > 0: curr_loras.add(lora_int_id) self.waiting.popleft() self._allocate(seq_group) self.running.append(seq_group) num_curr_seqs += num_new_seqs scheduled.append(seq_group) self.waiting.extendleft(leftover_waiting_sequences) if scheduled or ignored_seq_groups: scheduler_outputs = SchedulerOutputs( scheduled_seq_groups=scheduled, prompt_run=True, num_batched_tokens=len(seq_lens) * max(seq_lens) if seq_lens else 0, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, ignored_seq_groups=ignored_seq_groups, ) return scheduler_outputs # 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. self.running = self.policy.sort_by_priority(now, self.running) # Reserve new token slots for the running sequence groups. running: Deque[SequenceGroup] = deque() preempted: List[SequenceGroup] = [] while self.running: seq_group = self.running.popleft() while not self.block_manager.can_append_slot(seq_group): if self.running: # Preempt the lowest-priority sequence groups. victim_seq_group = self.running.pop() self._preempt(victim_seq_group, blocks_to_swap_out) preempted.append(victim_seq_group) else: # No other sequence groups can be preempted. # Preempt the current sequence group. self._preempt(seq_group, blocks_to_swap_out) preempted.append(seq_group) break else: # Append new slots to the sequence group. self._append_slot(seq_group, blocks_to_copy) running.append(seq_group) self.running = running # Swap in the sequence groups in the SWAPPED state if possible. self.swapped = self.policy.sort_by_priority(now, self.swapped) if not preempted: num_curr_seqs = sum(seq_group.get_max_num_running_seqs() for seq_group in self.running) curr_loras = set( seq_group.lora_int_id for seq_group in self.running) if self.lora_enabled else None leftover_swapped = deque() while self.swapped: seq_group = self.swapped[0] lora_int_id = 0 if self.lora_enabled: lora_int_id = seq_group.lora_int_id 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) self.swapped.popleft() continue # If the sequence group cannot be swapped in, stop. if not self.block_manager.can_swap_in(seq_group): break # 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() if (num_curr_seqs + num_new_seqs > self.scheduler_config.max_num_seqs): break if lora_int_id > 0: curr_loras.add(lora_int_id) self.swapped.popleft() self._swap_in(seq_group, blocks_to_swap_in) self._append_slot(seq_group, blocks_to_copy) num_curr_seqs += num_new_seqs self.running.append(seq_group) self.swapped.extendleft(leftover_swapped) # Each sequence in the generation phase only takes one token slot. # Therefore, the number of batched tokens is equal to the number of # sequences in the RUNNING state. num_batched_tokens = sum( seq_group.num_seqs(status=SequenceStatus.RUNNING) for seq_group in self.running) scheduler_outputs = SchedulerOutputs( scheduled_seq_groups=self.running, prompt_run=False, num_batched_tokens=num_batched_tokens, blocks_to_swap_in=blocks_to_swap_in, blocks_to_swap_out=blocks_to_swap_out, blocks_to_copy=blocks_to_copy, ignored_seq_groups=[], ) return scheduler_outputs 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 seq_group in scheduler_outputs.scheduled_seq_groups: seq_group.maybe_set_first_scheduled_time(now) seq_data: Dict[int, SequenceData] = {} block_tables: Dict[int, List[int]] = {} persistent_data: Dict[int, dict] = {} 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) persistent_data[seq_id] = seq.persistent_data self.block_manager.access_all_blocks_in_seq(seq, now) seq_group_metadata = SequenceGroupMetadata( request_id=seq_group.request_id, is_prompt=scheduler_outputs.prompt_run, seq_data=seq_data, sampling_params=seq_group.sampling_params, block_tables=block_tables, lora_request=seq_group.lora_request, persistent_data=persistent_data, computed_block_nums=self.block_manager. get_common_computed_block_ids(seq_group), state=seq_group.state, ) seq_group_metadata_list.append(seq_group_metadata) 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: 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(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_slot( self, seq_group: SequenceGroup, blocks_to_copy: Dict[int, List[int]], ) -> None: for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): ret = self.block_manager.append_slot(seq) if ret is not None: src_block, dst_block = ret if src_block in blocks_to_copy: blocks_to_copy[src_block].append(dst_block) else: blocks_to_copy[src_block] = [dst_block] def _preempt( self, seq_group: SequenceGroup, blocks_to_swap_out: Dict[int, int], preemption_mode: Optional[PreemptionMode] = None, ) -> None: # 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.") 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.block_manager.free(seq) # NOTE: For FCFS, we insert the preempted sequence group to the front # of the waiting queue. self.waiting.appendleft(seq_group) 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) self.swapped.append(seq_group) 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 mark_blocks_as_computed(self, seq_group: SequenceGroup): self.block_manager.mark_blocks_as_computed(seq_group)