384 lines
15 KiB
Python
384 lines
15 KiB
Python
import enum
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import time
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from typing import Dict, List, Optional, Tuple
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from cacheflow.master.block_manager import BlockSpaceManager
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from cacheflow.master.policy import PolicyFactory
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from cacheflow.sampling_params import SamplingParams
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from cacheflow.sequence import Sequence
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from cacheflow.sequence import SequenceGroup
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from cacheflow.sequence import SequenceGroupInputs
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from cacheflow.sequence import SequenceOutputs
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from cacheflow.sequence import SequenceStatus
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class PreemptionMode(enum.Enum):
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"""Preemption modes.
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1. Swapping: Swap out the blocks of the preempted sequences to CPU memory
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and swap them back in when the sequences are resumed.
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2. Recomputation: Discard the blocks of the preempted sequences and
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recompute them when the sequences are resumed, treating the sequences as
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new prompts.
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"""
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SWAP = enum.auto()
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RECOMPUTE = enum.auto()
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class Scheduler:
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def __init__(
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self,
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controllers: List,
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block_size: int,
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num_gpu_blocks: int,
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num_cpu_blocks: int,
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max_num_batched_tokens: int,
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) -> None:
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self.controllers = controllers
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self.block_size = block_size
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self.num_gpu_blocks = num_gpu_blocks
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self.num_cpu_blocks = num_cpu_blocks
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self.max_num_batched_tokens = max_num_batched_tokens
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# Instantiate the scheduling policy.
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self.policy = PolicyFactory.get_policy(policy_name='fcfs')
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# Create the block space manager.
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self.block_manager = BlockSpaceManager(
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block_size=block_size,
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num_gpu_blocks=num_gpu_blocks,
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num_cpu_blocks=num_cpu_blocks,
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)
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# Sequence groups in the WAITING state.
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self.waiting: List[SequenceGroup] = []
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# Sequence groups in the RUNNING state.
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self.running: List[SequenceGroup] = []
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# Mapping: group_id -> num_steps.
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self.num_steps: Dict[int, int] = {}
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# Mapping: group_id -> sampling params.
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self.sampling_params: Dict[int, SamplingParams] = {}
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# Sequence groups in the SWAPPED state.
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self.swapped: List[SequenceGroup] = []
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def add_sequence_groups(
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self,
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seq_groups: List[Tuple[SequenceGroup, SamplingParams]],
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) -> None:
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# Add sequence groups to the waiting queue.
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for seq_group, sampling_params in seq_groups:
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self.waiting.append(seq_group)
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self.sampling_params[seq_group.group_id] = sampling_params
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def _schedule(
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self,
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) -> Tuple[Dict[int, int], Dict[int, int], Dict[int, List[int]], List[int]]:
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# Blocks that need to be swaped or copied before model execution.
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blocks_to_swap_in: Dict[int, int] = {}
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blocks_to_swap_out: Dict[int, int] = {}
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blocks_to_copy: Dict[int, List[int]] = {}
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# Fix the current time.
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now = time.time()
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# NOTE(woosuk): We prioritize the sequence groups in the RUNNING state
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# in order to minimize the preemption overheads.
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# Preemption happens only when there is no available slot to keep all
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# the sequence groups in the RUNNING state.
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# In this case, the policy is responsible for deciding which sequence
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# groups to preempt.
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self.running = self.policy.sort_by_priority(now, self.running)
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# Reserve new token slots for the running sequence groups.
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running: List[SequenceGroup] = []
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preempted: List[SequenceGroup] = []
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while self.running:
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seq_group = self.running.pop(0)
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while not self.block_manager.can_append(seq_group):
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if self.running:
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# Preempt the lowest-priority sequence groups.
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victim_seq_group = self.running.pop(-1)
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self._preempt(victim_seq_group, blocks_to_swap_out)
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preempted.append(victim_seq_group)
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else:
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# No other sequence groups can be preempted.
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# Preempt the current sequence group.
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self._preempt(seq_group, blocks_to_swap_out)
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preempted.append(seq_group)
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break
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else:
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# Append new slots to the sequence group.
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self._append(seq_group, blocks_to_copy)
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running.append(seq_group)
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self.running = running
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# Swap in the sequence groups in the SWAPPED state if possible.
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self.swapped = self.policy.sort_by_priority(now, self.swapped)
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while self.swapped:
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seq_group = self.swapped[0]
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# If the sequence group has been preempted in this step, stop.
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if seq_group in preempted:
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break
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# If the sequence group cannot be swapped in, stop.
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if not self.block_manager.can_swap_in(seq_group):
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break
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seq_group = self.swapped.pop(0)
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self._swap_in(seq_group, blocks_to_swap_in)
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self._append(seq_group, blocks_to_copy)
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self.running.append(seq_group)
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num_batched_tokens = sum(
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seq_group.num_seqs(status=SequenceStatus.RUNNING)
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for seq_group in self.running
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)
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# Join waiting sequences if possible.
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prompt_group_ids: List[int] = []
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# NOTE(woosuk): The sequence groups in the SWAPPED state are strictly
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# prioritized over the sequence groups in the WAITING state.
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# This is because we want to bound the amount of CPU memory taken by
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# the swapped sequence groups.
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if not self.swapped:
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self.waiting = self.policy.sort_by_priority(now, self.waiting)
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while self.waiting:
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seq_group = self.waiting[0]
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# If the sequence group has been preempted in this step, stop.
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if seq_group in preempted:
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break
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# If the sequence group cannot be allocated, stop.
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if not self.block_manager.can_allocate(seq_group):
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break
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# If the number of batched tokens exceeds the limit, stop.
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num_prompt_tokens = seq_group.seqs[0].get_len()
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if (num_batched_tokens + num_prompt_tokens
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> self.max_num_batched_tokens):
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break
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seq_group = self.waiting.pop(0)
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self._allocate(seq_group)
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self.running.append(seq_group)
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num_batched_tokens += num_prompt_tokens
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prompt_group_ids.append(seq_group.group_id)
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return (blocks_to_swap_in,
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blocks_to_swap_out,
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blocks_to_copy,
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prompt_group_ids)
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def step(self) -> List[SequenceGroup]:
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# Schedule sequence groups.
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# This function call changes the internal states of the scheduler
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# such as self.running, self.swapped, and self.waiting.
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scheduler_output = self._schedule()
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blocks_to_swap_in = scheduler_output[0]
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blocks_to_swap_out = scheduler_output[1]
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blocks_to_copy = scheduler_output[2]
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prompt_group_ids = scheduler_output[3]
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# Create input data structures.
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input_seq_groups: List[SequenceGroupInputs] = []
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updated_seq_groups: List[SequenceGroup] = self.running.copy()
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for seq_group in self.running:
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group_id = seq_group.group_id
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is_prompt = group_id in prompt_group_ids
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input_tokens: Dict[int, List[int]] = {}
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seq_logprobs: Dict[int, float] = {}
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block_tables: Dict[int, List[int]] = {}
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for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
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seq_id = seq.seq_id
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block_tables[seq_id] = self.block_manager.get_block_table(seq)
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if is_prompt:
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input_tokens[seq_id] = seq.get_token_ids()
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else:
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input_tokens[seq_id] = [seq.get_last_token_id()]
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seq_logprobs[seq_id] = seq.cumulative_logprobs
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# NOTE(woosuk): Sequences in the same group have the same
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# sequence length
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seq_len = seq.get_len()
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input_seq_group = SequenceGroupInputs(
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group_id=group_id,
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is_prompt=is_prompt,
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input_tokens=input_tokens,
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context_len=seq_len,
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seq_logprobs=seq_logprobs,
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sampling_params=self.sampling_params[group_id],
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block_tables=block_tables,
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)
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input_seq_groups.append(input_seq_group)
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# Execute the first stage of the pipeline.
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if input_seq_groups or blocks_to_swap_in or blocks_to_swap_out:
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# Swap in and swap out should never happen at the same time.
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assert not (blocks_to_swap_in and blocks_to_swap_out)
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self.controllers[0].execute_stage(
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input_seq_groups,
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blocks_to_swap_in=blocks_to_swap_in,
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blocks_to_swap_out=blocks_to_swap_out,
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blocks_to_copy=blocks_to_copy,
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)
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return updated_seq_groups
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def post_step(
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self,
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seq_outputs: Dict[int, SequenceOutputs],
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) -> None:
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# Update the running sequences and free blocks.
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for seq_group in self.running:
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group_id = seq_group.group_id
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self.num_steps[group_id] += 1
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stop_token_ids = self.sampling_params[group_id].stop_token_ids
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# Process beam search results before processing the next tokens.
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for seq in seq_group.seqs:
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if seq.status == SequenceStatus.FINISHED:
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continue
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output = seq_outputs[seq.seq_id]
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if seq.seq_id != output.parent_seq_id:
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# The sequence is a fork of the parent sequence (beam search).
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# Free the current sequence.
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self.block_manager.free(seq)
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# Fork the parent sequence.
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parent_seq = seq_group.find(output.parent_seq_id)
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parent_seq.fork(seq)
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self.block_manager.fork(parent_seq, seq)
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# Process the next tokens.
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for seq in seq_group.seqs:
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if seq.status == SequenceStatus.FINISHED:
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continue
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# Append a new token to the sequence.
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output = seq_outputs[seq.seq_id]
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seq.append(output.output_token, output.logprobs)
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# Check if the sequence has generated a stop token.
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if output.output_token in stop_token_ids:
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self._free_seq(seq)
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continue
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# Check if the sequence has reached the maximum number of steps.
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max_num_steps = self.sampling_params[group_id].max_num_steps
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if self.num_steps[group_id] == max_num_steps:
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self._free_seq(seq)
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continue
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# Update the running sequences.
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running: List[SequenceGroup] = []
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for seq_group in self.running:
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if seq_group.is_finished():
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self._free_seq_group(seq_group)
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else:
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running.append(seq_group)
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self.running = running
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def _allocate(self, seq_group: SequenceGroup) -> None:
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self.block_manager.allocate(seq_group)
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for seq in seq_group.seqs:
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seq.status = SequenceStatus.RUNNING
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# FIXME(woosuk): Support interactive generation.
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if seq_group.group_id not in self.num_steps:
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self.num_steps[seq_group.group_id] = 0
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def _append(
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self,
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seq_group: SequenceGroup,
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blocks_to_copy: Dict[int, List[int]],
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) -> None:
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for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
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ret = self.block_manager.append(seq)
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if ret is not None:
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src_block, dst_block = ret
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if src_block in blocks_to_copy:
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blocks_to_copy[src_block].append(dst_block)
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else:
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blocks_to_copy[src_block] = [dst_block]
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def _preempt(
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self,
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seq_group: SequenceGroup,
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blocks_to_swap_out: Dict[int, int],
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preemption_mode: Optional[PreemptionMode] = None,
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) -> None:
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# If preemption mode is not specified, we determine the mode as follows:
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# We use recomputation by default since it incurs lower overhead than
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# swapping. However, when the sequence group has multiple sequences
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# (e.g., beam search), recomputation is not supported. In such a case,
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# we use swapping instead.
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# FIXME(woosuk): This makes our scheduling policy a bit bizarre.
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# As swapped sequences are prioritized over waiting sequences,
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# sequence groups with multiple sequences are implicitly prioritized
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# over sequence groups with a single sequence.
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# TODO(woosuk): Support recomputation for sequence groups with multiple
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# sequences. This may require a more sophisticated CUDA kernel.
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if preemption_mode is None:
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seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
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if len(seqs) == 1:
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preemption_mode = PreemptionMode.RECOMPUTE
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else:
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preemption_mode = PreemptionMode.SWAP
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if preemption_mode == PreemptionMode.RECOMPUTE:
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self._preempt_by_recompute(seq_group)
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elif preemption_mode == PreemptionMode.SWAP:
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self._preempt_by_swap(seq_group, blocks_to_swap_out)
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else:
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assert False, 'Invalid preemption mode.'
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def _preempt_by_recompute(
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self,
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seq_group: SequenceGroup,
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) -> None:
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seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
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assert len(seqs) == 1
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for seq in seqs:
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seq.status = SequenceStatus.WAITING
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self.block_manager.free(seq)
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self.waiting.append(seq_group)
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def _preempt_by_swap(
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self,
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seq_group: SequenceGroup,
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blocks_to_swap_out: Dict[int, int],
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) -> None:
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seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
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for seq in seqs:
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seq.status = SequenceStatus.SWAPPED
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self._swap_out(seq_group, blocks_to_swap_out)
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self.swapped.append(seq_group)
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def _free_seq(self, seq: Sequence) -> None:
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seq.status = SequenceStatus.FINISHED
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self.block_manager.free(seq)
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def _free_seq_group(self, seq_group: SequenceGroup) -> None:
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group_id = seq_group.group_id
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del self.num_steps[group_id]
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del self.sampling_params[group_id]
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def _swap_in(
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self,
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seq_group: SequenceGroup,
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blocks_to_swap_in: Dict[int, int],
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) -> None:
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mapping = self.block_manager.swap_in(seq_group)
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blocks_to_swap_in.update(mapping)
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for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED):
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seq.status = SequenceStatus.RUNNING
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def _swap_out(
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self,
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seq_group: SequenceGroup,
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blocks_to_swap_out: Dict[int, int],
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) -> None:
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assert self.block_manager.can_swap_out(seq_group)
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mapping = self.block_manager.swap_out(seq_group)
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blocks_to_swap_out.update(mapping)
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for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
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seq.status = SequenceStatus.SWAPPED
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