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import enum
import time
from typing import Dict, Iterable, List, Optional, Tuple, Union
from vllm.config import CacheConfig, SchedulerConfig
from .block_manager import AllocStatus, BlockSpaceManager
from vllm.core.policy import PolicyFactory
from vllm.logger import init_logger
from .sequence import (
Sequence,
SequenceData,
SequenceGroup,
SequenceGroupMetadata,
SequenceStatus,
)
logger = init_logger(__name__)
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: List[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
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
)
class Scheduler:
def __init__(
self,
scheduler_config: SchedulerConfig,
cache_config: CacheConfig,
) -> None:
self.scheduler_config = scheduler_config
self.cache_config = cache_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,
)
# TODO(zhuohan): Use deque instead of list for better performance.
# Sequence groups in the WAITING state.
self.waiting: List[SequenceGroup] = []
# Sequence groups in the RUNNING state.
self.running: List[SequenceGroup] = []
# Sequence groups in the SWAPPED state.
self.swapped: List[SequenceGroup] = []
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:
if isinstance(request_id, str):
request_id = (request_id,)
request_ids = set(request_id)
for state_queue in [self.waiting, self.running, self.swapped]:
# We need to reverse the list as we are removing elements
# from it as we iterate over it. If we don't do it,
# indices will get messed up and we will skip over elements.
for seq_group in reversed(state_queue):
if seq_group.request_id in request_ids:
# Remove the sequence group from the state queue.
state_queue.remove(seq_group)
for seq in seq_group.get_seqs():
if seq.is_finished():
continue
seq.status = SequenceStatus.FINISHED_ABORTED
self.free_seq(seq)
request_ids.remove(seq_group.request_id)
if not request_ids:
return
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 swaped 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
)
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.
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.pop(0)
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.pop(0)
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
seq_group = self.waiting.pop(0)
self._allocate(seq_group)
self.running.append(seq_group)
num_curr_seqs += num_new_seqs
scheduled.append(seq_group)
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(woosuk): 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: List[SequenceGroup] = []
preempted: List[SequenceGroup] = []
while self.running:
seq_group = self.running.pop(0)
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(-1)
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
)
while self.swapped:
seq_group = self.swapped[0]
# 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
seq_group = self.swapped.pop(0)
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)
# 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()
# Create input data structures.
seq_group_metadata_list: List[SequenceGroupMetadata] = []
for seq_group in scheduler_outputs.scheduled_seq_groups:
seq_data: Dict[int, SequenceData] = {}
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)
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,
)
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 = [
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(woosuk): 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(woosuk): 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.insert(0, 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(woosuk): 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
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