vllm/cacheflow/core/scheduler.py

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import enum
import time
from typing import Dict, List, Optional, Tuple
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from cacheflow.config import CacheConfig, SchedulerConfig
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from cacheflow.core.block_manager import BlockSpaceManager
from cacheflow.core.policy import PolicyFactory
from cacheflow.logger import init_logger
from cacheflow.sequence import (Sequence, SequenceData, SequenceGroup,
SequenceGroupMetadata, SequenceOutputs,
SequenceStatus)
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logger = init_logger(__name__)
_LOGGING_INTERVAL_SEC = 5
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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()
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class SchedulerOutputs:
def __init__(
self,
blocks_to_swap_in: Dict[int, int],
blocks_to_swap_out: Dict[int, int],
blocks_to_copy: Dict[int, List[int]],
) -> None:
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)
def is_empty(self) -> bool:
return (not self.blocks_to_swap_in
and not self.blocks_to_swap_out
and not self.blocks_to_copy)
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class Scheduler:
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def __init__(
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self,
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scheduler_config: SchedulerConfig,
cache_config: CacheConfig,
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log_stats: bool,
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) -> None:
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self.scheduler_config = scheduler_config
self.cache_config = cache_config
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self.log_stats = log_stats
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# Instantiate the scheduling policy.
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=self.cache_config.block_size,
num_gpu_blocks=self.cache_config.num_gpu_blocks,
num_cpu_blocks=self.cache_config.num_cpu_blocks,
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)
# 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.
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self.swapped: List[SequenceGroup] = []
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self.last_logging_time: float = 0.0
# List[timestamp, num_tokens]
self.num_input_tokens: List[Tuple[float, int]] = []
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def add_seq_group(self, seq_group: SequenceGroup) -> None:
# Add sequence groups to the waiting queue.
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self.waiting.append(seq_group)
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def abort_seq_group(self, request_id: str) -> None:
for state_queue in [self.waiting, self.running, self.swapped]:
for seq_group in state_queue:
if seq_group.request_id == request_id:
# Remove the sequence group from the state queue.
state_queue.remove(seq_group)
for seq in seq_group.seqs:
if seq.is_finished():
continue
self.free_seq(seq, SequenceStatus.FINISHED_ABORTED)
return
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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) -> Tuple[SchedulerOutputs, List[str]]:
# 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.time()
# NOTE(woosuk): We prioritize the sequence groups in the RUNNING state
# in order to minimize the preemption overheads.
# 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)
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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)
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break
else:
# Append new slots to the sequence group.
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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)
while self.swapped and not blocks_to_swap_out:
seq_group = self.swapped[0]
# If the sequence group has been preempted in this step, stop.
if seq_group in preempted:
break
# If the sequence group cannot be swapped in, stop.
if not self.block_manager.can_swap_in(seq_group):
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break
# The total number of sequences in the RUNNING state should not
# exceed the maximum number of sequences.
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num_new_seqs = seq_group.num_seqs(status=SequenceStatus.SWAPPED)
num_curr_seqs = len(self.running)
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)
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self._append_slot(seq_group, blocks_to_copy)
self.running.append(seq_group)
num_batched_tokens = sum(
seq_group.num_seqs(status=SequenceStatus.RUNNING)
for seq_group in self.running
)
# Join waiting sequences if possible.
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prompt_group_ids: List[str] = []
# NOTE(woosuk): The sequence groups in the SWAPPED state are strictly
# prioritized over the sequence groups in the WAITING state.
# This is because we want to bound the amount of CPU memory taken by
# the swapped sequence groups.
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if not self.swapped:
# 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]
# If the sequence group has been preempted in this step, stop.
if seq_group in preempted:
break
# If the sequence group cannot be allocated, stop.
if not self.block_manager.can_allocate(seq_group):
break
# If the number of batched tokens exceeds the limit, stop.
num_prompt_tokens = seq_group.get_seqs()[0].get_len()
if (num_batched_tokens + num_prompt_tokens
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> 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.
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num_new_seqs = seq_group.num_seqs(status=SequenceStatus.WAITING)
num_curr_seqs = len(self.running)
if num_curr_seqs + num_new_seqs > self.scheduler_config.max_num_seqs:
break
seq_group = self.waiting.pop(0)
self._allocate(seq_group)
self.running.append(seq_group)
num_batched_tokens += num_prompt_tokens
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prompt_group_ids.append(seq_group.request_id)
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scheduler_outputs = SchedulerOutputs(
blocks_to_swap_in=blocks_to_swap_in,
blocks_to_swap_out=blocks_to_swap_out,
blocks_to_copy=blocks_to_copy,
)
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if not self.log_stats:
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return scheduler_outputs, prompt_group_ids
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# TODO(woosuk): Move the below code to the engine.
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now = time.time()
if num_batched_tokens > 0:
self.num_input_tokens.append((now, num_batched_tokens))
elapsed_time = now - self.last_logging_time
if elapsed_time > _LOGGING_INTERVAL_SEC:
self.last_logging_time = now
self.num_input_tokens = [
(t, n) for t, n in self.num_input_tokens
if now - t < _LOGGING_INTERVAL_SEC
]
if len(self.num_input_tokens) > 1:
total_num_tokens = sum(n for _, n in self.num_input_tokens[:-1])
window = now - self.num_input_tokens[0][0]
avg_throughput = total_num_tokens / window
else:
avg_throughput = 0.0
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total_num_gpu_blocks = self.cache_config.num_gpu_blocks
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num_free_gpu_blocks = self.block_manager.get_num_free_gpu_blocks()
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num_used_gpu_blocks = total_num_gpu_blocks - num_free_gpu_blocks
gpu_cache_usage = num_used_gpu_blocks / total_num_gpu_blocks
total_num_cpu_blocks = self.cache_config.num_cpu_blocks
if total_num_cpu_blocks > 0:
num_free_cpu_blocks = self.block_manager.get_num_free_cpu_blocks()
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num_used_cpu_blocks = total_num_cpu_blocks - num_free_cpu_blocks
cpu_cache_usage = num_used_cpu_blocks / total_num_cpu_blocks
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else:
cpu_cache_usage = 0.0
logger.info(
f"Throughput: {avg_throughput:.1f} tokens/s, "
f"Running: {len(self.running)} reqs, "
f"Swapped: {len(self.swapped)} reqs, "
f"Pending: {len(self.waiting)} reqs, "
f"GPU KV cache usage: {gpu_cache_usage * 100:.1f}%, "
f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
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return scheduler_outputs, prompt_group_ids
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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.
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scheduler_outputs, prompt_group_ids = self._schedule()
# Create input data structures.
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seq_group_metadata_list: List[SequenceGroupMetadata] = []
for seq_group in self.running:
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is_prompt = seq_group.request_id in prompt_group_ids
seq_data: Dict[int, List[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)
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seq_group_metadata = SequenceGroupMetadata(
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request_id=seq_group.request_id,
is_prompt=is_prompt,
seq_data=seq_data,
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sampling_params=seq_group.sampling_params,
block_tables=block_tables,
)
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seq_group_metadata_list.append(seq_group_metadata)
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return seq_group_metadata_list, scheduler_outputs
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def update(
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self,
seq_outputs: Dict[int, SequenceOutputs],
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) -> List[SequenceGroup]:
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# Update the running sequences and free blocks.
for seq_group in self.running:
# Process beam search results before processing the new tokens.
for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
output = seq_outputs[seq.seq_id]
if seq.seq_id != output.parent_seq_id:
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# The sequence is a fork of the parent sequence (beam search).
# Free the current sequence.
self.block_manager.free(seq)
# Fork the parent sequence.
parent_seq = seq_group.find(output.parent_seq_id)
parent_seq.fork(seq)
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self.block_manager.fork(parent_seq, seq)
# Process the new tokens.
for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
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# Append a new token to the sequence.
output = seq_outputs[seq.seq_id]
seq.append_token_id(output.output_token, output.logprobs)
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# Return a shallow copy of the running queue to prevent the queue
# from being modified by the caller.
return self.running.copy()
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def free_seq(self, seq: Sequence, finish_status: SequenceStatus) -> None:
seq.status = finish_status
self.block_manager.free(seq)
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def free_finished_seq_groups(self) -> None:
self.running = [
seq_group for seq_group in self.running
if not seq_group.is_finished()
]
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def _allocate(self, seq_group: SequenceGroup) -> None:
self.block_manager.allocate(seq_group)
for seq in seq_group.get_seqs():
seq.status = SequenceStatus.RUNNING
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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):
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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 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:
seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
if len(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:
assert False, '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:
seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
for seq in seqs:
seq.status = SequenceStatus.SWAPPED
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:
assert self.block_manager.can_swap_out(seq_group)
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