283 lines
11 KiB
Python
283 lines
11 KiB
Python
from typing import Dict, List, Tuple
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from cacheflow.master.block_manager import BlockSpaceManager
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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 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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# 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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# Running sequence groups (FIFO).
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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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# Swapped sequence groups (LIFO).
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self.swapped: List[SequenceGroup] = []
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# Pending sequence groups (FIFO).
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self.pending: List[SequenceGroup] = []
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def add_sequence_groups(
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self,
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sequence_groups: List[Tuple[SequenceGroup, SamplingParams]],
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) -> None:
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# Add sequence groups to the pending queue.
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for seq_group, sampling_params in sequence_groups:
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self.pending.append(seq_group)
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self.sampling_params[seq_group.group_id] = sampling_params
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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 _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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self.running.append(seq_group)
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# FIXME(woosuk): Support interactive generation.
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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.seqs:
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if seq.status == SequenceStatus.FINISHED:
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continue
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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 _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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self.running.append(seq_group)
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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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self.swapped.append(seq_group)
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def step(self) -> List[SequenceGroup]:
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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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# 1. Reserve new slots for the running sequences.
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# NOTE: Here we implicitly assume FCFS scheduling.
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# That is, the most recently added sequence group is the first
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# to be swapped out.
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victim_idx = len(self.running) - 1
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for i, seq_group in enumerate(self.running):
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if i > victim_idx:
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# The i-th sequence group has already been swapped out.
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break
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# OOM. Swap out the victim sequence groups.
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while not self.block_manager.can_append(seq_group):
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victim_seq_group = self.running[victim_idx]
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self._swap_out(victim_seq_group, blocks_to_swap_out)
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victim_idx -= 1
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if i > victim_idx:
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# No other sequence groups can be swapped out.
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break
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else:
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self._append(seq_group, blocks_to_copy)
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self.running = self.running[:victim_idx + 1]
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# 2. Swap in the swapped sequences if possible.
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# NOTE: Here we implicitly assume FCFS scheduling.
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# The swapped sequences are in LIFO order.
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for i, seq_group in enumerate(reversed(self.swapped)):
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if self.block_manager.can_swap_in(seq_group):
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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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else:
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# OOM. Stop swapping.
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self.swapped = self.swapped[:len(self.swapped) - i]
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break
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else:
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# All swapped sequences are swapped in.
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self.swapped.clear()
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# Ensure that swap-in and swap-out never happen at the same timestep.
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if blocks_to_swap_in:
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assert not blocks_to_swap_out
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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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# 3. Join new sequences if possible.
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# NOTE: Here we implicitly assume FCFS scheduling.
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# TODO(woosuk): Add a batching policy to control the batch size.
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if not self.swapped:
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for i, seq_group in enumerate(self.pending):
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num_prompt_tokens = seq_group.seqs[0].get_len()
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if self.block_manager.can_allocate(seq_group):
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if (num_batched_tokens + num_prompt_tokens
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<= self.max_num_batched_tokens):
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self._allocate(seq_group)
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num_batched_tokens += num_prompt_tokens
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continue
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self.pending = self.pending[i:]
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break
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else:
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self.pending.clear()
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# 4. 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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num_steps = self.num_steps[group_id]
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# NOTE(woosuk): We assume that the number of steps is 0
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# for the prompt sequences.
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is_prompt = num_steps == 0
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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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# 5. 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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self.controllers[0].execute_stage(
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input_seq_groups,
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blocks_to_swap_in,
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blocks_to_swap_out,
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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 _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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