"""A block manager that manages token blocks.""" from itertools import chain from typing import Dict, List, Optional from typing import Sequence as GenericSequence from typing import Tuple from vllm.core.block.block_table import BlockTable from vllm.core.block.cpu_gpu_block_allocator import CpuGpuBlockAllocator from vllm.core.block.interfaces import Block from vllm.core.block.prefix_caching_block import (ComputedBlocksTracker, LastAccessBlocksTracker) from vllm.core.block.utils import check_no_caching_or_swa_for_blockmgr_encdec from vllm.core.interfaces import AllocStatus, BlockSpaceManager from vllm.sequence import Sequence, SequenceGroup, SequenceStatus from vllm.utils import Device SeqId = int EncoderSeqId = str class BlockSpaceManagerV2(BlockSpaceManager): """BlockSpaceManager which manages the allocation of KV cache. It owns responsibility for allocation, swapping, allocating memory for autoregressively-generated tokens, and other advanced features such as prefix caching, forking/copy-on-write, and sliding-window memory allocation. The current implementation is partial; in particular prefix caching and sliding-window are not feature complete. This class implements the design described in https://github.com/vllm-project/vllm/pull/3492. Lookahead slots The block manager has the notion of a "lookahead slot". These are slots in the KV cache that are allocated for a sequence. Unlike the other allocated slots, the content of these slots is undefined -- the worker may use the memory allocations in any way. In practice, a worker could use these lookahead slots to run multiple forward passes for a single scheduler invocation. Each successive forward pass would write KV activations to the corresponding lookahead slot. This allows low inter-token latency use-cases, where the overhead of continuous batching scheduling is amortized over >1 generated tokens. Speculative decoding uses lookahead slots to store KV activations of proposal tokens. See https://github.com/vllm-project/vllm/pull/3250 for more information on lookahead scheduling. Args: block_size (int): The size of each memory block. num_gpu_blocks (int): The number of memory blocks allocated on GPU. num_cpu_blocks (int): The number of memory blocks allocated on CPU. watermark (float, optional): The threshold used for memory swapping. Defaults to 0.01. sliding_window (Optional[int], optional): The size of the sliding window. Defaults to None. enable_caching (bool, optional): Flag indicating whether caching is enabled. Defaults to False. """ def __init__( self, block_size: int, num_gpu_blocks: int, num_cpu_blocks: int, watermark: float = 0.01, sliding_window: Optional[int] = None, enable_caching: bool = False, ) -> None: self.block_size = block_size self.num_total_gpu_blocks = num_gpu_blocks self.num_total_cpu_blocks = num_cpu_blocks self.sliding_window = sliding_window # max_block_sliding_window is the max number of blocks that need to be # allocated self.max_block_sliding_window = None if sliding_window is not None: # +1 here because // rounds down num_blocks = sliding_window // block_size + 1 # +1 here because the last block may not be full, # and so the sequence stretches one more block at the beginning # For example, if sliding_window is 3 and block_size is 4, # we may need 2 blocks when the second block only holds 1 token. self.max_block_sliding_window = num_blocks + 1 self.watermark = watermark assert watermark >= 0.0 self.enable_caching = enable_caching self.watermark_blocks = int(watermark * num_gpu_blocks) self.block_allocator = CpuGpuBlockAllocator.create( allocator_type="prefix_caching" if enable_caching else "naive", num_gpu_blocks=num_gpu_blocks, num_cpu_blocks=num_cpu_blocks, block_size=block_size, ) self.block_tables: Dict[SeqId, BlockTable] = {} self.cross_block_tables: Dict[EncoderSeqId, BlockTable] = {} self._computed_blocks_tracker = ComputedBlocksTracker( self.block_allocator) self._last_access_blocks_tracker = LastAccessBlocksTracker( self.block_allocator) def can_allocate(self, seq_group: SequenceGroup) -> AllocStatus: # FIXME(woosuk): Here we assume that all sequences in the group share # the same prompt. This may not be true for preempted sequences. check_no_caching_or_swa_for_blockmgr_encdec(self, seq_group) seq = seq_group.get_seqs(status=SequenceStatus.WAITING)[0] num_required_blocks = BlockTable.get_num_required_blocks( seq.get_token_ids(), block_size=self.block_size, ) if seq_group.is_encoder_decoder(): encoder_seq = seq_group.get_encoder_seq() assert encoder_seq is not None num_required_blocks += BlockTable.get_num_required_blocks( encoder_seq.get_token_ids(), block_size=self.block_size, ) if self.max_block_sliding_window is not None: num_required_blocks = min(num_required_blocks, self.max_block_sliding_window) num_free_gpu_blocks = self.block_allocator.get_num_free_blocks( device=Device.GPU) # Use watermark to avoid frequent cache eviction. if (self.num_total_gpu_blocks - num_required_blocks < self.watermark_blocks): return AllocStatus.NEVER if num_free_gpu_blocks - num_required_blocks >= self.watermark_blocks: return AllocStatus.OK else: return AllocStatus.LATER def _allocate_sequence(self, seq: Sequence) -> BlockTable: block_table = BlockTable( block_size=self.block_size, block_allocator=self.block_allocator, max_block_sliding_window=self.max_block_sliding_window, ) block_table.allocate(seq.get_token_ids()) return block_table def allocate(self, seq_group: SequenceGroup) -> None: # Allocate self-attention block tables for decoder sequences waiting_seqs = seq_group.get_seqs(status=SequenceStatus.WAITING) assert not (set(seq.seq_id for seq in waiting_seqs) & self.block_tables.keys()), "block table already exists" # NOTE: Here we assume that all sequences in the group have the same # prompt. seq = waiting_seqs[0] block_table: BlockTable = self._allocate_sequence(seq) self.block_tables[seq.seq_id] = block_table # Track seq self._computed_blocks_tracker.add_seq(seq.seq_id) self._last_access_blocks_tracker.add_seq(seq.seq_id) # Assign the block table for each sequence. for seq in waiting_seqs[1:]: self.block_tables[seq.seq_id] = block_table.fork() # Track seq self._computed_blocks_tracker.add_seq(seq.seq_id) self._last_access_blocks_tracker.add_seq(seq.seq_id) # Allocate cross-attention block table for encoder sequence # # NOTE: Here we assume that all sequences in the group have the same # encoder prompt. request_id = seq_group.request_id assert (request_id not in self.cross_block_tables), \ "block table already exists" check_no_caching_or_swa_for_blockmgr_encdec(self, seq_group) if seq_group.is_encoder_decoder(): encoder_seq = seq_group.get_encoder_seq() assert encoder_seq is not None block_table = self._allocate_sequence(encoder_seq) self.cross_block_tables[request_id] = block_table def can_append_slots(self, seq_group: SequenceGroup, num_lookahead_slots: int) -> bool: """Determine if there is enough space in the GPU KV cache to continue generation of the specified sequence group. We use a worst-case heuristic: assume each touched block will require a new allocation (either via CoW or new block). We can append slots if the number of touched blocks is less than the number of free blocks. "Lookahead slots" are slots that are allocated in addition to the slots for known tokens. The contents of the lookahead slots are not defined. This is used by speculative decoding when speculating future tokens. """ num_touched_blocks = 0 for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): block_table = self.block_tables[seq.seq_id] num_touched_blocks += ( block_table.get_num_blocks_touched_by_append_slots( token_ids=block_table.get_unseen_token_ids( seq.get_token_ids()), num_lookahead_slots=num_lookahead_slots, )) num_free_gpu_blocks = self.block_allocator.get_num_free_blocks( Device.GPU) return num_touched_blocks <= num_free_gpu_blocks def append_slots( self, seq: Sequence, num_lookahead_slots: int, ) -> List[Tuple[int, int]]: block_table = self.block_tables[seq.seq_id] block_table.append_token_ids( token_ids=block_table.get_unseen_token_ids(seq.get_token_ids()), num_lookahead_slots=num_lookahead_slots, num_computed_slots=seq.data.get_num_computed_tokens(), ) # Return any new copy-on-writes. new_cows = self.block_allocator.clear_copy_on_writes() return new_cows def free(self, seq: Sequence) -> None: seq_id = seq.seq_id if seq_id not in self.block_tables: # Already freed or haven't been scheduled yet. return # Update seq block ids with the latest access time self._last_access_blocks_tracker.update_seq_blocks_last_access( seq_id, self.block_tables[seq.seq_id].physical_block_ids) # Untrack seq self._last_access_blocks_tracker.remove_seq(seq_id) self._computed_blocks_tracker.remove_seq(seq_id) # Free table/blocks self.block_tables[seq_id].free() del self.block_tables[seq_id] def free_cross(self, seq_group: SequenceGroup) -> None: request_id = seq_group.request_id if request_id not in self.cross_block_tables: # Already freed or hasn't been scheduled yet. return self.cross_block_tables[request_id].free() del self.cross_block_tables[request_id] def get_block_table(self, seq: Sequence) -> List[int]: block_ids = self.block_tables[seq.seq_id].physical_block_ids return block_ids # type: ignore def get_cross_block_table(self, seq_group: SequenceGroup) -> List[int]: request_id = seq_group.request_id assert request_id in self.cross_block_tables block_ids = self.cross_block_tables[request_id].physical_block_ids assert all(b is not None for b in block_ids) return block_ids # type: ignore def access_all_blocks_in_seq(self, seq: Sequence, now: float): if self.enable_caching: # Record the latest access time for the sequence. The actual update # of the block ids is deferred to the sequence free(..) call, since # only during freeing of block ids, the blocks are actually added to # the evictor (which is when the most updated time is required) # (This avoids expensive calls to mark_blocks_as_accessed(..)) self._last_access_blocks_tracker.update_last_access( seq.seq_id, now) def mark_blocks_as_computed(self, seq_group: SequenceGroup, token_chunk_size: int): # If prefix caching is enabled, mark immutable blocks as computed # right after they have been scheduled (for prefill). This assumes # the scheduler is synchronous so blocks are actually computed when # scheduling the next batch. self.block_allocator.mark_blocks_as_computed([]) def get_common_computed_block_ids( self, seqs: List[Sequence]) -> GenericSequence[int]: """Determine which blocks for which we skip prefill. With prefix caching we can skip prefill for previously-generated blocks. Currently, the attention implementation only supports skipping cached blocks if they are a contiguous prefix of cached blocks. This method determines which blocks can be safely skipped for all sequences in the sequence group. """ computed_seq_block_ids = [] for seq in seqs: computed_seq_block_ids.append( self._computed_blocks_tracker. get_cached_computed_blocks_and_update( seq.seq_id, self.block_tables[seq.seq_id].physical_block_ids)) # NOTE(sang): This assumes seq_block_ids doesn't contain any None. return self.block_allocator.get_common_computed_block_ids( computed_seq_block_ids) # type: ignore def fork(self, parent_seq: Sequence, child_seq: Sequence) -> None: if parent_seq.seq_id not in self.block_tables: # Parent sequence has either been freed or never existed. return src_block_table = self.block_tables[parent_seq.seq_id] self.block_tables[child_seq.seq_id] = src_block_table.fork() # Track child seq self._computed_blocks_tracker.add_seq(child_seq.seq_id) self._last_access_blocks_tracker.add_seq(child_seq.seq_id) def can_swap_in(self, seq_group: SequenceGroup, num_lookahead_slots: int) -> AllocStatus: """Returns the AllocStatus for the given sequence_group with num_lookahead_slots. Args: sequence_group (SequenceGroup): The sequence group to swap in. num_lookahead_slots (int): Number of lookahead slots used in speculative decoding, default to 0. Returns: AllocStatus: The AllocStatus for the given sequence group. """ return self._can_swap(seq_group, Device.GPU, SequenceStatus.SWAPPED, num_lookahead_slots) def swap_in(self, seq_group: SequenceGroup) -> List[Tuple[int, int]]: """Returns the block id mapping (from CPU to GPU) generated by swapping in the given seq_group with num_lookahead_slots. Args: seq_group (SequenceGroup): The sequence group to swap in. Returns: List[Tuple[int, int]]: The mapping of swapping block from CPU to GPU. """ physical_block_id_mapping = [] for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED): blocks = self.block_tables[seq.seq_id].blocks if len(blocks) == 0: continue seq_swap_mapping = self.block_allocator.swap(blocks=blocks, src_device=Device.CPU, dst_device=Device.GPU) # Refresh the block ids of the table (post-swap) self.block_tables[seq.seq_id].update(blocks) seq_physical_block_id_mapping = { self.block_allocator.get_physical_block_id( Device.CPU, cpu_block_id): self.block_allocator.get_physical_block_id( Device.GPU, gpu_block_id) for cpu_block_id, gpu_block_id in seq_swap_mapping.items() } physical_block_id_mapping.extend( list(seq_physical_block_id_mapping.items())) return physical_block_id_mapping def can_swap_out(self, seq_group: SequenceGroup) -> bool: """Returns whether we can swap out the given sequence_group with num_lookahead_slots. Args: seq_group (SequenceGroup): The sequence group to swap in. num_lookahead_slots (int): Number of lookahead slots used in speculative decoding, default to 0. Returns: bool: Whether it's possible to swap out current sequence group. """ alloc_status = self._can_swap(seq_group, Device.CPU, SequenceStatus.RUNNING) if alloc_status == AllocStatus.OK: return True return False def swap_out(self, seq_group: SequenceGroup) -> List[Tuple[int, int]]: """Returns the block id mapping (from GPU to CPU) generated by swapping out the given sequence_group with num_lookahead_slots. Args: sequence_group (SequenceGroup): The sequence group to swap in. Returns: List[Tuple[int, int]]: The mapping of swapping block from GPU to CPU. """ physical_block_id_mapping = [] for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING): blocks = self.block_tables[seq.seq_id].blocks if len(blocks) == 0: continue seq_swap_mapping = self.block_allocator.swap(blocks=blocks, src_device=Device.GPU, dst_device=Device.CPU) # Refresh the block ids of the table (post-swap) self.block_tables[seq.seq_id].update(blocks) seq_physical_block_id_mapping = { self.block_allocator.get_physical_block_id( Device.GPU, gpu_block_id): self.block_allocator.get_physical_block_id( Device.CPU, cpu_block_id) for gpu_block_id, cpu_block_id in seq_swap_mapping.items() } physical_block_id_mapping.extend( list(seq_physical_block_id_mapping.items())) return physical_block_id_mapping def get_num_free_gpu_blocks(self) -> int: return self.block_allocator.get_num_free_blocks(Device.GPU) def get_num_free_cpu_blocks(self) -> int: return self.block_allocator.get_num_free_blocks(Device.CPU) def get_prefix_cache_hit_rate(self, device: Device) -> float: return self.block_allocator.get_prefix_cache_hit_rate(device) def _can_swap(self, seq_group: SequenceGroup, device: Device, status: SequenceStatus, num_lookahead_slots: int = 0) -> AllocStatus: """Returns the AllocStatus for swapping in/out the given sequence_group on to the 'device'. Args: sequence_group (SequenceGroup): The sequence group to swap in. device (Device): device to swap the 'seq_group' on. status (SequenceStatus): The status of sequence which is needed for action. RUNNING for swap out and SWAPPED for swap in num_lookahead_slots (int): Number of lookahead slots used in speculative decoding, default to 0. Returns: AllocStatus: The AllocStatus for swapping in/out the given sequence_group on to the 'device'. """ blocks = self._get_blocks_for_swap(seq_group, status) num_blocks_touched = self.block_allocator.get_num_blocks_touched( blocks, device, num_lookahead_slots) watermark_blocks = 0 if device == Device.GPU: watermark_blocks = self.watermark_blocks if self.block_allocator.get_num_total_blocks( device) < num_blocks_touched: return AllocStatus.NEVER elif self.block_allocator.get_num_free_blocks( device) - num_blocks_touched >= watermark_blocks: return AllocStatus.OK else: return AllocStatus.LATER def _get_blocks_for_swap(self, seq_group: SequenceGroup, status: SequenceStatus) -> List[Block]: """Returns the list of blocks those are touched by the seq_group Args: sequence_group (SequenceGroup): The sequence group to swap in. status (SequenceStatus): The status of sequence which is needed for action. RUNNING for swap out and SWAPPED for swap in Returns: The list of blocks those are touched by the seq_group. """ blocks: Dict[int, List[Block]] = {} for seq in seq_group.get_seqs(status=status): block_table = self.block_tables[seq.seq_id] if block_table.blocks is not None: blocks[seq.seq_id] = block_table.blocks combined_blocks = list(chain(*blocks.values())) return combined_blocks