Add minimal vLLM 0.16.1 build repo for BI-V150
This commit is contained in:
0
vllm/v1/core/__init__.py
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0
vllm/v1/core/__init__.py
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510
vllm/v1/core/block_pool.py
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vllm/v1/core/block_pool.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterable, Sequence
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from typing import Any
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from vllm.distributed.kv_events import (
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MEDIUM_GPU,
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AllBlocksCleared,
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BlockRemoved,
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BlockStored,
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KVCacheEvent,
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)
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from vllm.logger import init_logger
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from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector
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from vllm.v1.core.kv_cache_utils import (
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BlockHash,
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BlockHashList,
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BlockHashListWithBlockSize,
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BlockHashWithGroupId,
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ExternalBlockHash,
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FreeKVCacheBlockQueue,
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KVCacheBlock,
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generate_block_hash_extra_keys,
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get_block_hash,
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make_block_hash_with_group_id,
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maybe_convert_block_hash,
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)
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from vllm.v1.request import Request
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logger = init_logger(__name__)
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class BlockHashToBlockMap:
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"""
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Cache of blocks that are used for prefix caching. It caches blocks
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from hash directly to a block or multiple blocks
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(i.e. {block_hash: KVCacheBlocks})
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- Mostly block_hash maps to a single KVCacheBlock, and KVCacheBlocks
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would simply be a KVCacheBlock.
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- Otherwise, KVCacheBlocks is a dict from {block_id: KVCacheBlock}
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A cached block is a full block with a block hash that can be used
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for prefix caching.
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The cached block may be used by running requests or in the
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free_block_queue that could potentially be evicted.
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NOTE #1: We currently don't de-duplicate the blocks in the cache,
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meaning that if a block becomes full and is cached, we don't check
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if there is already an identical block in the cache. This is because
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we want to make sure the allocated block IDs won't change so that
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block tables are append-only.
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NOTE #2: The union type is introduced in order to reduce GC costs
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from the inner dict.
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"""
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def __init__(self):
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self._cache: dict[
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BlockHashWithGroupId, KVCacheBlock | dict[int, KVCacheBlock]
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] = {}
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def get_one_block(self, key: BlockHashWithGroupId) -> KVCacheBlock | None:
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"""
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Gets any block with the given block hash key.
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"""
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blocks = self._cache.get(key)
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if blocks is not None:
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if isinstance(blocks, KVCacheBlock):
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return blocks
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if isinstance(blocks, dict):
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return next(iter(blocks.values()))
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self._unexpected_blocks_type(blocks)
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return None
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def insert(self, key: BlockHashWithGroupId, block: KVCacheBlock) -> None:
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"""
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Inserts the KVCacheBlock to the cache
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"""
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blocks = self._cache.get(key)
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if blocks is None:
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# When key is not found, attach a single block to the key
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self._cache[key] = block
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elif isinstance(blocks, KVCacheBlock):
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# If there's a block with the same key, merge the original block
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# and the new block into a dict
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self._cache[key] = {blocks.block_id: blocks, block.block_id: block}
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elif isinstance(blocks, dict):
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# If it's already a dict, simply insert the block
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blocks[block.block_id] = block
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else:
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self._unexpected_blocks_type(blocks)
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def pop(self, key: BlockHashWithGroupId, block_id: int) -> KVCacheBlock | None:
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"""
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Checks if block_hash exists and pop block_id from the cache
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"""
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blocks = self._cache.pop(key, None)
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if blocks is None:
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# block_hash not found in the cache
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return None
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# TODO(Jialin): If key is found, block_id should always present
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# in blocks. We currently keep the original behaviour for safety.
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#
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# Will add block_id == blocks.block_id assertion and
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# use del blocks[block_id] instead as followup.
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if isinstance(blocks, KVCacheBlock):
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if blocks.block_id == block_id:
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return blocks
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# If the single block ID doesn't match, we should put the
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# block back (it should happen rarely)
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self._cache[key] = blocks
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return None
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if isinstance(blocks, dict):
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# Try to pop block_id from the block dict, and if dict still
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# contain blocks, put back to the cache.
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block = blocks.pop(block_id, None)
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if len(blocks) > 0:
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self._cache[key] = blocks
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return block
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self._unexpected_blocks_type(blocks)
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return None
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def __len__(self) -> int:
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return len(self._cache)
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def _unexpected_blocks_type(self, blocks: Any) -> None:
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raise AssertionError(f"Invalid KV cache block type {type(blocks)}")
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class BlockPool:
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"""BlockPool that manages KVCacheBlocks.
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It provides methods to allocate, free and cache the kv cache blocks. The
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free_block_queue stores the free blocks in eviction order to enable
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allocation, free, and cache eviction. The cached_block_hash_to_block
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maps between block hash and cached block to support finding cached blocks
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by their block hash.
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Args:
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num_gpu_blocks: The number of blocks in the pool.
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enable_caching: Whether to enable prefix caching.
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hash_block_size: The block size of which the block hashes are computed.
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The actual block size usually equals hash_block_size, but in cases
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where different KV cache groups have different block sizes, the
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actual block size can be a multiple of hash_block_size.
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enable_kv_cache_events: Whether to enable kv cache events.
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metrics_collector: Optional metrics collector for tracking block residency.
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"""
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def __init__(
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self,
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num_gpu_blocks: int,
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enable_caching: bool,
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hash_block_size: int,
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enable_kv_cache_events: bool = False,
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metrics_collector: KVCacheMetricsCollector | None = None,
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):
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assert isinstance(num_gpu_blocks, int) and num_gpu_blocks > 0
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self.num_gpu_blocks = num_gpu_blocks
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self.enable_caching = enable_caching
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self.hash_block_size = hash_block_size
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# All kv-cache blocks.
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self.blocks: list[KVCacheBlock] = [
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KVCacheBlock(idx) for idx in range(num_gpu_blocks)
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]
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# Free block queue that constructs and manipulates a doubly linked
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# list of free blocks (including eviction candidates when caching is
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# enabled).
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self.free_block_queue = FreeKVCacheBlockQueue(self.blocks)
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# Cache for block lookup
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self.cached_block_hash_to_block: BlockHashToBlockMap = BlockHashToBlockMap()
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# To represent a placeholder block with block_id=0.
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# The ref_cnt of null_block is not maintained, needs special care to
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# avoid freeing it.
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self.null_block = self.free_block_queue.popleft()
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self.null_block.is_null = True
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self.enable_kv_cache_events = enable_kv_cache_events
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self.kv_event_queue: list[KVCacheEvent] = []
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self.metrics_collector = metrics_collector
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def get_cached_block(
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self, block_hash: BlockHash, kv_cache_group_ids: list[int]
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) -> list[KVCacheBlock] | None:
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"""Get the cached block by the block hash for each group in
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`kv_cache_group_ids`, or None if cache miss for any group.
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If there are duplicated blocks, we return the first block in the cache.
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Args:
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block_hash: The hash value of the block.
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kv_cache_group_ids: The ids of the KV cache groups.
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Returns:
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The cached blocks if exists, or None.
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"""
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cached_blocks = []
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for group_id in kv_cache_group_ids:
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block_hash_with_group_id = make_block_hash_with_group_id(
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block_hash, group_id
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)
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block = self.cached_block_hash_to_block.get_one_block(
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block_hash_with_group_id
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)
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if not block:
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return None
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cached_blocks.append(block)
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return cached_blocks
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def cache_full_blocks(
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self,
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request: Request,
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blocks: list[KVCacheBlock],
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num_cached_blocks: int,
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num_full_blocks: int,
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block_size: int,
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kv_cache_group_id: int,
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) -> None:
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"""Cache a list of full blocks for prefix caching.
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This function takes a list of blocks that will have their block hash
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metadata to be updated and cached. Given a request, it updates the
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metadata for each block and caching it in the
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`cached_block_hash_to_block`.
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The block hashes values are computed by the Request object immediately
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when it is created and when new tokens are appended.
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Args:
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request: The request to cache the blocks.
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blocks: All blocks in the request.
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num_cached_blocks: The number of blocks that are already cached.
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num_full_blocks: The number of blocks that are full and should
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be cached after this function.
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block_size: Number of tokens in each block.
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kv_cache_group_id: The id of the KV cache group.
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"""
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if num_cached_blocks >= num_full_blocks:
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return
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new_full_blocks = blocks[num_cached_blocks:num_full_blocks]
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assert len(request.block_hashes) >= num_full_blocks
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if block_size == self.hash_block_size:
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# Common case.
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block_hashes: BlockHashList = request.block_hashes
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else:
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# block_size is a multiple of hash_block_size. This happens when
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# different KV cache groups have different block sizes.
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assert block_size % self.hash_block_size == 0
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# Recalculate block_hashes at the granularity of block_size, using
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# the original block_hashes (at the granularity of hash_block_size).
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block_hashes = BlockHashListWithBlockSize(
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request.block_hashes, self.hash_block_size, block_size
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)
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new_block_hashes = block_hashes[num_cached_blocks:]
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new_hashes: list[ExternalBlockHash] | None = (
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[] if self.enable_kv_cache_events else None
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)
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for i, blk in enumerate(new_full_blocks):
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# Some blocks may be null blocks when enabling sparse attention like
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# sliding window attention, or Mamba models with prefix-caching in
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# align mode. We skip null blocks here.
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if blk.is_null:
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continue
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assert blk.block_hash is None
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block_hash = new_block_hashes[i]
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# Update and added the full block to the cache.
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block_hash_with_group_id = make_block_hash_with_group_id(
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block_hash, kv_cache_group_id
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)
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blk.block_hash = block_hash_with_group_id
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self.cached_block_hash_to_block.insert(block_hash_with_group_id, blk)
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if new_hashes is not None:
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new_hashes.append(maybe_convert_block_hash(block_hash))
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if self.enable_kv_cache_events:
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if num_cached_blocks == 0:
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parent_block_hash: ExternalBlockHash | None = None
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else:
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parent_block_hash = maybe_convert_block_hash(
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block_hashes[num_cached_blocks - 1]
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)
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# Calculate token range for the blocks being cached
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start_token_idx = num_cached_blocks * block_size
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end_token_idx = num_full_blocks * block_size
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# Generate extra keys for each block individually.
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# Each block may have different extra_keys (e.g., different MM
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# features, or cache_salt only for the first block).
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# Skip null blocks to match the length of new_hashes.
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extra_keys_list: list[tuple[Any, ...] | None] = []
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curr_mm_idx = 0
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for i in range(num_cached_blocks, num_full_blocks):
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if blocks[i].is_null:
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continue
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block_start = i * block_size
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block_end = block_start + block_size
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extra_keys, curr_mm_idx = generate_block_hash_extra_keys(
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request, block_start, block_end, curr_mm_idx
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)
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extra_keys_list.append(extra_keys)
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self.kv_event_queue.append(
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BlockStored(
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block_hashes=new_hashes,
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parent_block_hash=parent_block_hash,
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token_ids=request.all_token_ids[start_token_idx:end_token_idx],
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block_size=block_size,
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lora_id=request.lora_request.adapter_id
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if request.lora_request
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else None,
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medium=MEDIUM_GPU,
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lora_name=request.lora_request.name
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if request.lora_request
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else None,
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extra_keys=extra_keys_list if extra_keys_list else None,
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)
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)
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def get_new_blocks(self, num_blocks: int) -> list[KVCacheBlock]:
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"""Get new blocks from the free block pool.
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Note that we do not check block cache in this function.
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Args:
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num_blocks: The number of blocks to allocate.
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Returns:
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A list of new block.
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"""
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if num_blocks > self.get_num_free_blocks():
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raise ValueError(f"Cannot get {num_blocks} free blocks from the pool")
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ret: list[KVCacheBlock] = self.free_block_queue.popleft_n(num_blocks)
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# In order to only iterate the list once, we duplicated code a bit
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if self.enable_caching:
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for block in ret:
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self._maybe_evict_cached_block(block)
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assert block.ref_cnt == 0
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block.ref_cnt += 1
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if self.metrics_collector:
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self.metrics_collector.on_block_allocated(block)
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else:
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for block in ret:
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assert block.ref_cnt == 0
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block.ref_cnt += 1
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if self.metrics_collector:
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self.metrics_collector.on_block_allocated(block)
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return ret
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def _maybe_evict_cached_block(self, block: KVCacheBlock) -> bool:
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"""
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If a block is cached in `cached_block_hash_to_block`, we reset its hash
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metadata and evict it from the cache.
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Args:
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block: The block to evict.
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Returns:
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True if the block is evicted, False otherwise.
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"""
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# Clean up metrics tracking first to prevent leaks
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if self.metrics_collector:
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self.metrics_collector.on_block_evicted(block)
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block_hash = block.block_hash
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if block_hash is None:
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# The block doesn't have hash, eviction is not needed
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return False
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if self.cached_block_hash_to_block.pop(block_hash, block.block_id) is None:
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# block not found in cached_block_hash_to_block,
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# eviction is not needed
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return False
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block.reset_hash()
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if self.enable_kv_cache_events:
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# FIXME (Chen): Not sure whether we should return `hash_value`
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# or `(hash_value, group_id)` here. But it's fine now because
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# we disable hybrid kv cache manager when kv cache event is
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# enabled, so there is only one group.
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self.kv_event_queue.append(
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BlockRemoved(
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block_hashes=[maybe_convert_block_hash(get_block_hash(block_hash))],
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medium=MEDIUM_GPU,
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)
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)
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return True
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def touch(self, blocks: Sequence[KVCacheBlock]) -> None:
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"""Touch a block increases its reference count by 1, and may remove
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the block from the free queue. This is used when a block is hit by
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another request with the same prefix.
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Args:
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blocks: A list of blocks to touch.
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"""
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for block in blocks:
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# ref_cnt=0 means this block is in the free list (i.e. eviction
|
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# candidate), so remove it.
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if block.ref_cnt == 0 and not block.is_null:
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self.free_block_queue.remove(block)
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block.ref_cnt += 1
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if self.metrics_collector:
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self.metrics_collector.on_block_accessed(block)
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def free_blocks(self, ordered_blocks: Iterable[KVCacheBlock]) -> None:
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"""Free a list of blocks. The blocks should be ordered by their
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eviction priority, where the first block will be evicted first.
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Args:
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ordered_blocks: A list of blocks to free ordered by their eviction
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priority.
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"""
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# Materialize the iterable to allow multiple passes.
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blocks_list = list(ordered_blocks)
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for block in blocks_list:
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block.ref_cnt -= 1
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self.free_block_queue.append_n(
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[block for block in blocks_list if block.ref_cnt == 0 and not block.is_null]
|
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)
|
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|
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def evict_blocks(self, block_ids: set[int]) -> None:
|
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"""evict blocks from the prefix cache by their block IDs.
|
||||
|
||||
only evicts blocks that are currently cached (have a hash). blocks
|
||||
with ref_cnt > 0 are not freed from the block pool, only evicted
|
||||
from the prefix cache hash table.
|
||||
|
||||
Args:
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block_ids: Set of block IDs to evict from cache.
|
||||
"""
|
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for block_id in block_ids:
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assert block_id < len(self.blocks), (
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||||
f"Invalid block_id {block_id} >= {len(self.blocks)}. "
|
||||
f"This indicates a bug in the KV connector - workers should "
|
||||
f"only report block IDs that were allocated by the scheduler."
|
||||
)
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||||
block = self.blocks[block_id]
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||||
self._maybe_evict_cached_block(block)
|
||||
|
||||
def reset_prefix_cache(self) -> bool:
|
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"""Reset prefix cache. This function may be used in RLHF
|
||||
flows to invalid prefix caching after the weights are updated,
|
||||
or used for resetting prefix caching status for benchmarking.
|
||||
|
||||
Returns:
|
||||
bool: True if the prefix cache is successfully reset,
|
||||
False otherwise.
|
||||
"""
|
||||
num_used_blocks = self.num_gpu_blocks - self.get_num_free_blocks()
|
||||
if num_used_blocks != 1: # The null block is always marked as used
|
||||
logger.warning(
|
||||
"Failed to reset prefix cache because some "
|
||||
"blocks (%d) are not freed yet",
|
||||
num_used_blocks - 1,
|
||||
)
|
||||
return False
|
||||
|
||||
# Remove all hashes so that no new blocks will hit.
|
||||
self.cached_block_hash_to_block = BlockHashToBlockMap()
|
||||
|
||||
# Remove all hashes from all blocks.
|
||||
for block in self.blocks:
|
||||
block.reset_hash()
|
||||
|
||||
if self.metrics_collector:
|
||||
self.metrics_collector.reset()
|
||||
|
||||
logger.info("Successfully reset prefix cache")
|
||||
|
||||
if self.enable_kv_cache_events:
|
||||
self.kv_event_queue.append(AllBlocksCleared())
|
||||
|
||||
return True
|
||||
|
||||
def get_num_free_blocks(self) -> int:
|
||||
"""Get the number of free blocks in the pool.
|
||||
|
||||
Returns:
|
||||
The number of free blocks.
|
||||
"""
|
||||
return self.free_block_queue.num_free_blocks
|
||||
|
||||
def get_usage(self) -> float:
|
||||
"""Get the KV cache usage.
|
||||
|
||||
Returns:
|
||||
The KV cache usage (between 0.0 and 1.0).
|
||||
"""
|
||||
|
||||
# Subtract 1 to account for null block.
|
||||
total_gpu_blocks = self.num_gpu_blocks - 1
|
||||
if not total_gpu_blocks:
|
||||
return 0
|
||||
return 1.0 - (self.get_num_free_blocks() / total_gpu_blocks)
|
||||
|
||||
def take_events(self) -> list[KVCacheEvent]:
|
||||
"""Atomically takes all events and clears the queue.
|
||||
|
||||
Returns:
|
||||
A list of KV cache events.
|
||||
"""
|
||||
if not self.enable_kv_cache_events:
|
||||
return []
|
||||
events = self.kv_event_queue
|
||||
self.kv_event_queue = []
|
||||
return events
|
||||
381
vllm/v1/core/encoder_cache_manager.py
Normal file
381
vllm/v1/core/encoder_cache_manager.py
Normal file
@@ -0,0 +1,381 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from collections import OrderedDict
|
||||
from collections.abc import Mapping
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from vllm.logger import init_logger
|
||||
from vllm.v1.request import Request
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.config import SchedulerConfig
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class EncoderCacheManager:
|
||||
"""Manages caching of encoder outputs for multimodal models in vLLM V1.
|
||||
|
||||
The EncoderCacheManager handles the lifecycle of multimodal encoder outputs
|
||||
(such as vision embeddings from images) during request processing. It
|
||||
provides memory-aware caching to avoid recomputing encoder outputs when the
|
||||
same multimodal inputs appear in different stages of request processing.
|
||||
|
||||
This manager is particularly important for:
|
||||
- Vision-language models (e.g., LLaVA) where image encoder outputs are
|
||||
cached
|
||||
- Any multimodal model where encoder computation is expensive and
|
||||
cacheable
|
||||
|
||||
The cache operates at the granularity of individual multimodal input items
|
||||
within requests, allowing for fine-grained memory management and enabling
|
||||
chunked processing of multimodal inputs.
|
||||
|
||||
Cache is enabled to share embeddings of same multimodal data
|
||||
item (identified by their hash value) between different requests,
|
||||
and eviction takes place at allocation time when there's no free
|
||||
space for new embeddings.
|
||||
Oldest cached embeddings with no request referenced will be first evicted.
|
||||
|
||||
NOTE: The EncoderCacheManager operates on the level of multimodal embeddings
|
||||
instead of encoder tokens (i.e. all tokens that represent the multimodal data
|
||||
in the input sequence). This means all break/text tokens in-between multimodal
|
||||
embeddings are not considered with respect to the cache size and the number
|
||||
of free slots.
|
||||
|
||||
Args:
|
||||
cache_size: Limit the size of the cache, measured by the number of
|
||||
encoder embeddings from the input sequence.
|
||||
|
||||
Attributes:
|
||||
cache_size: Total cache capacity in encoder embeddings.
|
||||
num_free_slots: Current available cache capacity in encoder embeddings.
|
||||
num_freeable_slots: Capacity that can be immediately reclaimed by
|
||||
evicting entries with zero references (in encoder embeddings).
|
||||
cached: Mapping from mm_hash to a set of request IDs that currently
|
||||
reference the cached entry. If the set is empty, the entry exists
|
||||
but is not referenced by any request and is eligible for
|
||||
reclamation.
|
||||
freeable: List of tuples (mm_hash, num_encoder_embeds) representing entries
|
||||
whose no current running request is needed and that can be freed to
|
||||
make space when needed.
|
||||
freed: List of mm_hash strings that were actually evicted since the
|
||||
last call to get_freed_mm_hashes(). This list is cleared on return.
|
||||
"""
|
||||
|
||||
def __init__(self, cache_size: int):
|
||||
self.cache_size = cache_size
|
||||
self.num_free_slots = cache_size
|
||||
self.num_freeable_slots = cache_size
|
||||
|
||||
# mm_hash of mm_data => ids of requests that reference the mm_data
|
||||
self.cached: dict[str, set[str]] = {}
|
||||
|
||||
# mm_hash of mm_data => num_encoder_embeds of the mm_data
|
||||
self.freeable: OrderedDict[str, int] = OrderedDict()
|
||||
self.freed: list[str] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset the encoder cache to its initial state.
|
||||
|
||||
This clears all cached encoder outputs and resets capacity tracking.
|
||||
Called when model weights are updated to invalidate stale embeddings.
|
||||
"""
|
||||
self.cached.clear()
|
||||
self.freeable.clear()
|
||||
self.freed.clear()
|
||||
self.num_free_slots = self.cache_size
|
||||
self.num_freeable_slots = self.cache_size
|
||||
|
||||
def check_and_update_cache(self, request: Request, input_id: int) -> bool:
|
||||
"""Check if encoder output for a specific multimodal input is cached.
|
||||
|
||||
If the encoder output is cached, update `cached` to add the request id
|
||||
to the set of request ids that reference the cached encoder output.
|
||||
If the encoder output was previously not referenced by any request,
|
||||
update `freeable` and `num_freeable_slots` accordingly.
|
||||
|
||||
Args:
|
||||
request: The request containing the multimodal input
|
||||
input_id: Index of the multimodal input within the request
|
||||
|
||||
Returns:
|
||||
True if the encoder output for this input is already cached
|
||||
"""
|
||||
mm_hash = request.mm_features[input_id].identifier
|
||||
# Not cached at all
|
||||
if mm_hash not in self.cached:
|
||||
return False
|
||||
|
||||
# Cached but currently not referenced by any request
|
||||
if not self.cached[mm_hash]:
|
||||
num_encoder_embeds = self.freeable.pop(mm_hash)
|
||||
self.num_freeable_slots -= num_encoder_embeds
|
||||
|
||||
self.cached[mm_hash].add(request.request_id)
|
||||
return True
|
||||
|
||||
def can_allocate(
|
||||
self,
|
||||
request: Request,
|
||||
input_id: int,
|
||||
encoder_compute_budget: int,
|
||||
num_embeds_to_schedule: int,
|
||||
) -> bool:
|
||||
"""Check if there's sufficient cache space for a multimodal input.
|
||||
If there is, return True and update EncoderCacheManager state.
|
||||
|
||||
If there is not enough free space in `num_free_slots` but there is
|
||||
enough reclaimable space in `num_freeable_slots`, entries will be
|
||||
evicted from `freeable` (their mm_hash appended to `freed`) until
|
||||
enough space is available, and then this method returns True.
|
||||
Older entries are evicted first.
|
||||
|
||||
Returns False only if the requested number of tokens exceeds both
|
||||
the free and reclaimable capacities combined.
|
||||
|
||||
Args:
|
||||
request: The request containing the multimodal input.
|
||||
input_id: Index of the multimodal input within the request.
|
||||
encoder_compute_budget: Number of encoder embeddings allowed to be
|
||||
computed when this method is invoked.
|
||||
num_embeds_to_schedule: Number of encoder embeddings already scheduled to be
|
||||
allocated with cache space when this method is invoked.
|
||||
|
||||
Returns:
|
||||
True if there's enough capacity to hold the encoder output for this
|
||||
input (possibly after reclaiming `freeable` entries); otherwise
|
||||
False.
|
||||
|
||||
Note: This method does not allocate physical memory for the encoder
|
||||
output but only the state of EncoderCacheManager.
|
||||
"""
|
||||
num_embeds = request.get_num_encoder_embeds(input_id)
|
||||
|
||||
# Not enough compute budget
|
||||
if num_embeds > encoder_compute_budget:
|
||||
return False
|
||||
|
||||
num_embeds += num_embeds_to_schedule
|
||||
|
||||
# Enough free slots
|
||||
if num_embeds <= self.num_free_slots:
|
||||
return True
|
||||
|
||||
# Not enough reclaimable slots
|
||||
if num_embeds > self.num_freeable_slots:
|
||||
return False
|
||||
|
||||
# Not enough free slots but enough reclaimable slots
|
||||
# NOTE: Eviction takes place here, but physical memory is not freed
|
||||
# until model runner is notified by the scheduler output.
|
||||
while num_embeds > self.num_free_slots:
|
||||
mm_hash, num_free_embeds = self.freeable.popitem(last=False)
|
||||
del self.cached[mm_hash]
|
||||
self.freed.append(mm_hash)
|
||||
self.num_free_slots += num_free_embeds
|
||||
return True
|
||||
|
||||
def allocate(self, request: Request, input_id: int) -> None:
|
||||
"""Allocate cache space for a multimodal input's encoder output.
|
||||
|
||||
This reserves cache space for storing the encoder output of the
|
||||
specified multimodal input. The actual encoder output storage happens in
|
||||
the model runner; this method updates the manager's bookkeeping.
|
||||
|
||||
Note:
|
||||
This method assumes can_allocate() returned True for the same input.
|
||||
"""
|
||||
|
||||
mm_hash = request.mm_features[input_id].identifier
|
||||
request_id = request.request_id
|
||||
if mm_hash not in self.cached:
|
||||
self.cached[mm_hash] = set()
|
||||
|
||||
num_encoder_embeds = request.get_num_encoder_embeds(input_id)
|
||||
|
||||
# NOTE: Encoder cache should always have enough space for encoder inputs
|
||||
# that are scheduled since eviction takes place at can_allocate().
|
||||
assert self.num_free_slots >= num_encoder_embeds
|
||||
assert self.num_freeable_slots >= num_encoder_embeds
|
||||
|
||||
self.cached[mm_hash].add(request_id)
|
||||
self.num_free_slots -= num_encoder_embeds
|
||||
self.num_freeable_slots -= num_encoder_embeds
|
||||
|
||||
def get_cached_input_ids(self, request: Request) -> set[int]:
|
||||
"""Get all cached multimodal input IDs for a request.
|
||||
|
||||
Returns the set of input IDs whose `mm_hash` exists in the cache map.
|
||||
This includes entries that are currently unreferenced (and thus present
|
||||
in `freeable`); for such entries, freeing for this request will be a
|
||||
no-op.
|
||||
"""
|
||||
return {
|
||||
input_id
|
||||
for input_id in range(len(request.mm_features))
|
||||
if request.mm_features[input_id].identifier in self.cached
|
||||
}
|
||||
|
||||
def free_encoder_input(self, request: Request, input_id: int) -> None:
|
||||
"""Free the request's reference to the encoder input (`mm_data`)
|
||||
|
||||
When the reference set for the corresponding `mm_hash` becomes empty,
|
||||
the entry is appended to `freeable` and `num_freeable_slots` is
|
||||
increased by the number of encoder embeddings for that input.
|
||||
|
||||
The entry is NOT physically freed until capacity is needed (e.g., by
|
||||
`can_allocate`).
|
||||
"""
|
||||
req_id = request.request_id
|
||||
mm_hash = request.mm_features[input_id].identifier
|
||||
# The mm_hash not in cache or the req_id set is empty
|
||||
if not self.cached.get(mm_hash, None):
|
||||
return
|
||||
self.cached[mm_hash].discard(req_id)
|
||||
if not self.cached[mm_hash]:
|
||||
num_encoder_embeds = request.get_num_encoder_embeds(input_id)
|
||||
self.freeable[mm_hash] = num_encoder_embeds
|
||||
self.num_freeable_slots += num_encoder_embeds
|
||||
|
||||
def free(self, request: Request) -> None:
|
||||
"""Free all encoder input cache reference held by *request*.
|
||||
|
||||
For each cached input ID, `free_encoder_input` is invoked.
|
||||
The data stays in memory until eviction is triggered by a future
|
||||
attempt allocation called by 'can_allocate'.
|
||||
|
||||
Typically called when a request is finished, cancelled, or aborted.
|
||||
"""
|
||||
input_ids = self.get_cached_input_ids(request)
|
||||
for input_id in input_ids:
|
||||
self.free_encoder_input(request, input_id)
|
||||
|
||||
def get_freed_mm_hashes(self) -> list[str]:
|
||||
"""Get and clear the list of recently freed encoder cache entries.
|
||||
|
||||
Returns:
|
||||
List of mm_hash strings that were actually evicted since the last
|
||||
call to be used by the scheduler to notify workers about which
|
||||
encoder outputs can be removed from their caches. The internal
|
||||
list is cleared after this call.
|
||||
"""
|
||||
freed = self.freed
|
||||
self.freed = []
|
||||
return freed
|
||||
|
||||
|
||||
def compute_mm_encoder_budget(
|
||||
scheduler_config: "SchedulerConfig",
|
||||
mm_max_toks_per_item: Mapping[str, int],
|
||||
) -> tuple[int, int]:
|
||||
"""Compute the encoder cache budget based on the model and scheduler
|
||||
configurations for a multimodal model.
|
||||
|
||||
Args:
|
||||
scheduler_config: Scheduler configuration.
|
||||
mm_max_toks_per_item: The maximum number of tokens per item for each
|
||||
non-text modality.
|
||||
|
||||
Returns:
|
||||
- Compute budget for encoder execution, measured in number of tokens
|
||||
from the input sequence.
|
||||
- Space budget for encoder cache size, measured in number of tokens
|
||||
from the input sequence.
|
||||
"""
|
||||
|
||||
if not mm_max_toks_per_item:
|
||||
logger.warning(
|
||||
"All non-text modalities supported by the model have been "
|
||||
"explicitly disabled via limit_mm_per_prompt. Encoder cache will "
|
||||
"not be initialized."
|
||||
)
|
||||
return 0, 0
|
||||
|
||||
max_tokens_per_mm_item = max(mm_max_toks_per_item.values())
|
||||
|
||||
if (
|
||||
scheduler_config.disable_chunked_mm_input
|
||||
and max_tokens_per_mm_item > scheduler_config.max_num_batched_tokens
|
||||
):
|
||||
raise ValueError(
|
||||
"Chunked MM input disabled but max_tokens_per_mm_item "
|
||||
f"({max_tokens_per_mm_item}) is larger than max_num_batched_tokens"
|
||||
f" ({scheduler_config.max_num_batched_tokens}). Please increase "
|
||||
"max_num_batched_tokens."
|
||||
)
|
||||
|
||||
encoder_compute_budget = max(
|
||||
scheduler_config.max_num_encoder_input_tokens, max_tokens_per_mm_item
|
||||
)
|
||||
encoder_cache_size = max(
|
||||
scheduler_config.encoder_cache_size, max_tokens_per_mm_item
|
||||
)
|
||||
|
||||
return encoder_compute_budget, encoder_cache_size
|
||||
|
||||
|
||||
# NOTE (NickLucche): Temporary implementation for encoder-decoder models that only
|
||||
# use the manager for scheduling purposes. Encoder-decoder models will eventually
|
||||
# utilize the cache and this class will fold into EncoderCacheManager, as
|
||||
# differences with MM models shrink.
|
||||
class EncoderDecoderCacheManager(EncoderCacheManager):
|
||||
def __init__(self, cache_size: int):
|
||||
self.cache_size = cache_size
|
||||
self.num_free_slots = cache_size
|
||||
self.allocated: list[str] = []
|
||||
self.to_free: list[str] = []
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Reset the encoder cache to its initial state."""
|
||||
self.num_free_slots = self.cache_size
|
||||
self.allocated.clear()
|
||||
self.to_free.clear()
|
||||
|
||||
def check_and_update_cache(self, request: Request, input_id: int) -> bool:
|
||||
return False
|
||||
|
||||
def can_allocate(
|
||||
self,
|
||||
request: Request,
|
||||
input_id: int,
|
||||
encoder_compute_budget: int,
|
||||
num_embeds_to_schedule: int,
|
||||
) -> bool:
|
||||
num_encoder_embeds = request.get_num_encoder_embeds(input_id)
|
||||
# Not enough compute budget
|
||||
if num_encoder_embeds > encoder_compute_budget:
|
||||
return False
|
||||
|
||||
num_encoder_embeds += num_embeds_to_schedule
|
||||
# Enough free slots
|
||||
return num_encoder_embeds <= self.num_free_slots
|
||||
|
||||
def allocate(self, request: Request, input_id: int) -> None:
|
||||
num_encoder_embeds = request.get_num_encoder_embeds(input_id)
|
||||
self.num_free_slots -= num_encoder_embeds
|
||||
|
||||
mm_hash = request.mm_features[input_id].identifier
|
||||
self.allocated.append(mm_hash)
|
||||
|
||||
def free(self, request: Request) -> None:
|
||||
for input_id in range(len(request.mm_features)):
|
||||
self.free_encoder_input(request, input_id)
|
||||
|
||||
def get_cached_input_ids(self, request: Request) -> set[int]:
|
||||
return set(range(len(request.mm_features)))
|
||||
|
||||
def get_freed_mm_hashes(self) -> list[str]:
|
||||
# As encoder cache is not used for enc-dec models, we can free the entries here
|
||||
# The actual free happens in the runner, *before* the model is executed.
|
||||
# Therefore, `freeable` acts as a buffer to free the entries only after the
|
||||
# model is executed, mimicking the state transition of `EncoderCacheManager`.
|
||||
to_free = self.to_free
|
||||
self.to_free = self.allocated
|
||||
self.allocated = []
|
||||
return to_free
|
||||
|
||||
def free_encoder_input(self, request: Request, input_id: int) -> None:
|
||||
num_encoder_embeds = request.get_num_encoder_embeds(input_id)
|
||||
self.num_free_slots += num_encoder_embeds
|
||||
591
vllm/v1/core/kv_cache_coordinator.py
Normal file
591
vllm/v1/core/kv_cache_coordinator.py
Normal file
@@ -0,0 +1,591 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
from abc import ABC, abstractmethod
|
||||
from collections.abc import Sequence
|
||||
from math import lcm
|
||||
|
||||
from vllm.v1.core.block_pool import BlockPool
|
||||
from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector
|
||||
from vllm.v1.core.kv_cache_utils import (
|
||||
BlockHash,
|
||||
BlockHashList,
|
||||
BlockHashListWithBlockSize,
|
||||
KVCacheBlock,
|
||||
)
|
||||
from vllm.v1.core.single_type_kv_cache_manager import (
|
||||
CrossAttentionManager,
|
||||
SingleTypeKVCacheManager,
|
||||
get_manager_for_kv_cache_spec,
|
||||
)
|
||||
from vllm.v1.kv_cache_interface import (
|
||||
FullAttentionSpec,
|
||||
KVCacheConfig,
|
||||
KVCacheSpec,
|
||||
)
|
||||
from vllm.v1.request import Request
|
||||
|
||||
|
||||
class KVCacheCoordinator(ABC):
|
||||
"""
|
||||
Coordinate the KV cache of different KV cache groups.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
use_eagle: bool,
|
||||
enable_caching: bool,
|
||||
enable_kv_cache_events: bool,
|
||||
dcp_world_size: int,
|
||||
pcp_world_size: int,
|
||||
hash_block_size: int,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
):
|
||||
self.kv_cache_config = kv_cache_config
|
||||
self.max_model_len = max_model_len
|
||||
self.enable_caching = enable_caching
|
||||
|
||||
self.block_pool = BlockPool(
|
||||
kv_cache_config.num_blocks,
|
||||
enable_caching,
|
||||
hash_block_size,
|
||||
enable_kv_cache_events,
|
||||
metrics_collector,
|
||||
)
|
||||
|
||||
# Needs special handling for find_longest_cache_hit if eagle is enabled
|
||||
self.use_eagle = use_eagle
|
||||
self.single_type_managers = tuple(
|
||||
get_manager_for_kv_cache_spec(
|
||||
kv_cache_spec=kv_cache_group.kv_cache_spec,
|
||||
block_pool=self.block_pool,
|
||||
enable_caching=enable_caching,
|
||||
kv_cache_group_id=i,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
)
|
||||
for i, kv_cache_group in enumerate(self.kv_cache_config.kv_cache_groups)
|
||||
)
|
||||
|
||||
def get_num_blocks_to_allocate(
|
||||
self,
|
||||
request_id: str,
|
||||
num_tokens: int,
|
||||
new_computed_blocks: tuple[Sequence[KVCacheBlock], ...],
|
||||
num_encoder_tokens: int,
|
||||
total_computed_tokens: int,
|
||||
num_tokens_main_model: int,
|
||||
) -> int:
|
||||
"""
|
||||
Get the number of blocks needed to be allocated for the request.
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
num_tokens: The total number of tokens that need a slot (including
|
||||
tokens that are already allocated).
|
||||
new_computed_blocks: The new computed blocks just hitting the
|
||||
prefix caching.
|
||||
num_encoder_tokens: The number of encoder tokens for allocating
|
||||
blocks for cross-attention.
|
||||
total_computed_tokens: Include both local and external tokens.
|
||||
num_tokens_main_model: The number of tokens for the main model (aka target
|
||||
model in spec decode). w/o spec decode, it is num_tokens;
|
||||
with spec decode, it is num_tokens - num_lookahead_tokens.
|
||||
|
||||
Returns:
|
||||
The number of blocks to allocate.
|
||||
"""
|
||||
num_blocks_to_allocate = 0
|
||||
for i, manager in enumerate(self.single_type_managers):
|
||||
if isinstance(manager, CrossAttentionManager):
|
||||
# For cross-attention, we issue a single static allocation
|
||||
# of blocks based on the number of encoder input tokens.
|
||||
num_blocks_to_allocate += manager.get_num_blocks_to_allocate(
|
||||
request_id, num_encoder_tokens, [], 0, num_encoder_tokens
|
||||
)
|
||||
else:
|
||||
num_blocks_to_allocate += manager.get_num_blocks_to_allocate(
|
||||
request_id,
|
||||
num_tokens,
|
||||
new_computed_blocks[i],
|
||||
total_computed_tokens,
|
||||
num_tokens_main_model,
|
||||
)
|
||||
return num_blocks_to_allocate
|
||||
|
||||
def allocate_new_computed_blocks(
|
||||
self,
|
||||
request_id: str,
|
||||
new_computed_blocks: tuple[Sequence[KVCacheBlock], ...],
|
||||
num_local_computed_tokens: int,
|
||||
num_external_computed_tokens: int,
|
||||
) -> None:
|
||||
"""
|
||||
Add the new computed blocks to the request. Optionally allocate new
|
||||
blocks for external computed tokens (if any).
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
new_computed_blocks: The new computed blocks just hitting the
|
||||
prefix cache.
|
||||
num_local_computed_tokens: The number of local computed tokens.
|
||||
num_external_computed_tokens: The number of external computed tokens.
|
||||
"""
|
||||
for i, manager in enumerate(self.single_type_managers):
|
||||
manager.allocate_new_computed_blocks(
|
||||
request_id,
|
||||
new_computed_blocks[i],
|
||||
num_local_computed_tokens,
|
||||
num_external_computed_tokens,
|
||||
)
|
||||
|
||||
def allocate_new_blocks(
|
||||
self,
|
||||
request_id: str,
|
||||
num_tokens: int,
|
||||
num_tokens_main_model: int,
|
||||
num_encoder_tokens: int = 0,
|
||||
) -> tuple[list[KVCacheBlock], ...]:
|
||||
"""
|
||||
Allocate new blocks for the request to give it at least `num_tokens`
|
||||
token slots.
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
num_tokens: The total number of tokens that need a slot (including
|
||||
tokens that are already allocated).
|
||||
num_tokens_main_model: The number of tokens for the main model (aka target
|
||||
model in spec decode). w/o spec decode, it is num_tokens;
|
||||
with spec decode, it is num_tokens - num_lookahead_tokens.
|
||||
num_encoder_tokens: The number of encoder tokens for allocating
|
||||
blocks for cross-attention.
|
||||
|
||||
Returns:
|
||||
The new allocated blocks.
|
||||
"""
|
||||
return tuple(
|
||||
manager.allocate_new_blocks(
|
||||
request_id,
|
||||
num_encoder_tokens
|
||||
if isinstance(manager, CrossAttentionManager)
|
||||
else num_tokens,
|
||||
num_tokens_main_model,
|
||||
)
|
||||
for manager in self.single_type_managers
|
||||
)
|
||||
|
||||
def cache_blocks(self, request: Request, num_computed_tokens: int) -> None:
|
||||
"""
|
||||
Cache the blocks for the request.
|
||||
|
||||
Args:
|
||||
request: The request.
|
||||
num_computed_tokens: The total number of tokens
|
||||
that need to be cached
|
||||
(including tokens that are already cached).
|
||||
"""
|
||||
for manager in self.single_type_managers:
|
||||
manager.cache_blocks(request, num_computed_tokens)
|
||||
|
||||
def free(self, request_id: str) -> None:
|
||||
"""
|
||||
Free the blocks for the request.
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
"""
|
||||
for manager in self.single_type_managers:
|
||||
manager.free(request_id)
|
||||
|
||||
def get_num_common_prefix_blocks(self, running_request_id: str) -> list[int]:
|
||||
"""
|
||||
Get the number of common prefix blocks for all requests with allocated
|
||||
KV cache for each kv cache group.
|
||||
|
||||
Args:
|
||||
running_request_id: The request ID of any running request, used to
|
||||
identify the common prefix blocks.
|
||||
|
||||
Returns:
|
||||
list[int]: The number of common prefix blocks for each kv cache group.
|
||||
"""
|
||||
return [
|
||||
manager.get_num_common_prefix_blocks(running_request_id)
|
||||
for manager in self.single_type_managers
|
||||
]
|
||||
|
||||
def remove_skipped_blocks(
|
||||
self, request_id: str, total_computed_tokens: int
|
||||
) -> None:
|
||||
"""
|
||||
Remove the blocks that are no longer needed from `blocks` and replace
|
||||
the removed blocks with null_block.
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
total_computed_tokens: The total number of computed tokens, including
|
||||
local computed tokens and external computed tokens.
|
||||
"""
|
||||
for manager in self.single_type_managers:
|
||||
manager.remove_skipped_blocks(request_id, total_computed_tokens)
|
||||
|
||||
def get_blocks(self, request_id: str) -> tuple[list[KVCacheBlock], ...]:
|
||||
"""
|
||||
Get the blocks for the request.
|
||||
"""
|
||||
return tuple(
|
||||
manager.req_to_blocks.get(request_id) or []
|
||||
for manager in self.single_type_managers
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
def find_longest_cache_hit(
|
||||
self,
|
||||
block_hashes: list[BlockHash],
|
||||
max_cache_hit_length: int,
|
||||
) -> tuple[tuple[list[KVCacheBlock], ...], int]:
|
||||
pass
|
||||
|
||||
def new_step_starts(self) -> None:
|
||||
"""Called when a new step is started."""
|
||||
for manager in self.single_type_managers:
|
||||
manager.new_step_starts()
|
||||
|
||||
|
||||
class KVCacheCoordinatorNoPrefixCache(KVCacheCoordinator):
|
||||
"""
|
||||
KV cache coordinator to use if prefix caching is disabled or unsupported.
|
||||
In contrast to UnitaryKVCacheCoordinator and HybridKVCacheCoordinator,
|
||||
supports arbitrary numbers of KV cache groups (including 0 groups).
|
||||
Does not implement any features related to prefix caching.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
use_eagle: bool,
|
||||
enable_kv_cache_events: bool,
|
||||
dcp_world_size: int,
|
||||
pcp_world_size: int,
|
||||
hash_block_size: int,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
):
|
||||
super().__init__(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
False,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
self.num_single_type_manager = len(self.single_type_managers)
|
||||
|
||||
def get_num_common_prefix_blocks(self, running_request_id: str) -> list[int]:
|
||||
return [0] * self.num_single_type_manager
|
||||
|
||||
def find_longest_cache_hit(
|
||||
self,
|
||||
block_hashes: list[BlockHash],
|
||||
max_cache_hit_length: int,
|
||||
) -> tuple[tuple[list[KVCacheBlock], ...], int]:
|
||||
blocks: tuple[list[KVCacheBlock], ...] = tuple(
|
||||
[] for _ in range(self.num_single_type_manager)
|
||||
)
|
||||
return blocks, 0
|
||||
|
||||
|
||||
class UnitaryKVCacheCoordinator(KVCacheCoordinator):
|
||||
"""
|
||||
KV cache coordinator for models with only one KV cache group. This is the
|
||||
case for models with only one KV cache type, e.g., all attention layers use
|
||||
full attention or all attention layers use sliding window attention.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
use_eagle: bool,
|
||||
enable_caching: bool,
|
||||
enable_kv_cache_events: bool,
|
||||
dcp_world_size: int,
|
||||
pcp_world_size: int,
|
||||
hash_block_size: int,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
):
|
||||
super().__init__(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
enable_caching,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
self.kv_cache_spec = self.kv_cache_config.kv_cache_groups[0].kv_cache_spec
|
||||
self.block_size = self.kv_cache_spec.block_size
|
||||
self.dcp_world_size = dcp_world_size
|
||||
self.pcp_world_size = pcp_world_size
|
||||
if dcp_world_size > 1:
|
||||
self.block_size *= dcp_world_size
|
||||
if pcp_world_size > 1:
|
||||
self.block_size *= pcp_world_size
|
||||
# For models using only Mamba, block_size is set to max_model_len when
|
||||
# prefix caching is disabled, and hash_block_size validation is skipped.
|
||||
assert not enable_caching or (hash_block_size == self.block_size), (
|
||||
"UnitaryKVCacheCoordinator assumes hash_block_size == block_size"
|
||||
)
|
||||
assert len(self.kv_cache_config.kv_cache_groups) == 1, (
|
||||
"UnitaryKVCacheCoordinator assumes only one kv cache group"
|
||||
)
|
||||
|
||||
def find_longest_cache_hit(
|
||||
self,
|
||||
block_hashes: list[BlockHash],
|
||||
max_cache_hit_length: int,
|
||||
) -> tuple[tuple[list[KVCacheBlock], ...], int]:
|
||||
hit_blocks = self.single_type_managers[0].find_longest_cache_hit(
|
||||
block_hashes=block_hashes,
|
||||
max_length=max_cache_hit_length,
|
||||
kv_cache_group_ids=[0],
|
||||
block_pool=self.block_pool,
|
||||
kv_cache_spec=self.kv_cache_spec,
|
||||
use_eagle=self.use_eagle,
|
||||
alignment_tokens=self.block_size,
|
||||
dcp_world_size=self.dcp_world_size,
|
||||
pcp_world_size=self.pcp_world_size,
|
||||
)
|
||||
return hit_blocks, len(hit_blocks[0]) * self.block_size
|
||||
|
||||
|
||||
class HybridKVCacheCoordinator(KVCacheCoordinator):
|
||||
"""
|
||||
KV cache coordinator for hybrid models with multiple KV cache types, and
|
||||
thus multiple kv cache groups.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
use_eagle: bool,
|
||||
enable_caching: bool,
|
||||
enable_kv_cache_events: bool,
|
||||
dcp_world_size: int,
|
||||
pcp_world_size: int,
|
||||
hash_block_size: int,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
):
|
||||
super().__init__(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
enable_caching,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
# hash_block_size: the block size used to compute block hashes.
|
||||
# The actual block size usually equals hash_block_size, but in cases where
|
||||
# different KV cache groups have different block sizes, the actual block size
|
||||
# can be a multiple of hash_block_size.
|
||||
self.hash_block_size = hash_block_size
|
||||
assert all(
|
||||
g.kv_cache_spec.block_size % hash_block_size == 0
|
||||
for g in kv_cache_config.kv_cache_groups
|
||||
), "block_size must be divisible by hash_block_size"
|
||||
assert dcp_world_size == 1, "DCP not support hybrid attn now."
|
||||
assert pcp_world_size == 1, "PCP not support hybrid attn now."
|
||||
self.verify_and_split_kv_cache_groups()
|
||||
|
||||
def verify_and_split_kv_cache_groups(self) -> None:
|
||||
"""
|
||||
Groups KV cache groups by their spec type for efficient batch processing
|
||||
during cache hit lookup.
|
||||
"""
|
||||
attention_groups: list[
|
||||
tuple[KVCacheSpec, list[int], type[SingleTypeKVCacheManager]]
|
||||
] = []
|
||||
|
||||
for i, g in enumerate(self.kv_cache_config.kv_cache_groups):
|
||||
manager_cls = self.single_type_managers[i].__class__
|
||||
spec = g.kv_cache_spec
|
||||
|
||||
# Try to find an existing group with the same spec
|
||||
for existing_spec, group_ids, existing_cls in attention_groups:
|
||||
if existing_spec == spec:
|
||||
assert manager_cls is existing_cls, (
|
||||
"Expected same manager class for identical KV cache specs."
|
||||
)
|
||||
group_ids.append(i)
|
||||
break
|
||||
else:
|
||||
attention_groups.append((spec, [i], manager_cls))
|
||||
|
||||
assert len(attention_groups) > 1, (
|
||||
"HybridKVCacheCoordinator requires at least two attention groups."
|
||||
)
|
||||
|
||||
# Put full attention first: its efficient left-to-right scan provides
|
||||
# a tighter initial bound, reducing work for subsequent groups.
|
||||
self.attention_groups = sorted(
|
||||
attention_groups,
|
||||
key=lambda x: not isinstance(x[0], FullAttentionSpec),
|
||||
)
|
||||
|
||||
# The LCM of the block sizes of all attention types.
|
||||
# The cache hit length must be a multiple of the LCM of the block sizes
|
||||
# to make sure the cache hit length is a multiple of the block size of
|
||||
# each attention type. Requiring this because we don't support partial
|
||||
# block cache hit yet.
|
||||
block_sizes = [spec.block_size for spec, _, _ in attention_groups]
|
||||
self.lcm_block_size = lcm(*block_sizes)
|
||||
|
||||
def find_longest_cache_hit(
|
||||
self,
|
||||
block_hashes: list[BlockHash],
|
||||
max_cache_hit_length: int,
|
||||
) -> tuple[tuple[list[KVCacheBlock], ...], int]:
|
||||
"""
|
||||
Find the longest cache hit using an iterative fixed-point algorithm.
|
||||
|
||||
Each attention type either accepts the current candidate length or
|
||||
reduces it. If any type reduces the length, restart checks over all
|
||||
types. This converges because length monotonically decreases and is
|
||||
bounded below by 0.
|
||||
|
||||
Args:
|
||||
block_hashes: The block hashes of the request.
|
||||
max_cache_hit_length: The maximum length of the cache hit.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- A tuple of the cache hit blocks for each single type manager.
|
||||
- The number of tokens of the longest cache hit.
|
||||
"""
|
||||
|
||||
def _get_block_hashes(kv_cache_spec: KVCacheSpec) -> BlockHashList:
|
||||
if kv_cache_spec.block_size == self.hash_block_size:
|
||||
return block_hashes
|
||||
return BlockHashListWithBlockSize(
|
||||
block_hashes, self.hash_block_size, kv_cache_spec.block_size
|
||||
)
|
||||
|
||||
num_groups = len(self.kv_cache_config.kv_cache_groups)
|
||||
hit_length = max_cache_hit_length
|
||||
hit_blocks_by_group: list[list[KVCacheBlock] | None] = [None] * num_groups
|
||||
|
||||
# Simple hybrid (1 full attn + 1 other): one iteration suffices.
|
||||
# Full attn is always first if it exists. This avoids EAGLE drops
|
||||
# being applied multiple times to non-full-attn groups.
|
||||
# FIXME (yifan): However, for complex hybrid models with multiple attn
|
||||
# groups, we still have the EAGLE spiral block dropping problem. See
|
||||
# discussion in issue https://github.com/vllm-project/vllm/issues/32802.
|
||||
is_simple_hybrid = len(self.attention_groups) == 2 and isinstance(
|
||||
self.attention_groups[0][0], FullAttentionSpec
|
||||
)
|
||||
|
||||
while True:
|
||||
curr_hit_length = hit_length
|
||||
|
||||
for spec, group_ids, manager_cls in self.attention_groups:
|
||||
is_full_attn = isinstance(spec, FullAttentionSpec)
|
||||
|
||||
# Full attention: reuse cached blocks (downward-closed property)
|
||||
cached_blocks = hit_blocks_by_group[group_ids[0]]
|
||||
if is_full_attn and cached_blocks is not None:
|
||||
# For full attention, we only need to compute the cache hit
|
||||
# length once. Starting from the second iteration, if the
|
||||
# curr_hit_length is reduced by other groups, we can simply
|
||||
# keep the first (curr_hit_length // block_size) blocks from
|
||||
# the last iteration.
|
||||
num_blocks = curr_hit_length // spec.block_size
|
||||
curr_hit_length = num_blocks * spec.block_size
|
||||
else:
|
||||
hit_blocks = manager_cls.find_longest_cache_hit(
|
||||
block_hashes=_get_block_hashes(spec),
|
||||
max_length=curr_hit_length,
|
||||
kv_cache_group_ids=group_ids,
|
||||
block_pool=self.block_pool,
|
||||
kv_cache_spec=spec,
|
||||
use_eagle=self.use_eagle,
|
||||
alignment_tokens=self.lcm_block_size,
|
||||
)
|
||||
curr_hit_length = len(hit_blocks[0]) * spec.block_size
|
||||
for group_id, blocks in zip(group_ids, hit_blocks):
|
||||
hit_blocks_by_group[group_id] = blocks
|
||||
|
||||
if curr_hit_length >= hit_length:
|
||||
break
|
||||
hit_length = curr_hit_length
|
||||
# Simple hybrid: exit after one iteration
|
||||
if is_simple_hybrid:
|
||||
break
|
||||
|
||||
# Truncate full attention blocks to final hit_length (if present)
|
||||
spec, group_ids, _ = self.attention_groups[0]
|
||||
if isinstance(spec, FullAttentionSpec):
|
||||
num_blocks = hit_length // spec.block_size
|
||||
for group_id in group_ids:
|
||||
if (blks := hit_blocks_by_group[group_id]) is not None:
|
||||
del blks[num_blocks:]
|
||||
|
||||
return tuple(
|
||||
blocks if blocks is not None else [] for blocks in hit_blocks_by_group
|
||||
), hit_length
|
||||
|
||||
|
||||
def get_kv_cache_coordinator(
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
use_eagle: bool,
|
||||
enable_caching: bool,
|
||||
enable_kv_cache_events: bool,
|
||||
dcp_world_size: int,
|
||||
pcp_world_size: int,
|
||||
hash_block_size: int,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
) -> KVCacheCoordinator:
|
||||
if not enable_caching:
|
||||
return KVCacheCoordinatorNoPrefixCache(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
if len(kv_cache_config.kv_cache_groups) == 1:
|
||||
return UnitaryKVCacheCoordinator(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
enable_caching,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
return HybridKVCacheCoordinator(
|
||||
kv_cache_config,
|
||||
max_model_len,
|
||||
use_eagle,
|
||||
enable_caching,
|
||||
enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=metrics_collector,
|
||||
)
|
||||
494
vllm/v1/core/kv_cache_manager.py
Normal file
494
vllm/v1/core/kv_cache_manager.py
Normal file
@@ -0,0 +1,494 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import itertools
|
||||
from collections.abc import Sequence
|
||||
from dataclasses import dataclass
|
||||
from typing import Literal, overload
|
||||
|
||||
from vllm.distributed.kv_events import KVCacheEvent
|
||||
from vllm.logger import init_logger
|
||||
from vllm.v1.core.kv_cache_coordinator import get_kv_cache_coordinator
|
||||
from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector
|
||||
from vllm.v1.core.kv_cache_utils import KVCacheBlock
|
||||
from vllm.v1.kv_cache_interface import KVCacheConfig
|
||||
from vllm.v1.metrics.stats import PrefixCacheStats
|
||||
from vllm.v1.request import Request
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class KVCacheBlocks:
|
||||
"""
|
||||
The allocation result of KVCacheManager, work as the interface between
|
||||
Scheduler and KVCacheManager, to hide KVCacheManager's internal data
|
||||
structure from the Scheduler.
|
||||
"""
|
||||
|
||||
blocks: tuple[Sequence[KVCacheBlock], ...]
|
||||
"""
|
||||
`blocks[i][j]` refers to the i-th kv_cache_group
|
||||
and the j-th block of tokens.We don't use block of
|
||||
tokens as the outer dimension because it assumes all
|
||||
kv_cache_groups have the same number of blocks, which is true for now but
|
||||
will be broken if we want to give different block_size to different
|
||||
kv_cache_groups in the future.
|
||||
|
||||
Each single type KVCacheBlocks could be represented as:
|
||||
- list[KVCacheBlock] for more than one KVCacheBlock
|
||||
- an empty tuple for requests without KVCacheBlock
|
||||
(a precomputed KVCacheBlocks is in KVCacheManager to avoid GC overhead)
|
||||
"""
|
||||
|
||||
def __add__(self, other: "KVCacheBlocks") -> "KVCacheBlocks":
|
||||
"""Adds two KVCacheBlocks instances."""
|
||||
return KVCacheBlocks(
|
||||
tuple(
|
||||
list(itertools.chain(blk1, blk2))
|
||||
for blk1, blk2 in zip(self.blocks, other.blocks)
|
||||
)
|
||||
)
|
||||
|
||||
@overload
|
||||
def get_block_ids(
|
||||
self,
|
||||
allow_none: Literal[False] = False,
|
||||
) -> tuple[list[int], ...]: ...
|
||||
|
||||
@overload
|
||||
def get_block_ids(
|
||||
self,
|
||||
allow_none: Literal[True] = True,
|
||||
) -> tuple[list[int], ...] | None: ...
|
||||
|
||||
def get_block_ids(
|
||||
self,
|
||||
allow_none: bool = False,
|
||||
) -> tuple[list[int], ...] | None:
|
||||
"""
|
||||
Converts the KVCacheBlocks instance to block_ids.
|
||||
|
||||
Returns:
|
||||
tuple[list[int], ...]: A tuple of lists where:
|
||||
- the outer tuple corresponds to KV cache groups
|
||||
- each inner list contains the block_ids of the blocks in that
|
||||
group
|
||||
"""
|
||||
if allow_none and all(len(group) == 0 for group in self.blocks):
|
||||
return None
|
||||
return tuple([blk.block_id for blk in group] for group in self.blocks)
|
||||
|
||||
def get_unhashed_block_ids(self) -> list[int]:
|
||||
"""Get block_ids of unhashed blocks from KVCacheBlocks instance."""
|
||||
assert len(self.blocks) == 1, "Only one group is supported"
|
||||
return [block.block_id for block in self.blocks[0] if block.block_hash is None]
|
||||
|
||||
def new_empty(self) -> "KVCacheBlocks":
|
||||
"""
|
||||
Creates a new KVCacheBlocks instance with no blocks.
|
||||
"""
|
||||
return KVCacheBlocks(tuple(() for _ in range(len(self.blocks))))
|
||||
|
||||
|
||||
class KVCacheManager:
|
||||
def __init__(
|
||||
self,
|
||||
kv_cache_config: KVCacheConfig,
|
||||
max_model_len: int,
|
||||
hash_block_size: int,
|
||||
enable_caching: bool = True,
|
||||
use_eagle: bool = False,
|
||||
log_stats: bool = False,
|
||||
enable_kv_cache_events: bool = False,
|
||||
dcp_world_size: int = 1,
|
||||
pcp_world_size: int = 1,
|
||||
metrics_collector: KVCacheMetricsCollector | None = None,
|
||||
) -> None:
|
||||
self.max_model_len = max_model_len
|
||||
|
||||
self.enable_caching = enable_caching
|
||||
self.use_eagle = use_eagle
|
||||
self.log_stats = log_stats
|
||||
self.metrics_collector = metrics_collector
|
||||
# FIXME: make prefix cache stats conditional on log_stats. We still need
|
||||
# this comment because when the log stats is enabled there are still
|
||||
# potential configs we could expose in the future.
|
||||
self.prefix_cache_stats = PrefixCacheStats() if log_stats else None
|
||||
|
||||
self.coordinator = get_kv_cache_coordinator(
|
||||
kv_cache_config=kv_cache_config,
|
||||
max_model_len=self.max_model_len,
|
||||
use_eagle=self.use_eagle,
|
||||
enable_caching=self.enable_caching,
|
||||
enable_kv_cache_events=enable_kv_cache_events,
|
||||
dcp_world_size=dcp_world_size,
|
||||
pcp_world_size=pcp_world_size,
|
||||
hash_block_size=hash_block_size,
|
||||
metrics_collector=self.metrics_collector,
|
||||
)
|
||||
self.num_kv_cache_groups = len(kv_cache_config.kv_cache_groups)
|
||||
self.block_pool = self.coordinator.block_pool
|
||||
self.kv_cache_config = kv_cache_config
|
||||
|
||||
# Pre-constructed KVCacheBlocks with no blocks, callers should use this
|
||||
# via create_kv_cache_blocks instead of creating new ones to avoid GC
|
||||
# overhead.
|
||||
#
|
||||
# We use nested tuples to ensure the empty KVCacheBlocks is immutable.
|
||||
self.empty_kv_cache_blocks = KVCacheBlocks(
|
||||
tuple(() for _ in range(self.num_kv_cache_groups))
|
||||
)
|
||||
|
||||
@property
|
||||
def usage(self) -> float:
|
||||
"""Get the KV cache usage.
|
||||
|
||||
Returns:
|
||||
The KV cache usage (between 0.0 and 1.0).
|
||||
"""
|
||||
return self.block_pool.get_usage()
|
||||
|
||||
def make_prefix_cache_stats(self) -> PrefixCacheStats | None:
|
||||
"""Get (and reset) the prefix cache stats.
|
||||
|
||||
Returns:
|
||||
The current prefix caching stats, or None if logging is disabled.
|
||||
"""
|
||||
if not self.log_stats:
|
||||
return None
|
||||
stats = self.prefix_cache_stats
|
||||
self.prefix_cache_stats = PrefixCacheStats()
|
||||
return stats
|
||||
|
||||
def get_computed_blocks(self, request: Request) -> tuple[KVCacheBlocks, int]:
|
||||
"""Get the computed (cached) blocks for the request.
|
||||
Note that the computed blocks must be full.
|
||||
|
||||
Args:
|
||||
request: The request to get the computed blocks.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- A list of blocks that are computed for the request.
|
||||
- The number of computed tokens.
|
||||
"""
|
||||
# We skip finding the prefix cache hit when prefix caching is
|
||||
# disabled or the request is marked as skipping kv cache read
|
||||
# (which happens when the request requires prompt logprobs
|
||||
# or calls a pooling model with all pooling).
|
||||
if not self.enable_caching or request.skip_reading_prefix_cache:
|
||||
return self.empty_kv_cache_blocks, 0
|
||||
|
||||
# NOTE: When all tokens hit the cache, we must recompute the last token
|
||||
# to obtain logits. Thus, set max_cache_hit_length to prompt_length - 1.
|
||||
# This can trigger recomputation of an entire block, rather than just
|
||||
# the single last token, because allocate_slots() requires
|
||||
# num_computed_tokens to be block-size aligned. Removing this limitation
|
||||
# could slightly improve performance in the future.
|
||||
max_cache_hit_length = request.num_tokens - 1
|
||||
computed_blocks, num_new_computed_tokens = (
|
||||
self.coordinator.find_longest_cache_hit(
|
||||
request.block_hashes, max_cache_hit_length
|
||||
)
|
||||
)
|
||||
|
||||
if self.log_stats:
|
||||
assert self.prefix_cache_stats is not None
|
||||
self.prefix_cache_stats.record(
|
||||
num_tokens=request.num_tokens,
|
||||
num_hits=num_new_computed_tokens,
|
||||
preempted=request.num_preemptions > 0,
|
||||
)
|
||||
|
||||
return self.create_kv_cache_blocks(computed_blocks), num_new_computed_tokens
|
||||
|
||||
def allocate_slots(
|
||||
self,
|
||||
request: Request,
|
||||
num_new_tokens: int,
|
||||
num_new_computed_tokens: int = 0,
|
||||
new_computed_blocks: KVCacheBlocks | None = None,
|
||||
num_lookahead_tokens: int = 0,
|
||||
num_external_computed_tokens: int = 0,
|
||||
delay_cache_blocks: bool = False,
|
||||
num_encoder_tokens: int = 0,
|
||||
) -> KVCacheBlocks | None:
|
||||
"""Add slots for a request with new tokens to append.
|
||||
|
||||
Args:
|
||||
request: The request to allocate slots.
|
||||
num_new_tokens: The number of new tokens to be allocated and computed.
|
||||
num_new_computed_tokens: The number of new computed tokens just
|
||||
hitting the prefix caching, excluding external tokens.
|
||||
new_computed_blocks: The cached blocks for the above new computed
|
||||
tokens, grouped as a tuple by kv cache groups.
|
||||
num_lookahead_tokens: The number of speculative tokens to allocate.
|
||||
This is used by spec decode proposers with kv-cache such
|
||||
as eagle.
|
||||
num_external_computed_tokens: The number of tokens that their
|
||||
KV caches are not cached by vLLM but cached by the connector.
|
||||
delay_cache_blocks: Whether to skip caching the blocks. This is
|
||||
used by P/D when allocating blocks used in a KV transfer
|
||||
which will complete in a future step.
|
||||
num_encoder_tokens: The number of encoder tokens to allocate for
|
||||
cross-attention in encoder-decoder models(e.g., Whisper).
|
||||
For decoder-only models, this should be 0.
|
||||
|
||||
Blocks layout:
|
||||
```
|
||||
----------------------------------------------------------------------
|
||||
| < comp > | < new_comp > | < ext_comp > | < new > | < lookahead > |
|
||||
----------------------------------------------------------------------
|
||||
| < to be computed > |
|
||||
----------------------------------------------------------------------
|
||||
| < to be allocated > |
|
||||
----------------------------------------------------------------------
|
||||
| < to be cached (roughly, |
|
||||
| details below)> |
|
||||
----------------------------------------------------------------------
|
||||
| Prefix-cached tokens from either vLLM |
|
||||
| or connector. Can be safely removed if |
|
||||
| they are outside sliding window. |
|
||||
----------------------------------------------------------------------
|
||||
| < cached by vLLM > | not cached by |
|
||||
| vLLM, but |
|
||||
| ref_cnt | ref_cnt not | cached by |
|
||||
| increased| increased yet| connector |
|
||||
----------------------------------------------------------------------
|
||||
```
|
||||
|
||||
Abbrivations:
|
||||
|
||||
```
|
||||
comp = request.num_computed_tokens
|
||||
new_comp = num_new_computed_tokens
|
||||
= len(new_computed_blocks) * block_size
|
||||
ext_comp = num_external_computed_tokens, cached by the connector
|
||||
new = num_new_tokens, including unverified draft tokens
|
||||
lookahead = num_lookahead_tokens
|
||||
```
|
||||
|
||||
NOTE: for new tokens which include both verified and unverified draft
|
||||
tokens, we only cache the verified tokens (by capping the number at
|
||||
`request.num_tokens`).
|
||||
|
||||
The allocation has three stages:
|
||||
- Free unnecessary blocks in `comp` and check
|
||||
if we have sufficient free blocks (return None if not).
|
||||
- Handle prefix tokens (`comp + new_comp + ext_comp`):
|
||||
- Free unnecessary blocks (e.g. outside sliding window)
|
||||
- Allocate new blocks for `ext_comp` tokens inside
|
||||
sliding window
|
||||
- Allocate new blocks for tokens to be computed (`new + lookahead`)
|
||||
|
||||
Returns:
|
||||
A list of new allocated blocks.
|
||||
"""
|
||||
# When loading KV data asynchronously, we may have zero new tokens to
|
||||
# compute while still allocating slots for externally computed tokens.
|
||||
if num_new_tokens == 0 and num_external_computed_tokens == 0:
|
||||
raise ValueError(
|
||||
"num_new_tokens must be greater than 0 when there are no "
|
||||
"external computed tokens"
|
||||
)
|
||||
|
||||
if new_computed_blocks is not None:
|
||||
new_computed_block_list = new_computed_blocks.blocks
|
||||
else:
|
||||
new_computed_block_list = self.empty_kv_cache_blocks.blocks
|
||||
|
||||
# The number of computed tokens is the number of computed tokens plus
|
||||
# the new prefix caching hits
|
||||
num_local_computed_tokens = (
|
||||
request.num_computed_tokens + num_new_computed_tokens
|
||||
)
|
||||
total_computed_tokens = min(
|
||||
num_local_computed_tokens + num_external_computed_tokens,
|
||||
self.max_model_len,
|
||||
)
|
||||
num_tokens_main_model = total_computed_tokens + num_new_tokens
|
||||
num_tokens_need_slot = min(
|
||||
num_tokens_main_model + num_lookahead_tokens,
|
||||
self.max_model_len,
|
||||
)
|
||||
|
||||
# Free the blocks that are skipped during the attention computation
|
||||
# (e.g., tokens outside the sliding window).
|
||||
# We can do this even if we cannot schedule this request due to
|
||||
# insufficient free blocks.
|
||||
# Should call this function before allocating new blocks to reduce
|
||||
# the number of evicted blocks.
|
||||
self.coordinator.remove_skipped_blocks(
|
||||
request.request_id, total_computed_tokens
|
||||
)
|
||||
|
||||
num_blocks_to_allocate = self.coordinator.get_num_blocks_to_allocate(
|
||||
request_id=request.request_id,
|
||||
num_tokens=num_tokens_need_slot,
|
||||
new_computed_blocks=new_computed_block_list,
|
||||
num_encoder_tokens=num_encoder_tokens,
|
||||
total_computed_tokens=num_local_computed_tokens
|
||||
+ num_external_computed_tokens,
|
||||
num_tokens_main_model=num_tokens_main_model,
|
||||
)
|
||||
|
||||
if num_blocks_to_allocate > self.block_pool.get_num_free_blocks():
|
||||
# Cannot allocate new blocks
|
||||
return None
|
||||
|
||||
if (
|
||||
new_computed_block_list is not self.empty_kv_cache_blocks.blocks
|
||||
or num_external_computed_tokens > 0
|
||||
):
|
||||
# Append the new computed blocks to the request blocks until now to
|
||||
# avoid the case where the new blocks cannot be allocated.
|
||||
self.coordinator.allocate_new_computed_blocks(
|
||||
request_id=request.request_id,
|
||||
new_computed_blocks=new_computed_block_list,
|
||||
num_local_computed_tokens=num_local_computed_tokens,
|
||||
num_external_computed_tokens=num_external_computed_tokens,
|
||||
)
|
||||
|
||||
new_blocks = self.coordinator.allocate_new_blocks(
|
||||
request.request_id,
|
||||
num_tokens_need_slot,
|
||||
num_tokens_main_model,
|
||||
num_encoder_tokens,
|
||||
)
|
||||
|
||||
# P/D: delay caching blocks if we have to recv from
|
||||
# remote. Update state for locally cached blocks.
|
||||
if not self.enable_caching or delay_cache_blocks:
|
||||
return self.create_kv_cache_blocks(new_blocks)
|
||||
|
||||
# NOTE(woosuk): We want to commit (cache) up to num_local_computed_tokens
|
||||
# + num_external_computed_tokens + num_new_tokens, but must exclude
|
||||
# "non-committable" tokens (e.g., draft tokens that could be rejected).
|
||||
# Therefore, we cap the number at `request.num_tokens`, ensuring only
|
||||
# "finalized" tokens are cached.
|
||||
num_tokens_to_cache = min(
|
||||
total_computed_tokens + num_new_tokens,
|
||||
request.num_tokens,
|
||||
)
|
||||
self.coordinator.cache_blocks(request, num_tokens_to_cache)
|
||||
|
||||
return self.create_kv_cache_blocks(new_blocks)
|
||||
|
||||
def free(self, request: Request) -> None:
|
||||
"""Free the blocks allocated for the request.
|
||||
We free the blocks in reverse order so that the tail blocks are evicted
|
||||
first when caching is enabled.
|
||||
|
||||
Args:
|
||||
request: The request to free the blocks.
|
||||
"""
|
||||
self.coordinator.free(request.request_id)
|
||||
|
||||
def remove_skipped_blocks(
|
||||
self, request_id: str, total_computed_tokens: int
|
||||
) -> None:
|
||||
"""Remove the blocks that are no longer needed from `blocks` and replace
|
||||
the removed blocks with null_block.
|
||||
|
||||
Args:
|
||||
request_id: The request ID.
|
||||
total_computed_tokens: The total number of computed tokens, including
|
||||
local computed tokens and external computed tokens.
|
||||
"""
|
||||
self.coordinator.remove_skipped_blocks(request_id, total_computed_tokens)
|
||||
|
||||
def evict_blocks(self, block_ids: set[int]) -> None:
|
||||
"""evict blocks from the prefix cache by their block IDs.
|
||||
|
||||
Args:
|
||||
block_ids: Set of block IDs to evict from cache.
|
||||
"""
|
||||
self.block_pool.evict_blocks(block_ids)
|
||||
|
||||
def reset_prefix_cache(self) -> bool:
|
||||
"""Reset prefix cache. This function may be used in RLHF
|
||||
flows to invalidate prefix caching after the weights are updated,
|
||||
or used for resetting prefix caching status for benchmarking.
|
||||
|
||||
Returns:
|
||||
bool: True if the prefix cache is successfully reset,
|
||||
False otherwise.
|
||||
"""
|
||||
if not self.block_pool.reset_prefix_cache():
|
||||
return False
|
||||
if self.log_stats:
|
||||
assert self.prefix_cache_stats is not None
|
||||
self.prefix_cache_stats.reset = True
|
||||
return True
|
||||
|
||||
def get_num_common_prefix_blocks(self, running_request_id: str) -> list[int]:
|
||||
"""Calculate the number of common prefix blocks for each kv cache group.
|
||||
|
||||
The function selects a running request and iterates through its blocks.
|
||||
A block is considered a common prefix block if ALL requests with
|
||||
allocated KV cache share it (i.e., ref_cnt equals the number of entries
|
||||
in req_to_blocks).
|
||||
|
||||
NOTE(woosuk): The number of requests with allocated KV cache is **greater
|
||||
than or equal to** the number of requests scheduled in the current step.
|
||||
This is because having allocated KV cache only indicates that:
|
||||
1. The request has not yet finished, and
|
||||
2. The request holds its blocks unfreed.
|
||||
|
||||
While all scheduled requests must have allocated KV cache, the inverse
|
||||
is not necessarily true. There may be requests with allocated KV cache
|
||||
that are not scheduled in the current step.
|
||||
|
||||
This can result in an edge case where the number of common prefix blocks
|
||||
is 0, even though all scheduled requests share a common prefix. This
|
||||
occurs because there may be unscheduled requests that do not share the
|
||||
common prefix. Currently, this case cannot be easily detected, so the
|
||||
function returns 0 in such cases.
|
||||
|
||||
Args:
|
||||
running_request_id: The request ID of any running request, used to
|
||||
identify the common prefix blocks.
|
||||
|
||||
Returns:
|
||||
list[int]: The number of common prefix blocks for each kv cache
|
||||
group.
|
||||
"""
|
||||
return self.coordinator.get_num_common_prefix_blocks(running_request_id)
|
||||
|
||||
def take_events(self) -> list[KVCacheEvent]:
|
||||
"""Take the KV cache events from the block pool.
|
||||
|
||||
Returns:
|
||||
A list of KV cache events.
|
||||
"""
|
||||
return self.block_pool.take_events()
|
||||
|
||||
def get_blocks(self, request_id: str) -> KVCacheBlocks:
|
||||
"""Get the blocks of a request."""
|
||||
return self.create_kv_cache_blocks(self.coordinator.get_blocks(request_id))
|
||||
|
||||
def get_block_ids(self, request_id: str) -> tuple[list[int], ...]:
|
||||
"""Get the block ids of a request."""
|
||||
return self.get_blocks(request_id).get_block_ids()
|
||||
|
||||
def cache_blocks(self, request: Request, num_computed_tokens: int) -> None:
|
||||
"""Cache the blocks for the request, if enabled.
|
||||
|
||||
Args:
|
||||
request: The request to cache the blocks.
|
||||
num_computed_tokens: The number of computed tokens, including tokens
|
||||
that are already cached and tokens to be cached.
|
||||
"""
|
||||
if self.enable_caching:
|
||||
self.coordinator.cache_blocks(request, num_computed_tokens)
|
||||
|
||||
def create_kv_cache_blocks(
|
||||
self, blocks: tuple[list[KVCacheBlock], ...]
|
||||
) -> KVCacheBlocks:
|
||||
# Only create new KVCacheBlocks for non-empty blocks
|
||||
return KVCacheBlocks(blocks) if any(blocks) else self.empty_kv_cache_blocks
|
||||
|
||||
def new_step_starts(self) -> None:
|
||||
"""Called when a new step is started."""
|
||||
self.coordinator.new_step_starts()
|
||||
96
vllm/v1/core/kv_cache_metrics.py
Normal file
96
vllm/v1/core/kv_cache_metrics.py
Normal file
@@ -0,0 +1,96 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
"""KV cache metrics tracking."""
|
||||
|
||||
import random
|
||||
import time
|
||||
from collections import deque
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.v1.core.kv_cache_utils import KVCacheBlock
|
||||
|
||||
from vllm.v1.metrics.stats import KVCacheEvictionEvent
|
||||
|
||||
|
||||
class BlockMetricsState:
|
||||
"""Tracks lifecycle metrics for a single KV cache block."""
|
||||
|
||||
def __init__(self):
|
||||
now_ns = time.monotonic_ns()
|
||||
self.birth_time_ns = now_ns
|
||||
self.last_access_ns = now_ns
|
||||
# Bounded to prevent unbounded growth if a block is accessed many times.
|
||||
self.access_history: deque[int] = deque(maxlen=4)
|
||||
|
||||
def record_access(self) -> None:
|
||||
now_ns = time.monotonic_ns()
|
||||
self.last_access_ns = now_ns
|
||||
self.access_history.append(now_ns)
|
||||
|
||||
def get_lifetime_seconds(self) -> float:
|
||||
now_ns = time.monotonic_ns()
|
||||
return (now_ns - self.birth_time_ns) / 1e9
|
||||
|
||||
def get_idle_time_seconds(self) -> float:
|
||||
now_ns = time.monotonic_ns()
|
||||
return (now_ns - self.last_access_ns) / 1e9
|
||||
|
||||
def get_reuse_gaps_seconds(self) -> list[float]:
|
||||
if len(self.access_history) < 2:
|
||||
return []
|
||||
history = list(self.access_history)
|
||||
return [(history[i] - history[i - 1]) / 1e9 for i in range(1, len(history))]
|
||||
|
||||
|
||||
class KVCacheMetricsCollector:
|
||||
"""Collects KV cache residency metrics with sampling."""
|
||||
|
||||
def __init__(self, sample_rate: float = 0.01):
|
||||
assert 0 < sample_rate <= 1.0, (
|
||||
f"sample_rate must be in (0, 1.0], got {sample_rate}"
|
||||
)
|
||||
self.sample_rate = sample_rate
|
||||
|
||||
self.block_metrics: dict[int, BlockMetricsState] = {}
|
||||
|
||||
self._eviction_events: list[KVCacheEvictionEvent] = []
|
||||
|
||||
def should_sample_block(self) -> bool:
|
||||
return random.random() < self.sample_rate
|
||||
|
||||
def on_block_allocated(self, block: "KVCacheBlock") -> None:
|
||||
if self.should_sample_block():
|
||||
self.block_metrics[block.block_id] = BlockMetricsState()
|
||||
|
||||
def on_block_accessed(self, block: "KVCacheBlock") -> None:
|
||||
metrics = self.block_metrics.get(block.block_id)
|
||||
if metrics:
|
||||
metrics.record_access()
|
||||
|
||||
def on_block_evicted(self, block: "KVCacheBlock") -> None:
|
||||
metrics = self.block_metrics.pop(block.block_id, None)
|
||||
if not metrics:
|
||||
return
|
||||
|
||||
lifetime = metrics.get_lifetime_seconds()
|
||||
idle_time = metrics.get_idle_time_seconds()
|
||||
reuse_gaps = tuple(metrics.get_reuse_gaps_seconds())
|
||||
|
||||
self._eviction_events.append(
|
||||
KVCacheEvictionEvent(
|
||||
lifetime_seconds=lifetime,
|
||||
idle_seconds=idle_time,
|
||||
reuse_gaps_seconds=reuse_gaps,
|
||||
)
|
||||
)
|
||||
|
||||
def reset(self) -> None:
|
||||
"""Clear all state on cache reset."""
|
||||
self.block_metrics.clear()
|
||||
self._eviction_events.clear()
|
||||
|
||||
def drain_events(self) -> list[KVCacheEvictionEvent]:
|
||||
events = self._eviction_events
|
||||
self._eviction_events = []
|
||||
return events
|
||||
1716
vllm/v1/core/kv_cache_utils.py
Normal file
1716
vllm/v1/core/kv_cache_utils.py
Normal file
File diff suppressed because it is too large
Load Diff
0
vllm/v1/core/sched/__init__.py
Normal file
0
vllm/v1/core/sched/__init__.py
Normal file
60
vllm/v1/core/sched/async_scheduler.py
Normal file
60
vllm/v1/core/sched/async_scheduler.py
Normal file
@@ -0,0 +1,60 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from vllm.logger import init_logger
|
||||
from vllm.v1.core.sched.output import SchedulerOutput
|
||||
from vllm.v1.core.sched.scheduler import Scheduler
|
||||
from vllm.v1.request import Request, RequestStatus
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class AsyncScheduler(Scheduler):
|
||||
def __init__(self, *args, **kwargs) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
# reusable read-only placeholder list for speculative decoding.
|
||||
self._spec_token_placeholders: list[int] = [-1] * self.num_spec_tokens
|
||||
|
||||
def _update_after_schedule(self, scheduler_output: SchedulerOutput) -> None:
|
||||
super()._update_after_schedule(scheduler_output)
|
||||
spec_decode_tokens = scheduler_output.scheduled_spec_decode_tokens
|
||||
for req_id in scheduler_output.num_scheduled_tokens:
|
||||
request = self.requests[req_id]
|
||||
if request.is_prefill_chunk:
|
||||
continue
|
||||
|
||||
scheduler_output.pending_structured_output_tokens |= (
|
||||
request.use_structured_output and request.num_output_placeholders > 0
|
||||
)
|
||||
# The request will generate a new token plus num_spec_tokens
|
||||
# in this scheduling step.
|
||||
cur_num_spec_tokens = len(spec_decode_tokens.get(req_id, ()))
|
||||
request.num_output_placeholders += 1 + cur_num_spec_tokens
|
||||
# Add placeholders for the new draft/spec tokens.
|
||||
# We will update the actual spec token ids in the worker process.
|
||||
request.spec_token_ids = self._spec_token_placeholders
|
||||
|
||||
def _update_request_with_output(
|
||||
self, request: Request, new_token_ids: list[int]
|
||||
) -> tuple[list[int], bool]:
|
||||
if request.discard_latest_async_tokens:
|
||||
# If the request is force preempted in reset_prefix_cache, we
|
||||
# should discard the latest async token.
|
||||
request.discard_latest_async_tokens = False
|
||||
return [], False
|
||||
|
||||
status_before_update = request.status
|
||||
new_token_ids, stopped = super()._update_request_with_output(
|
||||
request, new_token_ids
|
||||
)
|
||||
|
||||
# Update the number of output placeholders.
|
||||
request.num_output_placeholders -= len(new_token_ids)
|
||||
assert request.num_output_placeholders >= 0
|
||||
|
||||
# Cache the new tokens. Preempted requests should be skipped.
|
||||
if status_before_update == RequestStatus.RUNNING:
|
||||
self.kv_cache_manager.cache_blocks(
|
||||
request, request.num_computed_tokens - request.num_output_placeholders
|
||||
)
|
||||
return new_token_ids, stopped
|
||||
243
vllm/v1/core/sched/interface.py
Normal file
243
vllm/v1/core/sched/interface.py
Normal file
@@ -0,0 +1,243 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
import enum
|
||||
from abc import ABC, abstractmethod
|
||||
from collections.abc import Iterable
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
|
||||
from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
|
||||
from vllm.v1.engine import EngineCoreOutputs
|
||||
from vllm.v1.kv_cache_interface import KVCacheConfig
|
||||
from vllm.v1.metrics.stats import SchedulerStats
|
||||
from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
|
||||
from vllm.v1.request import Request, RequestStatus
|
||||
from vllm.v1.structured_output import StructuredOutputManager
|
||||
|
||||
|
||||
class PauseState(enum.IntEnum):
|
||||
"""Scheduler pause state.
|
||||
|
||||
- UNPAUSED: Normal operation
|
||||
- PAUSE_NEW: No new requests are scheduled, requests already in
|
||||
running state are scheduled.
|
||||
- PAUSE_ALL: No requests are scheduled
|
||||
"""
|
||||
|
||||
UNPAUSED = 0
|
||||
PAUSED_NEW = 1
|
||||
PAUSED_ALL = 2
|
||||
|
||||
|
||||
class SchedulerInterface(ABC):
|
||||
@abstractmethod
|
||||
def __init__(
|
||||
self,
|
||||
vllm_config: "VllmConfig",
|
||||
kv_cache_config: "KVCacheConfig",
|
||||
structured_output_manager: "StructuredOutputManager",
|
||||
block_size: int,
|
||||
mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
|
||||
include_finished_set: bool = False,
|
||||
log_stats: bool = False,
|
||||
) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def schedule(self) -> "SchedulerOutput":
|
||||
"""Schedule the requests to process in this scheduling step.
|
||||
|
||||
The scheduling decision is made at the iteration level. Each scheduling
|
||||
step corresponds to a single forward pass of the model. Therefore, this
|
||||
method is called repeatedly by a busy loop in the engine.
|
||||
|
||||
Essentially, the scheduler produces a dictionary of {req_id: num_tokens}
|
||||
that specifies how many tokens to process for each request in this
|
||||
scheduling step. For example, num_tokens can be as large as the number
|
||||
of prompt tokens for new requests, or it can be 1 for the requests that
|
||||
are auto-regressively generating new tokens one by one. Otherwise, it
|
||||
can be somewhere in between in case of chunked prefills, prefix caching,
|
||||
speculative decoding, etc.
|
||||
|
||||
Additionally, the scheduler also returns useful data about each request
|
||||
or the batch as a whole. The model runner will use this information in
|
||||
preparing inputs to the model.
|
||||
|
||||
Returns:
|
||||
A SchedulerOutput object containing information about the scheduled
|
||||
requests.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def get_grammar_bitmask(
|
||||
self, scheduler_output: "SchedulerOutput"
|
||||
) -> "GrammarOutput | None":
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def update_from_output(
|
||||
self,
|
||||
scheduler_output: "SchedulerOutput",
|
||||
model_runner_output: "ModelRunnerOutput",
|
||||
) -> dict[int, "EngineCoreOutputs"]:
|
||||
"""Update the scheduler state based on the model runner output.
|
||||
|
||||
This method is called after the model runner has processed the scheduled
|
||||
requests. The model runner output includes generated token ids, draft
|
||||
token ids for next step, etc. The scheduler uses this information to
|
||||
update its states, checks the finished requests, and returns the output
|
||||
for each request.
|
||||
|
||||
Returns:
|
||||
A dict of client index to EngineCoreOutputs object containing the
|
||||
outputs for each request originating from that client.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def update_draft_token_ids(self, draft_token_ids: "DraftTokenIds") -> None:
|
||||
"""Update requests with newly generated draft token ids, applying
|
||||
structured output grammar validation if needed.
|
||||
|
||||
Args:
|
||||
draft_token_ids: The input draft token ids for each request.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def update_draft_token_ids_in_output(
|
||||
self, draft_token_ids: "DraftTokenIds", scheduler_output: "SchedulerOutput"
|
||||
) -> None:
|
||||
"""Update scheduler output with newly generated draft token ids, applying
|
||||
structured output grammar validation if needed.
|
||||
|
||||
Args:
|
||||
draft_token_ids: The input draft token ids for each request.
|
||||
scheduler_output: Update the given scheduler_output
|
||||
with the corresponding draft token ids.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def add_request(self, request: "Request") -> None:
|
||||
"""Add a new request to the scheduler's internal queue.
|
||||
|
||||
Args:
|
||||
request: The new request being added.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def finish_requests(
|
||||
self,
|
||||
request_ids: str | Iterable[str] | None,
|
||||
finished_status: "RequestStatus",
|
||||
) -> list[tuple[str, int]]:
|
||||
"""Finish the requests in the scheduler's internal queue. If the request
|
||||
is not in the queue, this method will do nothing for that request.
|
||||
|
||||
This method is called in two cases:
|
||||
1. When the request is aborted by the client.
|
||||
2. When the frontend process detects a stop string of the request after
|
||||
de-tokenizing its generated tokens.
|
||||
|
||||
Args:
|
||||
request_ids: A single or a list of request IDs, or None to finish all.
|
||||
finished_status: The finished status of the given requests.
|
||||
|
||||
Returns:
|
||||
Tuple of (req_id, client_index) for requests that were aborted. Will not
|
||||
include any that were already finished.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def get_num_unfinished_requests(self) -> int:
|
||||
"""Number of unfinished requests in the scheduler's internal queue."""
|
||||
raise NotImplementedError
|
||||
|
||||
def has_unfinished_requests(self) -> bool:
|
||||
"""Returns True if there are unfinished requests in the scheduler's
|
||||
internal queue."""
|
||||
return self.get_num_unfinished_requests() > 0
|
||||
|
||||
@abstractmethod
|
||||
def has_finished_requests(self) -> bool:
|
||||
"""Returns True if there are finished requests that need to be cleared.
|
||||
NOTE: This is different from `not self.has_unfinished_requests()`.
|
||||
|
||||
The scheduler maintains an internal list of the requests finished in the
|
||||
previous step. This list is returned from the next call to schedule(),
|
||||
to be sent to the model runner in the next step to clear cached states
|
||||
for these finished requests.
|
||||
|
||||
This method checks if this internal list of finished requests is
|
||||
non-empty. This information is useful for DP attention.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def has_requests(self) -> bool:
|
||||
"""Returns True if there are unfinished requests, or finished requests
|
||||
not yet returned in SchedulerOutputs."""
|
||||
return self.has_unfinished_requests() or self.has_finished_requests()
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def pause_state(self) -> PauseState:
|
||||
"""Current pause state of the scheduler."""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def set_pause_state(self, pause_state: PauseState) -> None:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def reset_prefix_cache(
|
||||
self, reset_running_requests: bool = False, reset_connector: bool = False
|
||||
) -> bool:
|
||||
"""Reset the prefix cache for KV cache.
|
||||
|
||||
This is particularly required when the model weights are live-updated.
|
||||
|
||||
Args:
|
||||
reset_running_requests: If True, all the running requests will be
|
||||
preempted and moved to the waiting queue. Otherwise, this method
|
||||
will only reset the KV prefix cache when there is no running request
|
||||
taking KV cache.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def reset_encoder_cache(self) -> None:
|
||||
"""Reset the encoder cache to invalidate all cached encoder outputs.
|
||||
|
||||
This should be called when model weights are updated to ensure
|
||||
stale vision embeddings are not reused.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def get_request_counts(self) -> tuple[int, int]:
|
||||
"""Returns (num_running_reqs, num_waiting_reqs)."""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def make_stats(self) -> "SchedulerStats | None":
|
||||
"""Make a SchedulerStats object for logging.
|
||||
|
||||
The SchedulerStats object is created for every scheduling step.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def shutdown(self) -> None:
|
||||
"""Shutdown the scheduler."""
|
||||
raise NotImplementedError
|
||||
|
||||
def get_kv_connector(self) -> "KVConnectorBase_V1 | None":
|
||||
return None
|
||||
261
vllm/v1/core/sched/output.py
Normal file
261
vllm/v1/core/sched/output.py
Normal file
@@ -0,0 +1,261 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
from dataclasses import dataclass
|
||||
from functools import cached_property
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from vllm._bc_linter import bc_linter_include
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import torch
|
||||
|
||||
from vllm.distributed.ec_transfer.ec_connector.base import ECConnectorMetadata
|
||||
from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorMetadata
|
||||
from vllm.lora.request import LoRARequest
|
||||
from vllm.multimodal.inputs import MultiModalFeatureSpec
|
||||
from vllm.pooling_params import PoolingParams
|
||||
from vllm.sampling_params import SamplingParams
|
||||
from vllm.v1.request import Request
|
||||
else:
|
||||
ECConnectorMetadata = object
|
||||
KVConnectorMetadata = object
|
||||
LoRARequest = object
|
||||
MultiModalFeatureSpec = object
|
||||
PoolingParams = object
|
||||
SamplingParams = object
|
||||
Request = object
|
||||
|
||||
|
||||
@bc_linter_include
|
||||
@dataclass
|
||||
class NewRequestData:
|
||||
req_id: str
|
||||
prompt_token_ids: list[int] | None
|
||||
mm_features: list[MultiModalFeatureSpec]
|
||||
sampling_params: SamplingParams | None
|
||||
pooling_params: PoolingParams | None
|
||||
block_ids: tuple[list[int], ...]
|
||||
num_computed_tokens: int
|
||||
lora_request: LoRARequest | None
|
||||
prompt_embeds: "torch.Tensor | None" = None
|
||||
|
||||
# Only used for v2 model runner.
|
||||
prefill_token_ids: list[int] | None = None
|
||||
|
||||
@classmethod
|
||||
def from_request(
|
||||
cls,
|
||||
request: Request,
|
||||
block_ids: tuple[list[int], ...],
|
||||
prefill_token_ids: list[int] | None = None,
|
||||
) -> "NewRequestData":
|
||||
return cls(
|
||||
req_id=request.request_id,
|
||||
prompt_token_ids=request.prompt_token_ids,
|
||||
mm_features=request.mm_features,
|
||||
sampling_params=request.sampling_params,
|
||||
pooling_params=request.pooling_params,
|
||||
block_ids=block_ids,
|
||||
num_computed_tokens=request.num_computed_tokens,
|
||||
lora_request=request.lora_request,
|
||||
prompt_embeds=request.prompt_embeds,
|
||||
prefill_token_ids=prefill_token_ids,
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
prompt_embeds_shape = (
|
||||
self.prompt_embeds.shape if self.prompt_embeds is not None else None
|
||||
)
|
||||
return (
|
||||
f"NewRequestData("
|
||||
f"req_id={self.req_id},"
|
||||
f"prompt_token_ids={self.prompt_token_ids},"
|
||||
f"prefill_token_ids={self.prefill_token_ids},"
|
||||
f"mm_features={self.mm_features},"
|
||||
f"sampling_params={self.sampling_params},"
|
||||
f"block_ids={self.block_ids},"
|
||||
f"num_computed_tokens={self.num_computed_tokens},"
|
||||
f"lora_request={self.lora_request},"
|
||||
f"prompt_embeds_shape={prompt_embeds_shape}"
|
||||
")"
|
||||
)
|
||||
|
||||
# Version of __repr__ with the prompt data obfuscated
|
||||
def anon_repr(self) -> str:
|
||||
prompt_token_ids_len = (
|
||||
len(self.prompt_token_ids) if self.prompt_token_ids is not None else None
|
||||
)
|
||||
prompt_embeds_shape = (
|
||||
self.prompt_embeds.shape if self.prompt_embeds is not None else None
|
||||
)
|
||||
prefill_token_ids_len = (
|
||||
len(self.prefill_token_ids) if self.prefill_token_ids is not None else None
|
||||
)
|
||||
return (
|
||||
f"NewRequestData("
|
||||
f"req_id={self.req_id},"
|
||||
f"prompt_token_ids_len={prompt_token_ids_len},"
|
||||
f"prefill_token_ids_len={prefill_token_ids_len},"
|
||||
f"mm_features={self.mm_features},"
|
||||
f"sampling_params={self.sampling_params},"
|
||||
f"block_ids={self.block_ids},"
|
||||
f"num_computed_tokens={self.num_computed_tokens},"
|
||||
f"lora_request={self.lora_request},"
|
||||
f"prompt_embeds_shape={prompt_embeds_shape}"
|
||||
")"
|
||||
)
|
||||
|
||||
|
||||
@bc_linter_include
|
||||
@dataclass
|
||||
class CachedRequestData:
|
||||
req_ids: list[str]
|
||||
# For request ids not in resumed_req_ids, new_block_ids will be appended to
|
||||
# the request's block IDs. For those in the set, new_block_ids will be used as the
|
||||
# request's block IDs instead of appending to the existing block IDs.
|
||||
resumed_req_ids: set[str]
|
||||
# NOTE(woosuk): new_token_ids is only used for pipeline parallelism.
|
||||
# When PP is not used, new_token_ids will be empty.
|
||||
new_token_ids: list[list[int]]
|
||||
# For requests not scheduled in the last step, propagate the token ids to the
|
||||
# connector. Won't contain requests that were scheduled in the prior step.
|
||||
all_token_ids: dict[str, list[int]]
|
||||
new_block_ids: list[tuple[list[int], ...] | None]
|
||||
num_computed_tokens: list[int]
|
||||
num_output_tokens: list[int]
|
||||
|
||||
# Version of dataclass repr with token IDs obfuscated.
|
||||
def anon_repr(self) -> str:
|
||||
new_token_ids_lens = [len(toks) for toks in self.new_token_ids]
|
||||
all_token_ids_lens = {
|
||||
req_id: len(toks) for req_id, toks in self.all_token_ids.items()
|
||||
}
|
||||
return (
|
||||
f"CachedRequestData("
|
||||
f"req_ids={self.req_ids},"
|
||||
f"resumed_req_ids={self.resumed_req_ids},"
|
||||
f"new_token_ids_lens={new_token_ids_lens},"
|
||||
f"all_token_ids_lens={all_token_ids_lens},"
|
||||
f"new_block_ids={self.new_block_ids},"
|
||||
f"num_computed_tokens={self.num_computed_tokens},"
|
||||
f"num_output_tokens={self.num_output_tokens}"
|
||||
f")"
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return self.anon_repr()
|
||||
|
||||
@property
|
||||
def num_reqs(self) -> int:
|
||||
return len(self.req_ids)
|
||||
|
||||
@cached_property
|
||||
def _req_id_to_num_output_tokens(self) -> dict[str, int]:
|
||||
"""Cache mapping of req_id to num_output_tokens for O(1) lookup.
|
||||
|
||||
This cached property is safe because CachedRequestData instances
|
||||
are created fresh each scheduling iteration and not mutated during
|
||||
computation of iteration details.
|
||||
"""
|
||||
return dict(zip(self.req_ids, self.num_output_tokens))
|
||||
|
||||
def is_context_phase(self, req_id: str) -> bool:
|
||||
num_output_tokens = self._req_id_to_num_output_tokens.get(req_id)
|
||||
return num_output_tokens is not None and num_output_tokens == 0
|
||||
|
||||
@classmethod
|
||||
def make_empty(cls) -> "CachedRequestData":
|
||||
return cls(
|
||||
req_ids=[],
|
||||
resumed_req_ids=set(),
|
||||
new_token_ids=[],
|
||||
all_token_ids={},
|
||||
new_block_ids=[],
|
||||
num_computed_tokens=[],
|
||||
num_output_tokens=[],
|
||||
)
|
||||
|
||||
|
||||
@bc_linter_include
|
||||
@dataclass
|
||||
class SchedulerOutput:
|
||||
# list of the requests that are scheduled for the first time.
|
||||
# We cache the request's data in each worker process, so that we don't
|
||||
# need to re-send it every scheduling step.
|
||||
scheduled_new_reqs: list[NewRequestData]
|
||||
# list of the requests that have been scheduled before.
|
||||
# Since the request's data is already cached in the worker processes,
|
||||
# we only send the diff to minimize the communication cost.
|
||||
scheduled_cached_reqs: CachedRequestData
|
||||
|
||||
# req_id -> num_scheduled_tokens
|
||||
# Number of tokens scheduled for each request.
|
||||
num_scheduled_tokens: dict[str, int]
|
||||
# Total number of tokens scheduled for all requests.
|
||||
# Equal to sum(num_scheduled_tokens.values())
|
||||
total_num_scheduled_tokens: int
|
||||
# req_id -> spec_token_ids
|
||||
# If a request does not have any spec decode tokens, it will not be
|
||||
# included in the dictionary.
|
||||
scheduled_spec_decode_tokens: dict[str, list[int]]
|
||||
# req_id -> encoder input indices that need processing.
|
||||
# E.g., if a request has [0, 1], it could mean the vision encoder needs
|
||||
# to process that the request's 0-th and 1-th images in the current step.
|
||||
scheduled_encoder_inputs: dict[str, list[int]]
|
||||
# Number of common prefix blocks for all requests in each KV cache group.
|
||||
# This can be used for cascade attention.
|
||||
num_common_prefix_blocks: list[int]
|
||||
|
||||
# Request IDs that are finished in between the previous and the current
|
||||
# steps. This is used to notify the workers about the finished requests
|
||||
# so that they can free the cached states for those requests.
|
||||
finished_req_ids: set[str]
|
||||
# list of mm_hash strings associated with the encoder outputs to be
|
||||
# freed from the encoder cache.
|
||||
free_encoder_mm_hashes: list[str]
|
||||
|
||||
# Request IDs that are preempted in this step.
|
||||
# Only used for v2 model runner.
|
||||
preempted_req_ids: set[str] | None = None
|
||||
|
||||
# Whether any of the scheduled requests use structured output.
|
||||
# Set only in async scheduling case.
|
||||
has_structured_output_requests: bool = False
|
||||
|
||||
# Whether the scheduled requests have all the output tokens they
|
||||
# need to perform grammar bitmask computation.
|
||||
pending_structured_output_tokens: bool = False
|
||||
|
||||
# Used for adjusting acceptance rate calculation.
|
||||
num_invalid_spec_tokens: dict[str, int] | None = None
|
||||
|
||||
# KV Cache Connector metadata.
|
||||
kv_connector_metadata: KVConnectorMetadata | None = None
|
||||
|
||||
# EC Cache Connector metadata
|
||||
ec_connector_metadata: ECConnectorMetadata | None = None
|
||||
|
||||
@classmethod
|
||||
def make_empty(cls) -> "SchedulerOutput":
|
||||
return cls(
|
||||
scheduled_new_reqs=[],
|
||||
scheduled_cached_reqs=CachedRequestData.make_empty(),
|
||||
num_scheduled_tokens={},
|
||||
total_num_scheduled_tokens=0,
|
||||
scheduled_spec_decode_tokens={},
|
||||
scheduled_encoder_inputs={},
|
||||
num_common_prefix_blocks=[],
|
||||
finished_req_ids=set(),
|
||||
free_encoder_mm_hashes=[],
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GrammarOutput:
|
||||
# ids of structured output requests.
|
||||
structured_output_request_ids: list[str]
|
||||
# Bitmask ordered as structured_output_request_ids.
|
||||
grammar_bitmask: "npt.NDArray[np.int32]"
|
||||
208
vllm/v1/core/sched/request_queue.py
Normal file
208
vllm/v1/core/sched/request_queue.py
Normal file
@@ -0,0 +1,208 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
import heapq
|
||||
from abc import ABC, abstractmethod
|
||||
from collections import deque
|
||||
from collections.abc import Iterable, Iterator
|
||||
from enum import Enum
|
||||
|
||||
from vllm.v1.request import Request
|
||||
|
||||
|
||||
class SchedulingPolicy(Enum):
|
||||
"""Enum for scheduling policies."""
|
||||
|
||||
FCFS = "fcfs"
|
||||
PRIORITY = "priority"
|
||||
|
||||
|
||||
class RequestQueue(ABC):
|
||||
"""Abstract base class for request queues."""
|
||||
|
||||
@abstractmethod
|
||||
def add_request(self, request: Request) -> None:
|
||||
"""Add a request to the queue according to the policy."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def pop_request(self) -> Request:
|
||||
"""Pop a request from the queue according to the policy."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def peek_request(self) -> Request:
|
||||
"""Peek at the request at the front of the queue without removing it."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def prepend_request(self, request: Request) -> None:
|
||||
"""Prepend a request to the front of the queue."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def prepend_requests(self, requests: "RequestQueue") -> None:
|
||||
"""Prepend all requests from another queue to the front of this
|
||||
queue."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def remove_request(self, request: Request) -> None:
|
||||
"""Remove a specific request from the queue."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def remove_requests(self, requests: Iterable[Request]) -> None:
|
||||
"""Remove multiple specific requests from the queue."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def __bool__(self) -> bool:
|
||||
"""Check if queue has any requests."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def __len__(self) -> int:
|
||||
"""Get number of requests in queue."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def __iter__(self) -> Iterator[Request]:
|
||||
"""Iterate over the queue according to the policy."""
|
||||
pass
|
||||
|
||||
|
||||
class FCFSRequestQueue(deque[Request], RequestQueue):
|
||||
"""A first-come-first-served queue that supports deque operations."""
|
||||
|
||||
def add_request(self, request: Request) -> None:
|
||||
"""Add a request to the queue according to FCFS policy."""
|
||||
self.append(request)
|
||||
|
||||
def pop_request(self) -> Request:
|
||||
"""Pop a request from the queue according to FCFS policy."""
|
||||
return self.popleft()
|
||||
|
||||
def peek_request(self) -> Request:
|
||||
"""Peek at the next request in the queue without removing it."""
|
||||
if not self:
|
||||
raise IndexError("peek from an empty queue")
|
||||
return self[0]
|
||||
|
||||
def prepend_request(self, request: Request) -> None:
|
||||
"""Prepend a request to the front of the queue."""
|
||||
self.appendleft(request)
|
||||
|
||||
def prepend_requests(self, requests: RequestQueue) -> None:
|
||||
"""Prepend all requests from another queue to the front of this
|
||||
queue.
|
||||
|
||||
Note: The requests will be prepended in reverse order of their
|
||||
appearance in the `requests` queue.
|
||||
"""
|
||||
self.extendleft(requests)
|
||||
|
||||
def remove_request(self, request: Request) -> None:
|
||||
"""Remove a specific request from the queue."""
|
||||
self.remove(request)
|
||||
|
||||
def remove_requests(self, requests: Iterable[Request]) -> None:
|
||||
"""Remove multiple specific requests from the queue."""
|
||||
requests_to_remove = set(requests)
|
||||
filtered_requests = [req for req in self if req not in requests_to_remove]
|
||||
# deque does not support in-place filtering, so we need to clear
|
||||
# and extend
|
||||
self.clear()
|
||||
self.extend(filtered_requests)
|
||||
|
||||
def __bool__(self) -> bool:
|
||||
"""Check if queue has any requests."""
|
||||
return len(self) > 0
|
||||
|
||||
def __len__(self) -> int:
|
||||
"""Get number of requests in queue."""
|
||||
return super().__len__()
|
||||
|
||||
def __iter__(self) -> Iterator[Request]:
|
||||
"""Iterate over the queue according to FCFS policy."""
|
||||
return super().__iter__()
|
||||
|
||||
|
||||
class PriorityRequestQueue(RequestQueue):
|
||||
"""
|
||||
A priority queue that supports heap operations.
|
||||
|
||||
Respects the ordering defined in the Request class, where
|
||||
requests with a smaller value of `priority` are processed first.
|
||||
If multiple requests have the same priority, the one with the earlier
|
||||
`arrival_time` is processed first.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._heap: list[Request] = []
|
||||
|
||||
def add_request(self, request: Request) -> None:
|
||||
"""Add a request to the queue according to priority policy."""
|
||||
heapq.heappush(self._heap, request)
|
||||
|
||||
def pop_request(self) -> Request:
|
||||
"""Pop a request from the queue according to priority policy."""
|
||||
if not self._heap:
|
||||
raise IndexError("pop from empty heap")
|
||||
return heapq.heappop(self._heap)
|
||||
|
||||
def peek_request(self) -> Request:
|
||||
"""Peek at the next request in the queue without removing it."""
|
||||
if not self._heap:
|
||||
raise IndexError("peek from empty heap")
|
||||
return self._heap[0]
|
||||
|
||||
def prepend_request(self, request: Request) -> None:
|
||||
"""Add a request to the queue according to priority policy.
|
||||
|
||||
Note: In a priority queue, there is no concept of prepending to the
|
||||
front. Requests are ordered by (priority, arrival_time)."""
|
||||
self.add_request(request)
|
||||
|
||||
def prepend_requests(self, requests: RequestQueue) -> None:
|
||||
"""Add all requests from another queue according to priority policy.
|
||||
|
||||
Note: In a priority queue, there is no concept of prepending to the
|
||||
front. Requests are ordered by (priority, arrival_time)."""
|
||||
for request in requests:
|
||||
self.add_request(request)
|
||||
|
||||
def remove_request(self, request: Request) -> None:
|
||||
"""Remove a specific request from the queue."""
|
||||
self._heap.remove(request)
|
||||
heapq.heapify(self._heap)
|
||||
|
||||
def remove_requests(self, requests: Iterable[Request]) -> None:
|
||||
"""Remove multiple specific requests from the queue."""
|
||||
requests_to_remove = requests if isinstance(requests, set) else set(requests)
|
||||
self._heap = [r for r in self._heap if r not in requests_to_remove]
|
||||
heapq.heapify(self._heap)
|
||||
|
||||
def __bool__(self) -> bool:
|
||||
"""Check if queue has any requests."""
|
||||
return bool(self._heap)
|
||||
|
||||
def __len__(self) -> int:
|
||||
"""Get number of requests in queue."""
|
||||
return len(self._heap)
|
||||
|
||||
def __iter__(self) -> Iterator[Request]:
|
||||
"""Iterate over the queue according to priority policy."""
|
||||
heap_copy = self._heap[:]
|
||||
while heap_copy:
|
||||
yield heapq.heappop(heap_copy)
|
||||
|
||||
|
||||
def create_request_queue(policy: SchedulingPolicy) -> RequestQueue:
|
||||
"""Create request queue based on scheduling policy."""
|
||||
if policy == SchedulingPolicy.PRIORITY:
|
||||
return PriorityRequestQueue()
|
||||
elif policy == SchedulingPolicy.FCFS:
|
||||
return FCFSRequestQueue()
|
||||
else:
|
||||
raise ValueError(f"Unknown scheduling policy: {policy}")
|
||||
2212
vllm/v1/core/sched/scheduler.py
Normal file
2212
vllm/v1/core/sched/scheduler.py
Normal file
File diff suppressed because it is too large
Load Diff
64
vllm/v1/core/sched/utils.py
Normal file
64
vllm/v1/core/sched/utils.py
Normal file
@@ -0,0 +1,64 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
import contextlib
|
||||
|
||||
from vllm.v1.request import Request, RequestStatus
|
||||
|
||||
|
||||
def remove_all(lst: list, items_to_remove: set) -> list:
|
||||
"""Remove all items from a list that are in the items_to_remove set.
|
||||
|
||||
This method optimizes for the common case of removing a single item,
|
||||
falling back to list comprehension for multiple items.
|
||||
|
||||
Args:
|
||||
lst: The list to remove items from
|
||||
items_to_remove: Set of items to remove
|
||||
|
||||
Returns:
|
||||
Either the modified original list (for single item removal) or
|
||||
a new list (for multiple item removal). Callers should use the
|
||||
returned value.
|
||||
|
||||
Note:
|
||||
For single item removal, this modifies the original list in-place
|
||||
and returns it. For multiple items, it creates and returns a new list.
|
||||
"""
|
||||
if not items_to_remove:
|
||||
return lst
|
||||
|
||||
if len(items_to_remove) == 1:
|
||||
# Fast path for single item removal (most common case)
|
||||
item = next(iter(items_to_remove))
|
||||
with contextlib.suppress(ValueError):
|
||||
lst.remove(item)
|
||||
return lst
|
||||
# For multiple items, use list comprehension
|
||||
return [item for item in lst if item not in items_to_remove]
|
||||
|
||||
|
||||
def check_stop(request: Request, max_model_len: int) -> bool:
|
||||
assert not request.pooling_params
|
||||
|
||||
sampling_params = request.sampling_params
|
||||
assert sampling_params is not None
|
||||
|
||||
if request.num_output_tokens < sampling_params.min_tokens:
|
||||
return False
|
||||
|
||||
last_token_id = request.output_token_ids[-1]
|
||||
if last_token_id == sampling_params.eos_token_id:
|
||||
request.status = RequestStatus.FINISHED_STOPPED
|
||||
return True
|
||||
|
||||
if last_token_id in (sampling_params.stop_token_ids or ()):
|
||||
request.status = RequestStatus.FINISHED_STOPPED
|
||||
request.stop_reason = last_token_id
|
||||
return True
|
||||
if (
|
||||
request.num_tokens >= max_model_len
|
||||
or request.num_output_tokens >= request.max_tokens
|
||||
):
|
||||
request.status = RequestStatus.FINISHED_LENGTH_CAPPED
|
||||
return True
|
||||
return False
|
||||
1114
vllm/v1/core/single_type_kv_cache_manager.py
Normal file
1114
vllm/v1/core/single_type_kv_cache_manager.py
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user