151 lines
4.3 KiB
Python
151 lines
4.3 KiB
Python
import abc
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from collections import OrderedDict
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from dataclasses import dataclass
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from typing import List, Optional
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import torch
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from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
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class MultimodalCache(abc.ABC):
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@abc.abstractmethod
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def __init__(
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self,
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): ...
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@staticmethod
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def combine_hashes(mm_hashes: List[int]) -> Optional[int]:
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"""
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Get a combined hash from individual mm item hashes
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"""
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if not mm_hashes:
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return None
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return hash(tuple(mm_hashes))
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@abc.abstractmethod
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def get(
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self, mm_hashes: List[int], combined_hash: Optional[int] = None
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) -> Optional[torch.Tensor]:
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"""
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Extract the embedding with the hash-ids of the queried items. Try combined hash first, if missed, fallback to individual hashes
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The returned tensor may not be contiguous
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def set(
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self,
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mm_hash: int,
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embedding: torch.Tensor,
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mm_embedding_allocator: BaseTokenToKVPoolAllocator,
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) -> bool:
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"""
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Set the embedding to the pre-allocated locations with a hash id
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"""
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raise NotImplementedError()
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@abc.abstractmethod
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def has(self, mm_hash: int) -> bool:
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raise NotImplementedError()
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@abc.abstractmethod
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def free(
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self, mm_hash: int, mm_embedding_allocator: BaseTokenToKVPoolAllocator
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) -> bool:
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raise NotImplementedError()
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@abc.abstractmethod
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def clear(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def available_size(self):
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raise NotImplementedError()
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def _get_tensor_size(embedding: torch.Tensor):
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return embedding.element_size() * embedding.numel()
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@dataclass(kw_only=True)
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class EmbeddingResult:
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embedding: torch.Tensor
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class MultiModalStaticCache(MultimodalCache):
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"""
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A server-level cache for multimodal embedding.
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Embeddings are computed prior, and this cache does not really pre-alloc
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"""
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def __init__(
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self,
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max_size: int,
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):
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super().__init__()
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self.max_size = max_size
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self.mm_cache: OrderedDict[int, EmbeddingResult] = OrderedDict()
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self.current_size = 0
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def get(
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self, mm_hashes: List[int], combined_hash: Optional[int] = None
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) -> Optional[EmbeddingResult]:
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combined_hash = self.combine_hashes(mm_hashes)
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# MultiModalStaticCache does not fallback to individual item lookup
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embedding = self.mm_cache.get(combined_hash)
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if embedding is not None:
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self.mm_cache.move_to_end(combined_hash)
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return embedding
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def set(
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self,
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mm_hash: int,
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embedding: EmbeddingResult,
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loc: Optional[torch.Tensor] = None,
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) -> bool:
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assert isinstance(embedding, EmbeddingResult), embedding
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if mm_hash in self.mm_cache:
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self.mm_cache.move_to_end(mm_hash)
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return True
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data_size = _get_tensor_size(embedding.embedding)
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while self.current_size + data_size > self.max_size:
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if not self.mm_cache:
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return False
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lru_hash, lru_embedding = self.mm_cache.popitem(last=False)
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self.current_size -= _get_tensor_size(lru_embedding.embedding)
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self.mm_cache[mm_hash] = embedding
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self.current_size += data_size
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return True
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def get_single(self, mm_hash: int) -> Optional[EmbeddingResult]:
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"""Get a single cached embedding by its hash (no combine_hashes)."""
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embedding = self.mm_cache.get(mm_hash)
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if embedding is not None:
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self.mm_cache.move_to_end(mm_hash)
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return embedding
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def has(self, mm_hash: int) -> bool:
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return mm_hash in self.mm_cache
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def free(
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self, mm_hash: int, mm_embedding_allocator: BaseTokenToKVPoolAllocator
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) -> bool:
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if mm_hash not in self.mm_cache:
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return False
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old_embedding = self.mm_cache.pop(mm_hash)
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self.current_size -= _get_tensor_size(old_embedding.embedding)
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return True
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def clear(self):
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self.mm_cache.clear()
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self.current_size = 0
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def __len__(self):
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return len(self.mm_cache)
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def available_size(self):
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return self.__len__()
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