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"""
Thread-safe LRU prompt cache for MLX-based backends.
Ported from mlx_lm/server.py (MIT License, Copyright 2023-2024 Apple Inc.)
with thread-safety additions for LocalAI's gRPC backend.
Usage:
from mlx_cache import ThreadSafeLRUPromptCache
# In LoadModel:
self.lru_cache = ThreadSafeLRUPromptCache(max_size=10)
# In Predict/PredictStream:
prompt_cache, remaining_tokens = self.lru_cache.fetch_nearest_cache(model_key, tokens)
# ... generate ...
self.lru_cache.insert_cache(model_key, tokens, prompt_cache)
"""
import copy
import threading
from collections import deque
from dataclasses import dataclass
from typing import Any, List, Optional, Tuple
@dataclass
class CacheEntry:
"""A cache entry with reference counting."""
prompt_cache: List[Any]
count: int
@dataclass
class SearchResult:
"""Result of searching the cache trie."""
model: Any
exact: Optional[List[int]]
shorter: Optional[List[int]]
longer: Optional[List[int]]
common_prefix: int
class ThreadSafeLRUPromptCache:
"""
Thread-safe LRU cache with prefix matching for prompt KV caches.
This cache stores KV caches keyed by token sequences and supports:
- Exact match: Return the cache for the exact token sequence
- Shorter prefix match: Return a cache for a prefix of the tokens
- Longer prefix match: If a longer sequence is cached and can be trimmed
- LRU eviction: When max_size is exceeded, evict least recently used
Thread safety is provided via a threading.Lock that protects all
cache operations.
Args:
max_size: Maximum number of cache entries (default: 10)
can_trim_fn: Optional function to check if a cache can be trimmed
trim_fn: Optional function to trim a cache
"""
def __init__(
self,
max_size: int = 10,
can_trim_fn: Optional[Any] = None,
trim_fn: Optional[Any] = None,
):
self.max_size = max_size
self._cache = {}
self._lru = deque()
self._lock = threading.Lock()
# Optional trim functions (for longer prefix reuse)
self._can_trim_fn = can_trim_fn
self._trim_fn = trim_fn
def _search(self, model, tokens: List[int]) -> SearchResult:
"""
Search the cache for a prompt cache. Return exact or close match.
The cache is organized as a trie where each node is keyed by a token.
This allows efficient prefix matching.
"""
if model not in self._cache:
return SearchResult(model, None, None, None, 0)
current = self._cache[model]
last_cache_index = -1
index = 0
# Traverse the trie following the token sequence
while index < len(tokens) and tokens[index] in current:
current = current[tokens[index]]
if "cache" in current:
last_cache_index = index
index += 1
# Exact match - no need to search for longer or shorter caches
if last_cache_index == len(tokens) - 1:
return SearchResult(model, tuple(tokens), None, None, 0)
# Find the shorter cache (a prefix that has a cache)
# Note: Uses > 0 (not >= 0) to match upstream mlx_lm/server.py behavior.
# Single-token prefixes are not matched, which allows longer cached
# sequences to be preferred for trimming. This is acceptable because
# real prompts with chat templates are always many tokens.
shorter = None
if last_cache_index > 0:
shorter = tuple(tokens[: last_cache_index + 1])
# Check for caches that are longer than our token sequence
longer = None
common_prefix = index
if index > 0 and last_cache_index <= 0:
best = None
stack = [(current, [])]
while stack:
current, extra = stack.pop()
if "cache" in current:
if best is None or len(extra) < len(best):
best = extra
else:
for tok in current:
stack.append((current[tok], extra + [tok]))
if best is not None:
longer = tuple(tokens[:index] + best)
return SearchResult(model, None, shorter, longer, common_prefix)
def _get(self, model, tokens: Tuple[int, ...]) -> CacheEntry:
"""Get a cache entry by traversing the trie."""
current = self._cache[model]
for tok in tokens:
current = current[tok]
return current["cache"]
def _delete(self, model, tokens: Tuple[int, ...]) -> None:
"""Delete a cache entry and clean up empty trie nodes."""
path = [self._cache[model]]
for tok in tokens:
path.append(path[-1][tok])
del path[-1]["cache"]
# Clean up empty nodes bottom-up
for i in reversed(range(len(tokens))):
d_prev, d, t = path[i], path[i + 1], tokens[i]
if len(d) > 0:
break
del d_prev[t]
def _extract(self, model, tokens: Tuple[int, ...]) -> CacheEntry:
"""
Extract a cache entry for exclusive use.
If the entry has count > 1, deep copy and decrement.
If count == 1, remove from cache entirely.
"""
cache_entry = self._get(model, tokens)
if cache_entry.count == 1:
self._delete(model, tokens)
self._lru.remove((model, tokens))
return cache_entry
cache_entry.count -= 1
return CacheEntry(
copy.deepcopy(cache_entry.prompt_cache),
1,
)
def fetch_nearest_cache(
self, model, tokens: List[int]
) -> Tuple[Optional[List[Any]], List[int]]:
"""
Fetch the nearest cache for the given token sequence.
Thread-safe. Returns (cache, remaining_tokens) where:
- cache: The KV cache to use (or None if no cache found)
- remaining_tokens: Tokens that still need to be processed
Args:
model: Model identifier (used to namespace caches)
tokens: The full token sequence for the prompt
Returns:
Tuple of (prompt_cache, remaining_tokens)
"""
with self._lock:
tokens_tuple = tuple(tokens)
result = self._search(model, tokens)
# Exact match - extract and return
if result.exact is not None:
cache_entry = self._extract(result.model, result.exact)
return cache_entry.prompt_cache, []
# Shorter prefix match - extract and return remaining
if result.shorter is not None:
cache_entry = self._extract(result.model, result.shorter)
prefix_len = len(result.shorter)
return cache_entry.prompt_cache, list(tokens[prefix_len:])
# Longer prefix match - try to trim if possible
if result.longer is not None and self._can_trim_fn is not None:
cache_entry = self._get(result.model, result.longer)
if self._can_trim_fn(cache_entry.prompt_cache):
# Deep copy and trim
trimmed_cache = copy.deepcopy(cache_entry.prompt_cache)
prefix = min(len(tokens) - 1, result.common_prefix)
num_to_trim = len(result.longer) - prefix
if self._trim_fn is not None:
self._trim_fn(trimmed_cache, num_to_trim)
return trimmed_cache, list(tokens[prefix:])
# No match found
return None, list(tokens)
def insert_cache(
self, model, tokens: List[int], prompt_cache: List[Any]
) -> None:
"""
Insert a cache entry after generation completes.
Thread-safe. Handles LRU eviction if max_size is exceeded.
Args:
model: Model identifier (used to namespace caches)
tokens: The full token sequence (prompt + generated)
prompt_cache: The KV cache to store
"""
with self._lock:
tokens_tuple = tuple(tokens)
if model not in self._cache:
self._cache[model] = {}
current = self._cache[model]
# Build trie path
for tok in tokens_tuple:
if tok not in current:
current[tok] = {}
current = current[tok]
# Update or create entry
if "cache" in current:
current["cache"].count += 1
self._lru.remove((model, tokens_tuple))
else:
current["cache"] = CacheEntry(prompt_cache, 1)
# Update LRU order
self._lru.append((model, tokens_tuple))
# Evict if over capacity
if len(self._lru) > self.max_size:
evict_model, evict_tokens = self._lru.popleft()
self._delete(evict_model, evict_tokens)
def clear(self) -> None:
"""Clear all cache entries. Thread-safe."""
with self._lock:
self._cache.clear()
self._lru.clear()
def __len__(self) -> int:
"""Return the number of cache entries. Thread-safe."""
with self._lock:
return len(self._lru)
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