File size: 8,884 Bytes
2a5255e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import contextlib
import contextvars
import hashlib
import json
import os
import threading
import time
from collections import OrderedDict
from dataclasses import dataclass
from typing import Iterable, Optional
import numpy as np
import torch
from kimodo.sanitize import sanitize_texts
_ACTIVE_SESSION = contextvars.ContextVar("kimodo_demo_active_session", default=None)
@dataclass
class CacheStats:
hits: int = 0
misses: int = 0
disk_hits: int = 0
class EmbeddingCache:
"""Disk-backed text embedding cache with a small in-memory LRU."""
def __init__(
self,
*,
model_name: str,
encoder_id: str,
base_dir: Optional[str] = None,
max_mem_entries: int = 128,
) -> None:
cache_root = base_dir or os.environ.get(
"kimodo_EMBED_CACHE_DIR",
os.path.join("~", ".cache", "kimodo_demo", "embeddings"),
)
self.base_dir = os.path.expanduser(cache_root)
self.model_name = model_name
self.encoder_id = encoder_id
self.max_mem_entries = max_mem_entries
self.stats = CacheStats()
self._lock = threading.Lock()
self._mem_cache: OrderedDict[str, np.ndarray] = OrderedDict()
self._index = {}
self._index_loaded = False
def _model_dir(self) -> str:
return os.path.join(self.base_dir, self.model_name)
def _index_path(self) -> str:
return os.path.join(self._model_dir(), "index.json")
def _prewarm_marker_path(self, key: str) -> str:
return os.path.join(self._model_dir(), f"prewarm_{key}.json")
def has_prewarm_marker(self, key: str) -> bool:
return os.path.exists(self._prewarm_marker_path(key))
def write_prewarm_marker(self, key: str, *, prompt_count: int) -> None:
os.makedirs(self._model_dir(), exist_ok=True)
payload = {"prompt_count": prompt_count, "updated_at": time.time()}
tmp_path = f"{self._prewarm_marker_path(key)}.tmp"
with open(tmp_path, "w", encoding="utf-8") as f:
json.dump(payload, f)
os.replace(tmp_path, self._prewarm_marker_path(key))
def _load_index(self) -> None:
if self._index_loaded:
return
index_path = self._index_path()
if os.path.exists(index_path):
try:
with open(index_path, "r", encoding="utf-8") as f:
self._index = json.load(f)
except json.JSONDecodeError:
self._index = {}
self._index_loaded = True
def _save_index(self) -> None:
os.makedirs(self._model_dir(), exist_ok=True)
tmp_path = f"{self._index_path()}.tmp"
with open(tmp_path, "w", encoding="utf-8") as f:
json.dump(self._index, f)
os.replace(tmp_path, self._index_path())
def _make_key(self, text: str) -> str:
key_src = f"{self.model_name}|{self.encoder_id}|{text}"
return hashlib.sha256(key_src.encode("utf-8")).hexdigest()
def _entry_path(self, key: str) -> str:
return os.path.join(self._model_dir(), f"{key}.npy")
def _mem_get(self, key: str) -> Optional[np.ndarray]:
if key in self._mem_cache:
self._mem_cache.move_to_end(key)
return self._mem_cache[key]
return None
def _mem_put(self, key: str, value: np.ndarray) -> None:
self._mem_cache[key] = value
self._mem_cache.move_to_end(key)
while len(self._mem_cache) > self.max_mem_entries:
self._mem_cache.popitem(last=False)
def _disk_load(self, key: str) -> Optional[np.ndarray]:
path = self._entry_path(key)
if not os.path.exists(path):
return None
try:
return np.load(path)
except Exception:
return None
def _disk_save(self, key: str, value: np.ndarray) -> None:
os.makedirs(self._model_dir(), exist_ok=True)
np.save(self._entry_path(key), value)
self._index[key] = {
"length": int(value.shape[0]),
"dtype": str(value.dtype),
"updated_at": time.time(),
}
def _maybe_use_session_cache(self, texts: list[str]):
session = _ACTIVE_SESSION.get()
if session is None:
return None
if session.last_prompt_texts == texts and session.last_prompt_embeddings is not None:
return session.last_prompt_embeddings, session.last_prompt_lengths
return None
def _update_session_cache(self, texts: list[str], tensor: torch.Tensor, lengths: list[int]) -> None:
session = _ACTIVE_SESSION.get()
if session is None:
return
session.last_prompt_texts = texts
session.last_prompt_embeddings = tensor
session.last_prompt_lengths = lengths
def get_or_encode(self, texts: Iterable[str], encoder):
if isinstance(texts, str):
texts = [texts]
texts = sanitize_texts(list(texts))
if len(texts) == 0:
empty = torch.empty()
return empty, []
session_cache = self._maybe_use_session_cache(texts)
if session_cache is not None:
return session_cache
arrays: list[Optional[np.ndarray]] = [None] * len(texts)
lengths: list[int] = [0] * len(texts)
misses: list[tuple[int, str, str]] = []
with self._lock:
self._load_index()
for idx, text in enumerate(texts):
key = self._make_key(text)
cached = self._mem_get(key)
if cached is not None:
arrays[idx] = cached
lengths[idx] = cached.shape[0]
self.stats.hits += 1
continue
cached = self._disk_load(key)
if cached is not None:
arrays[idx] = cached
lengths[idx] = cached.shape[0]
self._mem_put(key, cached)
self.stats.disk_hits += 1
continue
misses.append((idx, text, key))
self.stats.misses += 1
if misses:
miss_texts = [text for _, text, _ in misses]
miss_tensor, miss_lengths = encoder(miss_texts)
miss_tensor = miss_tensor.detach().cpu()
miss_tensor_np = miss_tensor.numpy()
with self._lock:
self._load_index()
for miss_idx, length in enumerate(miss_lengths):
idx, _text, key = misses[miss_idx]
arr = miss_tensor_np[miss_idx, :length].copy()
arrays[idx] = arr
lengths[idx] = int(length)
self._mem_put(key, arr)
self._disk_save(key, arr)
self._save_index()
max_len = max(lengths) if lengths else 0
feat_dim = arrays[0].shape[-1] if arrays[0] is not None else 0
dtype = arrays[0].dtype if arrays[0] is not None else np.float32
padded = np.zeros((len(texts), max_len, feat_dim), dtype=dtype)
for idx, arr in enumerate(arrays):
if arr is None:
continue
padded[idx, : arr.shape[0]] = arr
result = torch.from_numpy(padded)
self._update_session_cache(texts, result, lengths)
return result, lengths
class CachedTextEncoder:
"""Wrapper around a text encoder to add disk-backed caching."""
def __init__(self, encoder, *, model_name: str, base_dir: Optional[str] = None):
self.encoder = encoder
self.model_name = model_name
encoder_id = f"{type(encoder).__name__}"
self.cache = EmbeddingCache(model_name=model_name, encoder_id=encoder_id, base_dir=base_dir)
def __call__(self, texts):
return self.cache.get_or_encode(texts, self.encoder)
def prewarm(self, texts) -> None:
if isinstance(texts, str):
texts = [texts]
texts = sanitize_texts(list(texts))
prewarm_key = hashlib.sha256("|".join(texts).encode("utf-8")).hexdigest()
if self.cache.has_prewarm_marker(prewarm_key):
return
self.cache.get_or_encode(texts, self.encoder)
self.cache.write_prewarm_marker(prewarm_key, prompt_count=len(texts))
def to(self, device=None, dtype=None):
if hasattr(self.encoder, "to"):
self.encoder.to(device=device, dtype=dtype)
return self
@contextlib.contextmanager
def session_context(self, session):
token = _ACTIVE_SESSION.set(session)
try:
yield
finally:
_ACTIVE_SESSION.reset(token)
def __getattr__(self, name):
return getattr(self.encoder, name)
|