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78d2329 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 | # import os
# import torch
# from dataclasses import dataclass, is_dataclass, replace, field
# from typing import TypeVar, Generic, Optional, Any, Callable, Iterable
# from pathlib import Path
# import pickle
# import tempfile
# import shutil
# import atexit
# import signal
# from typing import TypeVar, Generic
#
# T = TypeVar('T')
#
#
# @dataclass
# class DetachingCPUList(list[T]):
# cache_dir: Optional[Path] = field(default=None)
# save_serializer: Optional[Callable[[Any, Path], None]] = field(default=None)
# load_serializer: Optional[Callable[[Path], Any]] = field(default=None)
# detach_func: Optional[Callable[[Any], Any]] = field(default=None)
# remove_files_on_delete: bool = field(default=True)
# verbose: bool = field(default=True)
#
# _cache: dict = field(init=False, repr=False, default_factory=dict)
# _fd_map: dict = field(init=False, repr=False, default_factory=dict)
# _no_cache_set: set = field(init=False, repr=False, default_factory=set) # Track paths marked as no_cache
# _cache_dir: Path = field(init=False, repr=False)
# _tmp_dir_created: bool = field(init=False, repr=False, default=False)
#
# def __post_init__(self):
#
# # Set default serializers if not provided
# if self.save_serializer is None:
# def _save_pickle(obj, path: Path):
# path.parent.mkdir(parents=True, exist_ok=True)
# with open(path, "wb") as f:
# pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)
# self.save_serializer = _save_pickle
#
# # Set default deserializers if not provided
# if self.load_serializer is None:
# def _load_pickle(source):
# if isinstance(source, (str, Path)):
# with open(str(source), "rb") as f:
# return pickle.load(f)
# else:
# return pickle.load(source)
# self.load_serializer = _load_pickle
#
# # Set default detach_func if not provided
# if self.detach_func is None:
# self.detach_func = self._detach_recursive
#
# if self.cache_dir is None:
# tmp = tempfile.mkdtemp(prefix="detaching_cpu_list_")
# self._cache_dir = Path(tmp)
# self._tmp_dir_created = True
# else:
# self._cache_dir = Path(self.cache_dir)
# self._cache_dir.mkdir(parents=True, exist_ok=True)
#
# atexit.register(self._cleanup)
# for sig in (signal.SIGINT, signal.SIGTERM):
# try:
# old = signal.getsignal(sig)
# def _handler(signum, frame, _old=old):
# self._cleanup()
# if callable(_old) and _old not in (signal.SIG_DFL, signal.SIG_IGN):
# _old(signum, frame)
# signal.signal(sig, _handler)
# except Exception:
# pass
#
# # --------------------------
# # Core cleanup
# # --------------------------
# def _cleanup(self):
# # Close all file descriptors
# for fd in list(self._fd_map.values()):
# try:
# os.close(fd)
# except Exception:
# pass
# self._fd_map.clear()
# # Remove on-disk files (non-guaranteed mode)
# if self.remove_files_on_delete and self._tmp_dir_created and self._cache_dir.exists():
# shutil.rmtree(self._cache_dir, ignore_errors=True)
#
# # --------------------------
# # Save / load helpers
# # --------------------------
# def _save_unlinked(self, item: Any):
# """Save item to disk with guaranteed deletion (fd-based approach)."""
# tmp = tempfile.NamedTemporaryFile(delete=False, dir=str(self._cache_dir))
# temp_path = Path(tmp.name)
# tmp.close()
#
# assert self.save_serializer is not None, "save_serializer must be defined"
# self.save_serializer(item, temp_path)
# fd = os.open(str(temp_path), os.O_RDONLY)
# os.unlink(str(temp_path)) # unlink immediately - kernel guarantees cleanup on fd close
# pseudo = Path(f"/proc/self/fd/{fd}")
# token = pseudo.as_posix()
# self._fd_map[token] = fd
# if self.verbose:
# print(f"Saved item to fd {fd} with path {pseudo}")
# return pseudo
#
# def _load_from_fd(self, token: str):
# """Load from file descriptor path."""
# fd = self._fd_map.get(token)
# if fd is None:
# raise RuntimeError(f"FD {token} not available.")
# dupfd = os.dup(fd)
# assert self.load_serializer is not None, "load_serializer must be defined"
# with os.fdopen(dupfd, "rb") as f:
# f.seek(0)
# obj = self.load_serializer(f)
# if self.verbose:
# print(f"Loaded item from fd {fd} (token {token})")
# return obj
#
# def _load_from_disk(self, path: Path):
# """Load from regular file path."""
# assert self.load_serializer is not None, "load_serializer must be defined"
# if self.verbose:
# print(f"Loading from disk: {path}")
# return self.load_serializer(path)
#
# # --------------------------
# # Public interface
# # --------------------------
# def append(self, item, detach_and_cpu: bool = False, save_to_disk: bool = False, no_cache: bool = False):
# """
# Append an item to the list.
#
# Args:
# item: The item to append
# detach_and_cpu: If True, apply detach_func to move tensors to CPU
# save_to_disk: If True, save to disk using fd-based guaranteed deletion
# no_cache: If True, never cache this item in memory when accessed (always reload from disk)
# """
# # Validate save_to_disk requires detach capability
# if save_to_disk and not detach_and_cpu:
# raise ValueError("Cannot save to disk without detach_and_cpu=True")
#
# if not save_to_disk and no_cache:
# print("Warning: no_cache=True has no effect when save_to_disk=False")
#
# if detach_and_cpu and self.detach_func:
# item = self.detach_func(item)
#
# if save_to_disk:
# # Always use fd-based guaranteed deletion
# p = self._save_unlinked(item)
# super().append(p)
#
# # Mark as no_cache if requested
# if no_cache:
# self._no_cache_set.add(p.as_posix())
# else:
# super().append(item)
#
# def insert(self, index: int, item, detach_and_cpu: bool = False, save_to_disk: bool = False, no_cache: bool = False):
# """
# Insert an item at a specific index in the list.
#
# Args:
# index: The index to insert the item at
# item: The item to insert
# detach_and_cpu: If True, apply detach_func to move tensors to CPU
# save_to_disk: If True, save to disk using fd-based guaranteed deletion
# no_cache: If True, never cache this item in memory when accessed (always reload from disk)
# """
# # Validate save_to_disk requires detach capability
# if save_to_disk and not detach_and_cpu:
# raise ValueError("Cannot save to disk without detach_and_cpu=True")
#
# if detach_and_cpu and self.detach_func:
# item = self.detach_func(item)
#
# if save_to_disk:
# # Always use fd-based guaranteed deletion
# p = self._save_unlinked(item)
# super().insert(index, p)
#
# # Mark as no_cache if requested
# if no_cache:
# self._no_cache_set.add(p.as_posix())
# else:
# super().insert(index, item)
#
# def extend(self, items: Iterable[Any], **kwargs):
# for it in items:
# self.append(it, **kwargs)
#
# def __getitem__(self, index):
# """
# Return the item at `index`.
#
# - If the underlying stored value is a Path, load from disk/fd.
# - If marked as no_cache, always reload from disk (never cache).
# - Otherwise, cache after first load and return cached object on subsequent accesses.
# - If not a Path, return the in-memory value directly.
# """
# raw = super().__getitem__(index)
#
# # If it's not a Path, it's an in-memory object: return directly
# if not isinstance(raw, Path):
# return raw
#
# # it's a Path -> use its string as cache key (works for both normal paths and /proc/self/fd/<fd>)
# key = raw.as_posix()
#
# # If in cache, return cached object
# if key in self._cache:
# assert key not in self._no_cache_set, "Inconsistent state: item both cached and marked no_cache"
# return self._cache[key]
#
# # Always reload from disk/fd, never cache
# if str(raw).startswith("/proc/self/fd/"):
# obj = self._load_from_fd(key)
# else:
# raise NotImplementedError("Loading from disk is not implemented")
# # return self._load_from_disk(raw)
#
# if key in self._no_cache_set:
# pass # never cache
# else:
# # Cache it permanently (unless marked as no_cache)
# self._cache[key] = obj
#
# return obj
#
# def pop(self, index: int = -1):
# raw = super().pop(index)
# # If it was a Path, return the loaded object (and keep the fd open / mapping intact)
# if isinstance(raw, Path):
# key = raw.as_posix()
# # return cached value if present; else load now and cache it
# if key in self._cache:
# return self._cache[key]
# if str(raw).startswith("/proc/self/fd/"):
# obj = self._load_from_fd(key)
# else:
# obj = self._load_from_disk(raw)
# self._cache[key] = obj
# return obj
# return raw
#
# def clear(self):
# # do not close fds; keep them open until process exit as requested
# # keep cache intact if you want (or clear it if you prefer)
# # here we remove list entries but keep any cached objects and open fds
# super().clear()
#
# def __iter__(self):
# """Iterate over items, loading from disk as needed."""
# for i in range(len(self)):
# yield self[i]
#
# def __del__(self):
# self._cleanup()
#
# def _detach_recursive(self, obj):
# if isinstance(obj, torch.Tensor):
# return obj.detach().cpu()
# elif isinstance(obj, dict):
# return {k: self._detach_recursive(v) for k, v in obj.items()}
# elif isinstance(obj, (list, tuple)):
# t = type(obj)
# return t(self._detach_recursive(x) for x in obj)
# elif is_dataclass(obj):
# # Replace fields recursively (returns a new instance)
# return replace(obj, **{
# field.name: self._detach_recursive(getattr(obj, field.name))
# for field in obj.__dataclass_fields__.values()
# })
# else:
# return obj
from dataclasses import is_dataclass, replace, dataclass
import torch
@dataclass
class DetachingCPUList(list):
# TODO Naama: Add back disk saving
def append(self, item, detach_and_cpu=False, save_to_disk=False, no_cache=False):
if detach_and_cpu:
item = self._detach_recursive(item)
super().append(item)
def extend(self, iterable, detach_and_cpu=False):
if detach_and_cpu:
iterable = (self._detach_recursive(x) for x in iterable)
super().extend(iterable)
def insert(self, index, item, detach_and_cpu=False, save_to_disk=False, no_cache=False):
if detach_and_cpu:
item = self._detach_recursive(item)
super().insert(index, item)
def _detach_recursive(self, obj):
if isinstance(obj, torch.Tensor):
return obj.detach().cpu()
elif isinstance(obj, dict):
return {k: self._detach_recursive(v) for k, v in obj.items()}
elif isinstance(obj, (list, tuple)):
t = type(obj)
return t(self._detach_recursive(x) for x in obj)
elif is_dataclass(obj):
# Replace fields recursively (returns a new instance)
return replace(obj, **{
field.name: self._detach_recursive(getattr(obj, field.name))
for field in obj.__dataclass_fields__.values()
})
else:
return obj
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