File size: 13,661 Bytes
9d614f1 | 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 | """Deterministic sequential ring loader (PRIVATE to Hardware_Module) - Tier 3 (1-2 GB RAM).
The model skeleton is built with EMPTY parameters (zero-size tensors, real CPU buffers such as the
rotary inv_freq). Forward pre/post hooks then stream weights from the safetensors files in the exact
order embed -> layer 0 -> layer 1 -> ... -> layer N-1 -> norm -> lm_head (and wrap around to layer 0 for
the next token). At most `window` (default 2) decoder layers are resident at any time.
Parameters are always REPLACED by fresh nn.Parameter objects (never mutated in place), so autograd
graphs / checkpoint recomputation that still reference an evicted weight stay valid.
Extra capabilities used by the Elevator trainer:
pin(idxs)/unpin(idxs, save=True) keep trainable layers resident, persist them as overlay shards
export(dest) write the full (base + overlay) model as safetensors shards
"""
from __future__ import annotations
import gc
import json
import shutil
import time
from collections import deque
from pathlib import Path
from typing import Dict, Iterator, List, Optional, Tuple
import torch
from torch import nn
from safetensors import safe_open
from safetensors.torch import save_file
def _from_config(config, dtype):
from transformers import AutoModelForCausalLM
try:
return AutoModelForCausalLM.from_config(config, dtype=dtype)
except TypeError:
return AutoModelForCausalLM.from_config(config, torch_dtype=dtype)
class SequentialRingLoader:
EMBED_KEY = "model.embed_tokens.weight"
HEAD_KEY = "lm_head.weight"
def __init__(self, model_dir, dtype=torch.bfloat16, window: int = 2, evict_ends: bool = True,
overlay_dir: Optional[str] = None, row_recipe: Optional[Dict] = None, verbose: bool = True):
self.model_dir = Path(model_dir)
self.dtype = dtype
self.window = max(1, int(window))
self.evict_ends = bool(evict_ends)
self.overlay_dir = Path(overlay_dir) if overlay_dir else self.model_dir / ".rss_overlay"
self.row_recipe = {int(k): [int(x) for x in v] for k, v in (row_recipe or {}).items()}
self.verbose = verbose
self.trace: deque = deque(maxlen=256)
self.pinned: set = set()
self.loaded: set = set()
self.overlay_map: Dict[str, Path] = {}
self.weight_map: Dict[str, str] = {}
self._handles: Dict[str, object] = {}
self.model = None
self.segments: List[Dict] = []
self.by_name: Dict[str, Dict] = {}
self.num_layers = 0
self.original_tied = False
# ------------------------------------------------------------ indexing
def _index_weights(self) -> None:
index = self.model_dir / "model.safetensors.index.json"
if index.exists():
self.weight_map = dict(json.loads(index.read_text(encoding="utf-8"))["weight_map"])
else:
single = self.model_dir / "model.safetensors"
if not single.exists():
raise FileNotFoundError(f"no model.safetensors / index in {self.model_dir}")
with safe_open(str(single), framework="pt", device="cpu") as handle:
self.weight_map = {k: "model.safetensors" for k in handle.keys()}
def _handle(self, filename: str):
if filename not in self._handles:
self._handles[filename] = safe_open(str(self.model_dir / filename), framework="pt", device="cpu")
return self._handles[filename]
def _raw_get(self, key: str) -> torch.Tensor:
if key in self.overlay_map:
with safe_open(str(self.overlay_map[key]), framework="pt", device="cpu") as handle:
return handle.get_tensor(key)
return self._handle(self.weight_map[key]).get_tensor(key)
def _get(self, key: str) -> torch.Tensor:
tensor = self._raw_get(key)
if self.row_recipe and key in (self.EMBED_KEY, self.HEAD_KEY):
tensor = tensor.clone()
for new_id, sources in self.row_recipe.items():
if new_id < tensor.shape[0] and sources:
idx = torch.tensor([s for s in sources if s < tensor.shape[0]], dtype=torch.long)
if len(idx):
tensor[new_id] = tensor[idx].float().mean(0).to(tensor.dtype)
return tensor
# ---------------------------------------------------------------- build
def build(self):
from accelerate import init_empty_weights
from transformers import AutoConfig
self._index_weights()
config = AutoConfig.from_pretrained(str(self.model_dir))
self.original_tied = bool(getattr(config, "tie_word_embeddings", False))
config.tie_word_embeddings = False # embed and lm_head are streamed independently
with init_empty_weights(): # parameters on meta, buffers stay real
model = _from_config(config, self.dtype)
for module in model.modules():
for name, param in list(module._parameters.items()):
if param is not None:
module._parameters[name] = nn.Parameter(torch.empty(0, dtype=self.dtype), requires_grad=False)
model.eval()
model.config.use_cache = True
inner = model.model
self.num_layers = len(inner.layers)
self.segments = [{"name": "embed", "module": inner.embed_tokens, "prefix": "model.embed_tokens", "idx": None}]
for i, layer in enumerate(inner.layers):
self.segments.append({"name": f"layer{i}", "module": layer, "prefix": f"model.layers.{i}", "idx": i})
self.segments.append({"name": "norm", "module": inner.norm, "prefix": "model.norm", "idx": None})
self.segments.append({"name": "head", "module": model.lm_head, "prefix": "lm_head", "idx": None})
self.by_name = {s["name"]: s for s in self.segments}
self.model = model
for seg in self.segments:
self._install_hooks(seg)
if not self.evict_ends:
for name in ("embed", "norm", "head"):
self._ensure(name)
if self.verbose:
print(f" [ring] built skeleton: {self.num_layers} layers on disk, window={self.window}, "
f"evict_ends={self.evict_ends}, dtype={str(self.dtype).replace('torch.', '')}")
return model
# ------------------------------------------------------ key resolution
def _key_for(self, seg: Dict, rel: str) -> Optional[str]:
rel = rel.replace(".base_layer", "")
if "lora_" in rel or "modules_to_save" in rel or "original_module" in rel:
return None
key = f"{seg['prefix']}.{rel}"
if key in self.weight_map:
return key
if seg["name"] == "head" and rel == "weight" and self.EMBED_KEY in self.weight_map:
return self.EMBED_KEY # tied checkpoint: head shares the embedding matrix
return None
def _managed(self, seg: Dict) -> Iterator[Tuple[str, nn.Module, str]]:
for mod_name, mod in seg["module"].named_modules():
for pname, param in list(mod._parameters.items()):
if param is None:
continue
rel = f"{mod_name}.{pname}" if mod_name else pname
key = self._key_for(seg, rel)
if key:
yield key, mod, pname
# ----------------------------------------------------------- load/evict
def _ensure(self, name: str) -> None:
if name in self.loaded:
return
started = time.time()
for key, mod, pname in self._managed(self.by_name[name]):
old = mod._parameters[pname]
tensor = self._get(key).to(old.dtype)
mod._parameters[pname] = nn.Parameter(tensor, requires_grad=old.requires_grad)
self.loaded.add(name)
self.trace.append(("load", name, round(time.time() - started, 3)))
def _evict(self, name: str) -> None:
if name not in self.loaded or name in self.pinned:
return
for _key, mod, pname in self._managed(self.by_name[name]):
old = mod._parameters[pname]
mod._parameters[pname] = nn.Parameter(torch.empty(0, dtype=self.dtype), requires_grad=old.requires_grad)
self.loaded.discard(name)
self.trace.append(("evict", name, 0.0))
# ---------------------------------------------------------------- hooks
def _install_hooks(self, seg: Dict) -> None:
name, idx = seg["name"], seg["idx"]
module = seg["module"]
if idx is not None:
def pre(_m, _args, i=idx):
self._enter_layer(i)
def post(_m, _args, _out, i=idx):
if self.window == 1:
self._evict(f"layer{i}")
module.register_forward_pre_hook(pre)
module.register_forward_hook(post)
else:
def pre_end(_m, _args, n=name):
self._ensure(n)
def post_end(_m, _args, _out, n=name):
if self.evict_ends:
self._evict(n)
module.register_forward_pre_hook(pre_end)
module.register_forward_hook(post_end)
def _enter_layer(self, i: int) -> None:
keep = {f"layer{i}"}
self._ensure(f"layer{i}")
if self.window >= 2 and self.num_layers > 1:
nxt = f"layer{(i + 1) % self.num_layers}" # ring: last layer prefetches layer 0
self._ensure(nxt)
keep.add(nxt)
for name in list(self.loaded):
if name.startswith("layer") and name not in keep and name not in self.pinned:
self._evict(name)
# --------------------------------------------------- pin / overlay / io
def pin(self, idxs) -> None:
for i in idxs:
self._ensure(f"layer{i}")
self.pinned.add(f"layer{i}")
def _save_overlay(self, i: int) -> None:
seg = self.by_name[f"layer{i}"]
tensors = {}
for key, mod, pname in self._managed(seg):
tensors[key] = mod._parameters[pname].detach().to(self.dtype).contiguous().clone()
self.overlay_dir.mkdir(parents=True, exist_ok=True)
path = self.overlay_dir / f"layer_{i}_{int(time.time() * 1000)}.safetensors"
save_file(tensors, str(path), metadata={"format": "pt"})
for key in tensors:
old = self.overlay_map.get(key)
self.overlay_map[key] = path
if old and old != path and old.exists() and old not in set(self.overlay_map.values()):
old.unlink()
def unpin(self, idxs, save: bool = True) -> None:
for i in idxs:
name = f"layer{i}"
if save:
self._save_overlay(i)
self.pinned.discard(name)
for _key, mod, pname in self._managed(self.by_name[name]):
mod._parameters[pname].requires_grad_(False)
self._evict(name)
gc.collect()
def export(self, dest) -> Path:
"""Write base + overlay weights as per-segment safetensors shards (1 segment in RAM at a time)."""
dest = Path(dest)
dest.mkdir(parents=True, exist_ok=True)
covered = set()
order = [s["name"] for s in self.segments]
keys_by_segment: Dict[str, List[str]] = {}
for name in order:
seg = self.by_name[name]
keys = sorted({key for key, _m, _p in self._managed(seg)})
if name == "head" and self.original_tied:
keys = [] # keep the checkpoint tied: no duplicate matrix
keys_by_segment[name] = keys
covered.update(keys)
leftovers = sorted(k for k in self.weight_map if k not in covered
and not (self.original_tied and k == self.HEAD_KEY)
and not (k == self.EMBED_KEY))
total_files = len([n for n in order if keys_by_segment[n]]) or 1
weight_map_out: Dict[str, str] = {}
total_size = 0
shard_no = 0
for name in order:
keys = keys_by_segment[name]
if name == order[-1]:
keys = keys + leftovers
if not keys:
continue
shard_no += 1
fname = f"model-{shard_no:05d}-of-{total_files:05d}.safetensors"
tensors = {k: self._get(k).to(self.dtype).contiguous() for k in keys}
save_file(tensors, str(dest / fname), metadata={"format": "pt"})
for k, t in tensors.items():
weight_map_out[k] = fname
total_size += t.numel() * t.element_size()
del tensors
gc.collect()
(dest / "model.safetensors.index.json").write_text(
json.dumps({"metadata": {"total_size": total_size}, "weight_map": weight_map_out}, indent=1), encoding="utf-8")
for fname in ("config.json", "generation_config.json"):
if (self.model_dir / fname).exists():
shutil.copy(self.model_dir / fname, dest / fname)
if self.verbose:
print(f" [ring] exported {shard_no} shards ({total_size / 1e9:.2f} GB) -> {dest}")
return dest
def describe(self) -> str:
layers = sorted(int(n[5:]) for n in self.loaded if n.startswith("layer"))
try:
import psutil
rss = psutil.Process().memory_info().rss / 1024 ** 3
mem = f"{rss:.2f} GiB RSS"
except Exception:
mem = "n/a"
return f"ring: resident layers={layers} pinned={sorted(self.pinned)} | {mem}" |