File size: 14,755 Bytes
c61c435 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 | from __future__ import annotations
import json
from collections import Counter
from pathlib import Path
from typing import Any, Iterator
from .base import BaseModelInspector, InspectorError, ModelInspection, TensorStats, is_cancelled, report
from .statistics import (
discover_checkpoint_paths,
discover_config_files,
step_from_name,
tensor_stats_from_torch,
)
def _folder_size(path: Path) -> int:
if path.is_file():
return path.stat().st_size
total = 0
try:
for item in path.rglob("*"):
if item.is_file():
total += item.stat().st_size
except OSError:
return total
return total
def _read_configs(files: list[Path], root: Path) -> dict[str, Any]:
configs: dict[str, Any] = {}
for file in files[:40]:
try:
key = str(file.relative_to(root if root.is_dir() else root.parent))
except ValueError:
key = file.name
try:
configs[key] = json.loads(file.read_text(encoding="utf-8"))
except (OSError, UnicodeDecodeError, json.JSONDecodeError):
configs[key] = "<unreadable>"
return configs
def _resolution_from_configs(configs: dict[str, Any], settings: dict[str, Any] | None) -> int | None:
for source in (settings or {}, *[value for value in configs.values() if isinstance(value, dict)]):
for key in ("resolution", "sample_size", "image_size", "size"):
value = source.get(key) if isinstance(source, dict) else None
if isinstance(value, int):
return value
if isinstance(value, (list, tuple)) and value and isinstance(value[0], int):
return int(value[0])
try:
if value:
return int(value)
except (TypeError, ValueError):
pass
return None
def _iter_safetensors(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
from safetensors import safe_open
with safe_open(str(file), framework="pt", device="cpu") as handle:
metadata = handle.metadata() or {}
for key in handle.keys():
yield key, handle.get_tensor(key), metadata
def _extract_state_dict(payload: Any) -> dict[str, Any]:
try:
import torch
except Exception:
torch = None
if torch is not None and hasattr(payload, "shape"):
return {"tensor": payload}
if isinstance(payload, dict):
for key in ("state_dict", "model_state_dict", "model", "module", "unet", "network"):
value = payload.get(key)
if isinstance(value, dict) and any(hasattr(item, "shape") for item in value.values()):
return value
if any(hasattr(item, "shape") for item in payload.values()):
return payload
return {}
def _iter_torch_checkpoint(file: Path) -> Iterator[tuple[str, Any, dict[str, Any]]]:
import torch
try:
payload = torch.load(str(file), map_location="cpu", weights_only=True)
except TypeError:
payload = torch.load(str(file), map_location="cpu")
except Exception:
payload = torch.load(str(file), map_location="cpu", weights_only=False)
state = _extract_state_dict(payload)
metadata = {key: value for key, value in payload.items() if key not in state} if isinstance(payload, dict) else {}
for key, tensor in state.items():
if hasattr(tensor, "shape"):
yield str(key), tensor, metadata
def _weight_files(path: Path) -> list[Path]:
if path.is_file():
return [path]
ignored_names = {"optimizer.bin", "scheduler.bin", "scaler.pt"}
names = {
"diffusion_pytorch_model.safetensors",
"model.safetensors",
"pytorch_model.bin",
"adapter_model.safetensors",
"adapter_model.bin",
"checkpoint.pt",
"best_checkpoint.pt",
}
files: list[Path] = []
try:
for item in path.rglob("*"):
if item.is_file() and (item.name in names or item.suffix.casefold() in {".safetensors", ".pt", ".pth", ".bin", ".ckpt"}):
if item.name.casefold() not in ignored_names:
files.append(item)
except OSError:
return []
if (path / "model_index.json").is_file():
final_files = [
item for item in files
if not any(part.startswith("checkpoint-") for part in item.relative_to(path).parts)
]
if final_files:
files = final_files
return sorted(files, key=lambda item: (0 if item.name in names else 1, str(item)))
class GenericModelInspector(BaseModelInspector):
architecture = "Generic / Unknown"
def inspect(
self,
path: str | Path,
*,
recorded_architecture: str = "",
run_settings: dict[str, Any] | None = None,
progress=None,
cancelled=None,
) -> ModelInspection:
target = Path(path).expanduser()
if not target.exists():
raise InspectorError(f"Model path does not exist: {target}")
target = target.resolve()
report(progress, 3, "Finding model files")
config_files = discover_config_files(target)
configs = _read_configs(config_files, target)
files = _weight_files(target)
if not files:
message = "Model contains no readable tensor checkpoint"
return self._empty(target, recorded_architecture, run_settings, config_files, configs, message)
tensors: list[TensorStats] = []
dtypes: Counter[str] = Counter()
components: Counter[str] = Counter()
health: list[str] = []
messages: list[str] = []
metadata: dict[str, Any] = {}
for file_index, file in enumerate(files):
if is_cancelled(cancelled):
raise InspectorError("Inspection cancelled.")
report(progress, 8 + int(80 * file_index / max(1, len(files))), f"Reading {file.name}")
try:
if file.suffix.casefold() == ".safetensors":
iterator = _iter_safetensors(file)
else:
iterator = _iter_torch_checkpoint(file)
for name, tensor, file_metadata in iterator:
if is_cancelled(cancelled):
raise InspectorError("Inspection cancelled.")
prefix = file.parent.name if len(files) > 1 else ""
stat = tensor_stats_from_torch(f"{prefix}.{name}" if prefix and not name.startswith(prefix) else name, tensor)
tensors.append(stat)
dtypes[stat.dtype] += stat.parameter_count
components[stat.component] += stat.parameter_count
health.extend(f"{stat.name}: {item}" for item in stat.health)
if file_metadata:
metadata.update(file_metadata)
except Exception as exc:
health.append(f"{file.name}: unreadable checkpoint ({exc})")
if not tensors:
message = "Model contains no readable tensor checkpoint"
return self._empty(target, recorded_architecture, run_settings, config_files, configs, message, [*(health or []), message])
report(progress, 92, "Summarizing model")
total_parameters = sum(tensor.parameter_count for tensor in tensors)
parameter_memory = sum(tensor.memory_bytes for tensor in tensors)
largest = sorted(tensors, key=lambda item: item.parameter_count, reverse=True)[:20]
architecture, confidence, message = self._architecture_from_signals(
target, recorded_architecture, configs, [tensor.name for tensor in tensors]
)
messages.append(message)
duplicate_count = len(tensors) - len({tensor.name for tensor in tensors})
if duplicate_count:
health.append(f"Unusual: {duplicate_count} duplicate tensor names after folder merging")
checkpoints = [str(item) for item in discover_checkpoint_paths(target)]
return ModelInspection(
path=str(path),
resolved_path=str(target),
architecture=architecture,
confidence=confidence,
status="ok",
size_bytes=_folder_size(target),
config_files=[str(item) for item in config_files],
resolution=_resolution_from_configs(configs, run_settings),
epoch=self._number_from_metadata(metadata, "epoch"),
step=self._number_from_metadata(metadata, "step") or step_from_name(target.name),
tensor_count=len(tensors),
total_parameters=total_parameters,
trainable_parameters=self._trainable_parameters(tensors, architecture),
parameter_memory_bytes=parameter_memory,
dtypes=dict(dtypes),
components=dict(components),
largest_tensors=largest,
tensors=tensors,
health=health or ["No invalid tensor values found in sampled statistics."],
messages=messages,
lora=self._lora_info(tensors, configs),
configs=configs,
histogram=self._histogram(tensors),
tensor_size_distribution=[(tensor.name, tensor.parameter_count) for tensor in largest],
checkpoints=checkpoints,
loss_history=[],
)
def _empty(
self,
target: Path,
recorded_architecture: str,
run_settings: dict[str, Any] | None,
config_files: list[Path],
configs: dict[str, Any],
message: str,
health: list[str] | None = None,
) -> ModelInspection:
architecture, confidence, detection_message = self._architecture_from_signals(target, recorded_architecture, configs, [])
return ModelInspection(
path=str(target),
resolved_path=str(target),
architecture=architecture,
confidence=confidence,
status="warning",
size_bytes=_folder_size(target),
config_files=[str(item) for item in config_files],
resolution=_resolution_from_configs(configs, run_settings),
epoch=None,
step=step_from_name(target.name),
tensor_count=0,
total_parameters=0,
trainable_parameters=None,
parameter_memory_bytes=0,
dtypes={},
components={},
largest_tensors=[],
tensors=[],
health=health or [message],
messages=[detection_message, message],
configs=configs,
checkpoints=[str(item) for item in discover_checkpoint_paths(target)],
)
@staticmethod
def _number_from_metadata(metadata: dict[str, Any], key: str) -> int | None:
for candidate in (key, f"global_{key}", f"current_{key}"):
try:
value = metadata.get(candidate)
if value is not None:
return int(value)
except (TypeError, ValueError):
pass
return None
@staticmethod
def _trainable_parameters(tensors: list[TensorStats], architecture: str) -> int | None:
if architecture == "LoRA":
return sum(tensor.parameter_count for tensor in tensors)
return None
@staticmethod
def _architecture_from_signals(
target: Path,
recorded_architecture: str,
configs: dict[str, Any],
tensor_names: list[str],
) -> tuple[str, float, str]:
recorded = recorded_architecture.casefold()
joined_names = "\n".join(tensor_names).casefold()
config_text = json.dumps(configs, default=str).casefold()
folder_text = str(target).casefold()
signals = " ".join((joined_names, config_text, folder_text))
if "lora" in recorded or "lora" in signals or "adapter_config" in signals:
return "LoRA", 0.92, "Model recognized as LoRA"
if "maskgit" in recorded or "maskgit" in signals:
return "MaskGIT", 0.86, "Model recognized as MaskGIT"
if "flow" in recorded or "rectified_flow" in signals or "flow_model_info" in signals:
return "Flow Matching", 0.9, "Model recognized as Flow Matching"
if "ddpm" in recorded or "diffusers" in config_text or "unet" in signals or "scheduler_config" in signals:
return "DDPM / Diffusers", 0.88, "Model recognized as DDPM"
return "Generic / Unknown", 0.35, "Model type uncertain - using generic tensor inspection"
@staticmethod
def _lora_info(tensors: list[TensorStats], configs: dict[str, Any]) -> dict[str, Any]:
lora_tensors = [tensor for tensor in tensors if "lora" in tensor.name.casefold()]
if not lora_tensors:
return {}
down = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("down", "lora_a"))]
up = [tensor for tensor in lora_tensors if any(token in tensor.name.casefold() for token in ("up", "lora_b"))]
ranks = sorted({tensor.shape[0] for tensor in down if tensor.shape})
alpha = None
targets: set[str] = set()
for config in configs.values():
if isinstance(config, dict):
alpha = config.get("lora_alpha", config.get("alpha", alpha))
modules = config.get("target_modules")
if isinstance(modules, list):
targets.update(str(item) for item in modules)
if not targets:
for tensor in lora_tensors:
parts = tensor.name.split(".")
if len(parts) > 2:
targets.add(parts[-3])
return {
"rank": ", ".join(str(item) for item in ranks[:8]) if ranks else "unknown",
"alpha": alpha if alpha is not None else "unknown",
"target_modules": sorted(targets)[:20],
"down_matrices": len(down),
"up_matrices": len(up),
"adapter_parameter_count": sum(tensor.parameter_count for tensor in lora_tensors),
"average_abs_mean": (
sum(tensor.abs_mean or 0 for tensor in lora_tensors) / max(1, len(lora_tensors))
),
}
@staticmethod
def _histogram(tensors: list[TensorStats]) -> dict[str, list[float]]:
values = [tensor.abs_mean for tensor in tensors if tensor.abs_mean is not None]
if not values:
return {}
buckets = [0.0] * 10
high = max(values) or 1.0
for value in values:
index = min(9, int((value / high) * 10))
buckets[index] += 1
return {"abs_mean_bins": [round(high * index / 10, 6) for index in range(11)], "counts": buckets}
|