Spaces:
Running
Running
File size: 16,814 Bytes
1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 1099afe bdedf43 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 |
# DEPENDENCIES
import sys
import torch
from pathlib import Path
from transformers import AutoModel
from transformers import AutoTokenizer
from sentence_transformers import SentenceTransformer
# Add parent directory to path for imports
sys.path.append(str(Path(__file__).parent.parent))
from utils.logger import log_info
from utils.logger import log_error
from config.model_config import ModelConfig
from utils.logger import ContractAnalyzerLogger
from model_manager.model_registry import ModelInfo
from model_manager.model_registry import ModelType
from model_manager.model_registry import ModelStatus
from model_manager.model_registry import ModelRegistry
class ModelLoader:
"""
Smart model loader with automatic download, caching, and GPU support
"""
def __init__(self):
self.registry = ModelRegistry()
self.config = ModelConfig()
self.logger = ContractAnalyzerLogger.get_logger()
# Detect device
self.device = "cuda" if torch.cuda.is_available() else "cpu"
log_info(f"ModelLoader initialized", device = self.device, gpu_available = torch.cuda.is_available())
# Ensure directories exist
ModelConfig.ensure_directories()
log_info("Model directories ensured",
model_dir = str(self.config.MODEL_DIR),
cache_dir = str(self.config.CACHE_DIR),
)
def _check_model_files_exist(self, local_path: Path) -> bool:
"""
Check if all required model files exist in local path
"""
if not local_path.exists():
return False
# Check for essential files that indicate a complete model
essential_files = ["config.json",
"pytorch_model.bin",
"model.safetensors",
"vocab.txt",
"tokenizer_config.json"
]
# At least config.json and one model file should exist
has_config = (local_path / "config.json").exists()
has_model_file = any((local_path / file).exists() for file in ["pytorch_model.bin", "model.safetensors"])
return has_config and has_model_file
def load_legal_bert(self) -> tuple:
"""
Load Legal-BERT model and tokenizer (nlpaueb/legal-bert-base-uncased)
"""
# Check if already loaded
if self.registry.is_loaded(ModelType.LEGAL_BERT):
info = self.registry.get(ModelType.LEGAL_BERT)
log_info("Legal-BERT already loaded from cache",
memory_mb = info.memory_size_mb,
access_count = info.access_count,
)
return info.model, info.tokenizer
# Mark as loading
self.registry.register(ModelType.LEGAL_BERT,
ModelInfo(name = "legal-bert",
type = ModelType.LEGAL_BERT,
status = ModelStatus.LOADING,
)
)
try:
config = self.config.LEGAL_BERT
local_path = config["local_path"]
force_download = config.get("force_download", False)
# Check if we should use local cache
if self._check_model_files_exist(local_path) and not force_download:
log_info(f"Loading Legal-BERT from local cache", path=str(local_path))
model = AutoModel.from_pretrained(pretrained_model_name_or_path = str(local_path))
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = str(local_path))
else:
log_info(f"Downloading Legal-BERT from HuggingFace", model_name = config["model_name"])
model = AutoModel.from_pretrained(pretrained_model_name_or_path = config["model_name"])
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = config["model_name"])
# Save to local cache
log_info(f"Saving Legal-BERT to local cache", path = str(local_path))
local_path.mkdir(parents = True, exist_ok = True)
model.save_pretrained(save_directory = str(local_path))
tokenizer.save_pretrained(save_directory = str(local_path))
# Move to device
model.to(self.device)
model.eval()
# Calculate memory size
memory_mb = sum(p.nelement() * p.element_size() for p in model.parameters()) / (1024 * 1024)
# Register as loaded
self.registry.register(ModelType.LEGAL_BERT,
ModelInfo(name = "legal-bert",
type = ModelType.LEGAL_BERT,
status = ModelStatus.LOADED,
model = model,
tokenizer = tokenizer,
memory_size_mb = memory_mb,
metadata = {"device" : self.device, "model_name" : config["model_name"]}
)
)
log_info("Legal-BERT loaded successfully",
memory_mb = round(memory_mb, 2),
device = self.device,
parameters = sum(p.numel() for p in model.parameters()),
)
return model, tokenizer
except Exception as e:
log_error(e, context = {"component": "ModelLoader", "operation": "load_legal_bert", "model_name": self.config.LEGAL_BERT["model_name"]})
self.registry.register(ModelType.LEGAL_BERT,
ModelInfo(name = "legal-bert",
type = ModelType.LEGAL_BERT,
status = ModelStatus.ERROR,
error_message = str(e),
)
)
raise
def load_classifier_model(self) -> tuple:
"""
Load contract classification model using Legal-BERT with classification head
"""
# Check if already loaded
if self.registry.is_loaded(ModelType.CLASSIFIER):
info = self.registry.get(ModelType.CLASSIFIER)
log_info("Classifier model already loaded from cache",
memory_mb = info.memory_size_mb,
access_count = info.access_count,
)
return info.model, info.tokenizer
# Mark as loading
self.registry.register(ModelType.CLASSIFIER,
ModelInfo(name = "classifier",
type = ModelType.CLASSIFIER,
status = ModelStatus.LOADING,
)
)
try:
config = self.config.CLASSIFIER_MODEL
log_info("Loading classifier model (Legal-BERT based)",
embedding_dim = config["embedding_dim"],
hidden_dim = config["hidden_dim"],
num_categories = config["num_categories"],
)
# Use the Legal-BERT model but prepare it for classification
base_model, tokenizer = self.load_legal_bert()
# Register as loaded (sharing the same Legal-BERT instance)
self.registry.register(ModelType.CLASSIFIER,
ModelInfo(name = "classifier",
type = ModelType.CLASSIFIER,
status = ModelStatus.LOADED,
model = base_model,
tokenizer = tokenizer,
memory_size_mb = 0.0,
metadata = {"device" : self.device,
"base_model" : "legal-bert",
"embedding_dim" : config["embedding_dim"],
"num_classes" : config["num_categories"],
"purpose" : "contract_type_classification",
}
)
)
log_info("Classifier model loaded successfully",
base_model = "legal-bert",
num_categories = config["num_categories"],
note = "Using Legal-BERT for both clause extraction and classification",
)
return base_model, tokenizer
except Exception as e:
log_error(e, context = {"component": "ModelLoader", "operation": "load_classifier_model"})
self.registry.register(ModelType.CLASSIFIER,
ModelInfo(name = "classifier",
type = ModelType.CLASSIFIER,
status = ModelStatus.ERROR,
error_message = str(e),
)
)
raise
def load_embedding_model(self) -> SentenceTransformer:
"""
Load sentence transformer for embeddings
"""
# Check if already loaded
if self.registry.is_loaded(ModelType.EMBEDDING):
info = self.registry.get(ModelType.EMBEDDING)
log_info("Embedding model already loaded from cache",
memory_mb = info.memory_size_mb,
access_count = info.access_count,
)
return info.model
# Mark as loading
self.registry.register(ModelType.EMBEDDING,
ModelInfo(name = "embedding",
type = ModelType.EMBEDDING,
status = ModelStatus.LOADING,
)
)
try:
config = self.config.EMBEDDING_MODEL
local_path = config["local_path"]
force_download = config.get("force_download", False)
# Check if we should use local cache
if local_path.exists() and not force_download:
log_info("Loading embedding model from local cache", path = str(local_path))
model = SentenceTransformer(model_name_or_path = str(local_path))
else:
log_info("Downloading embedding model from HuggingFace", model_name = config["model_name"])
model = SentenceTransformer(model_name_or_path = config["model_name"])
# Save to local cache
log_info("Saving embedding model to local cache", path = str(local_path))
local_path.mkdir(parents = True, exist_ok = True)
model.save(str(local_path))
# Move to device
if self.device == "cuda":
model = model.to(self.device)
# Estimate memory size
memory_mb = 100
# Register as loaded
self.registry.register(ModelType.EMBEDDING,
ModelInfo(name = "embedding",
type = ModelType.EMBEDDING,
status = ModelStatus.LOADED,
model = model,
memory_size_mb = memory_mb,
metadata = {"device": self.device, "model_name": config["model_name"], "dimension": config["dimension"]}
)
)
log_info("Embedding model loaded successfully",
memory_mb = memory_mb,
device = self.device,
dimension = config["dimension"],
)
return model
except Exception as e:
log_error(e, context = {"component": "ModelLoader", "operation": "load_embedding_model", "model_name": self.config.EMBEDDING_MODEL["model_name"]})
self.registry.register(ModelType.EMBEDDING,
ModelInfo(name = "embedding",
type = ModelType.EMBEDDING,
status = ModelStatus.ERROR,
error_message = str(e),
)
)
raise
def ensure_models_downloaded(self):
"""
Ensure all required models are downloaded before use
"""
log_info("Ensuring all models are downloaded...")
try:
# Download Legal-BERT if needed
if not self.registry.is_loaded(ModelType.LEGAL_BERT):
config = self.config.LEGAL_BERT
local_path = config["local_path"]
if not self._check_model_files_exist(local_path):
log_info("Pre-downloading Legal-BERT...")
model = AutoModel.from_pretrained(pretrained_model_name_or_path = config["model_name"])
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = config["model_name"])
local_path.mkdir(parents = True, exist_ok = True)
model.save_pretrained(save_directory = str(local_path))
tokenizer.save_pretrained(save_directory = str(local_path))
log_info("Legal-BERT pre-downloaded successfully")
# Download embedding model if needed
if not self.registry.is_loaded(ModelType.EMBEDDING):
config = self.config.EMBEDDING_MODEL
local_path = config["local_path"]
if not local_path.exists():
log_info("Pre-downloading embedding model...")
model = SentenceTransformer(model_name_or_path = config["model_name"])
local_path.mkdir(parents = True, exist_ok = True)
model.save(str(local_path))
log_info("Embedding model pre-downloaded successfully")
# Note: Classifier model is a stub, no download needed
log_info("Classifier model stub - no download required (uses Legal-BERT)")
log_info("All models are ready for use")
except Exception as e:
log_error(e, context={"component": "ModelLoader", "operation": "ensure_models_downloaded"})
raise
def get_registry_stats(self) -> dict:
"""
Get statistics about loaded models
"""
stats = self.registry.get_stats()
log_info("Retrieved registry statistics",
total_models = stats["total_models"],
loaded_models = stats["loaded_models"],
total_memory_mb = stats["total_memory_mb"],
)
return stats
def clear_cache(self):
"""
Clear all models from memory
"""
log_info("Clearing all models from cache")
self.registry.clear_all()
log_info("All models cleared from cache") |