""" Advanced Chunk Loader for large models with memory constraints Optimized for CPU-only training on 16GB RAM systems """ import os import gc import mmap import logging import asyncio from typing import Dict, Any, List, Optional, Iterator, Union from pathlib import Path import torch import torch.nn as nn from transformers import AutoModel, AutoConfig, AutoTokenizer from safetensors import safe_open import numpy as np from .memory_manager import AdvancedMemoryManager logger = logging.getLogger(__name__) class ModelChunk: """Represents a chunk of a large model""" def __init__(self, chunk_id: str, parameters: Dict[str, torch.Tensor], metadata: Dict[str, Any]): self.chunk_id = chunk_id self.parameters = parameters self.metadata = metadata self.is_loaded = True self.memory_size_mb = sum(p.numel() * p.element_size() for p in parameters.values()) / 1024**2 def unload(self): """Unload chunk from memory""" if self.is_loaded: del self.parameters self.parameters = {} self.is_loaded = False gc.collect() logger.debug(f"Unloaded chunk {self.chunk_id}") def __del__(self): if hasattr(self, 'is_loaded') and self.is_loaded: self.unload() class AdvancedChunkLoader: """ Advanced chunk loader for handling large models with memory constraints """ def __init__(self, memory_manager: AdvancedMemoryManager, chunk_size_mb: float = 500.0): """ Initialize chunk loader Args: memory_manager: Memory manager instance chunk_size_mb: Target size for each chunk in MB """ self.memory_manager = memory_manager self.chunk_size_mb = chunk_size_mb self.chunk_size_bytes = chunk_size_mb * 1024**2 self.loaded_chunks = {} self.chunk_cache = {} self.max_cached_chunks = 3 # Register cleanup callback self.memory_manager.register_cleanup_callback(self._cleanup_chunks) logger.info(f"Chunk loader initialized with {chunk_size_mb}MB chunks") async def load_model_in_chunks(self, model_path: str, **kwargs) -> Dict[str, Any]: """ Load a large model in chunks Args: model_path: Path to model (local or HF repo) **kwargs: Additional loading parameters Returns: Model metadata and chunk information """ with self.memory_manager.memory_context("load_model_in_chunks"): logger.info(f"Loading model in chunks: {model_path}") # First, get model config and size estimation config = await self._load_model_config(model_path, **kwargs) estimated_size_mb = self._estimate_model_size(config) logger.info(f"Estimated model size: {estimated_size_mb:.1f}MB") if estimated_size_mb <= self.chunk_size_mb * 2: # Small model, load normally return await self._load_small_model(model_path, config, **kwargs) else: # Large model, use chunking return await self._load_large_model_chunked(model_path, config, **kwargs) async def _load_model_config(self, model_path: str, **kwargs) -> AutoConfig: """Load model configuration""" try: hf_token = kwargs.get('token') or os.getenv('HF_TOKEN') trust_remote_code = kwargs.get('trust_remote_code', False) config = AutoConfig.from_pretrained( model_path, trust_remote_code=trust_remote_code, token=hf_token, timeout=30 ) return config except Exception as e: logger.error(f"Failed to load config for {model_path}: {e}") raise def _estimate_model_size(self, config: AutoConfig) -> float: """Estimate model size in MB""" try: # Get basic parameters hidden_size = getattr(config, 'hidden_size', 768) num_layers = getattr(config, 'num_hidden_layers', getattr(config, 'num_layers', 12)) vocab_size = getattr(config, 'vocab_size', 50000) # Rough estimation for transformer models embedding_params = vocab_size * hidden_size layer_params = num_layers * (hidden_size * hidden_size * 4) # Simplified total_params = embedding_params + layer_params # Convert to MB (4 bytes per parameter for float32) size_mb = (total_params * 4) / (1024 ** 2) return max(size_mb, 100) # Minimum 100MB except Exception: return 2000 # Default 2GB if estimation fails async def _load_small_model(self, model_path: str, config: AutoConfig, **kwargs) -> Dict[str, Any]: """Load small model normally""" logger.info(f"Loading small model normally: {model_path}") hf_token = kwargs.get('token') or os.getenv('HF_TOKEN') trust_remote_code = kwargs.get('trust_remote_code', False) try: # Load model with CPU optimization model = AutoModel.from_pretrained( model_path, config=config, torch_dtype=torch.float32, trust_remote_code=trust_remote_code, token=hf_token, low_cpu_mem_usage=True, device_map='cpu' ) # Load tokenizer/processor tokenizer = None try: tokenizer = AutoTokenizer.from_pretrained( model_path, token=hf_token, trust_remote_code=trust_remote_code ) except: logger.warning(f"Could not load tokenizer for {model_path}") return { 'model': model, 'tokenizer': tokenizer, 'config': config, 'is_chunked': False, 'source': model_path, 'estimated_size_mb': self._estimate_model_size(config) } except Exception as e: logger.error(f"Failed to load small model {model_path}: {e}") raise async def _load_large_model_chunked(self, model_path: str, config: AutoConfig, **kwargs) -> Dict[str, Any]: """Load large model using chunking strategy""" logger.info(f"Loading large model with chunking: {model_path}") # Create chunks metadata chunks_info = await self._create_chunks_metadata(model_path, config, **kwargs) # Load first chunk to get model structure first_chunk = await self._load_chunk(model_path, chunks_info[0], **kwargs) return { 'model': None, # No single model object for chunked models 'chunks_info': chunks_info, 'first_chunk': first_chunk, 'config': config, 'is_chunked': True, 'source': model_path, 'total_chunks': len(chunks_info), 'estimated_size_mb': self._estimate_model_size(config) } async def _create_chunks_metadata(self, model_path: str, config: AutoConfig, **kwargs) -> List[Dict[str, Any]]: """Create metadata for model chunks""" # This is a simplified chunking strategy # In practice, you'd analyze the model structure more carefully estimated_size_mb = self._estimate_model_size(config) num_chunks = max(1, int(estimated_size_mb / self.chunk_size_mb)) chunks_info = [] for i in range(num_chunks): chunk_info = { 'chunk_id': f"chunk_{i}", 'start_layer': i * (config.num_hidden_layers // num_chunks), 'end_layer': min((i + 1) * (config.num_hidden_layers // num_chunks), config.num_hidden_layers), 'estimated_size_mb': estimated_size_mb / num_chunks, 'parameters': [] # Will be populated during loading } chunks_info.append(chunk_info) return chunks_info async def _load_chunk(self, model_path: str, chunk_info: Dict[str, Any], **kwargs) -> ModelChunk: """Load a specific chunk of the model""" chunk_id = chunk_info['chunk_id'] with self.memory_manager.memory_context(f"load_chunk_{chunk_id}"): logger.debug(f"Loading chunk {chunk_id}") # For now, this is a placeholder implementation # In practice, you'd implement layer-wise loading parameters = {} # Create dummy parameters for demonstration # Replace with actual chunk loading logic hidden_size = getattr(kwargs.get('config', {}), 'hidden_size', 768) chunk_params = torch.randn(hidden_size, hidden_size) * 0.02 parameters[f'{chunk_id}_weight'] = chunk_params metadata = { 'chunk_id': chunk_id, 'layer_range': (chunk_info['start_layer'], chunk_info['end_layer']), 'parameter_count': sum(p.numel() for p in parameters.values()) } chunk = ModelChunk(chunk_id, parameters, metadata) self.loaded_chunks[chunk_id] = chunk # Manage cache await self._manage_chunk_cache() return chunk async def _manage_chunk_cache(self): """Manage chunk cache to prevent memory overflow""" if len(self.loaded_chunks) > self.max_cached_chunks: # Remove oldest chunks chunks_to_remove = list(self.loaded_chunks.keys())[:-self.max_cached_chunks] for chunk_id in chunks_to_remove: chunk = self.loaded_chunks.pop(chunk_id) chunk.unload() logger.debug(f"Removed chunk {chunk_id} from cache") def _cleanup_chunks(self): """Cleanup callback for memory manager""" logger.info("Cleaning up loaded chunks") for chunk in self.loaded_chunks.values(): chunk.unload() self.loaded_chunks.clear() gc.collect() async def get_chunk_iterator(self, model_info: Dict[str, Any]) -> Iterator[ModelChunk]: """Get iterator for model chunks""" if not model_info.get('is_chunked', False): # Not a chunked model yield model_info['model'] return chunks_info = model_info['chunks_info'] model_path = model_info['source'] for chunk_info in chunks_info: chunk = await self._load_chunk(model_path, chunk_info) yield chunk # Optionally unload chunk after yielding # chunk.unload() def get_memory_usage(self) -> Dict[str, float]: """Get current memory usage of loaded chunks""" total_memory_mb = sum(chunk.memory_size_mb for chunk in self.loaded_chunks.values()) return { 'total_chunks_memory_mb': total_memory_mb, 'loaded_chunks_count': len(self.loaded_chunks), 'average_chunk_size_mb': total_memory_mb / len(self.loaded_chunks) if self.loaded_chunks else 0 }