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"""
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
}
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