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"""
Helion-OSC Sharded Model Loader
Efficiently loads 116 safetensors shards (2.8GB each)
"""
import torch
import json
import os
from pathlib import Path
from typing import Dict, Optional, List
import logging
from tqdm import tqdm
from safetensors.torch import load_file
from transformers import AutoConfig, AutoTokenizer
import psutil
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ShardedModelLoader:
"""
Loader for sharded safetensors model files
Optimized for 116 shards of 2.8GB each
"""
def __init__(self, model_path: str):
"""
Initialize the sharded model loader
Args:
model_path: Path to the inference directory containing shards
"""
self.model_path = Path(model_path)
self.config_path = self.model_path / "model_config.json"
self.index_path = self.model_path / "model.safetensors.index.json"
# Load configuration
logger.info(f"Loading configuration from {self.config_path}")
with open(self.config_path, 'r') as f:
self.config = json.load(f)
# Load weight index
logger.info(f"Loading weight index from {self.index_path}")
with open(self.index_path, 'r') as f:
self.index = json.load(f)
self.metadata = self.index.get("metadata", {})
self.weight_map = self.index.get("weight_map", {})
logger.info(f"Model: {self.metadata.get('model_type', 'unknown')}")
logger.info(f"Total shards: {self.metadata.get('total_shards', 0)}")
logger.info(f"Total size: {self.metadata.get('total_size', 0) / 1e9:.2f} GB")
logger.info(f"Total parameters: {self.config['architectures_info']['total_parameters']}")
logger.info(f"Active parameters: {self.config['architectures_info']['active_parameters']}")
def get_shard_path(self, shard_name: str) -> Path:
"""Get full path to a shard file"""
return self.model_path / shard_name
def get_available_memory(self) -> Dict[str, float]:
"""Get available system memory"""
memory = psutil.virtual_memory()
result = {
"ram_total_gb": memory.total / 1e9,
"ram_available_gb": memory.available / 1e9,
"ram_percent_used": memory.percent
}
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
gpu_mem = torch.cuda.get_device_properties(i).total_memory
gpu_allocated = torch.cuda.memory_allocated(i)
result[f"gpu_{i}_total_gb"] = gpu_mem / 1e9
result[f"gpu_{i}_available_gb"] = (gpu_mem - gpu_allocated) / 1e9
return result
def load_shard(self, shard_name: str, device: str = "cpu") -> Dict[str, torch.Tensor]:
"""
Load a single shard file
Args:
shard_name: Name of the shard file
device: Device to load tensors to
Returns:
Dictionary of weight tensors
"""
shard_path = self.get_shard_path(shard_name)
if not shard_path.exists():
raise FileNotFoundError(f"Shard not found: {shard_path}")
logger.debug(f"Loading shard: {shard_name}")
return load_file(str(shard_path), device=device)
def load_sharded_weights(
self,
device: str = "cpu",
low_memory: bool = False,
show_progress: bool = True
) -> Dict[str, torch.Tensor]:
"""
Load all sharded weights
Args:
device: Device to load weights to
low_memory: Use memory-efficient loading
show_progress: Show progress bar
Returns:
Dictionary of all model weights
"""
logger.info("Loading sharded model weights...")
# Check available memory
mem_info = self.get_available_memory()
logger.info(f"Available RAM: {mem_info['ram_available_gb']:.2f} GB")
if "gpu_0_available_gb" in mem_info:
logger.info(f"Available GPU 0: {mem_info['gpu_0_available_gb']:.2f} GB")
# Get unique shard files
shard_files = sorted(set(self.weight_map.values()))
total_shards = len(shard_files)
logger.info(f"Loading {total_shards} shard files...")
all_weights = {}
# Create progress bar
pbar = tqdm(shard_files, disable=not show_progress, desc="Loading shards")
for shard_name in pbar:
pbar.set_description(f"Loading {shard_name}")
# Load shard
shard_weights = self.load_shard(shard_name, device=device)
# Add to all weights
all_weights.update(shard_weights)
# Clear memory if low_memory mode
if low_memory:
del shard_weights
if torch.cuda.is_available():
torch.cuda.empty_cache()
logger.info(f"Loaded {len(all_weights)} weight tensors")
return all_weights
def get_layer_weights(self, layer_idx: int) -> List[str]:
"""
Get all weight keys for a specific layer
Args:
layer_idx: Layer index
Returns:
List of weight keys for that layer
"""
prefix = f"model.layers.{layer_idx}."
return [k for k in self.weight_map.keys() if k.startswith(prefix)]
def get_shard_for_weight(self, weight_key: str) -> Optional[str]:
"""
Get shard file name for a specific weight
Args:
weight_key: Weight key/name
Returns:
Shard file name or None
"""
return self.weight_map.get(weight_key)
def verify_shards(self) -> Dict[str, bool]:
"""
Verify all shard files exist
Returns:
Dictionary mapping shard names to existence status
"""
logger.info("Verifying shard files...")
shard_files = set(self.weight_map.values())
verification = {}
for shard_name in tqdm(sorted(shard_files), desc="Verifying"):
shard_path = self.get_shard_path(shard_name)
verification[shard_name] = shard_path.exists()
missing = [s for s, exists in verification.items() if not exists]
if missing:
logger.warning(f"Missing {len(missing)} shard files:")
for shard in missing[:10]: # Show first 10
logger.warning(f" - {shard}")
if len(missing) > 10:
logger.warning(f" ... and {len(missing) - 10} more")
else:
logger.info("✓ All shard files present")
return verification
def load_metadata(self) -> Dict:
"""Load model metadata"""
return {
"config": self.config,
"index": self.index,
"total_shards": self.metadata.get("total_shards", 0),
"total_size_gb": self.metadata.get("total_size", 0) / 1e9,
"architecture": self.config.get("architectures_info", {}),
"num_layers": self.config.get("num_hidden_layers", 0),
"hidden_size": self.config.get("hidden_size", 0),
"vocab_size": self.config.get("vocab_size", 0)
}
def load_full_model(
model_path: str,
device: str = "cuda" if torch.cuda.is_available() else "cpu",
low_memory: bool = False
):
"""
Convenience function to load the full model
Args:
model_path: Path to inference directory
device: Device to load model to
low_memory: Use low memory loading
Returns:
Loaded model weights and metadata
"""
loader = ShardedModelLoader(model_path)
# Verify shards first
verification = loader.verify_shards()
missing = sum(1 for exists in verification.values() if not exists)
if missing > 0:
raise FileNotFoundError(
f"Cannot load model: {missing} shard files are missing. "
f"Please download all 116 shard files."
)
# Load weights
weights = loader.load_sharded_weights(
device=device,
low_memory=low_memory,
show_progress=True
)
# Load metadata
metadata = loader.load_metadata()
return weights, metadata
def inspect_model(model_path: str):
"""
Inspect model structure without loading weights
Args:
model_path: Path to inference directory
"""
loader = ShardedModelLoader(model_path)
print("\n" + "="*80)
print("HELION-OSC MODEL INSPECTION")
print("="*80)
metadata = loader.load_metadata()
print(f"\nModel Type: {metadata['architecture'].get('model_description', 'N/A')}")
print(f"Architecture: {metadata['architecture'].get('architecture_type', 'N/A')}")
print(f"Total Parameters: {metadata['architecture'].get('total_parameters', 'N/A')}")
print(f"Active Parameters: {metadata['architecture'].get('active_parameters', 'N/A')}")
print(f"\nModel Configuration:")
print(f" Layers: {metadata['num_layers']}")
print(f" Hidden Size: {metadata['hidden_size']}")
print(f" Vocabulary Size: {metadata['vocab_size']}")
print(f" Attention Heads: {metadata['config'].get('num_attention_heads', 'N/A')}")
print(f" KV Heads: {metadata['config'].get('num_key_value_heads', 'N/A')}")
print(f"\nMoE Configuration:")
arch = metadata['architecture']
print(f" Number of Experts: {arch.get('num_experts', 'N/A')}")
print(f" Experts per Token: {arch.get('experts_per_token', 'N/A')}")
print(f" Shared Experts: {arch.get('num_shared_experts', 'N/A')}")
print(f"\nStorage Information:")
print(f" Total Shards: {metadata['total_shards']}")
print(f" Total Size: {metadata['total_size_gb']:.2f} GB")
print(f" Shard Size: ~2.8 GB each")
print(f" Format: safetensors")
print(f" Precision: bfloat16")
print(f"\nContext Length:")
print(f" Max Position Embeddings: {metadata['config'].get('max_position_embeddings', 'N/A')}")
print(f" RoPE Theta: {metadata['config'].get('rope_theta', 'N/A')}")
print("\n" + "="*80)
# Verify shards
print("\nVerifying shard files...")
verification = loader.verify_shards()
present = sum(1 for exists in verification.values() if exists)
total = len(verification)
print(f"\nShard Status: {present}/{total} files present")
if present == total:
print("✓ All shard files are available")
else:
print(f"✗ Missing {total - present} shard files")
def main():
"""Main CLI interface"""
import argparse
parser = argparse.ArgumentParser(description="Helion-OSC Sharded Model Loader")
parser.add_argument(
"model_path",
type=str,
help="Path to inference directory"
)
parser.add_argument(
"--action",
choices=["inspect", "verify", "load"],
default="inspect",
help="Action to perform"
)
parser.add_argument(
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device to load model to"
)
parser.add_argument(
"--low-memory",
action="store_true",
help="Use low memory mode"
)
args = parser.parse_args()
if args.action == "inspect":
inspect_model(args.model_path)
elif args.action == "verify":
loader = ShardedModelLoader(args.model_path)
loader.verify_shards()
elif args.action == "load":
logger.info("Loading full model...")
weights, metadata = load_full_model(
args.model_path,
device=args.device,
low_memory=args.low_memory
)
logger.info(f"Successfully loaded {len(weights)} weight tensors")
logger.info(f"Model ready on {args.device}")
if __name__ == "__main__":
main() |