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#!/usr/bin/env python3
"""
Enhanced checkpointing system for BitTransformerLM with multiple training runs support.
Optimized for Claude Code environment with HF Pro + 20GB persistent storage.
"""
import os
import json
import shutil
import logging
from pathlib import Path
from typing import Dict, Any, Optional, List, Union
from datetime import datetime
import torch
from huggingface_hub import HfApi, hf_hub_download
from bit_transformer.error_handling import with_error_recovery, safe_operation
from bit_transformer.types import PathLike, ModelConfig, TrainingConfig
logger = logging.getLogger(__name__)
class EnhancedCheckpointManager:
"""Advanced checkpoint management for multiple training runs with HF integration."""
def __init__(self,
base_dir: PathLike = "/data/checkpoints",
hf_repo_id: str = "WCNegentropy/BitTransformerLM",
hf_token: Optional[str] = None,
max_local_checkpoints: int = 5):
self.base_dir = Path(base_dir)
self.base_dir.mkdir(parents=True, exist_ok=True)
self.hf_repo_id = hf_repo_id
self.hf_token = hf_token or os.getenv("HF_TOKEN")
self.api = HfApi(token=self.hf_token) if self.hf_token else None
self.max_local_checkpoints = max_local_checkpoints
# Training session tracking
self.sessions_dir = self.base_dir / "training_sessions"
self.sessions_dir.mkdir(exist_ok=True)
# Best models storage
self.best_models_dir = self.base_dir / "best_models"
self.best_models_dir.mkdir(exist_ok=True)
def create_training_session(self,
session_name: str,
model_config: ModelConfig,
training_config: TrainingConfig) -> str:
"""Create a new training session with metadata."""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
session_id = f"{session_name}_{timestamp}"
session_dir = self.sessions_dir / session_id
session_dir.mkdir(exist_ok=True)
# Save session metadata
metadata = {
"session_id": session_id,
"session_name": session_name,
"created_at": timestamp,
"model_config": model_config,
"training_config": training_config,
"checkpoints": [],
"best_metric": None,
"status": "active"
}
with open(session_dir / "metadata.json", "w") as f:
json.dump(metadata, f, indent=2, default=str)
logger.info(f"Created training session: {session_id}")
return session_id
@with_error_recovery(recovery_value=False)
def save_checkpoint(self,
model: torch.nn.Module,
session_id: str,
epoch: int,
metrics: Dict[str, float],
optimizer_state: Optional[Dict] = None,
scheduler_state: Optional[Dict] = None,
additional_data: Optional[Dict] = None) -> bool:
"""Save checkpoint with comprehensive metadata."""
session_dir = self.sessions_dir / session_id
if not session_dir.exists():
raise ValueError(f"Training session {session_id} not found")
# Create checkpoint directory
checkpoint_name = f"checkpoint_epoch_{epoch:04d}"
checkpoint_dir = session_dir / checkpoint_name
checkpoint_dir.mkdir(exist_ok=True)
# Save model state
model_path = checkpoint_dir / "model.pt"
torch.save({
'model_state_dict': model.state_dict(),
'epoch': epoch,
'metrics': metrics,
'model_config': getattr(model, 'config', {}),
'timestamp': datetime.now().isoformat()
}, model_path)
# Save optimizer state if provided
if optimizer_state:
torch.save(optimizer_state, checkpoint_dir / "optimizer.pt")
# Save scheduler state if provided
if scheduler_state:
torch.save(scheduler_state, checkpoint_dir / "scheduler.pt")
# Save additional data
if additional_data:
with open(checkpoint_dir / "additional_data.json", "w") as f:
json.dump(additional_data, f, indent=2, default=str)
# Update session metadata
self._update_session_metadata(session_id, checkpoint_name, metrics)
# Cleanup old checkpoints to save space
self._cleanup_old_checkpoints(session_dir)
logger.info(f"Saved checkpoint {checkpoint_name} for session {session_id}")
return True
def load_checkpoint(self,
session_id: str,
checkpoint_name: Optional[str] = None,
model: Optional[torch.nn.Module] = None) -> Dict[str, Any]:
"""Load checkpoint with all associated data."""
session_dir = self.sessions_dir / session_id
if not session_dir.exists():
raise ValueError(f"Training session {session_id} not found")
# Use latest checkpoint if none specified
if checkpoint_name is None:
checkpoints = [d for d in session_dir.iterdir()
if d.is_dir() and d.name.startswith("checkpoint_")]
if not checkpoints:
raise ValueError(f"No checkpoints found for session {session_id}")
checkpoint_name = max(checkpoints, key=lambda x: x.name).name
checkpoint_dir = session_dir / checkpoint_name
if not checkpoint_dir.exists():
raise ValueError(f"Checkpoint {checkpoint_name} not found in session {session_id}")
# Load model state
model_path = checkpoint_dir / "model.pt"
checkpoint_data = torch.load(model_path, map_location='cpu', weights_only=False)
if model is not None:
model.load_state_dict(checkpoint_data['model_state_dict'])
# Load optimizer state if exists
optimizer_state = None
optimizer_path = checkpoint_dir / "optimizer.pt"
if optimizer_path.exists():
optimizer_state = torch.load(optimizer_path, map_location='cpu', weights_only=False)
# Load scheduler state if exists
scheduler_state = None
scheduler_path = checkpoint_dir / "scheduler.pt"
if scheduler_path.exists():
scheduler_state = torch.load(scheduler_path, map_location='cpu', weights_only=False)
# Load additional data if exists
additional_data = {}
additional_path = checkpoint_dir / "additional_data.json"
if additional_path.exists():
with open(additional_path) as f:
additional_data = json.load(f)
return {
'model_data': checkpoint_data,
'optimizer_state': optimizer_state,
'scheduler_state': scheduler_state,
'additional_data': additional_data,
'checkpoint_path': str(checkpoint_dir)
}
def save_best_model(self,
session_id: str,
model: torch.nn.Module,
metric_name: str,
metric_value: float,
is_better_func: callable = lambda x, y: x > y) -> bool:
"""Save model if it achieves best performance."""
best_model_path = self.best_models_dir / f"{session_id}_best.pt"
best_meta_path = self.best_models_dir / f"{session_id}_best_meta.json"
# Check if this is the best model so far
current_best = None
if best_meta_path.exists():
with open(best_meta_path) as f:
current_best = json.load(f)
if current_best is None or is_better_func(metric_value, current_best['metric_value']):
# Save new best model
torch.save({
'model_state_dict': model.state_dict(),
'metric_name': metric_name,
'metric_value': metric_value,
'session_id': session_id,
'timestamp': datetime.now().isoformat()
}, best_model_path)
# Save metadata
with open(best_meta_path, "w") as f:
json.dump({
'metric_name': metric_name,
'metric_value': metric_value,
'session_id': session_id,
'timestamp': datetime.now().isoformat()
}, f, indent=2)
logger.info(f"New best model saved for session {session_id}: {metric_name}={metric_value}")
return True
return False
def push_to_hf(self,
session_id: str,
checkpoint_name: Optional[str] = None,
include_optimizer: bool = False) -> bool:
"""Push checkpoint to HuggingFace Hub."""
if not self.api:
logger.error("HuggingFace API not available - check token")
return False
try:
checkpoint_data = self.load_checkpoint(session_id, checkpoint_name)
checkpoint_dir = Path(checkpoint_data['checkpoint_path'])
# Upload model weights
self.api.upload_file(
path_or_fileobj=str(checkpoint_dir / "model.pt"),
path_in_repo=f"checkpoints/{session_id}/model.pt",
repo_id=self.hf_repo_id,
commit_message=f"Upload checkpoint {checkpoint_name or 'latest'} from session {session_id}"
)
# Upload optimizer state if requested and exists
if include_optimizer and (checkpoint_dir / "optimizer.pt").exists():
self.api.upload_file(
path_or_fileobj=str(checkpoint_dir / "optimizer.pt"),
path_in_repo=f"checkpoints/{session_id}/optimizer.pt",
repo_id=self.hf_repo_id
)
logger.info(f"Successfully pushed checkpoint to HuggingFace: {self.hf_repo_id}")
return True
except Exception as e:
logger.error(f"Failed to push to HuggingFace: {e}")
return False
def pull_from_hf(self,
session_id: str,
local_session_id: Optional[str] = None) -> bool:
"""Pull checkpoint from HuggingFace Hub."""
if not self.api:
logger.error("HuggingFace API not available - check token")
return False
try:
local_session = local_session_id or session_id
local_dir = self.sessions_dir / local_session / "checkpoint_from_hf"
local_dir.mkdir(parents=True, exist_ok=True)
# Download model weights
model_file = hf_hub_download(
repo_id=self.hf_repo_id,
filename=f"checkpoints/{session_id}/model.pt",
local_dir=str(local_dir),
local_dir_use_symlinks=False
)
logger.info(f"Successfully pulled checkpoint from HuggingFace to {local_dir}")
return True
except Exception as e:
logger.error(f"Failed to pull from HuggingFace: {e}")
return False
def get_storage_usage(self) -> Dict[str, Any]:
"""Get detailed storage usage breakdown."""
def get_dir_size(path: Path) -> int:
total = 0
for item in path.rglob('*'):
if item.is_file():
total += item.stat().st_size
return total
usage = {
'total_gb': get_dir_size(self.base_dir) / 1e9,
'sessions_gb': get_dir_size(self.sessions_dir) / 1e9,
'best_models_gb': get_dir_size(self.best_models_dir) / 1e9,
'num_sessions': len(list(self.sessions_dir.iterdir())),
'num_best_models': len(list(self.best_models_dir.glob('*_best.pt'))),
}
# Get per-session breakdown
sessions = []
for session_dir in self.sessions_dir.iterdir():
if session_dir.is_dir():
sessions.append({
'session_id': session_dir.name,
'size_gb': get_dir_size(session_dir) / 1e9,
'num_checkpoints': len(list(session_dir.glob('checkpoint_*')))
})
usage['sessions'] = sorted(sessions, key=lambda x: x['size_gb'], reverse=True)
return usage
def _update_session_metadata(self, session_id: str, checkpoint_name: str, metrics: Dict[str, float]):
"""Update session metadata with new checkpoint info."""
metadata_path = self.sessions_dir / session_id / "metadata.json"
with open(metadata_path) as f:
metadata = json.load(f)
metadata['checkpoints'].append({
'name': checkpoint_name,
'metrics': metrics,
'timestamp': datetime.now().isoformat()
})
# Update best metric if applicable
if 'loss' in metrics:
if metadata['best_metric'] is None or metrics['loss'] < metadata['best_metric'].get('loss', float('inf')):
metadata['best_metric'] = metrics.copy()
with open(metadata_path, "w") as f:
json.dump(metadata, f, indent=2, default=str)
def _cleanup_old_checkpoints(self, session_dir: Path):
"""Remove oldest checkpoints to stay within limits."""
checkpoints = sorted([d for d in session_dir.iterdir()
if d.is_dir() and d.name.startswith("checkpoint_")],
key=lambda x: x.stat().st_mtime)
while len(checkpoints) > self.max_local_checkpoints:
old_checkpoint = checkpoints.pop(0)
shutil.rmtree(old_checkpoint)
logger.info(f"Cleaned up old checkpoint: {old_checkpoint.name}")
# Convenience functions for easy usage
def create_checkpoint_manager(hf_token: str = "os.environ.get('HF_TOKEN', 'your-token-here')") -> EnhancedCheckpointManager:
"""Create a pre-configured checkpoint manager for this environment."""
return EnhancedCheckpointManager(
base_dir="/data/checkpoints",
hf_repo_id="WCNegentropy/BitTransformerLM",
hf_token=hf_token,
max_local_checkpoints=3 # Conservative for 20GB storage
)
if __name__ == "__main__":
# Demo usage
manager = create_checkpoint_manager()
usage = manager.get_storage_usage()
print(f"Current storage usage: {usage['total_gb']:.2f} GB")
print(f"Number of training sessions: {usage['num_sessions']}") |