src/monitoring.py

"""
Trackio Monitoring Integration for SmolLM3 Fine-tuning
Provides comprehensive experiment tracking and monitoring capabilities with HF Datasets support
"""

import os
import json
import logging
from typing import Dict, Any, Optional, List
from datetime import datetime
import torch
from pathlib import Path

# Import the real API client
try:
    from scripts.trackio_tonic.trackio_api_client import TrackioAPIClient
    TRACKIO_AVAILABLE = True
except ImportError:
    TRACKIO_AVAILABLE = False
    print("Warning: Trackio API client not available. Install with: pip install requests")

# Check if there's a conflicting trackio package installed
try:
    import trackio
    print(f"Warning: Found installed trackio package at {trackio.__file__}")
    print("This may conflict with our custom TrackioAPIClient. Using custom implementation only.")
except ImportError:
    pass  # No conflicting package found

logger = logging.getLogger(__name__)

class SmolLM3Monitor:
    """Monitoring and tracking for SmolLM3 fine-tuning experiments with HF Datasets support"""
    
    def __init__(
        self,
        experiment_name: str,
        trackio_url: Optional[str] = None,
        trackio_token: Optional[str] = None,
        enable_tracking: bool = True,
        log_artifacts: bool = True,
        log_metrics: bool = True,
        log_config: bool = True,
        hf_token: Optional[str] = None,
        dataset_repo: Optional[str] = None
    ):
        self.experiment_name = experiment_name
        self.enable_tracking = enable_tracking and TRACKIO_AVAILABLE
        self.log_artifacts = log_artifacts
        self.log_metrics_enabled = log_metrics  # Rename to avoid conflict
        self.log_config_enabled = log_config  # Rename to avoid conflict
        
        # HF Datasets configuration
        self.hf_token = hf_token or os.environ.get('HF_TOKEN')
        self.dataset_repo = dataset_repo or os.environ.get('TRACKIO_DATASET_REPO', 'tonic/trackio-experiments')
        
        # Ensure dataset repository is properly set
        if not self.dataset_repo or self.dataset_repo.strip() == '':
            logger.warning("⚠️ Dataset repository not set, using default")
            self.dataset_repo = 'tonic/trackio-experiments'
        
        # Initialize experiment metadata first
        self.experiment_id = None
        self.start_time = datetime.now()
        self.metrics_history = []
        self.artifacts = []
        
        # Initialize Trackio API client
        self.trackio_client = None
        if self.enable_tracking:
            self._setup_trackio(trackio_url, trackio_token)
        
        # Initialize HF Datasets client
        self.hf_dataset_client = None
        if self.hf_token:
            self._setup_hf_datasets()
        
        logger.info("Initialized monitoring for experiment: %s", experiment_name)
        logger.info("Dataset repository: %s", self.dataset_repo)
        
        # Create experiment in Trackio if tracking is enabled
        if self.enable_tracking and self.trackio_client:
            self._create_experiment()
    
    def _setup_hf_datasets(self):
        """Setup HF Datasets client for persistent storage"""
        try:
            from datasets import Dataset
            from huggingface_hub import HfApi
            
            self.hf_dataset_client = {
                'Dataset': Dataset,
                'HfApi': HfApi,
                'api': HfApi(token=self.hf_token)
            }
            logger.info("✅ HF Datasets client initialized for %s", self.dataset_repo)
            
        except ImportError:
            logger.warning("⚠️ datasets or huggingface-hub not available. Install with: pip install datasets huggingface-hub")
            self.hf_dataset_client = None
        except Exception as e:
            logger.error("Failed to initialize HF Datasets client: %s", e)
            self.hf_dataset_client = None
    
    def _setup_trackio(self, trackio_url: Optional[str], trackio_token: Optional[str]):
        """Setup Trackio API client"""
        try:
            # Get Trackio configuration from environment or parameters
            space_id = trackio_url or os.getenv('TRACKIO_SPACE_ID')
            
            if not space_id:
                # Use the deployed Trackio Space ID
                space_id = "Tonic/trackio-monitoring-20250727"
                logger.info(f"Using default Trackio Space ID: {space_id}")
            
            # Get HF token for Space resolution
            hf_token = self.hf_token or trackio_token or os.getenv('HF_TOKEN')
            
            self.trackio_client = TrackioAPIClient(space_id, hf_token)
            
            # Test connection to Trackio Space
            try:
                # Test connection first
                connection_test = self.trackio_client.test_connection()
                if connection_test.get('error'):
                    logger.warning(f"Trackio Space not accessible: {connection_test['error']}")
                    logger.info("Continuing with HF Datasets only")
                    self.enable_tracking = False
                    return
                logger.info("✅ Trackio Space connection successful")
                
            except Exception as e:
                logger.warning(f"Trackio Space not accessible: {e}")
                logger.info("Continuing with HF Datasets only")
                self.enable_tracking = False
                return
                
        except Exception as e:
            logger.error(f"Failed to setup Trackio: {e}")
            self.enable_tracking = False
    
    def _create_experiment(self):
        """Create experiment in Trackio and set experiment_id"""
        try:
            if not self.trackio_client:
                logger.warning("Trackio client not available, skipping experiment creation")
                return
            
            # Create experiment with timestamp to ensure uniqueness
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            experiment_name = f"{self.experiment_name}_{timestamp}"
            
            result = self.trackio_client.create_experiment(
                name=experiment_name,
                description=f"SmolLM3 fine-tuning experiment: {self.experiment_name}"
            )
            
            if result.get('success'):
                # Extract experiment ID from the response
                response_data = result.get('data', '')
                if 'ID: ' in response_data:
                    # Extract ID from response like "✅ Experiment created successfully!\nID: exp_20250727_151252\nName: test_experiment_api_fix\nStatus: running"
                    lines = response_data.split('\n')
                    for line in lines:
                        if line.startswith('ID: '):
                            self.experiment_id = line.replace('ID: ', '').strip()
                            break
                
                if not self.experiment_id:
                    # Fallback: generate experiment ID
                    self.experiment_id = f"exp_{timestamp}"
                
                logger.info(f"✅ Experiment created successfully: {self.experiment_id}")
            else:
                logger.warning(f"Failed to create experiment: {result}")
                # Fallback: generate experiment ID
                timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
                self.experiment_id = f"exp_{timestamp}"
                
        except Exception as e:
            logger.error(f"Failed to create experiment: {e}")
            # Fallback: generate experiment ID
            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
            self.experiment_id = f"exp_{timestamp}"
    
    def _save_to_hf_dataset(self, experiment_data: Dict[str, Any]):
        """Save experiment data to HF Dataset"""
        if not self.hf_dataset_client or not self.dataset_repo:
            logger.warning("⚠️ HF Datasets not available or dataset repo not set")
            return False
        
        try:
            # Ensure dataset repository is not empty
            if not self.dataset_repo or self.dataset_repo.strip() == '':
                logger.error("❌ Dataset repository is empty")
                return False
            
            # Validate dataset repository format
            if '/' not in self.dataset_repo:
                logger.error(f"❌ Invalid dataset repository format: {self.dataset_repo}")
                return False
            
            Dataset = self.hf_dataset_client['Dataset']
            api = self.hf_dataset_client['api']
            
            # Create dataset from experiment data with correct structure
            # Match the structure used in setup_hf_dataset.py
            dataset_data = [{
                'experiment_id': self.experiment_id or f"exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
                'name': self.experiment_name,
                'description': "SmolLM3 fine-tuning experiment",
                'created_at': self.start_time.isoformat(),
                'status': 'running',
                'metrics': json.dumps(self.metrics_history),
                'parameters': json.dumps(experiment_data),
                'artifacts': json.dumps(self.artifacts),
                'logs': json.dumps([]),
                'last_updated': datetime.now().isoformat()
            }]
            
            # Create dataset from the experiment data
            dataset = Dataset.from_list(dataset_data)
            
            # Push to hub
            dataset.push_to_hub(
                self.dataset_repo,
                token=self.hf_token,
                private=True
            )
            
            logger.info(f"✅ Experiment data saved to HF Dataset: {self.dataset_repo}")
            return True
            
        except Exception as e:
            logger.error(f"Failed to save to HF Dataset: {e}")
            return False
    
    def log_configuration(self, config: Dict[str, Any]):
        """Log experiment configuration"""
        if not self.enable_tracking or not self.log_config_enabled:
            return
        
        try:
            # Log configuration as parameters
            if self.trackio_client:
                try:
                    result = self.trackio_client.log_parameters(
                        experiment_id=self.experiment_id,
                        parameters=config
                    )
                    
                    if "success" in result:
                        logger.info("Configuration logged to Trackio")
                    else:
                        logger.warning("Failed to log configuration to Trackio: %s", result)
                except Exception as e:
                    logger.warning("Trackio configuration logging failed: %s", e)
            
            # Save to HF Dataset
            self._save_to_hf_dataset(config)
            
            # Also save config locally
            config_path = "config_{}_{}.json".format(
                self.experiment_name, 
                self.start_time.strftime('%Y%m%d_%H%M%S')
            )
            with open(config_path, 'w') as f:
                json.dump(config, f, indent=2, default=str)
            
            self.artifacts.append(config_path)
            logger.info("Configuration saved to %s", config_path)
            
        except Exception as e:
            logger.error("Failed to log configuration: %s", e)
    
    def log_config(self, config: Dict[str, Any]):
        """Alias for log_configuration for backward compatibility"""
        return self.log_configuration(config)
    
    def log_metrics(self, metrics: Dict[str, Any], step: Optional[int] = None):
        """
        Log training metrics. Supports advanced metrics such as:
        - total_tokens, truncated_tokens, padding_tokens
        - throughput, step_time, batch_size, seq_len
        - token_acc, train/gate_ortho, train/center, etc.
        """
        if not self.enable_tracking or not self.log_metrics_enabled:
            return
        
        try:
            # Add timestamp
            metrics['timestamp'] = datetime.now().isoformat()
            if step is not None:
                metrics['step'] = step
            
            # Log to Trackio (if available)
            if self.trackio_client:
                try:
                    result = self.trackio_client.log_metrics(
                        experiment_id=self.experiment_id,
                        metrics=metrics,
                        step=step
                    )
                    
                    if "success" in result:
                        logger.debug("Metrics logged to Trackio")
                    else:
                        logger.warning("Failed to log metrics to Trackio: %s", result)
                except Exception as e:
                    logger.warning("Trackio logging failed: %s", e)
            
            # Store locally
            self.metrics_history.append(metrics)
            
            # Save to HF Dataset periodically
            if len(self.metrics_history) % 10 == 0:  # Save every 10 metrics
                self._save_to_hf_dataset({'metrics': self.metrics_history})
            
            logger.debug("Metrics logged: %s", metrics)
            
        except Exception as e:
            logger.error("Failed to log metrics: %s", e)
    
    def log_model_checkpoint(self, checkpoint_path: str, step: Optional[int] = None):
        """Log model checkpoint"""
        if not self.enable_tracking or not self.log_artifacts:
            return
        
        try:
            # For now, just log the checkpoint path as a parameter
            # The actual file upload would need additional API endpoints
            checkpoint_info = {
                "checkpoint_path": checkpoint_path,
                "checkpoint_step": step,
                "checkpoint_size": os.path.getsize(checkpoint_path) if os.path.exists(checkpoint_path) else 0
            }
            
            if self.trackio_client:
                result = self.trackio_client.log_parameters(
                    experiment_id=self.experiment_id,
                    parameters=checkpoint_info
                )
                
                if "success" in result:
                    logger.info("Checkpoint logged to Trackio")
                else:
                    logger.error("Failed to log checkpoint to Trackio: %s", result)
            
            self.artifacts.append(checkpoint_path)
            logger.info("Checkpoint logged: %s", checkpoint_path)
            
        except Exception as e:
            logger.error("Failed to log checkpoint: %s", e)
    
    def log_evaluation_results(self, results: Dict[str, Any], step: Optional[int] = None):
        """Log evaluation results"""
        if not self.enable_tracking:
            return
        
        try:
            # Add evaluation prefix to metrics
            eval_metrics = {f"eval_{k}": v for k, v in results.items()}
            
            self.log_metrics(eval_metrics, step)
            
            # Save evaluation results locally
            eval_path = "eval_results_step_{}_{}.json".format(
                step or "unknown",
                self.start_time.strftime('%Y%m%d_%H%M%S')
            )
            with open(eval_path, 'w') as f:
                json.dump(results, f, indent=2, default=str)
            
            self.artifacts.append(eval_path)
            logger.info("Evaluation results logged and saved to %s", eval_path)
            
        except Exception as e:
            logger.error("Failed to log evaluation results: %s", e)
    
    def log_system_metrics(self, step: Optional[int] = None):
        """Log system metrics (GPU, memory, etc.)"""
        if not self.enable_tracking:
            return
        
        try:
            system_metrics = {}
            
            # GPU metrics
            if torch.cuda.is_available():
                for i in range(torch.cuda.device_count()):
                    system_metrics['gpu_{}_memory_allocated'.format(i)] = torch.cuda.memory_allocated(i) / 1024**3  # GB
                    system_metrics['gpu_{}_memory_reserved'.format(i)] = torch.cuda.memory_reserved(i) / 1024**3  # GB
                    system_metrics['gpu_{}_utilization'.format(i)] = torch.cuda.utilization(i) if hasattr(torch.cuda, 'utilization') else 0
            
            # CPU and memory metrics (basic)
            try:
                import psutil
                system_metrics['cpu_percent'] = psutil.cpu_percent()
                system_metrics['memory_percent'] = psutil.virtual_memory().percent
            except ImportError:
                logger.warning("psutil not available, skipping CPU/memory metrics")
            
            self.log_metrics(system_metrics, step)
            
        except Exception as e:
            logger.error("Failed to log system metrics: %s", e)
    
    def log_training_summary(self, summary: Dict[str, Any]):
        """Log training summary at the end"""
        if not self.enable_tracking:
            return
        
        try:
            # Add experiment duration
            end_time = datetime.now()
            duration = (end_time - self.start_time).total_seconds()
            summary['experiment_duration_seconds'] = duration
            summary['experiment_duration_hours'] = duration / 3600
            
            # Log final summary to Trackio
            if self.trackio_client:
                result = self.trackio_client.log_parameters(
                    experiment_id=self.experiment_id,
                    parameters=summary
                )
                
                if "success" in result:
                    logger.info("Training summary logged to Trackio")
                else:
                    logger.error("Failed to log training summary to Trackio: %s", result)
            
            # Save to HF Dataset
            self._save_to_hf_dataset(summary)
            
            # Save summary locally
            summary_path = "training_summary_{}_{}.json".format(
                self.experiment_name,
                self.start_time.strftime('%Y%m%d_%H%M%S')
            )
            with open(summary_path, 'w') as f:
                json.dump(summary, f, indent=2, default=str)
            
            self.artifacts.append(summary_path)
            logger.info("Training summary logged and saved to %s", summary_path)
            
        except Exception as e:
            logger.error("Failed to log training summary: %s", e)
    
    def create_monitoring_callback(self):
        """Create a callback for integration with Hugging Face Trainer"""
        from transformers import TrainerCallback
        
        class TrackioCallback(TrainerCallback):
            """
            Trainer callback for logging metrics, including advanced metrics:
            - total_tokens, truncated_tokens, padding_tokens
            - throughput, step_time, batch_size, seq_len
            - token_acc, train/gate_ortho, train/center, etc.
            """
            def __init__(self, monitor):
                super().__init__()
                self.monitor = monitor
                logger.info("TrackioCallback initialized")
                self.last_step_time = None

            def on_init_end(self, args, state, control, **kwargs):
                """Called when training initialization is complete"""
                try:
                    logger.info("Training initialization completed")
                except Exception as e:
                    logger.error("Error in on_init_end: %s", e)
            
            def on_log(self, args, state, control, logs=None, **kwargs):
                """Called when logs are created"""
                import time
                try:
                    step = getattr(state, 'global_step', None)
                    # Timing and throughput
                    now = time.time()
                    if self.last_step_time is not None:
                        step_time = now - self.last_step_time
                        logs['step_time'] = step_time
                        # Throughput: tokens/sec if total_tokens is available
                        if hasattr(self, 'last_total_tokens') and self.last_total_tokens is not None:
                            throughput = (logs.get('total_tokens', 0) / step_time) if step_time > 0 else 0
                            logs['throughput'] = throughput
                    self.last_step_time = now

                    # Token stats from batch (if available in kwargs)
                    batch = kwargs.get('inputs', None)
                    if batch is not None:
                        for key in ['total_tokens', 'padding_tokens', 'truncated_tokens', 'batch_size', 'seq_len']:
                            if key in batch:
                                logs[key] = batch[key]
                        self.last_total_tokens = batch.get('total_tokens', None)
                    else:
                        self.last_total_tokens = None

                    # Token accuracy (if possible)
                    if 'labels' in logs and 'predictions' in logs:
                        labels = logs['labels']
                        preds = logs['predictions']
                        if hasattr(labels, 'shape') and hasattr(preds, 'shape'):
                            correct = (preds == labels).sum().item()
                            total = labels.numel()
                            logs['token_acc'] = correct / total if total > 0 else 0.0

                    self.monitor.log_metrics(logs, step)
                    self.monitor.log_system_metrics(step)
                except Exception as e:
                    logger.error("Error in on_log: %s", e)
            
            def on_save(self, args, state, control, **kwargs):
                """Called when a checkpoint is saved"""
                try:
                    step = getattr(state, 'global_step', None)
                    if step is not None:
                        checkpoint_path = os.path.join(args.output_dir, "checkpoint-{}".format(step))
                        if os.path.exists(checkpoint_path):
                            self.monitor.log_model_checkpoint(checkpoint_path, step)
                except Exception as e:
                    logger.error("Error in on_save: %s", e)
            
            def on_evaluate(self, args, state, control, metrics=None, **kwargs):
                """Called when evaluation is performed"""
                try:
                    if metrics and isinstance(metrics, dict):
                        step = getattr(state, 'global_step', None)
                        self.monitor.log_evaluation_results(metrics, step)
                except Exception as e:
                    logger.error("Error in on_evaluate: %s", e)
            
            def on_train_begin(self, args, state, control, **kwargs):
                """Called when training begins"""
                try:
                    logger.info("Training started")
                except Exception as e:
                    logger.error("Error in on_train_begin: %s", e)
            
            def on_train_end(self, args, state, control, **kwargs):
                """Called when training ends"""
                try:
                    logger.info("Training completed")
                    if self.monitor:
                        self.monitor.close()
                except Exception as e:
                    logger.error("Error in on_train_end: %s", e)
        
        callback = TrackioCallback(self)
        logger.info("TrackioCallback created successfully")
        return callback
    
    def get_experiment_url(self) -> Optional[str]:
        """Get the URL to view the experiment in Trackio"""
        if self.trackio_client and self.experiment_id:
            return "{}?tab=view_experiments".format(self.trackio_client.space_url)
        return None
    
    def close(self):
        """Close the monitoring session"""
        if self.enable_tracking and self.trackio_client:
            try:
                # Mark experiment as completed
                result = self.trackio_client.update_experiment_status(
                    experiment_id=self.experiment_id,
                    status="completed"
                )
                if "success" in result:
                    logger.info("Monitoring session closed")
                else:
                    logger.error("Failed to close monitoring session: %s", result)
            except Exception as e:
                logger.error("Failed to close monitoring session: %s", e)
        
        # Final save to HF Dataset
        if self.hf_dataset_client:
            self._save_to_hf_dataset({'status': 'completed'})

# Utility function to create monitor from config
def create_monitor_from_config(config, experiment_name: Optional[str] = None) -> SmolLM3Monitor:
    """Create a monitor instance from configuration"""
    if experiment_name is None:
        experiment_name = getattr(config, 'experiment_name', 'smollm3_experiment')
    
    return SmolLM3Monitor(
        experiment_name=experiment_name,
        trackio_url=getattr(config, 'trackio_url', None),
        trackio_token=getattr(config, 'trackio_token', None),
        enable_tracking=getattr(config, 'enable_tracking', True),
        log_artifacts=getattr(config, 'log_artifacts', True),
        log_metrics=getattr(config, 'log_metrics', True),
        log_config=getattr(config, 'log_config', True),
        hf_token=getattr(config, 'hf_token', None),
        dataset_repo=getattr(config, 'dataset_repo', None)
    )