import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR, OneCycleLR from torch.cuda.amp import GradScaler, autocast import torch.distributed as dist import torch.multiprocessing as mp from torch.nn.parallel import DistributedDataParallel as DDP import os import logging from tqdm import tqdm import wandb from torch.utils.data.distributed import DistributedSampler logger = logging.getLogger(__name__) class AdvancedTrainer: """ Advanced training framework with mixed precision, distributed training, and modern optimization techniques. """ def __init__(self, model, train_dataset, val_dataset, config): self.config = config self.model = model self.train_dataset = train_dataset self.val_dataset = val_dataset # Distributed training setup self.world_size = int(os.environ.get('WORLD_SIZE', 1)) self.rank = int(os.environ.get('RANK', 0)) self.local_rank = int(os.environ.get('LOCAL_RANK', 0)) self.is_distributed = self.world_size > 1 self.is_main_process = self.rank == 0 if self.is_distributed: self._setup_distributed() # Mixed precision training self.scaler = GradScaler() if config.use_mixed_precision else None # Optimizer with advanced scheduling self.optimizer = self._create_optimizer() self.scheduler = self._create_scheduler() # Loss functions with label smoothing self.criterion = { 'emotion': nn.CrossEntropyLoss(label_smoothing=0.1), 'intent': nn.CrossEntropyLoss(label_smoothing=0.1), 'engagement': self._create_regression_loss(), 'confidence': self._create_regression_loss(), 'contrastive': nn.CrossEntropyLoss() } # Weights for multi-task loss self.task_weights = config.task_weights # Initialize wandb for main process if self.is_main_process and config.use_wandb: wandb.init(project="emotia-training", config=config.__dict__) def _setup_distributed(self): """Setup distributed training""" torch.cuda.set_device(self.local_rank) dist.init_process_group( backend='nccl', init_method='env://', world_size=self.world_size, rank=self.rank ) # Wrap model with DDP self.model = DDP(self.model, device_ids=[self.local_rank]) def _create_optimizer(self): """Create advanced optimizer""" if self.config.optimizer == 'adamw': optimizer = optim.AdamW( self.model.parameters(), lr=self.config.lr, weight_decay=self.config.weight_decay, betas=(0.9, 0.999) ) elif self.config.optimizer == 'lion': # LION optimizer (more memory efficient) from lion_pytorch import Lion optimizer = Lion( self.model.parameters(), lr=self.config.lr, weight_decay=self.config.weight_decay ) else: optimizer = optim.Adam( self.model.parameters(), lr=self.config.lr, weight_decay=self.config.weight_decay ) return optimizer def _create_scheduler(self): """Create advanced learning rate scheduler""" if self.config.scheduler == 'cosine': scheduler = CosineAnnealingLR( self.optimizer, T_max=self.config.epochs, eta_min=self.config.min_lr ) elif self.config.scheduler == 'one_cycle': scheduler = OneCycleLR( self.optimizer, max_lr=self.config.lr, epochs=self.config.epochs, steps_per_epoch=len(self.train_dataset) // (self.config.batch_size * self.world_size), pct_start=0.3, anneal_strategy='cos' ) else: scheduler = None return scheduler def _create_regression_loss(self): """Create regression loss with uncertainty""" def uncertainty_loss(pred_mean, pred_var, target): # Negative log likelihood for Gaussian distribution loss = 0.5 * torch.log(pred_var) + 0.5 * (target - pred_mean)**2 / pred_var return loss.mean() return uncertainty_loss def train_epoch(self, epoch): """Train for one epoch with advanced techniques""" self.model.train() if self.is_distributed: sampler = DistributedSampler(self.train_dataset, shuffle=True) dataloader = torch.utils.data.DataLoader( self.train_dataset, batch_size=self.config.batch_size, sampler=sampler, num_workers=self.config.num_workers, pin_memory=True ) else: dataloader = torch.utils.data.DataLoader( self.train_dataset, batch_size=self.config.batch_size, shuffle=True, num_workers=self.config.num_workers, pin_memory=True ) total_loss = 0 num_batches = 0 progress_bar = tqdm(dataloader, desc=f"Epoch {epoch+1}") if self.is_main_process else dataloader for batch in progress_bar: # Move to device batch = {k: v.cuda(self.local_rank) if torch.is_tensor(v) else v for k, v in batch.items()} self.optimizer.zero_grad() # Mixed precision training if self.scaler: with autocast(): outputs = self.model(**batch) loss = self._compute_loss(outputs, batch) self.scaler.scale(loss).backward() self.scaler.step(self.optimizer) self.scaler.update() else: outputs = self.model(**batch) loss = self._compute_loss(outputs, batch) loss.backward() self.optimizer.step() # Update scheduler (for OneCycleLR) if isinstance(self.scheduler, OneCycleLR): self.scheduler.step() total_loss += loss.item() num_batches += 1 # Update progress bar if self.is_main_process: progress_bar.set_postfix({'loss': f'{loss.item():.4f}'}) avg_loss = total_loss / num_batches # Step scheduler (for CosineAnnealingLR) if isinstance(self.scheduler, CosineAnnealingLR): self.scheduler.step() return avg_loss def _compute_loss(self, outputs, batch): """Compute multi-task loss with uncertainty""" total_loss = 0 # Emotion classification if 'emotion_logits' in outputs and 'emotion' in batch: emotion_loss = self.criterion['emotion'](outputs['emotion_logits'], batch['emotion']) total_loss += self.task_weights['emotion'] * emotion_loss # Intent classification if 'intent_logits' in outputs and 'intent' in batch: intent_loss = self.criterion['intent'](outputs['intent_logits'], batch['intent']) total_loss += self.task_weights['intent'] * intent_loss # Engagement regression with uncertainty if 'engagement_mean' in outputs and 'engagement_var' in outputs and 'engagement' in batch: engagement_loss = self.criterion['engagement']( outputs['engagement_mean'], outputs['engagement_var'], batch['engagement'] ) total_loss += self.task_weights['engagement'] * engagement_loss # Confidence regression with uncertainty if 'confidence_mean' in outputs and 'confidence_var' in outputs and 'confidence' in batch: confidence_loss = self.criterion['confidence']( outputs['confidence_mean'], outputs['confidence_var'], batch['confidence'] ) total_loss += self.task_weights['confidence'] * confidence_loss # Contrastive loss for multi-modal alignment if hasattr(self.model, 'contrastive_loss') and 'embeddings' in outputs: contrastive_loss = self.model.contrastive_loss(outputs['embeddings']) total_loss += self.config.contrastive_weight * contrastive_loss return total_loss def validate(self, epoch): """Validation with comprehensive metrics""" self.model.eval() if self.is_distributed: sampler = DistributedSampler(self.val_dataset, shuffle=False) dataloader = torch.utils.data.DataLoader( self.val_dataset, batch_size=self.config.batch_size, sampler=sampler, num_workers=self.config.num_workers, pin_memory=True ) else: dataloader = torch.utils.data.DataLoader( self.val_dataset, batch_size=self.config.batch_size, shuffle=False, num_workers=self.config.num_workers, pin_memory=True ) total_loss = 0 num_batches = 0 all_emotion_preds = [] all_emotion_labels = [] all_intent_preds = [] all_intent_labels = [] with torch.no_grad(): for batch in dataloader: batch = {k: v.cuda(self.local_rank) if torch.is_tensor(v) else v for k, v in batch.items()} outputs = self.model(**batch) loss = self._compute_loss(outputs, batch) total_loss += loss.item() num_batches += 1 # Collect predictions for metrics if 'emotion_logits' in outputs: all_emotion_preds.extend(outputs['emotion_logits'].argmax(dim=1).cpu().numpy()) all_emotion_labels.extend(batch['emotion'].cpu().numpy()) if 'intent_logits' in outputs: all_intent_preds.extend(outputs['intent_logits'].argmax(dim=1).cpu().numpy()) all_intent_labels.extend(batch['intent'].cpu().numpy()) avg_loss = total_loss / num_batches # Compute metrics metrics = self._compute_metrics(all_emotion_preds, all_emotion_labels, all_intent_preds, all_intent_labels) return avg_loss, metrics def _compute_metrics(self, emotion_preds, emotion_labels, intent_preds, intent_labels): """Compute comprehensive evaluation metrics""" from sklearn.metrics import accuracy_score, f1_score, precision_recall_fscore_support metrics = {} if emotion_preds and emotion_labels: metrics.update({ 'emotion_accuracy': accuracy_score(emotion_labels, emotion_preds), 'emotion_f1_macro': f1_score(emotion_labels, emotion_preds, average='macro'), 'emotion_f1_weighted': f1_score(emotion_labels, emotion_preds, average='weighted'), }) if intent_preds and intent_labels: metrics.update({ 'intent_accuracy': accuracy_score(intent_labels, intent_preds), 'intent_f1_macro': f1_score(intent_labels, intent_preds, average='macro'), 'intent_f1_weighted': f1_score(intent_labels, intent_preds, average='weighted'), }) return metrics def train(self): """Main training loop""" best_val_loss = float('inf') patience_counter = 0 for epoch in range(self.config.epochs): # Train epoch train_loss = self.train_epoch(epoch) # Validate val_loss, val_metrics = self.validate(epoch) # Log metrics if self.is_main_process: logger.info(f"Epoch {epoch+1}: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}") for metric_name, metric_value in val_metrics.items(): logger.info(f"{metric_name}: {metric_value:.4f}") # Wandb logging if self.config.use_wandb: wandb.log({ 'epoch': epoch, 'train_loss': train_loss, 'val_loss': val_loss, **val_metrics, 'lr': self.optimizer.param_groups[0]['lr'] }) # Save best model if val_loss < best_val_loss: best_val_loss = val_loss patience_counter = 0 if self.is_main_process: self.save_checkpoint(epoch, val_loss, val_metrics) else: patience_counter += 1 # Early stopping if patience_counter >= self.config.patience: logger.info("Early stopping triggered") break # Final cleanup if self.is_distributed: dist.destroy_process_group() def save_checkpoint(self, epoch, val_loss, val_metrics): """Save model checkpoint""" checkpoint = { 'epoch': epoch, 'model_state_dict': self.model.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), 'scheduler_state_dict': self.scheduler.state_dict() if self.scheduler else None, 'scaler_state_dict': self.scaler.state_dict() if self.scaler else None, 'val_loss': val_loss, 'val_metrics': val_metrics, 'config': self.config } checkpoint_path = f"{self.config.checkpoint_dir}/checkpoint_epoch_{epoch}.pth" torch.save(checkpoint, checkpoint_path) logger.info(f"Saved checkpoint: {checkpoint_path}") @staticmethod def load_checkpoint(checkpoint_path, model, optimizer=None, scheduler=None, scaler=None): """Load model checkpoint""" checkpoint = torch.load(checkpoint_path) model.load_state_dict(checkpoint['model_state_dict']) if optimizer and 'optimizer_state_dict' in checkpoint: optimizer.load_state_dict(checkpoint['optimizer_state_dict']) if scheduler and 'scheduler_state_dict' in checkpoint: scheduler.load_state_dict(checkpoint['scheduler_state_dict']) if scaler and 'scaler_state_dict' in checkpoint: scaler.load_state_dict(checkpoint['scaler_state_dict']) return checkpoint['epoch'], checkpoint['val_loss'], checkpoint['val_metrics']