""" Training script with built-in benchmark evaluation. """ import torch import argparse import sys import os from datetime import datetime # Better error reporting on Windows os.environ['CUDA_LAUNCH_BLOCKING'] = '0' # Set to '1' for debugging if needed torch.backends.cudnn.benchmark = True # Fix Windows output buffering if sys.platform == 'win32': import sys if hasattr(sys.stdout, 'reconfigure'): sys.stdout.reconfigure(encoding='utf-8', errors='replace') sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from config.config import Config from model.architecture import NovelAIModel from training.trainer import Trainer from utils.data_loader import create_data_loaders, load_text_file from utils.tokenizer import SimpleTokenizer from evaluation.benchmarks import BenchmarkSuite def main(): parser = argparse.ArgumentParser(description='Train and evaluate the novel AI model') parser.add_argument('--config', type=str, default=None, help='Path to config file') parser.add_argument('--train_data', type=str, required=True, help='Path to training data file') parser.add_argument('--val_data', type=str, default=None, help='Path to validation data file') parser.add_argument('--test_data', type=str, nargs='+', help='Path(s) to test data file(s)') parser.add_argument('--test_names', type=str, nargs='+', help='Names for test datasets') parser.add_argument('--output_dir', type=str, default='./checkpoints', help='Output directory') parser.add_argument('--epochs', type=int, default=None, help='Number of epochs') parser.add_argument('--batch_size', type=int, default=None, help='Batch size') parser.add_argument('--learning_rate', type=float, default=None, help='Learning rate') parser.add_argument('--eval_interval', type=int, default=1, help='Evaluate every N epochs') parser.add_argument('--save_best', action='store_true', help='Save best model based on validation') args = parser.parse_args() # Load config if args.config and os.path.exists(args.config): config = Config.load(args.config) else: config = Config.default() # Override config if args.epochs is not None: config.training.num_epochs = args.epochs if args.batch_size is not None: config.training.batch_size = args.batch_size if args.learning_rate is not None: config.training.learning_rate = args.learning_rate config.training.train_data_path = args.train_data config.training.val_data_path = args.val_data config.training.save_dir = args.output_dir # Set device - FORCE GPU if available if torch.cuda.is_available(): device = 'cuda' print(f"[GPU] Available: {torch.cuda.get_device_name(0)}") print(f"[GPU] CUDA Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB") torch.cuda.empty_cache() # Clear any previous allocations else: device = 'cpu' print("[WARNING] No GPU available, using CPU (will be very slow)") config.training.device = device print(f"Using device: {device}") # Load data print("Loading data...") train_texts = load_text_file(args.train_data) print(f"Loaded {len(train_texts)} training texts") val_texts = None if args.val_data: val_texts = load_text_file(args.val_data) print(f"Loaded {len(val_texts)} validation texts") # Create data loaders and tokenizer print("Creating data loaders...") train_loader, val_loader, tokenizer = create_data_loaders( train_texts=train_texts, val_texts=val_texts, max_length=config.model.max_seq_length, batch_size=config.training.batch_size, num_workers=0 ) print(f"Vocabulary size: {tokenizer.vocab_size}") print(f"Training batches: {len(train_loader)}") if val_loader: print(f"Validation batches: {len(val_loader)}") # Update model vocab size config.model.vocab_size = tokenizer.vocab_size # Create model print("Creating model...") formula_config = { 'w1': config.model.formula_w1, 'w2': config.model.formula_w2, 'w3': config.model.formula_w3, 'lambda_decay': config.model.formula_lambda, 'k_fatigue': config.model.formula_k, 'learnable_weights': config.model.formula_learnable_weights, 'learnable_decay': config.model.formula_learnable_decay, } model = NovelAIModel( vocab_size=config.model.vocab_size, embedding_dim=config.model.embedding_dim, num_layers=config.model.num_layers, num_heads=config.model.num_heads, feedforward_dim=config.model.feedforward_dim, max_seq_length=config.model.max_seq_length, dropout=config.model.dropout, formula_config=formula_config, use_memory_buffer=False, # Disable to avoid CUDA memory issues memory_buffer_size=config.model.memory_buffer_size, use_formula_attention=config.model.use_formula_attention, ) # Move to GPU immediately model = model.to(device) print(f"Model moved to {device}") # Print model info total_params = sum(p.numel() for p in model.parameters()) trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) print(f"Model parameters: {total_params:,}") print(f"Trainable parameters: {trainable_params:,}") # Create trainer trainer = Trainer( model=model, train_loader=train_loader, val_loader=val_loader, learning_rate=config.training.learning_rate, weight_decay=config.training.weight_decay, warmup_steps=config.training.warmup_steps, max_grad_norm=config.training.max_grad_norm, device=device, save_dir=config.training.save_dir, log_interval=config.training.log_interval, ) # Save config config.save(os.path.join(config.training.save_dir, 'config.json')) print(f"Saved config to {os.path.join(config.training.save_dir, 'config.json')}") # Load test datasets if provided test_loaders = {} if args.test_data: test_names = args.test_names if args.test_names else [f"test_{i}" for i in range(len(args.test_data))] for test_path, test_name in zip(args.test_data, test_names): print(f"Loading test dataset: {test_name} from {test_path}") test_texts = load_text_file(test_path) _, test_loader, _ = create_data_loaders( train_texts=test_texts, val_texts=None, tokenizer=tokenizer, max_length=config.model.max_seq_length, batch_size=config.training.batch_size, num_workers=0, build_vocab=False ) test_loaders[test_name] = test_loader print(f" Loaded {len(test_texts)} test texts") # Train print("\n" + "="*70) print("Starting Training") print("="*70) best_val_loss = float('inf') try: for epoch in range(config.training.num_epochs): print(f"\n{'='*70}") print(f"Epoch {epoch + 1}/{config.training.num_epochs}") print(f"{'='*70}") try: # Train print("Starting training epoch...") train_metrics = trainer.train_epoch() print("Training epoch completed successfully") # Validate val_metrics = trainer.validate() # Update best model if val_metrics and val_metrics.get('val_loss', float('inf')) < best_val_loss: best_val_loss = val_metrics['val_loss'] if args.save_best: # Save best model checkpoint_path = os.path.join(config.training.save_dir, 'best_model.pt') torch.save({ 'epoch': epoch, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': trainer.optimizer.state_dict(), 'val_loss': best_val_loss, }, checkpoint_path) print(f"\nSaved best model to {checkpoint_path}") # Evaluate on test sets if args.eval_interval > 0 and (epoch + 1) % args.eval_interval == 0 and test_loaders: print("\n" + "="*70) print(f"Running Benchmarks (Epoch {epoch + 1})") print("="*70) benchmark_suite = BenchmarkSuite(model, tokenizer, device=device) test_results = benchmark_suite.evaluate_multiple_datasets(test_loaders) # Save evaluation results results_path = os.path.join(config.training.save_dir, f'eval_results_epoch_{epoch+1}.json') import json with open(results_path, 'w') as f: json.dump(test_results, f, indent=2) print(f"\nSaved evaluation results to {results_path}") except Exception as e: print(f"\nERROR during epoch {epoch + 1}: {e}") import traceback traceback.print_exc() print("Continuing to next epoch...") continue except KeyboardInterrupt: print("\nTraining interrupted by user") except Exception as e: print(f"\nFATAL ERROR: {e}") import traceback traceback.print_exc() raise # Final evaluation if test_loaders: print("\n" + "="*70) print("Final Benchmark Evaluation") print("="*70) benchmark_suite = BenchmarkSuite(model, tokenizer, device=device) final_results = benchmark_suite.evaluate_multiple_datasets(test_loaders) # Generate comparison report report = benchmark_suite.generate_comparison_report(final_results) print(report) # Save final results final_results_path = os.path.join(config.training.save_dir, 'final_benchmark_results.json') import json with open(final_results_path, 'w') as f: json.dump(final_results, f, indent=2) print(f"\nSaved final benchmark results to {final_results_path}") print("\nTraining and evaluation complete!") if __name__ == '__main__': main()