""" Quantumaurora: Advanced Transformer-based Language Model Version: 1.0.0 Created: 2025 """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import Dataset, DataLoader from transformers import PreTrainedTokenizerFast from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders import math from typing import Optional, Dict, List, Tuple from torch.cuda.amp import autocast, GradScaler from torch.nn.parallel import DistributedDataParallel import torch.distributed as dist import torch.multiprocessing as mp from torch.utils.checkpoint import checkpoint import json import os from datetime import datetime class QuantumauroraConfig: """Configuration class for Quantumaurora model""" def __init__(self, vocab_size: int = 50000, d_model: int = 512, num_heads: int = 8, num_layers: int = 6, d_ff: int = 2048, dropout: float = 0.1, attention_type: str = "full", use_checkpointing: bool = True, max_sequence_length: int = 2048, model_version: str = "1.0.0"): self.vocab_size = vocab_size self.d_model = d_model self.num_heads = num_heads self.num_layers = num_layers self.d_ff = d_ff self.dropout = dropout self.attention_type = attention_type self.use_checkpointing = use_checkpointing self.max_sequence_length = max_sequence_length self.model_version = model_version self.model_type = "quantumaurora" def save(self, path: str): """Save configuration to JSON file""" config_dict = self.__dict__ config_dict['timestamp'] = datetime.now().isoformat() with open(path, 'w') as f: json.dump(config_dict, f, indent=2) @classmethod def load(cls, path: str) -> 'QuantumauroraConfig': """Load configuration from JSON file""" with open(path, 'r') as f: config_dict = json.load(f) # Remove timestamp from loaded config if 'timestamp' in config_dict: del config_dict['timestamp'] return cls(**config_dict) class Quantumaurora(nn.Module): """ Quantumaurora: Advanced Transformer-based Language Model A state-of-the-art language model featuring: - Multi-head attention with sparse/local patterns - Multiple pre-training objectives - Gradient checkpointing - Mixed precision training - Distributed training support """ def __init__(self, config: QuantumauroraConfig): super().__init__() self.config = config # Model components self.token_embedding = nn.Embedding(config.vocab_size, config.d_model) self.positional_encoding = PositionalEncoding(config.d_model) self.transformer_blocks = nn.ModuleList([ TransformerBlock( config.d_model, config.num_heads, config.d_ff, config.dropout, config.attention_type ) for _ in range(config.num_layers) ]) self.pretraining_objectives = PreTrainingObjectives( config.d_model, config.vocab_size ) self.dropout = nn.Dropout(config.dropout) def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]: x = self.token_embedding(x) x = self.positional_encoding(x) x = self.dropout(x) for transformer_block in self.transformer_blocks: if self.config.use_checkpointing and self.training: x = checkpoint(transformer_block, x, mask) else: x = transformer_block(x, mask) return self.pretraining_objectives(x) def save_pretrained(self, path: str): """Save model and configuration""" os.makedirs(path, exist_ok=True) # Save configuration config_path = os.path.join(path, 'config.json') self.config.save(config_path) # Save model weights model_path = os.path.join(path, 'model.pt') torch.save(self.state_dict(), model_path) # Save tokenizer if available if hasattr(self, 'tokenizer'): tokenizer_path = os.path.join(path, 'tokenizer.json') self.tokenizer.save(tokenizer_path) @classmethod def from_pretrained(cls, path: str) -> 'Quantumaurora': """Load pretrained model and configuration""" config = QuantumauroraConfig.load(os.path.join(path, 'config.json')) model = cls(config) model_path = os.path.join(path, 'model.pt') model.load_state_dict(torch.load(model_path)) # Load tokenizer if available tokenizer_path = os.path.join(path, 'tokenizer.json') if os.path.exists(tokenizer_path): model.tokenizer = PreTrainedTokenizerFast.from_file(tokenizer_path) return model class QuantumauroraTrainer: """Training manager for Quantumaurora model""" def __init__(self, model: Quantumaurora, train_dataloader: DataLoader, optimizer: torch.optim.Optimizer, device: str = "cuda", use_mixed_precision: bool = True, distributed: bool = True): self.model = model self.train_dataloader = train_dataloader self.optimizer = optimizer self.device = device self.use_mixed_precision = use_mixed_precision self.distributed = distributed if use_mixed_precision: self.scaler = GradScaler() if distributed: self.model = DistributedDataParallel(model) def train(self, num_epochs: int, save_dir: str = None): """Main training loop""" best_loss = float('inf') for epoch in range(num_epochs): losses = self.train_epoch(epoch) # Save checkpoint if this is the best model if save_dir and losses['total'] < best_loss: best_loss = losses['total'] self.model.save_pretrained(os.path.join(save_dir, f'checkpoint-{epoch}')) print(f"Epoch {epoch+1}/{num_epochs}") for loss_name, loss_value in losses.items(): print(f"{loss_name}: {loss_value:.4f}") def main(): """Example usage of Quantumaurora""" # Initialize configuration config = QuantumauroraConfig( vocab_size=50000, d_model=768, num_heads=12, num_layers=12, attention_type="sparse" ) # Initialize model model = Quantumaurora(config) # Multi-GPU training if available world_size = torch.cuda.device_count() if world_size > 1: mp.spawn( train_distributed, args=(world_size, model, dataset), nprocs=world_size, join=True ) else: # Single GPU training trainer = QuantumauroraTrainer( model=model, train_dataloader=train_dataloader, optimizer=torch.optim.Adam(model.parameters()), use_mixed_precision=True, distributed=False ) trainer.train( num_epochs=10, save_dir='quantumaurora_checkpoints' ) if __name__ == "__main__": main()