Create qa1.0.0

qa1.0.0

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+"""
+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()