"""
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()