scripts/training/quick_training_run.py

#!/usr/bin/env python3
"""
Full end-to-end BitTransformerLM training run with all optimizations!
Small scale test to validate our enhanced system.
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import logging
from pathlib import Path
import time
from typing import List, Dict, Any

# Import our enhanced modules
from bit_transformer.model import BitTransformerLM
from bit_transformer.compression import compress_bits_batch, model_output_decompress
from bit_transformer.error_handling import safe_model_forward, setup_error_logging
from bit_transformer.types import BitSequence, TelemetryDict
from enhanced_checkpoint_system import create_checkpoint_manager

# Setup logging
logger = setup_error_logging("INFO")

class SimpleBitDataset(Dataset):
    """Simple dataset of bit sequences for training."""
    
    def __init__(self, num_samples: int = 1000, seq_length: int = 128):
        self.num_samples = num_samples
        self.seq_length = seq_length
        self.data = self._generate_bit_sequences()
    
    def _generate_bit_sequences(self) -> List[torch.Tensor]:
        """Generate diverse bit sequences with different patterns."""
        sequences = []
        
        # Pattern 1: Alternating sequences
        for i in range(self.num_samples // 4):
            pattern = torch.tensor([i % 2] * self.seq_length, dtype=torch.long)
            sequences.append(pattern)
        
        # Pattern 2: Random sequences
        for i in range(self.num_samples // 4):
            pattern = torch.randint(0, 2, (self.seq_length,), dtype=torch.long)
            sequences.append(pattern)
        
        # Pattern 3: Structured patterns (runs)
        for i in range(self.num_samples // 4):
            pattern = []
            pos = 0
            while pos < self.seq_length:
                run_length = min(np.random.randint(1, 20), self.seq_length - pos)
                bit_value = np.random.randint(0, 2)
                pattern.extend([bit_value] * run_length)
                pos += run_length
            pattern = torch.tensor(pattern[:self.seq_length], dtype=torch.long)
            sequences.append(pattern)
        
        # Pattern 4: Fibonacci-like sequences
        remaining = self.num_samples - len(sequences)
        for i in range(remaining):
            pattern = [0, 1]
            while len(pattern) < self.seq_length:
                pattern.append(pattern[-1] ^ pattern[-2])  # XOR of last two bits
            pattern = torch.tensor(pattern[:self.seq_length], dtype=torch.long)
            sequences.append(pattern)
        
        return sequences
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        sequence = self.data[idx]
        # For language modeling, input is sequence[:-1], target is sequence[1:]
        return sequence[:-1], sequence[1:]


def compute_safety_metrics(predictions: torch.Tensor, targets: torch.Tensor) -> Dict[str, float]:
    """Compute K/C/S safety metrics."""
    pred_bits = (predictions > 0.5).float().flatten()
    
    # K metric (Negentropy): Measure of order vs randomness
    if len(pred_bits) > 0:
        prob_1 = pred_bits.mean().item()
        prob_0 = 1 - prob_1
        if prob_0 > 0 and prob_1 > 0:
            entropy = -prob_0 * np.log2(prob_0) - prob_1 * np.log2(prob_1)
            negentropy = 1.0 - entropy  # Higher = more ordered
        else:
            negentropy = 1.0 if prob_1 == 1.0 or prob_1 == 0.0 else 0.0
    else:
        negentropy = 0.0
    
    # C metric (Complexity): Simple run-length approximation
    changes = (pred_bits[1:] != pred_bits[:-1]).sum().item()
    complexity = min(changes / len(pred_bits), 1.0) if len(pred_bits) > 1 else 0.0
    
    # S metric (Symbiosis): Alignment with target distribution
    target_bits = targets.float().flatten()
    if len(target_bits) > 0:
        target_mean = target_bits.mean()
        pred_mean = pred_bits.mean()
        symbiosis = 1.0 - abs(target_mean - pred_mean).item()
    else:
        symbiosis = 1.0
    
    return {
        'K_negentropy': negentropy,
        'C_complexity': complexity, 
        'S_symbiosis': symbiosis
    }


def train_bittransformer():
    """Main training function with all optimizations."""
    
    logger.info("🚀 Starting BitTransformerLM end-to-end training run!")
    
    # Model configuration - small but meaningful
    model_config = {
        'd_model': 256,
        'nhead': 8,
        'num_layers': 4,
        'dim_feedforward': 512,
        'max_seq_len': 128,
        'use_checkpoint': True,
        'chunk_size': None,  # Disable chunking for small model
    }
    
    training_config = {
        'batch_size': 16,
        'learning_rate': 1e-3,
        'num_epochs': 10,
        'save_every_n_epochs': 2,
        'log_every_n_steps': 10
    }
    
    # Initialize enhanced checkpoint manager
    checkpoint_manager = create_checkpoint_manager()
    session_id = checkpoint_manager.create_training_session(
        session_name="end_to_end_test",
        model_config=model_config,
        training_config=training_config
    )
    
    logger.info(f"📝 Created training session: {session_id}")
    
    # Create dataset and dataloader
    logger.info("📊 Creating training dataset...")
    dataset = SimpleBitDataset(num_samples=800, seq_length=model_config['max_seq_len'])
    dataloader = DataLoader(dataset, batch_size=training_config['batch_size'], shuffle=True)
    
    # Initialize model
    logger.info("🧠 Initializing BitTransformerLM model...")
    model = BitTransformerLM(
        d_model=model_config['d_model'],
        nhead=model_config['nhead'],
        num_layers=model_config['num_layers'],
        dim_feedforward=model_config['dim_feedforward'],
        max_seq_len=model_config['max_seq_len'],
        use_checkpoint=model_config['use_checkpoint'],
        chunk_size=model_config['chunk_size']
    )
    
    # Count parameters
    total_params = sum(p.numel() for p in model.parameters())
    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    logger.info(f"🔢 Model parameters: {total_params:,} total, {trainable_params:,} trainable")
    
    # Setup optimizer and loss
    optimizer = optim.AdamW(model.parameters(), lr=training_config['learning_rate'])
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=training_config['num_epochs'])
    criterion = nn.CrossEntropyLoss()
    
    # Training loop
    logger.info("🏃‍♂️ Starting training loop...")
    
    for epoch in range(training_config['num_epochs']):
        model.train()
        epoch_loss = 0.0
        epoch_metrics = {'K_negentropy': 0.0, 'C_complexity': 0.0, 'S_symbiosis': 0.0}
        num_batches = 0
        
        start_time = time.time()
        
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            optimizer.zero_grad()
            
            # Forward pass with safety monitoring
            try:
                # BitTransformerLM returns (logits, telemetry)
                output = safe_model_forward(model, inputs)
                if isinstance(output, tuple):
                    logits, telemetry = output
                else:
                    logits = output
                    telemetry = {}
                
                # BitTransformerLM outputs logits for binary classification
                # Shape should be [batch, seq_len, 2] for binary vocab
                if logits.dim() == 2:
                    # If [batch*seq_len, 2], already flattened
                    logits_flat = logits
                    targets_flat = targets.reshape(-1)
                else:
                    # If [batch, seq_len, 2], flatten
                    logits_flat = logits.reshape(-1, 2)
                    targets_flat = targets.reshape(-1)
                
                loss = criterion(logits_flat, targets_flat)
                
                # Backward pass
                loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                
                # Compute metrics
                with torch.no_grad():
                    # Handle different logits shapes for predictions
                    if logits.dim() == 2:
                        # [batch*seq_len, 2] -> reshape back to [batch, seq_len, 2]
                        batch_size = inputs.shape[0]
                        seq_len = inputs.shape[1]
                        logits_reshaped = logits.reshape(batch_size, seq_len, 2)
                        predictions = torch.softmax(logits_reshaped, dim=-1)[:, :, 1]  # Prob of bit=1
                    else:
                        # [batch, seq_len, 2]
                        predictions = torch.softmax(logits, dim=-1)[:, :, 1]  # Prob of bit=1
                    
                    safety_metrics = compute_safety_metrics(predictions, targets)
                    
                    epoch_loss += loss.item()
                    for key, value in safety_metrics.items():
                        epoch_metrics[key] += value
                    num_batches += 1
                
                # Logging
                if batch_idx % training_config['log_every_n_steps'] == 0:
                    logger.info(f"Epoch {epoch+1}/{training_config['num_epochs']}, "
                              f"Batch {batch_idx}/{len(dataloader)}, "
                              f"Loss: {loss.item():.4f}, "
                              f"K: {safety_metrics['K_negentropy']:.3f}, "
                              f"C: {safety_metrics['C_complexity']:.3f}, "
                              f"S: {safety_metrics['S_symbiosis']:.3f}")
                
            except Exception as e:
                logger.error(f"Error in batch {batch_idx}: {e}")
                continue
        
        # End of epoch processing
        scheduler.step()
        epoch_time = time.time() - start_time
        
        if num_batches > 0:
            avg_loss = epoch_loss / num_batches
            avg_metrics = {k: v / num_batches for k, v in epoch_metrics.items()}
            
            logger.info(f"✅ Epoch {epoch+1} completed in {epoch_time:.2f}s")
            logger.info(f"📊 Avg Loss: {avg_loss:.4f}")
            logger.info(f"📈 Safety Metrics - K: {avg_metrics['K_negentropy']:.3f}, "
                       f"C: {avg_metrics['C_complexity']:.3f}, "
                       f"S: {avg_metrics['S_symbiosis']:.3f}")
            
            # Save checkpoint
            if (epoch + 1) % training_config['save_every_n_epochs'] == 0:
                checkpoint_success = checkpoint_manager.save_checkpoint(
                    model=model,
                    session_id=session_id,
                    epoch=epoch + 1,
                    metrics={
                        'loss': avg_loss,
                        'learning_rate': scheduler.get_last_lr()[0],
                        **avg_metrics
                    },
                    optimizer_state=optimizer.state_dict(),
                    scheduler_state=scheduler.state_dict()
                )
                
                if checkpoint_success:
                    logger.info(f"💾 Checkpoint saved for epoch {epoch+1}")
                
                # Save best model if loss improved
                checkpoint_manager.save_best_model(
                    session_id=session_id,
                    model=model,
                    metric_name='loss',
                    metric_value=avg_loss,
                    is_better_func=lambda x, y: x < y  # Lower loss is better
                )
    
    logger.info("🎉 Training completed successfully!")
    
    # Test inference and compression
    logger.info("🧪 Testing model inference and compression...")
    
    model.eval()
    with torch.no_grad():
        # Create a test sequence
        test_input = torch.randint(0, 2, (1, 64), dtype=torch.long)
        logger.info(f"📥 Input sequence: {test_input.squeeze().tolist()}")
        
        # Model inference
        output_logits = model(test_input)
        output_probs = torch.softmax(output_logits, dim=-1)
        predicted_bits = torch.argmax(output_probs, dim=-1)
        
        logger.info(f"📤 Predicted sequence: {predicted_bits.squeeze().tolist()}")
        
        # Test compression
        compressed = compress_bits_batch(predicted_bits)
        logger.info(f"🗜️ Compressed length: {len(compressed[0])} (original: {predicted_bits.shape[-1]})")
        
        # Decompress to verify
        decompressed = model_output_decompress(compressed)
        compression_success = torch.equal(predicted_bits, decompressed)
        logger.info(f"✅ Compression/decompression successful: {compression_success}")
    
    # Final storage usage report
    storage_usage = checkpoint_manager.get_storage_usage()
    logger.info(f"💾 Final storage usage: {storage_usage['total_gb']:.3f} GB")
    logger.info(f"📁 Training sessions: {storage_usage['num_sessions']}")
    
    return session_id, model, checkpoint_manager


if __name__ == "__main__":
    try:
        session_id, trained_model, manager = train_bittransformer()
        print(f"\n🎉 SUCCESS! Training session completed: {session_id}")
        print(f"🔍 Use checkpoint_manager.load_checkpoint('{session_id}') to resume")
        
    except Exception as e:
        logger.error(f"❌ Training failed: {e}")
        import traceback
        traceback.print_exc()
        raise