train_hcf.py

import os
import json
import torch
import logging
from pathlib import Path
from dataclasses import dataclass
from typing import Optional, List, Dict, Tuple, Any
import transformers
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)
from datasets import Dataset, load_dataset
import numpy as np
from accelerate import Accelerator
from safetensors import safe_open
from safetensors.torch import save_file, load_file

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class TensorInfo:
    """Stores metadata about tensor indices and shape"""
    shape: Tuple[int, ...]
    dtype: str
    indices: Optional[torch.Tensor] = None
    hcf_patterns: Optional[Dict] = None

class SafeTensorHCFAnalyzer:
    """
    Analyzes HCF patterns in model weights using SafeTensors format.
    Handles efficient loading and analysis of large model weights.
    """
    
    def __init__(self, tolerance: float = 1e-5):
        self.tolerance = tolerance
        self.tensor_info = {}
        self.metadata = {}
        
    def load_safetensor_file(self, 
                          filepath: str,
                          device: str = 'cpu',
                          load_indices: bool = True) -> Dict[str, TensorInfo]:
        """
        Load and parse a SafeTensor file with proper memory management.
        
        Args:
            filepath: Path to .safetensors file
            device: Device to load tensors to
            load_indices: Whether to load weight indices
            
        Returns:
            Dictionary mapping tensor names to their metadata
        """
        try:
            # First load metadata only to check structure
            with safe_open(filepath, framework="pt") as f:
                self.metadata = json.loads(f.metadata()) if f.metadata() else {}
                
            # Load tensors efficiently
            tensors = load_file(filepath, device=device)
            
            for tensor_name, tensor in tensors.items():
                self.tensor_info[tensor_name] = TensorInfo(
                    shape=tuple(tensor.shape),
                    dtype=str(tensor.dtype)
                )
                
                # Load indices if available in metadata
                if load_indices and tensor_name in self.metadata:
                    if 'indices' in self.metadata[tensor_name]:
                        indices_data = self.metadata[tensor_name]['indices']
                        if isinstance(indices_data, list):
                            self.tensor_info[tensor_name].indices = torch.tensor(
                                indices_data, device=device
                            )
                        elif isinstance(indices_data, str) and os.path.exists(indices_data):
                            # Load indices from separate file if provided as path
                            self.tensor_info[tensor_name].indices = torch.load(indices_data)
                            
            return self.tensor_info
            
        except Exception as e:
            raise RuntimeError(f"Error loading SafeTensor file: {str(e)}")
    
    def analyze_safetensor_weights(self, 
                               filepath: str,
                               batch_size: int = 1000) -> Dict:
        """
        Analyze weights from SafeTensor file in memory-efficient batches.
        
        Args:
            filepath: Path to .safetensors file
            batch_size: Number of weights to process at once
            
        Returns:
            Analysis results including HCF patterns and optimization opportunities
        """
        results = {
            'tensor_hcfs': {},
            'shared_patterns': [],
            'optimization_suggestions': [],
            'memory_impact': {}
        }
        
        # Process tensors in batches
        with safe_open(filepath, framework="pt") as f:
            for tensor_name in f.keys():
                # Get tensor info
                tensor_data = f.get_tensor(tensor_name)
                tensor_size = np.prod(tensor_data.shape)
                
                if tensor_name in self.tensor_info and self.tensor_info[tensor_name].indices is not None:
                    indices = self.tensor_info[tensor_name].indices
                    unique_indices = torch.unique(indices)
                    
                    # Process each index group
                    tensor_hcfs = {}
                    for idx in unique_indices:
                        mask = (indices == idx)
                        indexed_weights = tensor_data[mask]
                        
                        # Process in batches if needed
                        if len(indexed_weights) > batch_size:
                            hcf = self._process_large_weight_group(indexed_weights, batch_size)
                        else:
                            hcf = self._calculate_hcf(indexed_weights)
                            
                        tensor_hcfs[idx.item()] = hcf
                        
                    results['tensor_hcfs'][tensor_name] = tensor_hcfs
                    
                    # Find optimization opportunities
                    patterns = self._analyze_weight_patterns(tensor_data, indices)
                    self.tensor_info[tensor_name].hcf_patterns = patterns
                    
                    # Calculate potential memory savings
                    savings = self._estimate_memory_savings(patterns, tensor_data.dtype)
                    results['memory_impact'][tensor_name] = {
                        'original_size': tensor_size * tensor_data.element_size(),
                        'potential_savings': savings
                    }
        
        # Find shared patterns across tensors
        results['shared_patterns'] = self._find_shared_patterns()
        results['optimization_suggestions'] = self._generate_optimization_suggestions(results)
        
        return results
    
    def _calculate_hcf(self, weights: torch.Tensor) -> float:
        """Calculate HCF for a tensor of weights, with tolerance for floating point"""
        # Implementation placeholder - actual implementation would depend on specific needs
        if len(weights) == 0:
            return 0.0
        return 1.0  # Simplified for example
    
    def _gcd_float(self, a: float, b: float) -> float:
        """Calculate greatest common divisor for floating point numbers"""
        # Implementation placeholder
        return min(a, b)  # Simplified for example
    
    def _process_large_weight_group(self, 
                                weights: torch.Tensor, 
                                batch_size: int) -> float:
        """Process large weight groups in batches to manage memory."""
        current_hcf = None
        
        for i in range(0, len(weights), batch_size):
            batch = weights[i:i + batch_size]
            batch_hcf = self._calculate_hcf(batch)
            
            if current_hcf is None:
                current_hcf = batch_hcf
            elif batch_hcf > self.tolerance:
                current_hcf = self._gcd_float(current_hcf, batch_hcf)
                
        return current_hcf if current_hcf is not None else 0.0
    
    def _analyze_weight_patterns(self, 
                             weights: torch.Tensor, 
                             indices: torch.Tensor) -> Dict:
        """Analyze weight patterns within indexed groups."""
        patterns = {}
        unique_indices = torch.unique(indices)
        
        for idx in unique_indices:
            mask = (indices == idx)
            pattern_weights = weights[mask]
            
            patterns[idx.item()] = {
                'mean': float(pattern_weights.mean()),
                'std': float(pattern_weights.std()),
                'size': len(pattern_weights),
                'hcf': self._calculate_hcf(pattern_weights)
            }
            
        return patterns
    
    def _estimate_memory_savings(self, patterns: Dict, dtype: torch.dtype) -> int:
        """Estimate potential memory savings from patterns"""
        # Implementation placeholder
        return sum(p['size'] for p in patterns.values()) // 2  # Simplified estimate
    
    def _find_shared_patterns(self) -> List[Dict]:
        """Find patterns that could be shared across tensors."""
        shared_patterns = []
        pattern_groups = {}
        
        for tensor_name, info in self.tensor_info.items():
            if info.hcf_patterns:
                for idx, pattern in info.hcf_patterns.items():
                    # Create pattern signature
                    signature = f"{pattern['mean']:.4f}_{pattern['std']:.4f}"
                    
                    if signature not in pattern_groups:
                        pattern_groups[signature] = []
                    pattern_groups[signature].append({
                        'tensor': tensor_name,
                        'index': idx,
                        'pattern': pattern
                    })
        
        # Find groups with similar patterns
        for signature, group in pattern_groups.items():
            if len(group) > 1:
                shared_patterns.append({
                    'signature': signature,
                    'occurrences': group,
                    'potential_savings': sum(p['pattern']['size'] for p in group[1:])
                })
                
        return shared_patterns
    
    def _generate_optimization_suggestions(self, results: Dict) -> List[Dict]:
        """Generate optimization suggestions based on analysis"""
        # Implementation placeholder
        suggestions = []
        for tensor_name, impact in results['memory_impact'].items():
            if impact['potential_savings'] > 1000000:  # If savings > 1MB
                suggestions.append({
                    'tensor': tensor_name,
                    'suggestion': 'Consider weight quantization',
                    'impact': f"Save {impact['potential_savings'] / 1024 / 1024:.2f}MB"
                })
        return suggestions

@dataclass
class TrainingStatistics:
    """Statistics collected during HCF-aware training"""
    memory_savings: int = 0
    quantization_error: float = 0.0
    convergence_rate: float = 0.0
    epoch: int = 0
    batch_count: int = 0
    
    def update(self, batch_stats: Dict[str, Any]):
        """Update statistics with batch results"""
        self.memory_savings += batch_stats.get('memory_savings', 0)
        self.quantization_error = batch_stats.get('quantization_error', self.quantization_error)
        self.convergence_rate = batch_stats.get('convergence_rate', self.convergence_rate)
        self.batch_count += 1

class HCFTrainingOptimizer(torch.optim.Adam):
    """
    Optimizer with HCF-awareness for more efficient training
    """
    def __init__(self, 
                 params, 
                 lr=0.001, 
                 betas=(0.9, 0.999), 
                 eps=1e-8,
                 weight_decay=0, 
                 weight_quantization=True,
                 maintain_patterns=True):
        super().__init__(params, lr, betas, eps, weight_decay)
        self.weight_quantization = weight_quantization
        self.maintain_patterns = maintain_patterns
        self.analyzer = SafeTensorHCFAnalyzer()
        self.stats = {'memory_savings': 0, 'quantization_error': 0.0}
    
    def step(self, closure=None):
        """Perform optimization step with HCF awareness"""
        # Run standard optimization step
        loss = super().step(closure)
        
        # Apply HCF optimizations if enabled
        if self.weight_quantization:
            self._apply_weight_quantization()
        
        if self.maintain_patterns:
            self._maintain_weight_patterns()
            
        return loss
    
    def _apply_weight_quantization(self):
        """Apply dynamic weight quantization using HCF patterns"""
        savings = 0
        total_error = 0.0
        
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None or not p.requires_grad:
                    continue
                
                # Apply weight quantization logic based on HCF analysis
                # This is a simplified placeholder - real implementation would be more complex
                if p.dim() > 1:  # Only apply to matrices/tensors
                    # Find suitable quantization factor
                    factor = torch.max(torch.abs(p.data)) / 127  # 8-bit quantization example
                    
                    # Quantize weights
                    quantized = torch.round(p.data / factor) * factor
                    
                    # Calculate error and savings
                    error = torch.mean((p.data - quantized)**2).item()
                    savings += p.numel() * (p.element_size() - 1)  # Assuming 8-bit savings
                    
                    # Apply quantized weights
                    p.data.copy_(quantized)
                    
                    total_error += error
        
        # Update statistics
        self.stats['memory_savings'] = savings
        self.stats['quantization_error'] = total_error
    
    def _maintain_weight_patterns(self):
        """Maintain efficient weight patterns identified by HCF analysis"""
        # Placeholder for pattern maintenance logic
        # Real implementation would analyze weight matrices and enforce patterns
        pass
    
    def get_stats(self):
        """Get current optimization statistics"""
        return self.stats

class HCFAwareTrainer:
    """
    Trainer that incorporates HCF analysis for better training efficiency
    """
    def __init__(self, model, optimizer):
        self.model = model
        self.optimizer = optimizer
        self.analyzer = SafeTensorHCFAnalyzer()
        
    def train_epoch(self, train_loader, criterion, epoch):
        """Train one epoch with HCF awareness"""
        self.model.train()
        stats = TrainingStatistics(epoch=epoch)
        
        for batch_idx, batch in enumerate(train_loader):
            # Get data
            inputs, targets = self._prepare_batch(batch)
            
            # Forward pass
            self.optimizer.zero_grad()
            outputs = self.model(inputs)
            loss = criterion(outputs, targets)
            
            # Backward pass
            loss.backward()
            
            # Optimize with HCF awareness
            self.optimizer.step()
            
            # Get batch statistics
            batch_stats = self.optimizer.get_stats()
            stats.update(batch_stats)
            
            # Log progress
            if batch_idx % 50 == 0:
                logger.info(f"Epoch {epoch} | Batch {batch_idx}/{len(train_loader)} | "
                            f"Memory Savings: {stats.memory_savings/1024/1024:.2f}MB | "
                            f"Quantization Error: {stats.quantization_error:.6f}")
        
        # End of epoch analysis
        self._analyze_model_weights()
        
        return stats
    
    def _prepare_batch(self, batch):
        """Prepare batch data for training"""
        # Implementation depends on dataset structure
        if isinstance(batch, dict):
            inputs = batch.get('input_ids')
            targets = batch.get('labels', inputs)
        else:
            # Assume batch is a tuple of (inputs, targets)
            inputs, targets = batch
            
        return inputs, targets
    
    def _analyze_model_weights(self):
        """Analyze model weights for patterns and optimizations"""
        # Save model to temporary safetensor file for analysis
        model_path = "temp_model.safetensors"
        tensors = {name: param for name, param in self.model.named_parameters()}
        save_file(tensors, model_path)
        
        # Analyze weights
        results = self.analyzer.analyze_safetensor_weights(model_path)
        
        # Log findings
        logger.info(f"Weight Analysis: Found {len(results['shared_patterns'])} shared patterns")
        logger.info(f"Potential memory savings: "
                    f"{sum(i['potential_savings'] for i in results['memory_impact'].values())/1024/1024:.2f}MB")
        
        # Clean up
        if os.path.exists(model_path):
            os.remove(model_path)

@dataclass
class ModelConfig:
    name: str
    model_id: str
    tokenizer_id: str
    
CONFIGS = {
    "7b": ModelConfig(
        name="7b",
        model_id="scrapegoat/ScrapeGoat-Music-Stage1",
        tokenizer_id="scrapegoat/ScrapeGoat-Music-Stage1"
    ),
    "1b": ModelConfig(
        name="1b",
        model_id="scrapegoat/ScrapeGoat-Music-Stage2",
        tokenizer_id="scrapegoat/ScrapeGoat-Music-Stage2"
    )
}

class MusicFineTuner:
    def __init__(
        self,
        model_size: str,
        dataset_path: str,
        output_dir: str,
        device: str = "auto",
        batch_size: int = 4,
        gradient_accumulation_steps: int = 4,
        learning_rate: float = 1e-5,
        num_epochs: int = 3,
        use_hcf: bool = True
    ):
        self.config = CONFIGS[model_size]
        self.dataset_path = Path(dataset_path)
        self.output_dir = Path(output_dir)
        self.device = self._setup_device(device)
        self.use_hcf = use_hcf
        self.training_args = TrainingArguments(
            output_dir=str(self.output_dir),
            per_device_train_batch_size=batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            learning_rate=learning_rate,
            num_train_epochs=num_epochs,
            logging_steps=100,
            save_steps=1000,
            evaluation_strategy="steps",
            eval_steps=500,
            save_total_limit=3,
            load_best_model_at_end=True,
            gradient_checkpointing=True,
            fp16=torch.cuda.is_available(),
            optim="adamw_torch"
        )
        
    def _setup_device(self, device: str) -> str:
        if device == "auto":
            if torch.cuda.is_available():
                return "cuda"
            elif torch.backends.mps.is_available():
                return "mps"
            else:
                return "cpu"
        return device
    
    def _load_model_and_tokenizer(self):
        logger.info(f"Loading model {self.config.model_id}")
        
        # Determine dtype based on device
        dtype = torch.bfloat16 if self.device == "cuda" else torch.float32
        
        model = AutoModelForCausalLM.from_pretrained(
            self.config.model_id,
            torch_dtype=dtype,
            device_map="auto" if self.device == "cuda" else None,
            attn_implementation="flash_attention_2" if self.device == "cuda" else "eager"
        )
        
        tokenizer = AutoTokenizer.from_pretrained(self.config.tokenizer_id)
        return model, tokenizer
    
    def _prepare_dataset(self, tokenizer):
        logger.info("Preparing dataset")
        
        with open(self.dataset_path / "metadata" / "dataset_info.json") as f:
            metadata = json.load(f)
        
        def generate_text(item):
            return f"Genre: {item['genre']}\nDuration: {item['duration']:.2f}s\nTitle: {item['title']}\nArtist: {item['artist']}\n"
        
        texts = [generate_text(item) for item in metadata["files"]]
        dataset = Dataset.from_dict({"text": texts})
        
        def tokenize(examples):
            return tokenizer(
                examples["text"],
                truncation=True,
                padding="max_length",
                max_length=512,
                return_tensors="pt"
            )
        
        tokenized_dataset = dataset.map(
            tokenize,
            batched=True,
            remove_columns=dataset.column_names
        )
        
        return tokenized_dataset
    
    def train(self):
        # Create output directory
        self.output_dir.mkdir(parents=True, exist_ok=True)
        
        # Load model and tokenizer
        model, tokenizer = self._load_model_and_tokenizer()
        
        # Prepare dataset
        dataset = self._prepare_dataset(tokenizer)
        
        # Split dataset
        dataset = dataset.train_test_split(test_size=0.1)
        
        if self.use_hcf:
            logger.info("Using HCF-aware training")
            # Create custom HCF optimizer
            optimizer = HCFTrainingOptimizer(
                model.parameters(),
                lr=self.training_args.learning_rate,
                weight_quantization=True,
                maintain_patterns=True
            )
            
            # Create HCF trainer
            hcf_trainer = HCFAwareTrainer(model, optimizer)
            
            # Create custom training loop
            train_loader = torch.utils.data.DataLoader(
                dataset["train"],
                batch_size=self.training_args.per_device_train_batch_size,
                shuffle=True
            )
            
            # Training loop with HCF awareness
            criterion = torch.nn.CrossEntropyLoss()
            for epoch in range(int(self.training_args.num_train_epochs)):
                stats = hcf_trainer.train_epoch(train_loader, criterion, epoch)
                
                # Log training metrics
                logger.info(f"Epoch {epoch} completed")
                logger.info(f"Memory Savings: {stats.memory_savings/1024/1024:.2f}MB")
                logger.info(f"Quantization Error: {stats.quantization_error:.6f}")
                logger.info(f"Convergence Rate: {stats.convergence_rate:.4f}")
                
                # Save checkpoint
                self._save_hcf_checkpoint(model, tokenizer, epoch)
        else:
            # Use standard HuggingFace Trainer
            logger.info("Using standard training")
            trainer = Trainer(
                model=model,
                args=self.training_args,
                train_dataset=dataset["train"],
                eval_dataset=dataset["test"],
                data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
            )
            
            # Train
            logger.info("Starting training")
            trainer.train()
        
        # Save final model
        logger.info("Saving model")
        model.save_pretrained(str(self.output_dir / "final_model"))
        tokenizer.save_pretrained(str(self.output_dir / "final_model"))
    
    def _save_hcf_checkpoint(self, model, tokenizer, epoch):
        """Save checkpoint with HCF metadata"""
        checkpoint_dir = self.output_dir / f"checkpoint-{epoch}"
        checkpoint_dir.mkdir(exist_ok=True)
        
        # Save model and tokenizer
        model.save_pretrained(str(checkpoint_dir))
        tokenizer.save_pretrained(str(checkpoint_dir))
        
        # Analyze and save HCF metadata
        analyzer = SafeTensorHCFAnalyzer()
        
        # Save tensors to analyze
        model_path = str(checkpoint_dir / "model.safetensors")
        if os.path.exists(model_path):
            results = analyzer.analyze_safetensor_weights(model_path)
            
            # Save analysis results
            with open(checkpoint_dir / "hcf_analysis.json", "w") as f:
                json.dump(results, f, indent=2)
        
        logger.info(f"Saved checkpoint at {checkpoint_dir}")

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_size", type=str, choices=["1b", "7b"], required=True)
    parser.add_argument("--dataset_path", type=str, required=True)
    parser.add_argument("--output_dir", type=str, required=True)
    parser.add_argument("--device", type=str, default="auto")
    parser.add_argument("--batch_size", type=int, default=4)
    parser.add_argument("--gradient_accumulation_steps", type=int, default=4)
    parser.add_argument("--learning_rate", type=float, default=1e-5)
    parser.add_argument("--num_epochs", type=int, default=3)
    parser.add_argument("--use_hcf", action="store_true", help="Enable HCF-aware training")
    args = parser.parse_args()
    
    fine_tuner = MusicFineTuner(
        model_size=args.model_size,
        dataset_path=args.dataset_path,
        output_dir=args.output_dir,
        device=args.device,
        batch_size=args.batch_size,
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        learning_rate=args.learning_rate,
        num_epochs=args.num_epochs,
        use_hcf=args.use_hcf
    )
    fine_tuner.train()