e2e_qlora_training_example.py

"""
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║ ThemisDB - Hybrid Database System                                   ║
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  File:            e2e_qlora_training_example.py                      ║
  Version:         0.0.47                                             ║
  Last Modified:   2026-04-15 18:43:53                                ║
  Author:          unknown                                            ║
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  Quality Metrics:                                                    ║
    • Maturity Level:  🟢 PRODUCTION-READY                             ║
    • Quality Score:   100.0/100                                      ║
    • Total Lines:     386                                            ║
    • Open Issues:     TODOs: 0, Stubs: 0                             ║
╠═════════════════════════════════════════════════════════════════════╣
  Status: ✅ Production Ready                                          ║
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"""

#!/usr/bin/env python3
"""
End-to-End QLoRA Training Pipeline Example for ThemisDB

This example demonstrates the complete workflow:
1. Check model compatibility
2. Prepare training data
3. Train QLoRA adapter with Axolotl integration
4. Monitor resource usage
5. Convert adapter to GGUF
6. Load and test in ThemisDB

Usage:
    python e2e_qlora_training_example.py --model <model_path> --data <data_file>
"""

import os
import sys
import json
import argparse
import logging
from pathlib import Path
from typing import List, Dict

# Add ThemisDB Python bridge to path
sys.path.insert(0, str(Path(__file__).parent.parent / "src" / "llm" / "lora_framework"))

from axolotl_bridge import (
    ThemisDBTrainingConfig,
    LoRAHyperparameters,
    QLoRAConfig,
    AxolotlBridge,
    DataExporter
)

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


class E2EQLoRATrainingPipeline:
    """End-to-end QLoRA training pipeline"""
    
    def __init__(self, base_model: str, output_dir: str = "./qlora_output"):
        self.base_model = base_model
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)
        
        logger.info(f"E2E QLoRA Training Pipeline initialized")
        logger.info(f"  Base model: {base_model}")
        logger.info(f"  Output dir: {output_dir}")
    
    def step1_check_compatibility(self) -> Dict:
        """
        Step 1: Check model compatibility
        
        Calls ThemisDB's ModelCompatibilityChecker to validate:
        - Model format (GGUF, SafeTensors, etc.)
        - Architecture support
        - Quantization compatibility
        """
        logger.info("=" * 80)
        logger.info("STEP 1: Model Compatibility Check")
        logger.info("=" * 80)
        
        # In production, this would call the C++ ModelCompatibilityChecker
        # For now, simulate the check
        
        compatibility = {
            "is_compatible": True,
            "format": "GGUF",
            "architecture": "LLaMA",
            "warnings": [],
            "recommendations": {
                "quantization": "nf4",
                "rank": 16,
                "batch_size": 8,
                "target_modules": ["q_proj", "v_proj", "k_proj", "o_proj"]
            }
        }
        
        if compatibility["is_compatible"]:
            logger.info("✅ Model is compatible with QLoRA training")
            logger.info(f"  Format: {compatibility['format']}")
            logger.info(f"  Architecture: {compatibility['architecture']}")
            logger.info(f"  Recommended quantization: {compatibility['recommendations']['quantization']}")
            logger.info(f"  Recommended rank: {compatibility['recommendations']['rank']}")
        else:
            logger.error("❌ Model is not compatible")
            for error in compatibility.get("errors", []):
                logger.error(f"  - {error}")
            sys.exit(1)
        
        return compatibility
    
    def step2_prepare_training_data(self, data_file: str) -> List[Dict]:
        """
        Step 2: Prepare training data
        
        Load and validate training data.
        """
        logger.info("\n" + "=" * 80)
        logger.info("STEP 2: Prepare Training Data")
        logger.info("=" * 80)
        
        # Load training data
        with open(data_file, 'r') as f:
            if data_file.endswith('.jsonl'):
                training_data = [json.loads(line) for line in f]
            else:
                training_data = json.load(f)
        
        logger.info(f"✅ Loaded {len(training_data)} training samples")
        
        # Validate data format
        required_keys = {"input", "output"}
        for i, sample in enumerate(training_data):
            if not required_keys.issubset(sample.keys()):
                logger.error(f"Sample {i} missing required keys: {required_keys}")
                sys.exit(1)
        
        logger.info("  Data format validated")
        
        return training_data
    
    def step3_configure_training(self, compatibility: Dict) -> ThemisDBTrainingConfig:
        """
        Step 3: Configure training
        
        Create ThemisDB training configuration using compatibility recommendations.
        """
        logger.info("\n" + "=" * 80)
        logger.info("STEP 3: Configure Training")
        logger.info("=" * 80)
        
        # Use recommendations from compatibility check
        recs = compatibility["recommendations"]
        
        # LoRA hyperparameters
        hyperparameters = LoRAHyperparameters(
            rank=recs["rank"],
            alpha=32.0,  # 2x rank is common
            dropout=0.05,
            learning_rate=2e-4,
            batch_size=recs["batch_size"],
            num_epochs=3,
            max_seq_length=2048,
            optimizer="adamw",
            lr_scheduler="cosine",
            warmup_steps=100,
            target_modules=recs["target_modules"]
        )
        
        # QLoRA configuration
        qlora = QLoRAConfig(
            enabled=True,
            quantization_type=recs["quantization"],
            block_size=64,
            use_double_quantization=True,
            layer_by_layer=True,
            use_paged_optimizer=False
        )
        
        # Complete training config
        config = ThemisDBTrainingConfig(
            adapter_id="my_legal_qa_adapter",
            base_model_path=self.base_model,
            output_dir=str(self.output_dir / "adapter"),
            hyperparameters=hyperparameters,
            qlora=qlora,
            mixed_precision=True,
            gradient_accumulation_steps=4,
            enable_checkpointing=True,
            checkpoint_interval_steps=100,
            checkpoint_dir=str(self.output_dir / "checkpoints")
        )
        
        logger.info("✅ Training configuration created")
        logger.info(f"  LoRA rank: {hyperparameters.rank}")
        logger.info(f"  Quantization: {qlora.quantization_type}")
        logger.info(f"  Batch size: {hyperparameters.batch_size}")
        logger.info(f"  Learning rate: {hyperparameters.learning_rate}")
        logger.info(f"  Epochs: {hyperparameters.num_epochs}")
        
        return config
    
    def step4_train_adapter(self, config: ThemisDBTrainingConfig, 
                           training_data: List[Dict]) -> Dict:
        """
        Step 4: Train QLoRA adapter
        
        Train using Axolotl with ThemisDB configuration.
        """
        logger.info("\n" + "=" * 80)
        logger.info("STEP 4: Train QLoRA Adapter")
        logger.info("=" * 80)
        
        # Create bridge
        bridge = AxolotlBridge(config)
        
        # Train and convert
        logger.info("Starting training with Axolotl...")
        result = bridge.train_and_convert(training_data, convert_to_gguf=True)
        
        if result.get("success", False):
            logger.info("✅ Training completed successfully")
            logger.info(f"  Adapter path: {result.get('adapter_path')}")
            if result.get("gguf_conversion", {}).get("success", False):
                logger.info(f"  GGUF file: {result['gguf_conversion']['output_file']}")
        else:
            logger.error("❌ Training failed")
            logger.error(f"  Error: {result.get('error')}")
            sys.exit(1)
        
        return result
    
    def step5_analyze_resources(self) -> Dict:
        """
        Step 5: Analyze resource usage
        
        Parse resource profiling logs from training.
        """
        logger.info("\n" + "=" * 80)
        logger.info("STEP 5: Analyze Resource Usage")
        logger.info("=" * 80)
        
        # Look for resource profile file
        profile_files = list((self.output_dir / "checkpoints").glob("resource_profile_*.jsonl"))
        
        if not profile_files:
            logger.warning("No resource profile file found")
            return {}
        
        profile_file = profile_files[0]
        logger.info(f"Reading resource profile: {profile_file}")
        
        # Parse snapshots
        snapshots = []
        with open(profile_file, 'r') as f:
            for line in f:
                snapshots.append(json.loads(line))
        
        # Compute statistics
        if snapshots:
            peak_gpu_mb = max(s["gpu_memory"]["allocated_mb"] for s in snapshots)
            avg_gpu_util = sum(s["gpu_utilization_pct"] for s in snapshots) / len(snapshots)
            avg_throughput = sum(s["throughput"]["samples_per_sec"] for s in snapshots if s["throughput"]["samples_per_sec"] > 0)
            avg_throughput = avg_throughput / len([s for s in snapshots if s["throughput"]["samples_per_sec"] > 0]) if avg_throughput > 0 else 0
            
            logger.info("✅ Resource usage analyzed")
            logger.info(f"  Peak GPU memory: {peak_gpu_mb:.2f} MB")
            logger.info(f"  Avg GPU utilization: {avg_gpu_util:.1f}%")
            logger.info(f"  Avg throughput: {avg_throughput:.1f} samples/s")
            
            return {
                "peak_gpu_mb": peak_gpu_mb,
                "avg_gpu_util": avg_gpu_util,
                "avg_throughput": avg_throughput,
                "num_snapshots": len(snapshots)
            }
        
        return {}
    
    def step6_verify_adapter(self, gguf_file: str):
        """
        Step 6: Verify adapter
        
        Test the trained adapter with a sample query.
        """
        logger.info("\n" + "=" * 80)
        logger.info("STEP 6: Verify Adapter")
        logger.info("=" * 80)
        
        if not Path(gguf_file).exists():
            logger.error(f"GGUF file not found: {gguf_file}")
            return
        
        logger.info(f"Adapter file: {gguf_file}")
        logger.info(f"File size: {Path(gguf_file).stat().st_size / (1024*1024):.2f} MB")
        
        # In production, this would load the adapter into ThemisDB and test it
        logger.info("✅ Adapter verification complete")
        logger.info("\nTo use this adapter in ThemisDB:")
        logger.info(f"  SELECT LORA_QUERY(")
        logger.info(f"    '{self.base_model}',")
        logger.info(f"    'my_legal_qa_adapter',")
        logger.info(f"    'What is breach of contract?'")
        logger.info(f"  );")
    
    def run_complete_pipeline(self, data_file: str):
        """Run the complete end-to-end pipeline"""
        logger.info("\n" + "=" * 80)
        logger.info("QLoRA End-to-End Training Pipeline")
        logger.info("=" * 80)
        logger.info("")
        
        try:
            # Step 1: Check compatibility
            compatibility = self.step1_check_compatibility()
            
            # Step 2: Prepare data
            training_data = self.step2_prepare_training_data(data_file)
            
            # Step 3: Configure training
            config = self.step3_configure_training(compatibility)
            
            # Step 4: Train adapter
            train_result = self.step4_train_adapter(config, training_data)
            
            # Step 5: Analyze resources
            resource_stats = self.step5_analyze_resources()
            
            # Step 6: Verify adapter
            if train_result.get("gguf_conversion", {}).get("success", False):
                gguf_file = train_result["gguf_conversion"]["output_file"]
                self.step6_verify_adapter(gguf_file)
            
            # Summary
            logger.info("\n" + "=" * 80)
            logger.info("PIPELINE COMPLETE - Summary")
            logger.info("=" * 80)
            logger.info(f"✅ Model: {compatibility['architecture']} ({compatibility['format']})")
            logger.info(f"✅ Training samples: {len(training_data)}")
            logger.info(f"✅ Quantization: {config.qlora.quantization_type}")
            logger.info(f"✅ LoRA rank: {config.hyperparameters.rank}")
            if resource_stats:
                logger.info(f"✅ Peak GPU memory: {resource_stats['peak_gpu_mb']:.2f} MB")
                logger.info(f"✅ Avg throughput: {resource_stats['avg_throughput']:.1f} samples/s")
            logger.info(f"✅ Output: {self.output_dir}")
            logger.info("")
            logger.info("Next steps:")
            logger.info("1. Review resource profile: checkpoints/resource_profile_*.jsonl")
            logger.info("2. Test adapter with ThemisDB LORA_QUERY function")
            logger.info("3. Monitor adapter performance in production")
            logger.info("=" * 80)
            
        except Exception as e:
            logger.error(f"\n❌ Pipeline failed: {e}", exc_info=True)
            sys.exit(1)


def main():
    parser = argparse.ArgumentParser(description="E2E QLoRA Training Pipeline")
    parser.add_argument("--model", required=True, help="Path to base model")
    parser.add_argument("--data", required=True, help="Path to training data (JSON/JSONL)")
    parser.add_argument("--output", default="./qlora_output", help="Output directory")
    
    args = parser.parse_args()
    
    # Check files exist
    if not Path(args.model).exists():
        logger.error(f"Model file not found: {args.model}")
        sys.exit(1)
    
    if not Path(args.data).exists():
        logger.error(f"Data file not found: {args.data}")
        sys.exit(1)
    
    # Run pipeline
    pipeline = E2EQLoRATrainingPipeline(args.model, args.output)
    pipeline.run_complete_pipeline(args.data)


if __name__ == "__main__":
    main()