Advanced Optimization Techniques

#!/bin/bash
# Cricket Momentum Analyzer - Advanced Optimization Techniques Setup
# This script creates a complete project structure for training optimized
# Hugging Face Transformer models on cricket match commentary data

echo "================================"
echo "Cricket Momentum Analyzer Setup"
echo "================================"

# Create project directory structure with modular organization
mkdir -p cricket-momentum-analyzer/{data,models,scripts,results,notebooks}
cd cricket-momentum-analyzer

# Create subdirectories for organized data and model management
mkdir -p data/{raw_commentary,processed}
mkdir -p models/{pretrained,quantized,distilled}
mkdir -p scripts/{data_processing,training,optimization,evaluation}
mkdir -p results/{metrics,logs,visualizations}

echo "[✓] Project directory structure created"

# Create Python virtual environment for dependency isolation
python3 -m venv cricket_env
source cricket_env/bin/activate  # Activate virtual environment

echo "[✓] Virtual environment activated"

# Upgrade pip to latest version for compatibility
pip install --upgrade pip setuptools wheel

echo "[✓] Pip upgraded successfully"

# Install PyTorch with CUDA 11.8 support for GPU acceleration
pip install torch==2.0.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

echo "[✓] PyTorch 2.0.1 with CUDA support installed"

# Install Hugging Face Transformers library for state-of-the-art models
pip install transformers==4.35.2

# Install datasets library for efficient data loading and processing
pip install datasets==2.14.6

# Install scikit-learn for evaluation metrics and preprocessing
pip install scikit-learn==1.3.2

# Install model optimization and quantization libraries
pip install intel-extension-for-pytorch==2.0.1

# Install additional optimization tools
pip install optimum[onnxruntime]==1.16.0
pip install onnx==1.15.0
pip install onnxruntime==1.17.0

echo "[✓] All dependencies installed successfully"

# Create main configuration file for the project
cat > config.yaml << 'EOF'
# Cricket Momentum Analyzer - Configuration

project_name: "cricket-momentum-analyzer"
version: "1.0.0"

# Cricket-themed dataset and model parameters
dataset:
  match_types: ["test", "odi", "t20"]
  commentary_source: "ipl_commentary_2023"
  train_test_split: 0.8
  validation_split: 0.1
  max_sequence_length: 512
  batch_size: 16

# Model optimization configurations
optimization:
  quantization:
    method: "dynamic"  # dynamic, static, qat
    precision: "int8"
  distillation:
    teacher_model: "bert-base-cased"
    student_model: "distilbert-base-cased"
    temperature: 4.0
    alpha: 0.7
  pruning:
    method: "structured"
    sparsity: 0.3

# Training parameters
training:
  epochs: 3
  learning_rate: 2e-5
  optimizer: "adamw"
  weight_decay: 0.01
  warmup_steps: 500
  gradient_accumulation_steps: 2

# Cricket player and match identifiers
cricket_players:
  batsmen: ["Rohit Sharma", "Virat Kohli", "Steve Smith"]
  bowlers: ["Jasprit Bumrah", "Pat Cummins", "Jofra Archer"]
  
cricket_metrics:
  runs_scored: "momentum"
  wickets_fallen: "pressure"
  strike_rate: "aggression"
  bowling_economy: "control"
EOF

echo "[✓] Configuration file created: config.yaml"

# Create requirements.txt for reproducible environment
cat > requirements.txt << 'EOF'
torch==2.0.1
torchvision==0.15.2
torchaudio==2.0.1
transformers==4.35.2
datasets==2.14.6
scikit-learn==1.3.2
intel-extension-for-pytorch==2.0.1
optimum==1.16.0
onnx==1.15.0
onnxruntime==1.17.0
pyyaml==6.0
pandas==2.1.3
numpy==1.24.3
matplotlib==3.8.2
tqdm==4.66.1
accelerate==0.24.1
peft==0.7.1
EOF

echo "[✓] Requirements file created: requirements.txt"

# Create README documenting the project structure and optimization techniques
cat > README.md << 'EOF'
# Cricket Momentum Analyzer - Advanced Optimization Techniques

An advanced project demonstrating Hugging Face Transformers optimization techniques applied to cricket match analysis.

## Project Structure

```
cricket-momentum-analyzer/
├── data/
│   ├── raw_commentary/          # Raw match commentary data
│   └── processed/               # Preprocessed datasets
├── models/
│   ├── pretrained/              # Original transformer models
│   ├── quantized/               # INT8 quantized models
│   └── distilled/               # Knowledge-distilled models
├── scripts/
│   ├── data_processing/         # Data loading and preprocessing
│   ├── training/                # Model training scripts
│   ├── optimization/            # Quantization, pruning, distillation
│   └── evaluation/              # Performance evaluation
├── results/
│   ├── metrics/                 # Performance metrics
│   ├── logs/                    # Training logs
│   └── visualizations/          # Performance charts
├── config.yaml                  # Project configuration
├── requirements.txt             # Python dependencies
└── README.md                    # This file
```

## Key Features

1. **Quantization**: Convert models to INT8 for 4x smaller model size
2. **Distillation**: Transfer knowledge from BERT to DistilBERT for 40% faster inference
3. **Pruning**: Remove 30% of model weights while maintaining accuracy
4. **Optimization**: Use torch.quantization and intel-extension-for-pytorch

## Cricket-Themed Variables

- **match_id**: Unique cricket match identifier
- **innings_count**: Number of innings completed
- **CricketPlayer**: Player entity with stats (Rohit Sharma, Jasprit Bumrah)
- **momentum_score**: Model output predicting match momentum
- **commentary_batch**: Batch of match commentary for processing

## Installation

```bash
# Activate virtual environment
source cricket_env/bin/activate

# Install dependencies
pip install -r requirements.txt
```

## Usage

```bash
# Run data preprocessing
python scripts/data_processing/load_commentary.py

# Train unoptimized baseline model
python scripts/training/train_baseline.py

# Apply quantization optimization
python scripts/optimization/quantize_model.py

# Apply knowledge distillation
python scripts/optimization/distill_model.py

# Apply pruning optimization
python scripts/optimization/prune_model.py

# Evaluate and compare optimizations
python scripts/evaluation/compare_models.py
```

## Optimization Results

Expected improvements after applying all techniques:
- Model Size: 4x reduction (quantization)
- Inference Speed: 3x faster (distillation)
- Memory Usage: 2x lower
- Accuracy Drop: <2% relative

## Dependencies

- PyTorch 2.0.1 with CUDA support
- Transformers 4.35.2
- Datasets 2.14.6
- Intel Extension for PyTorch 2.0.1
- Optimum 1.16.0 (ONNX Runtime support)

## Cricket Domain Concepts

This project analyzes:
- **Match Momentum**: Predicted from commentary patterns
- **Player Performance**: Batting and bowling efficiency
- **Innings Dynamics**: Pressure and aggression metrics
- **Match Progression**: Real-time probability updates

EOF

echo "[✓] README.md created with full documentation"

# Create Python module structure
touch __init__.py
mkdir -p scripts/data_processing
mkdir -p scripts/training
mkdir -p scripts/optimization
mkdir -p scripts/evaluation

# Create module initialization files
touch scripts/__init__.py
touch scripts/data_processing/__init__.py
touch scripts/training/__init__.py
touch scripts/optimization/__init__.py
touch scripts/evaluation/__init__.py

echo "[✓] Python module structure initialized"

# Display final setup summary
echo ""
echo "================================"
echo "Setup Complete!"
echo "================================"
echo ""
echo "Cricket Momentum Analyzer project created successfully"
echo "Project location: $(pwd)"
echo ""
echo "Next steps:"
echo "1. Activate environment: source cricket_env/bin/activate"
echo "2. Review configuration: cat config.yaml"
echo "3. View project structure: tree -L 2"
echo ""
echo "Key files created:"
echo "  ✓ Project directory with 7 subdirectories"
echo "  ✓ Virtual environment (cricket_env)"
echo "  ✓ Configuration file (config.yaml)"
echo "  ✓ Requirements file (requirements.txt)"
echo "  ✓ Documentation (README.md)"
echo "  ✓ Python module structure"
echo ""
echo "Installed packages:"
echo "  ✓ PyTorch 2.0.1 (with CUDA 11.8)"
echo "  ✓ Transformers 4.35.2"
echo "  ✓ Datasets 2.14.6"
echo "  ✓ scikit-learn 1.3.2"
echo "  ✓ Intel Extension for PyTorch 2.0.1"
echo "  ✓ Optimum 1.16.0 (ONNX Runtime)"
echo ""
echo "Ready to implement:"
echo "  • Data loading for cricket commentary"
echo "  • Dynamic quantization (INT8)"
echo "  • Knowledge distillation (BERT → DistilBERT)"
echo "  • Structured pruning (30% sparsity)"
echo ""

# models/base_model.py
import torch
import torch.nn as nn
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from torch.utils.data import DataLoader, Dataset
import time

class CricketMatchDataset(Dataset):
    """Cricket match commentary dataset for momentum analysis"""
    def __init__(self, commentaries, labels, tokenizer, max_length=128):
        self.commentaries = commentaries
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __len__(self):
        return len(self.commentaries)

    def __getitem__(self, idx):
        """
        Returns encoded match commentary similar to how a coach analyzes
        critical deliveries from a batsman's innings
        """
        commentary = self.commentaries[idx]
        label = self.labels[idx]
        
        # Tokenize like extracting key patterns from match data
        encoded = self.tokenizer(
            commentary,
            max_length=self.max_length,
            padding='max_length',
            truncation=True,
            return_tensors='pt'
        )
        
        return {
            'input_ids': encoded['input_ids'].squeeze(),
            'attention_mask': encoded['attention_mask'].squeeze(),
            'labels': torch.tensor(label, dtype=torch.long)
        }


class CricketMomentumModel(nn.Module):
    """
    DistilBERT-based model for analyzing match momentum (like Rishabh Pant's form).
    Demonstrates baseline establishment before optimization:
    - Full BERT: 110M parameters (watching all 200 matches)
    - DistilBERT: 70M parameters (70% of key patterns)
    """
    def __init__(self, num_labels=2, model_name='distilbert-base-uncased'):
        super(CricketMomentumModel, self).__init__()
        
        # DistilBERT: The optimized version of BERT (distilled knowledge)
        self.base_transformer = DistilBertForSequenceClassification.from_pretrained(
            model_name,
            num_labels=num_labels
        )
        
        # Track model statistics for baseline establishment
        self.player_name = "DistilBERT-Optimizer"
        self.parameter_count = sum(p.numel() for p in self.base_transformer.parameters())
        
    def forward(self, input_ids, attention_mask):
        """
        Forward pass analyzing match momentum through commentary
        """
        outputs = self.base_transformer(
            input_ids=input_ids,
            attention_mask=attention_mask
        )
        return outputs


class BaselineEstablisher:
    """
    Establishes baseline performance metrics before optimization.
    Like a coach measuring Rohit Sharma's current strike rate and technique.
    """
    def __init__(self, model, device='cpu'):
        self.model = model
        self.device = device
        self.baseline_metrics = {}
        
    def establish_baseline(self, eval_dataloader, epoch_id=0):
        """
        Measure current model performance on validation set.
        This is like the coach's initial assessment before coaching begins.
        """
        self.model.eval()
        total_loss = 0.0
        correct_predictions = 0
        total_samples = 0
        inference_times = []
        
        with torch.no_grad():
            for batch in eval_dataloader:
                input_ids = batch['input_ids'].to(self.device)
                attention_mask = batch['attention_mask'].to(self.device)
                labels = batch['labels'].to(self.device)
                
                # Measure inference time (like timing a delivery)
                start_time = time.time()
                outputs = self.model(input_ids, attention_mask)
                inference_time = time.time() - start_time
                inference_times.append(inference_time)
                
                logits = outputs.logits
                loss = nn.CrossEntropyLoss()(logits, labels)
                
                total_loss += loss.item()
                predictions = torch.argmax(logits, dim=1)
                correct_predictions += (predictions == labels).sum().item()
                total_samples += labels.size(0)
        
        # Calculate baseline metrics
        avg_loss = total_loss / len(eval_dataloader)
        accuracy = correct_predictions / total_samples
        avg_inference_time = sum(inference_times) / len(inference_times)
        
        self.baseline_metrics[f'epoch_{epoch_id}'] = {
            'loss': avg_loss,
            'accuracy': accuracy,
            'avg_inference_time_ms': avg_inference_time * 1000,
            'total_parameters': self.model.parameter_count
        }
        
        return avg_loss, accuracy, avg_inference_time


# Example usage demonstrating the cricket analogy
if __name__ == "__main__":
    # Sample cricket match commentaries (like Jasprit Bumrah bowling analysis)
    sample_commentaries = [
        "Bumrah delivers a yorker, batsman plays a perfect drive. Four runs!",
        "Brilliant bowling from Jasprit. The batsman struggles against the short ball.",
        "Rohit Sharma comes down the track and smashes it over mid-wicket. Six!",
        "Edge caught! The batsman couldn't handle the rising delivery.",
    ]
    
    sample_labels = [1, 0, 1, 0]  # 1 = positive momentum, 0 = negative momentum
    
    # Initialize tokenizer and model
    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
    
    # Create dataset
    cricket_dataset = CricketMatchDataset(
        commentaries=sample_commentaries,
        labels=sample_labels,
        tokenizer=tokenizer,
        max_length=64
    )
    
    # Create data loader (like organizing match statistics)
    match_dataloader = DataLoader(cricket_dataset, batch_size=2, shuffle=True)
    
    # Initialize model
    momentum_model = CricketMomentumModel(num_labels=2)
    print(f"Model Parameters: {momentum_model.parameter_count / 1e6:.1f}M")
    print(f"(DistilBERT uses ~70M params, down from BERT's 110M - like distilling crucial patterns)\n")
    
    # Establish baseline (coach's initial assessment)
    baseline_establisher = BaselineEstablisher(momentum_model)
    loss, accuracy, inference_time = baseline_establisher.establish_baseline(match_dataloader)
    
    print("🏏 Baseline Establishment (Before Optimization):")
    print(f"  Loss: {loss:.4f}")
    print(f"  Accuracy: {accuracy:.2%}")
    print(f"  Avg Inference Time: {inference_time*1000:.2f}ms")
    print("\nThis baseline is like measuring Rishabh Pant's current strike rate")
    print("before the coaching intervention begins.")

# scripts/optimize_model.py
import torch
import torch.nn as nn
from torch.quantization import quantize_dynamic, QConfig, default_qat_qconfig
from torch.nn.utils import prune
from transformers import DistilBertForSequenceClassification
import copy

class QuantizedCricketClassifier:
    """Dynamic quantization for cricket momentum classifier"""
    def __init__(self, baseline_model, quantization_type='dynamic'):
        self.baseline_model = baseline_model
        self.quantization_type = quantization_type
        self.quantized_model = None
        self.pruned_model = None
        
    def compress_to_hd(self):
        """
        Quantize model from full precision (4K) to 8-bit (HD compression)
        Like Jasprit Bumrah's yorker biomechanics: essential angles preserved,
        imperceptible decimal variations discarded
        """
        if self.quantization_type == 'dynamic':
            # Dynamic quantization: weights stored in int8, activations in float32
            self.quantized_model = quantize_dynamic(
                self.baseline_model,
                {nn.Linear},  # Only quantize Linear layers (like key bowlers)
                dtype=torch.qint8
            )
            print("✓ Model compressed to HD (8-bit weights, float32 activations)")
            return self.quantized_model
        
        elif self.quantization_type == 'static':
            # Static QAT: Calibrate on representative cricket data
            self.baseline_model.qconfig = default_qat_qconfig
            self.quantized_model = torch.quantization.prepare_qat(self.baseline_model)
            print("✓ Model prepared for static quantization-aware training")
            return self.quantized_model
    
    def squad_optimization(self, pruning_amount=0.2):
        """
        Prune low-contribution parameters (like 5th bowler with 2% match impact)
        Captain's mid-tournament decision: remove squad members barely contributing
        """
        self.pruned_model = copy.deepcopy(self.baseline_model)
        
        # Identify and remove low-magnitude weights (unproductive bowlers)
        total_parameters = 0
        pruned_parameters = 0
        
        for name, module in self.pruned_model.named_modules():
            if isinstance(module, nn.Linear):
                # Structured pruning by magnitude
                prune.l1_unstructured(module, name='weight', amount=pruning_amount)
                prune.remove(module, 'weight')  # Permanently remove pruned weights
                
                total_params_in_layer = module.weight.numel()
                pruned_params_in_layer = int(total_params_in_layer * pruning_amount)
                
                total_parameters += total_params_in_layer
                pruned_parameters += pruned_params_in_layer
                
                print(f"  📍 Layer '{name}': Pruned {pruning_amount*100:.1f}% "
                      f"({pruned_params_in_layer}/{total_params_in_layer} parameters)")
        
        pruning_ratio = (pruned_parameters / total_parameters * 100) if total_parameters > 0 else 0
        print(f"✓ Squad optimized: Removed {pruning_ratio:.2f}% of low-impact parameters\n")
        
        return self.pruned_model
    
    def combined_pipeline(self, pruning_amount=0.2):
        """
        Full optimization pipeline: Quantization + Pruning
        Match preparation: Compress footage (quantization) + select best XI (pruning)
        """
        print("🏏 CRICKET MATCH OPTIMIZATION PIPELINE")
        print("=" * 50)
        
        # Stage 1: Compression (4K → HD)
        print("\n[Stage 1] Compressing training footage to HD...")
        quantized = self.compress_to_hd()
        
        # Stage 2: Squad selection
        print("\n[Stage 2] Optimizing squad by removing bench players...")
        optimized = self.squad_optimization(pruning_amount=pruning_amount)
        
        # Model size comparison (like comparing match archives)
        original_size = sum(p.numel() for p in self.baseline_model.parameters())
        optimized_size = sum(p.numel() for p in optimized.parameters())
        
        compression_ratio = (1 - optimized_size / original_size) * 100
        
        print(f"\n[Results] Match Archive Compression")
        print(f"  Original model size: {original_size:,} parameters")
        print(f"  Optimized model size: {optimized_size:,} parameters")
        print(f"  Total compression: {compression_ratio:.2f}%")
        print(f"  Upload speed improvement: ~{compression_ratio:.0f}% faster ⚡")
        
        return quantized, optimized


class CricketPerformanceAnalyzer:
    """Analyze cricket player statistics with optimized models"""
    
    def __init__(self, model):
        self.model = model
        self.rohit_sharma = {"name": "Rohit Sharma", "role": "Opener", "centuries": 31}
        self.jasprit_bumrah = {"name": "Jasprit Bumrah", "role": "Bowler", "wickets": 289}
        self.player_stats = []
    
    def track_innings_performance(self, innings_count, match_id, player_name, runs_scored):
        """Track cricket player performance across innings"""
        performance_record = {
            "innings_count": innings_count,
            "match_id": match_id,
            "player_name": player_name,
            "runs_scored": runs_scored,
            "model_compressed": hasattr(self.model, 'quantized_model') and self.model.quantized_model is not None
        }
        self.player_stats.append(performance_record)
        return performance_record
    
    def get_player_momentum(self):
        """Calculate momentum from tracked stats"""
        if not self.player_stats:
            return 0
        total_runs = sum(stat["runs_scored"] for stat in self.player_stats)
        return total_runs / len(self.player_stats)


# ============================================================================
# DEMONSTRATION: Full optimization workflow
# ============================================================================

if __name__ == "__main__":
    print("\n🏏 HUGGING FACE TRANSFORMERS - ADVANCED OPTIMIZATION DEMO\n")
    
    # Initialize baseline cricket sentiment classifier
    print("Loading baseline DistilBERT model...")
    baseline_cricket_model = DistilBertForSequenceClassification.from_pretrained(
        'distilbert-base-uncased',
        num_labels=2,  # Binary: Match-winning performance or not
        output_hidden_states=False
    )
    
    # Create optimizer instance
    optimizer = QuantizedCricketClassifier(baseline_cricket_model, quantization_type='dynamic')
    
    # Run combined optimization pipeline
    quantized_model, optimized_model = optimizer.combined_pipeline(pruning_amount=0.15)
    
    # Performance tracking
    print("\n" + "=" * 50)
    print("TRACKING PLAYER PERFORMANCE WITH OPTIMIZED MODEL")
    print("=" * 50)
    
    analyzer = CricketPerformanceAnalyzer(optimizer)
    
    # Track innings for Rohit Sharma
    analyzer.track_innings_performance(
        innings_count=1, 
        match_id="IND_vs_AUS_2024", 
        player_name="Rohit Sharma", 
        runs_scored=87
    )
    
    # Track yorker performance for Jasprit Bumrah
    analyzer.track_innings_performance(
        innings_count=2, 
        match_id="IND_vs_AUS_2024", 
        player_name="Jasprit Bumrah", 
        runs_scored=2  # Bowlers contribute via wickets, but we track runs scored against them
    )
    
    average_momentum = analyzer.get_player_momentum()
    print(f"\n📊 Average match momentum (optimized inference): {average_momentum:.2f} runs/innings")
    print("\n✅ Optimization complete! Model ready for production inference.")
    print("   Reduced memory footprint = Faster deployment across edge devices 🚀")