docs/ARCHITECTURE_EN.md

System Architecture Document

(Google Gemini Translation)

This document details the system architecture, design principles, and implementation specifics of the emotion and physiological state change prediction model.

Table of Contents

1. System Overview
2. Overall Architecture
3. Model Architecture
4. Data Processing Workflow
5. Training Workflow
6. Inference Workflow
7. Module Design
8. Design Patterns
9. Performance Optimization
10. Extensibility Design

System Overview

Design Goals

This system aims to implement an efficient, scalable, and maintainable emotion and physiological state change prediction model. The main design goals include:

1. High Performance: Support GPU acceleration and optimize inference speed.
2. Modularity: Clear module partitioning for easy maintenance and extension.
3. Configurability: Flexible configuration system to support hyperparameter tuning.
4. Usability: Comprehensive CLI tools and Python API.
5. Extensibility: Support new model architectures and loss functions.
6. Observability: Complete logging and monitoring system.

Technology Stack

• Deep Learning Framework: PyTorch 1.12+
• Data Processing: NumPy, Pandas, scikit-learn
• Configuration Management: PyYAML, OmegaConf
• Visualization: Matplotlib, Seaborn, Plotly
• Command Line: argparse, Click
• Logging System: Loguru
• Experiment Tracking: MLflow, Weights & Biases
• Performance Analysis: py-spy, memory-profiler

Overall Architecture

System Architecture Diagram

┌─────────────────────────────────────────────────────────────────┐
│                        User Interface Layer                       │
├─────────────────────────────────────────────────────────────────┤
│  CLI Tool  │  Python API  │  Web API  │  Jupyter Notebook       │
├─────────────────────────────────────────────────────────────────┤
│                        Business Logic Layer                     │
├─────────────────────────────────────────────────────────────────┤
│  Training Manager  │  Inference Engine  │  Evaluator  │  Config Manager  │  Log Manager  │
├─────────────────────────────────────────────────────────────────┤
│                        Core Model Layer                         │
├─────────────────────────────────────────────────────────────────┤
│  PAD Predictor  │  Loss Function  │  Evaluation Metrics  │  Model Factory  │  Optimizer  │
├─────────────────────────────────────────────────────────────────┤
│                        Data Processing Layer                    │
├─────────────────────────────────────────────────────────────────┤
│  Data Loader  │  Preprocessor  │  Data Augmenter  │  Synthetic Data Generator        │
├─────────────────────────────────────────────────────────────────┤
│                        Infrastructure Layer                     │
├─────────────────────────────────────────────────────────────────┤
│  File System  │  GPU Computing  │  Memory Management  │  Exception Handling  │  Utility Functions  │
└─────────────────────────────────────────────────────────────────┘

Module Dependency Relationships

CLI Module → Business Logic Layer → Core Model Layer → Data Processing Layer → Infrastructure Layer
   ↓
Config Manager → All Modules
   ↓
Log Manager → All Modules

Model Architecture

Network Structure

The PAD predictor employs a Multi-Layer Perceptron (MLP) architecture:

Input Layer (7 dimensions)
    ↓
Hidden Layer 1 (128 neurons) + ReLU + Dropout(0.3)
    ↓
Hidden Layer 2 (64 neurons) + ReLU + Dropout(0.3)
    ↓
Hidden Layer 3 (32 neurons) + ReLU
    ↓
Output Layer (5 neurons) + Linear Activation

Detailed Network Components

Input Layer

• Dimensions: 7-dimensional feature vector
• Feature Composition:
  • User PAD: 3 dimensions (Pleasure, Arousal, Dominance)
  • Vitality: 1 dimension (Physiological Vitality Value)
  • Current PAD: 3 dimensions (Current Emotional State)

Hidden Layer Design Principles

1. Layer-by-Layer Compression: Gradually reduce the number of neurons from 128 → 64 → 32.
2. Activation Function: Use ReLU activation function to avoid vanishing gradients.
3. Regularization: Use Dropout in the first two layers to prevent overfitting.
4. Weight Initialization: Use Xavier uniform initialization, suitable for ReLU activation.

Output Layer Design

• Dimensions: 3-dimensional output vector
• Output Composition:
  • ΔPAD: 3 dimensions (Change in Emotion: ΔPleasure, ΔArousal, ΔDominance)
  • ΔPressure: Dynamically calculated from PAD changes (Formula: 1.0 × (-ΔP) + 0.8 × (ΔA) + 0.6 × (-ΔD))
• Activation Function: Linear activation, suitable for regression tasks.

Model Configuration System

# Default architecture configuration
DEFAULT_ARCHITECTURE = {
    'input_dim': 7,
    'output_dim': 3,
    'hidden_dims': [512, 256, 128],
    'dropout_rate': 0.3,
    'activation': 'relu',
    'weight_init': 'xavier_uniform',
    'bias_init': 'zeros'
}

# Configurable parameters
CONFIGURABLE_PARAMS = {
    'hidden_dims': {
        'type': list,
        'default': [128, 64, 32],
        'constraints': [
            lambda x: len(x) >= 1,
            lambda x: all(isinstance(n, int) and n > 0 for n in x),
            lambda x: x == sorted(x, reverse=True)  # Decreasing sequence
        ]
    },
    'dropout_rate': {
        'type': float,
        'default': 0.3,
        'range': [0.0, 0.9]
    },
    'activation': {
        'type': str,
        'default': 'relu',
        'choices': ['relu', 'tanh', 'sigmoid', 'leaky_relu']
    }
}

Data Processing Workflow

Data Pipeline

Raw Data → Data Validation → Feature Extraction → Data Preprocessing → Data Augmentation → Batch Generation
    ↓
Model Training/Inference

Data Preprocessing Workflow

1. Data Validation

class DataValidator:
    """Data validator to ensure data quality"""
    
    def validate_input_shape(self, data: np.ndarray) -> bool:
        """Validate input data shape"""
        return data.shape[1] == 7
    
    def validate_value_ranges(self, data: np.ndarray) -> Dict[str, bool]:
        """Validate value ranges"""
        return {
            'pad_features_valid': np.all(data[:, :6] >= -1) and np.all(data[:, :6] <= 1),
            'vitality_valid': np.all(data[:, 3] >= 0) and np.all(data[:, 3] <= 100)
        }
    
    def check_missing_values(self, data: np.ndarray) -> Dict[str, Any]:
        """Check for missing values"""
        return {
            'has_missing': np.isnan(data).any(),
            'missing_count': np.isnan(data).sum(),
            'missing_ratio': np.isnan(data).mean()
        }

2. Feature Engineering

class FeatureEngineer:
    """Feature engineer"""
    
    def extract_pad_features(self, data: np.ndarray) -> np.ndarray:
        """Extract PAD features"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        return np.hstack([user_pad, current_pad])
    
    def compute_pad_differences(self, data: np.ndarray) -> np.ndarray:
        """Compute PAD differences"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        return user_pad - current_pad
    
    def create_interaction_features(self, data: np.ndarray) -> np.ndarray:
        """Create interaction features"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        
        # PAD dot product
        pad_interaction = np.sum(user_pad * current_pad, axis=1, keepdims=True)
        
        # PAD Euclidean distance
        pad_distance = np.linalg.norm(user_pad - current_pad, axis=1, keepdims=True)
        
        return np.hstack([data, pad_interaction, pad_distance])

3. Data Standardization

class DataNormalizer:
    """Data normalizer"""
    
    def __init__(self, method: str = 'standard'):
        self.method = method
        self.scalers = {}
    
    def fit_pad_features(self, features: np.ndarray):
        """Fit PAD feature scaler"""
        if self.method == 'standard':
            self.scalers['pad'] = StandardScaler()
        elif self.method == 'minmax':
            self.scalers['pad'] = MinMaxScaler(feature_range=(-1, 1))
        
        self.scalers['pad'].fit(features)
    
    def fit_vitality_feature(self, features: np.ndarray):
        """Fit vitality feature scaler"""
        if self.method == 'standard':
            self.scalers['vitality'] = StandardScaler()
        elif self.method == 'minmax':
            self.scalers['vitality'] = MinMaxScaler(feature_range=(0, 1))
        
        self.scalers['vitality'].fit(features.reshape(-1, 1))

Data Augmentation Strategies

class DataAugmenter:
    """Data augmenter"""
    
    def __init__(self, noise_std: float = 0.01, mixup_alpha: float = 0.2):
        self.noise_std = noise_std
        self.mixup_alpha = mixup_alpha
    
    def add_gaussian_noise(self, features: np.ndarray) -> np.ndarray:
        """Add Gaussian noise"""
        noise = np.random.normal(0, self.noise_std, features.shape)
        return features + noise
    
    def mixup_augmentation(self, features: np.ndarray, labels: np.ndarray) -> tuple:
        """Mixup data augmentation"""
        batch_size = features.shape[0]
        lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
        
        # Randomly shuffle indices
        index = np.random.permutation(batch_size)
        
        # Mix features and labels
        mixed_features = lam * features + (1 - lam) * features[index]
        mixed_labels = lam * labels + (1 - lam) * labels[index]
        
        return mixed_features, mixed_labels

Training Workflow

Training Architecture

Config Loading → Data Preparation → Model Initialization → Training Loop → Model Saving → Result Evaluation

Training Manager Design

class ModelTrainer:
    """Model training manager"""
    
    def __init__(self, model, preprocessor=None, device='auto'):
        self.model = model
        self.preprocessor = preprocessor
        self.device = self._setup_device(device)
        self.logger = logging.getLogger(__name__)
        
        # Training state
        self.training_state = {
            'epoch': 0,
            'best_loss': float('inf'),
            'patience_counter': 0,
            'training_history': []
        }
    
    def setup_training(self, config: Dict[str, Any]):
        """Set up the training environment"""
        # Optimizer setup
        self.optimizer = self._create_optimizer(config['optimizer'])
        
        # Learning rate scheduler
        self.scheduler = self._create_scheduler(config['scheduler'])
        
        # Loss function
        self.criterion = self._create_criterion(config['loss'])
        
        # Early stopping mechanism
        self.early_stopping = self._setup_early_stopping(config['early_stopping'])
        
        # Checkpoint management
        self.checkpoint_manager = CheckpointManager(config['checkpointing'])
    
    def train_epoch(self, train_loader: DataLoader) -> Dict[str, float]:
        """Train for one epoch"""
        self.model.train()
        epoch_loss = 0.0
        num_batches = len(train_loader)
        
        for batch_idx, (features, labels) in enumerate(train_loader):
            features = features.to(self.device)
            labels = labels.to(self.device)
            
            # Forward pass
            self.optimizer.zero_grad()
            outputs = self.model(features)
            loss = self.criterion(outputs, labels)
            
            # Backward pass
            loss.backward()
            
            # Gradient clipping
            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
            
            # Parameter update
            self.optimizer.step()
            
            epoch_loss += loss.item()
            
            # Logging
            if batch_idx % 100 == 0:
                self.logger.debug(f'Batch {batch_idx}/{num_batches}, Loss: {loss.item():.6f}')
        
        return {'train_loss': epoch_loss / num_batches}
    
    def validate_epoch(self, val_loader: DataLoader) -> Dict[str, float]:
        """Validate for one epoch"""
        self.model.eval()
        val_loss = 0.0
        num_batches = len(val_loader)
        
        with torch.no_grad():
            for features, labels in val_loader:
                features = features.to(self.device)
                labels = labels.to(self.device)
                
                outputs = self.model(features)
                loss = self.criterion(outputs, labels)
                
                val_loss += loss.item()
        
        return {'val_loss': val_loss / num_batches}

Training Strategies

1. Learning Rate Scheduling

class LearningRateScheduler:
    """Learning rate scheduling strategy"""
    
    @staticmethod
    def cosine_annealing_scheduler(optimizer, T_max, eta_min=1e-6):
        """Cosine annealing scheduler"""
        return torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, T_max=T_max, eta_min=eta_min
        )
    
    @staticmethod
    def reduce_on_plateau_scheduler(optimizer, patience=5, factor=0.5):
        """ReduceLROnPlateau scheduler"""
        return torch.optim.lr_scheduler.ReduceLROnPlateau(
            optimizer, mode='min', patience=patience, factor=factor
        )
    
    @staticmethod
    def warmup_cosine_scheduler(optimizer, warmup_epochs, total_epochs):
        """Warmup cosine scheduler"""
        def lr_lambda(epoch):
            if epoch < warmup_epochs:
                return epoch / warmup_epochs
            else:
                progress = (epoch - warmup_epochs) / (total_epochs - warmup_epochs)
                return 0.5 * (1 + math.cos(math.pi * progress))
        
        return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)

2. Early Stopping Mechanism

class EarlyStopping:
    """Early stopping mechanism"""
    
    def __init__(self, patience=10, min_delta=1e-4, mode='min'):
        self.patience = patience
        self.min_delta = min_delta
        self.mode = mode
        self.counter = 0
        self.best_score = None
        
        if mode == 'min':
            self.is_better = lambda x, y: x < y - min_delta
        else:
            self.is_better = lambda x, y: x > y + min_delta
    
    def __call__(self, score):
        if self.best_score is None:
            self.best_score = score
            return False
        
        if self.is_better(score, self.best_score):
            self.best_score = score
            self.counter = 0
            return False
        else:
            self.counter += 1
            return self.counter >= self.patience

Inference Workflow

Inference Architecture

Model Loading → Input Validation → Data Preprocessing → Model Inference → Result Post-processing → Output Formatting

Inference Engine Design

class InferenceEngine:
    """High-performance inference engine"""
    
    def __init__(self, model, preprocessor=None, device='auto'):
        self.model = model
        self.preprocessor = preprocessor
        self.device = self._setup_device(device)
        self.model.to(self.device)
        self.model.eval()
        
        # Performance optimization
        self._optimize_model()
        
        # Warm-up
        self._warmup_model()
    
    def _optimize_model(self):
        """Optimize model performance"""
        # TorchScript optimization
        try:
            self.model = torch.jit.script(self.model)
            self.logger.info("Model optimized to TorchScript format")
        except Exception as e:
            self.logger.warning(f"TorchScript optimization failed: {e}")
        
        # Mixed precision
        if self.device.type == 'cuda':
            self.scaler = torch.cuda.amp.GradScaler()
    
    def _warmup_model(self, num_warmup=5):
        """Warm up the model"""
        dummy_input = torch.randn(1, 7).to(self.device)
        
        with torch.no_grad():
            for _ in range(num_warmup):
                _ = self.model(dummy_input)
        
        self.logger.info(f"Model warm-up completed, warm-up runs: {num_warmup}")
    
    def predict_single(self, input_data: Union[List, np.ndarray]) -> Dict[str, Any]:
        """Single sample inference"""
        # Input validation
        validated_input = self._validate_input(input_data)
        
        # Data preprocessing
        processed_input = self._preprocess_input(validated_input)
        
        # Model inference
        with torch.no_grad():
            if self.device.type == 'cuda':
                with torch.cuda.amp.autocast():
                    output = self.model(processed_input)
            else:
                output = self.model(processed_input)
        
        # Result post-processing
        result = self._postprocess_output(output)
        
        return result
    
    def predict_batch(self, input_batch: Union[List, np.ndarray]) -> List[Dict[str, Any]]:
        """Batch inference"""
        # Input validation and preprocessing
        validated_batch = self._validate_batch(input_batch)
        processed_batch = self._preprocess_batch(validated_batch)
        
        # Batch inference
        batch_size = min(32, len(processed_batch))
        results = []
        
        for i in range(0, len(processed_batch), batch_size):
            batch_input = processed_batch[i:i+batch_size]
            
            with torch.no_grad():
                if self.device.type == 'cuda':
                    with torch.cuda.amp.autocast():
                        batch_output = self.model(batch_input)
                else:
                    batch_output = self.model(batch_input)
            
            # Post-processing
            batch_results = self._postprocess_batch(batch_output)
            results.extend(batch_results)
        
        return results

Performance Optimization Strategies

1. Memory Optimization

class MemoryOptimizer:
    """Memory optimizer"""
    
    @staticmethod
    def optimize_memory_usage():
        """Optimize memory usage"""
        # Clear GPU cache
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        
        # Set memory allocation strategy
        if torch.cuda.is_available():
            torch.cuda.set_per_process_memory_fraction(0.9)
    
    @staticmethod
    def monitor_memory_usage():
        """Monitor memory usage"""
        if torch.cuda.is_available():
            allocated = torch.cuda.memory_allocated() / 1024**3  # GB
            cached = torch.cuda.memory_reserved() / 1024**3  # GB
            return {'allocated': allocated, 'cached': cached}
        return {'allocated': 0, 'cached': 0}

2. Computation Optimization

class ComputeOptimizer:
    """Computation optimizer"""
    
    @staticmethod
    def enable_tf32():
        """Enable TF32 acceleration (Ampere architecture GPUs)"""
        if torch.cuda.is_available():
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.allow_tf32 = True
    
    @staticmethod
    def optimize_dataloader(dataloader, num_workers=4, pin_memory=True):
        """Optimize data loader"""
        return DataLoader(
            dataloader.dataset,
            batch_size=dataloader.batch_size,
            shuffle=dataloader.shuffle,
            num_workers=num_workers,
            pin_memory=pin_memory and torch.cuda.is_available(),
            persistent_workers=True if num_workers > 0 else False
        )

Module Design

Core Modules

1. Model Module (src.models/)

# Model module structure
src/models/
├── __init__.py
├── pad_predictor.py      # Core predictor
├── loss_functions.py     # Loss functions
├── metrics.py           # Evaluation metrics
├── model_factory.py     # Model factory
└── base_model.py        # Base model class

Design Principles:

• Single Responsibility: Each class is responsible for only one specific function.
• Open/Closed Principle: Open for extension, closed for modification.
• Dependency Inversion: Depend on abstractions, not concretions.

2. Data Module (src.data/)

# Data module structure
src/data/
├── __init__.py
├── dataset.py           # Dataset class
├── data_loader.py       # Data loader
├── preprocessor.py      # Data preprocessor
├── synthetic_generator.py # Synthetic data generator
└── data_validator.py    # Data validator

Design Patterns:

• Strategy Pattern: Different data preprocessing strategies.
• Factory Pattern: Data generator factory.
• Observer Pattern: Data quality monitoring.

3. Utility Module (src.utils/)

# Utility module structure
src/utils/
├── __init__.py
├── inference_engine.py  # Inference engine
├── trainer.py          # Trainer
├── logger.py           # Logging utility
├── config.py           # Configuration management
└── exceptions.py       # Custom exceptions

Features:

• High-performance inference engine
• Flexible training management
• Structured logging system
• Unified configuration management

Design Patterns

1. Factory Pattern

class ModelFactory:
    """Model factory class"""
    
    _models = {
        'pad_predictor': PADPredictor,
        'advanced_predictor': AdvancedPADPredictor,
        'ensemble_predictor': EnsemblePredictor
    }
    
    @classmethod
    def create_model(cls, model_type: str, config: Dict[str, Any]):
        """Create a model instance"""
        if model_type not in cls._models:
            raise ValueError(f"Unsupported model type: {model_type}")
        
        model_class = cls._models[model_type]
        return model_class(**config)
    
    @classmethod
    def register_model(cls, name: str, model_class):
        """Register a new model type"""
        cls._models[name] = model_class

2. Strategy Pattern

class LossStrategy(ABC):
    """Abstract base class for loss strategies"""
    
    @abstractmethod
    def compute_loss(self, predictions, targets):
        pass

class WeightedMSELoss(LossStrategy):
    """Weighted Mean Squared Error Loss"""
    
    def compute_loss(self, predictions, targets):
        # Implement weighted MSE
        pass

class HuberLoss(LossStrategy):
    """Huber Loss"""
    
    def compute_loss(self, predictions, targets):
        # Implement Huber loss
        pass

class LossContext:
    """Loss context"""
    
    def __init__(self, strategy: LossStrategy):
        self._strategy = strategy
    
    def set_strategy(self, strategy: LossStrategy):
        self._strategy = strategy
    
    def compute_loss(self, predictions, targets):
        return self._strategy.compute_loss(predictions, targets)

3. Observer Pattern

class TrainingObserver(ABC):
    """Abstract base class for training observers"""
    
    @abstractmethod
    def on_epoch_start(self, epoch, metrics):
        pass
    
    @abstractmethod
    def on_epoch_end(self, epoch, metrics):
        pass

class LoggingObserver(TrainingObserver):
    """Logging observer"""
    
    def on_epoch_end(self, epoch, metrics):
        self.logger.info(f"Epoch {epoch}: {metrics}")

class CheckpointObserver(TrainingObserver):
    """Checkpoint observer"""
    
    def on_epoch_end(self, epoch, metrics):
        if self.should_save_checkpoint(metrics):
            self.save_checkpoint(epoch, metrics)

class TrainingSubject:
    """Training subject"""
    
    def __init__(self):
        self._observers = []
    
    def attach(self, observer: TrainingObserver):
        self._observers.append(observer)
    
    def detach(self, observer: TrainingObserver):
        self._observers.remove(observer)
    
    def notify_epoch_end(self, epoch, metrics):
        for observer in self._observers:
            observer.on_epoch_end(epoch, metrics)

4. Builder Pattern

class ModelBuilder:
    """Model builder"""
    
    def __init__(self):
        self.input_dim = 7
        self.output_dim = 3
        self.hidden_dims = [128, 64, 32]
        self.dropout_rate = 0.3
        self.activation = 'relu'
    
    def with_dimensions(self, input_dim, output_dim):
        self.input_dim = input_dim
        self.output_dim = output_dim
        return self
    
    def with_hidden_layers(self, hidden_dims):
        self.hidden_dims = hidden_dims
        return self
    
    def with_dropout(self, dropout_rate):
        self.dropout_rate = dropout_rate
        return self
    
    def with_activation(self, activation):
        self.activation = activation
        return self
    
    def build(self):
        return PADPredictor(
            input_dim=self.input_dim,
            output_dim=self.output_dim,
            hidden_dims=self.hidden_dims,
            dropout_rate=self.dropout_rate
        )

# Example usage
model = (ModelBuilder()
         .with_dimensions(7, 5)
         .with_hidden_layers([256, 128, 64])
         .with_dropout(0.3)
         .build())

Performance Optimization

1. Model Optimization

Quantization

class ModelQuantizer:
    """Model quantizer"""
    
    @staticmethod
    def quantize_model(model, calibration_data):
        """Dynamically quantize the model"""
        model.eval()
        
        # Dynamic quantization
        quantized_model = torch.quantization.quantize_dynamic(
            model, {nn.Linear}, dtype=torch.qint8
        )
        
        return quantized_model
    
    @staticmethod
    def quantize_aware_training(model, train_loader):
        """Quantization-aware training"""
        model.eval()
        model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
        torch.quantization.prepare_qat(model, inplace=True)
        
        # Quantization-aware training
        for epoch in range(num_epochs):
            for batch in train_loader:
                # Training steps
                pass
        
        # Convert to quantized model
        quantized_model = torch.quantization.convert(model.eval(), inplace=False)
        return quantized_model

Model Pruning

class ModelPruner:
    """Model pruner"""
    
    @staticmethod
    def prune_model(model, pruning_ratio=0.2):
        """Structured pruning"""
        import torch.nn.utils.prune as prune
        
        # Prune all linear layers
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.l1_unstructured(module, name='weight', amount=pruning_ratio)
        
        return model
    
    @staticmethod
    def remove_pruning(model):
        """Remove pruning reparameterization"""
        import torch.nn.utils.prune as prune
        
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.remove(module, 'weight')
        
        return model

2. Inference Optimization

Batch Inference Optimization

class BatchInferenceOptimizer:
    """Batch inference optimizer"""
    
    def __init__(self, model, device):
        self.model = model
        self.device = device
        self.optimal_batch_size = self._find_optimal_batch_size()
    
    def _find_optimal_batch_size(self):
        """Find the optimal batch size"""
        batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128]
        best_batch_size = 1
        best_throughput = 0
        
        dummy_input = torch.randn(1, 7).to(self.device)
        
        for batch_size in batch_sizes:
            try:
                # Test batch size
                batch_input = dummy_input.repeat(batch_size, 1)
                
                start_time = time.time()
                with torch.no_grad():
                    for _ in range(10):
                        _ = self.model(batch_input)
                end_time = time.time()
                
                throughput = (batch_size * 10) / (end_time - start_time)
                
                if throughput > best_throughput:
                    best_throughput = throughput
                    best_batch_size = batch_size
                    
            except RuntimeError:
                break  # Out of memory
        
        return best_batch_size

Extensibility Design

1. Plugin System

class PluginManager:
    """Plugin manager"""
    
    def __init__(self):
        self.plugins = {}
        self.hooks = defaultdict(list)
    
    def register_plugin(self, name: str, plugin):
        """Register a plugin"""
        self.plugins[name] = plugin
        
        # Register plugin hooks
        if hasattr(plugin, 'get_hooks'):
            for hook_name, hook_func in plugin.get_hooks().items():
                self.hooks[hook_name].append(hook_func)
    
    def execute_hooks(self, hook_name: str, *args, **kwargs):
        """Execute hooks"""
        for hook_func in self.hooks[hook_name]:
            hook_func(*args, **kwargs)

class PluginBase(ABC):
    """Base class for plugins"""
    
    @abstractmethod
    def initialize(self, config):
        pass
    
    @abstractmethod
    def cleanup(self):
        pass
    
    def get_hooks(self):
        return {}

2. Configuration Extension

class ConfigManager:
    """Configuration manager"""
    
    def __init__(self):
        self.config_schemas = {}
        self.config_validators = {}
    
    def register_config_schema(self, name: str, schema: Dict):
        """Register a configuration schema"""
        self.config_schemas[name] = schema
    
    def register_validator(self, name: str, validator: callable):
        """Register a configuration validator"""
        self.config_validators[name] = validator
    
    def validate_config(self, config: Dict[str, Any]) -> bool:
        """Validate configuration"""
        for name, validator in self.config_validators.items():
            if name in config:
                if not validator(config[name]):
                    raise ValueError(f"Configuration validation failed: {name}")
        return True

3. Model Registration System

class ModelRegistry:
    """Model registration system"""
    
    _models = {}
    _model_metadata = {}
    
    @classmethod
    def register(cls, name: str, metadata: Dict = None):
        """Model registration decorator"""
        def decorator(model_class):
            cls._models[name] = model_class
            cls._model_metadata[name] = metadata or {}
            return model_class
        return decorator
    
    @classmethod
    def create_model(cls, name: str, **kwargs):
        """Create a model"""
        if name not in cls._models:
            raise ValueError(f"Unregistered model: {name}")
        
        model_class = cls._models[name]
        return model_class(**kwargs)
    
    @classmethod
    def list_models(cls):
        """List all registered models"""
        return list(cls._models.keys())

# Example usage
@ModelRegistry.register("advanced_pad", 
                       {"description": "Advanced PAD Predictor", "version": "2.0"})
class AdvancedPADPredictor(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        # Model implementation
        pass

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This architecture document describes the overall design and implementation details of the system. As the project evolves, the architecture will continue to be optimized and extended. For suggestions or questions, please provide feedback via GitHub Issues.