model.py

import os
import xml.etree.ElementTree as ET
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Any, Optional
from collections import defaultdict
from accelerate import Accelerator

class DynamicModel(nn.Module):
    def __init__(self, sections: Dict[str, List[Dict[str, Any]]]):
        """
        Initialize the DynamicModel with configurable neural network sections.

        Args:
            sections (Dict[str, List[Dict[str, Any]]]): Dictionary mapping section names to lists of layer configurations.
                Each layer configuration is a dictionary containing:
                - input_size (int): Size of input features
                - output_size (int): Size of output features
                - activation (str, optional): Activation function name ('relu', 'tanh', 'sigmoid', etc.)
                - dropout (float, optional): Dropout rate
                - batch_norm (bool, optional): Whether to use batch normalization
                - hidden_layers (List[Dict[str, Any]], optional): List of hidden layer configurations
                - memory_augmentation (bool, optional): Whether to add a memory augmentation layer
                - hybrid_attention (bool, optional): Whether to add a hybrid attention layer
                - dynamic_flash_attention (bool, optional): Whether to add a dynamic flash attention layer

        Example:
            sections = {
                'encoder': [
                    {'input_size': 128, 'output_size': 256, 'activation': 'relu', 'batch_norm': True},
                    {'input_size': 256, 'output_size': 512, 'activation': 'leaky_relu', 'dropout': 0.1}
                ],
                'decoder': [
                    {'input_size': 512, 'output_size': 256, 'activation': 'elu'},
                    {'input_size': 256, 'output_size': 128, 'activation': 'tanh'}
                ]
            }
        """
        super(DynamicModel, self).__init__()
        self.sections = nn.ModuleDict()

        # Default section configuration if none provided
        if not sections:
            sections = {
                'default': [{
                    'input_size': 128,
                    'output_size': 256,
                    'activation': 'relu',
                    'batch_norm': True,
                    'dropout': 0.1
                }]
            }

        # Initialize each section with its layer configurations
        for section_name, layers in sections.items():
            self.sections[section_name] = nn.ModuleList()
            for layer_params in layers:
                self.sections[section_name].append(self.create_layer(layer_params))

    def create_layer(self, layer_params: Dict[str, Any]) -> nn.Module:
        """
        Creates a neural network layer based on provided parameters.

        Args:
            layer_params (Dict[str, Any]): Dictionary containing layer configuration
                Required keys:
                - input_size (int): Size of input features
                - output_size (int): Size of output features
                Optional keys:
                - activation (str): Activation function name ('relu', 'tanh', 'sigmoid', None)
                - dropout (float): Dropout rate if needed
                - batch_norm (bool): Whether to use batch normalization
                - hidden_layers (List[Dict[str, Any]]): List of hidden layer configurations
                - memory_augmentation (bool): Whether to add a memory augmentation layer
                - hybrid_attention (bool): Whether to add a hybrid attention layer
                - dynamic_flash_attention (bool): Whether to add a dynamic flash attention layer

        Returns:
            nn.Module: Configured neural network layer with activation

        Raises:
            KeyError: If required parameters are missing
            ValueError: If activation function is not supported
        """
        layers = []

        # Add linear layer
        layers.append(nn.Linear(layer_params['input_size'], layer_params['output_size']))

        # Add batch normalization if specified
        if layer_params.get('batch_norm', False):
            layers.append(nn.BatchNorm1d(layer_params['output_size']))

        # Add activation function
        activation = layer_params.get('activation', 'relu')
        if activation == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif activation == 'tanh':
            layers.append(nn.Tanh())
        elif activation == 'sigmoid':
            layers.append(nn.Sigmoid())
        elif activation == 'leaky_relu':
            layers.append(nn.LeakyReLU(negative_slope=0.01, inplace=True))
        elif activation == 'elu':
            layers.append(nn.ELU(alpha=1.0, inplace=True))
        elif activation is not None:
            raise ValueError(f"Unsupported activation function: {activation}")

        # Add dropout if specified
        if dropout_rate := layer_params.get('dropout', 0.0):
            layers.append(nn.Dropout(p=dropout_rate))

        # Add hidden layers if specified
        if hidden_layers := layer_params.get('hidden_layers', []):
            for hidden_layer_params in hidden_layers:
                layers.append(self.create_layer(hidden_layer_params))

        # Add memory augmentation layer if specified
        if layer_params.get('memory_augmentation', False):
            layers.append(MemoryAugmentationLayer(layer_params['output_size']))

        # Add hybrid attention layer if specified
        if layer_params.get('hybrid_attention', False):
            layers.append(HybridAttentionLayer(layer_params['output_size']))

        # Add dynamic flash attention layer if specified
        if layer_params.get('dynamic_flash_attention', False):
            layers.append(DynamicFlashAttentionLayer(layer_params['output_size']))

        return nn.Sequential(*layers)

    def forward(self, x: torch.Tensor, section_name: Optional[str] = None) -> torch.Tensor:
        """
        Forward pass through the dynamic model architecture.

        Args:
            x (torch.Tensor): Input tensor to process
            section_name (Optional[str]): Specific section to process. If None, processes all sections

        Returns:
            torch.Tensor: Processed output tensor

        Raises:
            KeyError: If specified section_name doesn't exist
        """
        if section_name is not None:
            if section_name not in self.sections:
                raise KeyError(f"Section '{section_name}' not found in model")
            for layer in self.sections[section_name]:
                x = layer(x)
        else:
            for section_name, layers in self.sections.items():
                for layer in layers:
                    x = layer(x)
        return x

class MemoryAugmentationLayer(nn.Module):
    def __init__(self, size: int):
        super(MemoryAugmentationLayer, self).__init__()
        self.memory = nn.Parameter(torch.randn(size))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x + self.memory

class HybridAttentionLayer(nn.Module):
    def __init__(self, size: int):
        super(HybridAttentionLayer, self).__init__()
        self.attention = nn.MultiheadAttention(size, num_heads=8)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x.unsqueeze(1)  # Add sequence dimension
        attn_output, _ = self.attention(x, x, x)
        return attn_output.squeeze(1)

class DynamicFlashAttentionLayer(nn.Module):
    def __init__(self, size: int):
        super(DynamicFlashAttentionLayer, self).__init__()
        self.attention = nn.MultiheadAttention(size, num_heads=8)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x.unsqueeze(1)  # Add sequence dimension
        attn_output, _ = self.attention(x, x, x)
        return attn_output.squeeze(1)

def parse_xml_file(file_path: str) -> List[Dict[str, Any]]:
    """
    Parses an XML configuration file to extract layer parameters for neural network construction.

    Args:
        file_path (str): Path to the XML configuration file

    Returns:
        List[Dict[str, Any]]: List of dictionaries containing layer configurations

    Raises:
        ET.ParseError: If XML file is malformed
        KeyError: If required attributes are missing in XML
    """
    tree = ET.parse(file_path)
    root = tree.getroot()

    layers = []
    for layer in root.findall('.//layer'):
        layer_params = {}
        layer_params['input_size'] = int(layer.get('input_size', 128))
        layer_params['output_size'] = int(layer.get('output_size', 256))
        layer_params['activation'] = layer.get('activation', 'relu').lower()

        # Validate activation function
        if layer_params['activation'] not in ['relu', 'tanh', 'sigmoid', 'none']:
            raise ValueError(f"Unsupported activation function: {layer_params['activation']}")

        # Validate dimensions
        if layer_params['input_size'] <= 0 or layer_params['output_size'] <= 0:
            raise ValueError("Layer dimensions must be positive integers")

        layers.append(layer_params)

    if not layers:
        # Fallback to default configuration if no layers found
        layers.append({
            'input_size': 128,
            'output_size': 256,
            'activation': 'relu'
        })

    return layers

def create_model_from_folder(folder_path: str) -> DynamicModel:
    """
    Creates a DynamicModel instance by parsing XML files in the specified folder structure.

    Each subfolder represents a model section, and XML files within contain layer configurations.
    The function recursively walks through the folder structure, processing all XML files to build
    the model architecture.

    Args:
        folder_path (str): Path to the root folder containing XML configuration files

    Returns:
        DynamicModel: A configured neural network model based on the XML specifications

    Raises:
        FileNotFoundError: If the specified folder path doesn't exist
        ET.ParseError: If XML parsing fails for any configuration file
    """
    sections = defaultdict(list)

    if not os.path.exists(folder_path):
        print(f"Warning: Folder {folder_path} does not exist. Creating model with default configuration.")
        return DynamicModel({})

    xml_files_found = False
    for root, dirs, files in os.walk(folder_path):
        for file in files:
            if file.endswith('.xml'):
                xml_files_found = True
                file_path = os.path.join(root, file)
                try:
                    layers = parse_xml_file(file_path)
                    section_name = os.path.basename(root)
                    sections[section_name].extend(layers)
                except Exception as e:
                    print(f"Error processing {file_path}: {str(e)}")

    if not xml_files_found:
        print("Warning: No XML files found. Creating model with default configuration.")
        return DynamicModel({})

    return DynamicModel(dict(sections))

def main():
    """
    Main function that demonstrates the creation and training of a dynamic PyTorch model.

    This function:
    1. Creates a dynamic model from XML configurations
    2. Sets up distributed training environment using Accelerator
    3. Configures optimization components (optimizer, loss function)
    4. Creates synthetic dataset for demonstration
    5. Implements distributed training loop with loss tracking

    The model architecture is determined by XML files in the 'Xml_Data' folder,
    where each subfolder represents a model section containing layer configurations.
    """
    folder_path = 'Xml_Data'
    model = create_model_from_folder(folder_path)

    print(f"Created dynamic PyTorch model with sections: {list(model.sections.keys())}")

    # Dynamically determine input size from first layer configuration
    first_section = next(iter(model.sections.keys()))
    first_layer = model.sections[first_section][0]
    input_features = first_layer[0].in_features

    # Validate model with sample input
    sample_input = torch.randn(1, input_features)
    output = model(sample_input)
    print(f"Sample output shape: {output.shape}")

    # Initialize distributed training components
    accelerator = Accelerator()

    # Configure training parameters and optimization components
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()
    num_epochs = 10

    # Generate synthetic dataset for demonstration purposes
    dataset = torch.utils.data.TensorDataset(
        torch.randn(100, input_features),
        torch.randint(0, 2, (100,))
    )
    train_dataloader = torch.utils.data.DataLoader(
        dataset, 
        batch_size=16, 
        shuffle=True
    )

    # Prepare model, optimizer, and dataloader for distributed training
    model, optimizer, train_dataloader = accelerator.prepare(
        model, 
        optimizer, 
        train_dataloader
    )

    # Execute training loop with distributed processing
    for epoch in range(num_epochs):
        model.train()
        total_loss = 0
        for batch_idx, (inputs, labels) in enumerate(train_dataloader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            accelerator.backward(loss)
            optimizer.step()
            total_loss += loss.item()

        avg_loss = total_loss / len(train_dataloader)
        print(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.4f}")

if __name__ == "__main__":
    main()