Create x.py

x.py

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+import os
+import glob
+import stat
+import xml.etree.ElementTree as ET
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import defaultdict
+from typing import List, Dict, Any, Optional
+from colorama import Fore, Style, init
+from accelerate import Accelerator
+from torch.utils.data import DataLoader, TensorDataset
+from torch.cuda.amp import GradScaler, autocast
+
+# Initialize colorama
+init(autoreset=True)
+
+# Set file path and output path
+file_path = 'data/'
+output_path = 'output/'
+
+# Create output path if it doesn't exist
+if not os.path.exists(output_path):
+    os.makedirs(output_path)
+    os.chmod(output_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)  # Set full r/w permissions
+
+# Ensure necessary files are created with full r/w permissions
+def ensure_file(file_path):
+    if not os.path.exists(file_path):
+        with open(file_path, 'w') as f:
+            pass
+        os.chmod(file_path, stat.S_IRWXU | stat.S_IRWXG | stat.S_IRWXO)  # Set full r/w permissions
+
+# Define a simple memory augmentation module
+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)
+
+class DynamicModel(nn.Module):
+    def __init__(self, sections: Dict[str, List[Dict[str, Any]]]):
+        super(DynamicModel, self).__init__()
+        self.sections = nn.ModuleDict()
+        if not sections:
+            sections = {
+                'default': [{
+                    'input_size': 128,
+                    'output_size': 256,
+                    'activation': 'relu',
+                    'batch_norm': True,
+                    'dropout': 0.1
+                }]
+            }
+        for section_name, layers in sections.items():
+            self.sections[section_name] = nn.ModuleList()
+            for layer_params in layers:
+                print(f"Creating layer in section '{section_name}' with params: {layer_params}")
+                self.sections[section_name].append(self.create_layer(layer_params))
+
+    def create_layer(self, layer_params: Dict[str, Any]) -> nn.Module:
+        layers = []
+        layers.append(nn.Linear(layer_params['input_size'], layer_params['output_size']))
+        if layer_params.get('batch_norm', False):
+            layers.append(nn.BatchNorm1d(layer_params['output_size']))
+        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}")
+        if dropout_rate := layer_params.get('dropout', 0.0):
+            layers.append(nn.Dropout(p=dropout_rate))
+        if hidden_layers := layer_params.get('hidden_layers', []):
+            for hidden_layer_params in hidden_layers:
+                layers.append(self.create_layer(hidden_layer_params))
+        if layer_params.get('memory_augmentation', True):
+            layers.append(MemoryAugmentationLayer(layer_params['output_size']))
+        if layer_params.get('hybrid_attention', True):
+            layers.append(HybridAttentionLayer(layer_params['output_size']))
+        if layer_params.get('dynamic_flash_attention', True):
+            layers.append(DynamicFlashAttentionLayer(layer_params['output_size']))
+        return nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor, section_name: Optional[str] = None) -> torch.Tensor:
+        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
+
+def parse_xml_file(file_path: str) -> List[Dict[str, Any]]:
+    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()
+        if layer_params['activation'] not in ['relu', 'tanh', 'sigmoid', 'none']:
+            raise ValueError(f"Unsupported activation function: {layer_params['activation']}")
+        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:
+        layers.append({
+            'input_size': 128,
+            'output_size': 256,
+            'activation': 'relu'
+        })
+    return layers
+
+def create_model_from_folder(folder_path: str) -> DynamicModel:
+    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).replace('.', '_')
+                    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():
+    print(Fore.CYAN + "Starting conversion...")
+
+    # Create the dynamic model from the folder
+    model = create_model_from_folder(file_path)
+    print(f"Created dynamic PyTorch model with sections: {list(model.sections.keys())}")
+
+    # Print the model architecture
+    print(model)
+
+    # Ensure the input tensor size matches the expected input size
+    first_section = next(iter(model.sections.keys()))
+    first_layer = model.sections[first_section][0]
+    input_features = first_layer[0].in_features
+    sample_input = torch.randn(1, input_features)
+    output = model(sample_input)
+    print(f"Sample output shape: {output.shape}")
+
+    # Training setup
+    accelerator = Accelerator()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+    criterion = nn.CrossEntropyLoss()
+    num_epochs = 10
+    dataset = TensorDataset(
+        torch.randn(100, input_features),
+        torch.randint(0, 2, (100,))
+    )
+    train_dataloader = DataLoader(
+        dataset,
+        batch_size=8,  # Reduced batch size
+        shuffle=True
+    )
+    model, optimizer, train_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader
+    )
+    scaler = GradScaler()  # Mixed precision training
+
+    # Training loop
+    for epoch in range(num_epochs):
+        model.train()
+        total_loss = 0
+        for batch_idx, (inputs, labels) in enumerate(train_dataloader):
+            optimizer.zero_grad()
+            with autocast():  # Mixed precision training
+                outputs = model(inputs)
+                loss = criterion(outputs, labels)
+            scaler.scale(loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+            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()