Update core/graph_mamba.py

core/graph_mamba.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch_geometric.utils import degree, to_dense_adj
+from torch_geometric.nn import GCNConv
+import math
+import logging
+
+logger = logging.getLogger(__name__)
+
+class CognitiveMomentumEngine(nn.Module):
+    """Core cognitive momentum system from the document"""
+    def __init__(self, d_model):
+        super().__init__()
+        self.d_model = d_model
+        
+        # Momentum tracking
+        self.register_buffer('momentum_vectors', torch.zeros(d_model))
+        self.register_buffer('cognitive_mass', torch.ones(d_model))
+        self.register_buffer('kinetic_energy', torch.zeros(d_model))
+        self.register_buffer('potential_energy', torch.zeros(d_model))
+        
+        # Field interactions
+        self.attraction_projection = nn.Linear(d_model, d_model)
+        self.repulsion_projection = nn.Linear(d_model, d_model)
+        
+        # Crystallization threshold
+        self.crystallization_threshold = 0.1
+        self.memory_decay = 0.99
+        
+    def update_momentum(self, concept_features, force, dt=0.1):
+        """Apply cognitive momentum physics"""
+        # F = ma => a = F/m
+        acceleration = force / (self.cognitive_mass + 1e-8)
+        
+        # Update velocity: v = v₀ + at
+        current_velocity = self.momentum_vectors / (self.cognitive_mass + 1e-8)
+        new_velocity = current_velocity + acceleration * dt
+        
+        # Update momentum: p = mv
+        self.momentum_vectors = self.cognitive_mass * new_velocity
+        
+        # Update energy
+        self.kinetic_energy = 0.5 * self.cognitive_mass * (new_velocity ** 2)
+        
+        return self.momentum_vectors
+    
+    def crystallize_knowledge(self):
+        """Compress low-momentum concepts"""
+        low_momentum_mask = torch.abs(self.momentum_vectors) < self.crystallization_threshold
+        
+        # Compress crystallized knowledge
+        crystallized_pattern = self.momentum_vectors[low_momentum_mask].mean()
+        
+        # Reset crystallized components
+        self.momentum_vectors[low_momentum_mask] = crystallized_pattern * 0.1
+        
+        return crystallized_pattern
+    
+    def forward(self, x):
+        """Apply momentum to features"""
+        if x.dim() == 2:
+            x = x.unsqueeze(0)
+        batch_size, seq_len, d_model = x.shape
+        
+        # Compute forces from feature interactions
+        attraction_force = self.attraction_projection(x)
+        repulsion_force = self.repulsion_projection(x)
+        
+        # Net force
+        net_force = attraction_force - repulsion_force * 0.1
+        
+        # Simple momentum application
+        momentum_enhanced = x + net_force * 0.1
+        
+        # Crystallize periodically
+        if torch.rand(1) < 0.1:
+            self.crystallize_knowledge()
+        
+        return momentum_enhanced
+
+class AstrocyteLayer(nn.Module):
+    """Multi-timescale processing with momentum"""
+    def __init__(self, d_model, astrocyte_ratio=2.0):
+        super().__init__()
+        self.d_model = d_model
+        self.d_astrocyte = int(d_model * astrocyte_ratio)
+        
+        # Fast neuronal processing
+        self.neuron_fast = nn.Linear(d_model, d_model)
+        self.neuron_dropout = nn.Dropout(0.1)
+        
+        # Slow astrocyte processing
+        self.astrocyte_slow = nn.Linear(d_model, self.d_astrocyte)
+        self.astrocyte_integration = nn.Linear(self.d_astrocyte, d_model)
+        self.astrocyte_dropout = nn.Dropout(0.1)
+        
+        # Cognitive momentum
+        self.momentum_engine = CognitiveMomentumEngine(d_model)
+        
+        # Multi-timescale gates
+        self.fast_gate = nn.Linear(d_model, d_model)
+        self.slow_gate = nn.Linear(self.d_astrocyte, d_model)
+        
+        # Memory for slow dynamics
+        self.register_buffer('astrocyte_memory', torch.zeros(1, self.d_astrocyte))
+        self.memory_decay = 0.9
+        
+    def forward(self, x):
+        batch_size = x.size(0) if x.dim() == 3 else 1
+        if x.dim() == 2:
+            x = x.unsqueeze(0)
+        
+        if self.astrocyte_memory.size(0) != batch_size:
+            self.astrocyte_memory = torch.zeros(batch_size, self.d_astrocyte, device=x.device)
+        
+        # Apply cognitive momentum
+        x_momentum = self.momentum_engine(x)
+        
+        # Fast neuronal response
+        fast_out = self.neuron_dropout(torch.tanh(self.neuron_fast(x_momentum)))
+        
+        # Slow astrocyte integration
+        astrocyte_input = self.astrocyte_slow(x_momentum)
+        self.astrocyte_memory = self.memory_decay * self.astrocyte_memory + (1 - self.memory_decay) * astrocyte_input.mean(dim=1)
+        slow_out = self.astrocyte_dropout(torch.tanh(self.astrocyte_integration(self.astrocyte_memory))).unsqueeze(1).expand(-1, x.size(1), -1)
+        
+        # Multi-timescale gating
+        fast_gate = torch.sigmoid(self.fast_gate(x_momentum))
+        slow_gate = torch.sigmoid(self.slow_gate(self.astrocyte_memory)).unsqueeze(1).expand(-1, x.size(1), -1)
+        
+        # Combine with momentum
+        output = fast_gate * fast_out + slow_gate * slow_out
+        
+        return output.squeeze(0) if output.size(0) == 1 else output
+
+class PhysicsInformedMamba(nn.Module):
+    """Mamba with physics constraints and momentum"""
+    def __init__(self, d_model, d_state=8):
+        super().__init__()
+        self.d_model = d_model
+        self.d_inner = d_model * 2
+        self.d_state = d_state
+        
+        self.in_proj = nn.Linear(d_model, self.d_inner * 2, bias=False)
+        self.conv1d = nn.Conv1d(self.d_inner, self.d_inner, 4, groups=self.d_inner, padding=3)
+        self.x_proj = nn.Linear(self.d_inner, d_state * 2 + 1, bias=False)
+        self.dt_proj = nn.Linear(1, self.d_inner, bias=True)
+        
+        # Physics constraints
+        A = torch.arange(1, d_state + 1, dtype=torch.float32).unsqueeze(0).repeat(self.d_inner, 1)
+        self.A_log = nn.Parameter(torch.log(A))
+        self.D = nn.Parameter(torch.ones(self.d_inner))
+        self.out_proj = nn.Linear(self.d_inner, d_model, bias=False)
+        
+        # Energy conservation
+        self.energy_projection = nn.Linear(d_model, d_model)
+        
+    def forward(self, x):
+        if x.dim() == 2:
+            x = x.unsqueeze(0)
+        
+        batch, length, _ = x.shape
+        
+        # Energy conservation
+        total_energy = x.norm(dim=-1, keepdim=True)
+        
+        xz = self.in_proj(x)
+        x_inner, z = xz.chunk(2, dim=-1)
+        
+        # Convolution
+        x_inner = x_inner.transpose(1, 2)
+        x_inner = self.conv1d(x_inner)[:, :, :length]
+        x_inner = x_inner.transpose(1, 2)
+        x_inner = F.silu(x_inner)
+        
+        # State space with physics
+        y = self.selective_scan(x_inner)
+        y = y * F.silu(z)
+        
+        # Apply energy conservation
+        output = self.out_proj(y)
+        output_energy = output.norm(dim=-1, keepdim=True)
+        energy_scale = total_energy / (output_energy + 1e-8)
+        output = output * energy_scale
+        
+        return output
+    
+    def selective_scan(self, x):
+        batch, length, d_inner = x.shape
+        
+        deltaBC = self.x_proj(x)
+        delta, B, C = torch.split(deltaBC, [1, self.d_state, self.d_state], dim=-1)
+        delta = F.softplus(self.dt_proj(delta))
+        
+        deltaA = torch.exp(delta.unsqueeze(-1) * (-torch.exp(self.A_log)))
+        deltaB = delta.unsqueeze(-1) * B.unsqueeze(2)
+        
+        states = torch.zeros(batch, d_inner, self.d_state, device=x.device)
+        outputs = []
+        
+        for i in range(length):
+            states = deltaA[:, i] * states + deltaB[:, i] * x[:, i, :, None]
+            y = (states @ C[:, i, :, None]).squeeze(-1) + self.D * x[:, i]
+            outputs.append(y)
+            
+        return torch.stack(outputs, dim=1)
+
+class CognitiveMambaGraphMamba(nn.Module):
+    """Revolutionary cognitive momentum architecture"""
+    def __init__(self, config):
+        super().__init__()
+        
+        self.config = config
+        d_model = config['model']['d_model']
+        n_layers = config['model']['n_layers']
+        input_dim = config.get('input_dim', 1433)
+        
+        # Input processing
+        self.input_proj = nn.Linear(input_dim, d_model)
+        self.input_norm = nn.LayerNorm(d_model)
+        
+        # GCN backbone for graph structure
+        self.gcn_layers = nn.ModuleList([
+            GCNConv(d_model, d_model) for _ in range(n_layers)
+        ])
+        
+        # Revolutionary components
+        self.astrocyte_layers = nn.ModuleList([
+            AstrocyteLayer(d_model) for _ in range(n_layers)
+        ])
+        
+        self.physics_mamba = PhysicsInformedMamba(d_model)
+        
+        # Global cognitive momentum
+        self.global_momentum = CognitiveMomentumEngine(d_model)
+        
+        # Layer norms
+        self.norms = nn.ModuleList([
+            nn.LayerNorm(d_model) for _ in range(n_layers)
+        ])
+        
+        # Multi-path fusion
+        self.fusion_weights = nn.Parameter(torch.tensor([0.4, 0.3, 0.3]))  # GCN, Astrocyte, Mamba
+        
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = None
+        
+    def forward(self, x, edge_index, batch=None):
+        # Input processing
+        h = self.input_norm(self.input_proj(x))
+        
+        # Multi-path processing with momentum
+        for i in range(len(self.gcn_layers)):
+            gcn = self.gcn_layers[i]
+            astrocyte = self.astrocyte_layers[i] 
+            norm = self.norms[i]
+            # Path 1: GCN (graph structure)
+            h_gcn = F.relu(gcn(h, edge_index))
+            h_gcn = self.dropout(h_gcn)
+            
+            # Path 2: Astrocyte (multi-timescale with momentum)
+            h_astrocyte = astrocyte(h.unsqueeze(0)).squeeze(0)
+            
+            # Path 3: Physics-informed Mamba (sequential with physics)
+            h_mamba = self.physics_mamba(h.unsqueeze(0)).squeeze(0)
+            
+            # Apply global cognitive momentum
+            h_combined = torch.stack([h_gcn, h_astrocyte, h_mamba], dim=0)  # (3, nodes, features)
+            h_combined = h_combined.permute(1, 0, 2)  # (nodes, 3, features)
+            h_momentum = self.global_momentum(h_combined.unsqueeze(0)).squeeze(0)  # (nodes, 3, features)
+            h_momentum = h_momentum.mean(dim=1)  # (nodes, features)
+            
+            # Weighted fusion
+            weights = F.softmax(self.fusion_weights, dim=0)
+            h_fused = weights[0] * h_gcn + weights[1] * h_astrocyte + weights[2] * h_mamba + h_momentum * 0.1
+            
+            # Residual + norm
+            h = norm(h + h_fused)
+        
+        return h
+    
+    def _init_classifier(self, num_classes, device):
+        if self.classifier is None:
+            self.classifier = nn.Sequential(
+                nn.Dropout(0.1),
+                nn.Linear(self.config['model']['d_model'], num_classes)
+            ).to(device)
+    
+    def get_performance_stats(self):
+        total_params = sum(p.numel() for p in self.parameters())
+        return {
+            'total_params': total_params,
+            'device': next(self.parameters()).device,
+            'dtype': next(self.parameters()).dtype,
+            'model_size': f"{total_params/1000:.1f}K parameters"
+        }
+
+class LegacyGraphMamba(nn.Module):
+    """Fallback simple version"""
+    def __init__(self, config):
+        super().__init__()
+        self.cognitive_mamba = CognitiveMambaGraphMamba(config)
+        self.config = config
+        self.classifier = None
+        
+    def forward(self, x, edge_index, batch=None):
+        return self.cognitive_mamba(x, edge_index, batch)
+    
+    def _init_classifier(self, num_classes, device):
+        self.classifier = nn.Sequential(
+            nn.Dropout(0.1),
+            nn.Linear(self.config['model']['d_model'], num_classes)
+        ).to(device)
+        self.cognitive_mamba.classifier = self.classifier
+        return self.classifier
+    
+    def get_performance_stats(self):
+        return self.cognitive_mamba.get_performance_stats()
+
+def create_astrocyte_config():
+    """Revolutionary cognitive momentum configuration"""
+    return {
+        'model': {
+            'd_model': 128,
+            'd_state': 8,
+            'd_conv': 4,
+            'expand': 2,
+            'n_layers': 4,
+            'dropout': 0.1
+        },
+        'data': {
+            'batch_size': 1,
+            'test_split': 0.2
+        },
+        'training': {
+            'learning_rate': 0.003,
+            'weight_decay': 0.001,
+            'epochs': 500,
+            'patience': 100,
+            'warmup_epochs': 25,
+            'min_lr': 1e-7,
+            'label_smoothing': 0.0,
+            'max_gap': 0.3
+        },
+        'ordering': {
+            'strategy': 'none',
+            'preserve_locality': True
+        },
+        'input_dim': 1433
+    }
+
+# Aliases
+AstrocyteGraphMamba = CognitiveMambaGraphMamba
+GraphMamba = CognitiveMambaGraphMamba
+HybridGraphMamba = LegacyGraphMamba
+QuantumEnhancedGraphMamba = LegacyGraphMamba
+create_regularized_config = create_astrocyte_config