Update core/graph_mamba.py

core/graph_mamba.py

@@ -1,358 +0,0 @@
-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