""" Coherence Monitoring and Self-Reflection Module This module implements the coherence assessment and self-reflection mechanisms that are central to the toroidal diffusion model architecture. """ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from typing import Dict, List, Tuple, Optional from collections import deque class CoherenceMetrics: """ Computes various coherence metrics for assessing generation quality. """ @staticmethod def semantic_coherence(features: torch.Tensor, window_size: int = 3) -> torch.Tensor: """ Compute semantic coherence based on local feature consistency. Args: features: Feature tensor of shape (batch, channels, height, width) window_size: Size of the local window for coherence computation Returns: coherence: Semantic coherence score """ batch_size, channels, height, width = features.shape # Compute local variance within windows kernel = torch.ones(1, 1, window_size, window_size, device=features.device) / (window_size ** 2) # Mean within windows local_mean = F.conv2d(features, kernel.repeat(channels, 1, 1, 1), groups=channels, padding=window_size//2) # Variance within windows local_var = F.conv2d((features - local_mean) ** 2, kernel.repeat(channels, 1, 1, 1), groups=channels, padding=window_size//2) # Coherence is inverse of variance (lower variance = higher coherence) coherence = 1.0 / (1.0 + local_var.mean(dim=1, keepdim=True)) return coherence @staticmethod def structural_coherence(features: torch.Tensor) -> torch.Tensor: """ Compute structural coherence based on gradient consistency. Args: features: Feature tensor Returns: coherence: Structural coherence score """ # Compute gradients grad_x = torch.diff(features, dim=3, prepend=features[:, :, :, -1:]) grad_y = torch.diff(features, dim=2, prepend=features[:, :, -1:, :]) # Gradient magnitude grad_mag = torch.sqrt(grad_x ** 2 + grad_y ** 2) # Coherence based on gradient smoothness grad_smoothness = 1.0 / (1.0 + torch.std(grad_mag, dim=1, keepdim=True)) return grad_smoothness @staticmethod def temporal_coherence(features_sequence: List[torch.Tensor]) -> torch.Tensor: """ Compute temporal coherence across a sequence of features. Args: features_sequence: List of feature tensors from different timesteps Returns: coherence: Temporal coherence score """ if len(features_sequence) < 2: return torch.ones_like(features_sequence[0][:, :1]) # Compute frame-to-frame differences temporal_diffs = [] for i in range(1, len(features_sequence)): diff = torch.abs(features_sequence[i] - features_sequence[i-1]) temporal_diffs.append(diff.mean(dim=1, keepdim=True)) # Average temporal difference avg_temporal_diff = torch.stack(temporal_diffs).mean(dim=0) # Coherence is inverse of temporal variation temporal_coherence = 1.0 / (1.0 + avg_temporal_diff) return temporal_coherence class SelfReflectionModule(nn.Module): """ Implements self-reflection mechanisms for the toroidal diffusion model. This module analyzes the current generation state and provides feedback for improving coherence and quality. """ def __init__(self, feature_dim: int, reflection_depth: int = 3): super().__init__() self.feature_dim = feature_dim self.reflection_depth = reflection_depth # Reflection network layers num_groups = min(8, feature_dim) if feature_dim >= 8 else 1 self.reflection_layers = nn.ModuleList([ nn.Sequential( nn.Conv2d(feature_dim, feature_dim, 3, padding=1), nn.GroupNorm(num_groups, feature_dim), nn.SiLU(), nn.Conv2d(feature_dim, feature_dim, 3, padding=1), nn.GroupNorm(num_groups, feature_dim), nn.SiLU() ) for _ in range(reflection_depth) ]) # Coherence assessment head self.coherence_head = nn.Sequential( nn.Conv2d(feature_dim, feature_dim // 2, 1), nn.SiLU(), nn.Conv2d(feature_dim // 2, 1, 1), nn.Sigmoid() ) # Correction suggestion head self.correction_head = nn.Sequential( nn.Conv2d(feature_dim, feature_dim, 3, padding=1), nn.GroupNorm(num_groups, feature_dim), nn.SiLU(), nn.Conv2d(feature_dim, feature_dim, 3, padding=1) ) def analyze_coherence(self, features: torch.Tensor) -> Dict[str, torch.Tensor]: """ Analyze the coherence of current features. Args: features: Input feature tensor Returns: analysis: Dictionary containing coherence metrics """ semantic_coh = CoherenceMetrics.semantic_coherence(features) structural_coh = CoherenceMetrics.structural_coherence(features) # Overall coherence score overall_coherence = self.coherence_head(features) return { 'semantic_coherence': semantic_coh, 'structural_coherence': structural_coh, 'overall_coherence': overall_coherence, 'mean_coherence': (semantic_coh + structural_coh + overall_coherence) / 3 } def generate_corrections(self, features: torch.Tensor, coherence_analysis: Dict[str, torch.Tensor]) -> torch.Tensor: """ Generate correction suggestions based on coherence analysis. Args: features: Input feature tensor coherence_analysis: Coherence analysis results Returns: corrections: Suggested corrections to improve coherence """ # Weight corrections by coherence deficiency coherence_weight = 1.0 - coherence_analysis['mean_coherence'] # Generate corrections corrections = self.correction_head(features) # Apply coherence-weighted corrections weighted_corrections = corrections * coherence_weight return weighted_corrections def reflect(self, features: torch.Tensor) -> Dict[str, torch.Tensor]: """ Perform self-reflection on the current features. Args: features: Input feature tensor Returns: reflection_result: Dictionary containing analysis and corrections """ # Multi-layer reflection reflected_features = features for layer in self.reflection_layers: reflected_features = layer(reflected_features) + reflected_features # Residual connection # Analyze coherence coherence_analysis = self.analyze_coherence(reflected_features) # Generate corrections corrections = self.generate_corrections(reflected_features, coherence_analysis) return { 'reflected_features': reflected_features, 'coherence_analysis': coherence_analysis, 'corrections': corrections, 'original_features': features } def forward(self, features: torch.Tensor) -> Dict[str, torch.Tensor]: """ Forward pass performing self-reflection. Args: features: Input feature tensor Returns: reflection_result: Self-reflection results """ return self.reflect(features) class MultiPassRefinement(nn.Module): """ Implements multi-pass refinement mechanism for iterative improvement. This module performs multiple passes of generation and refinement, using self-reflection to guide the improvement process. """ def __init__(self, feature_dim: int, max_passes: int = 3, coherence_threshold: float = 0.8): super().__init__() self.feature_dim = feature_dim self.max_passes = max_passes self.coherence_threshold = coherence_threshold # Self-reflection module self.reflection_module = SelfReflectionModule(feature_dim) # Refinement network num_groups = min(8, feature_dim) if feature_dim >= 8 else 1 self.refinement_net = nn.Sequential( nn.Conv2d(feature_dim * 2, feature_dim, 3, padding=1), # features + corrections nn.GroupNorm(num_groups, feature_dim), nn.SiLU(), nn.Conv2d(feature_dim, feature_dim, 3, padding=1), nn.GroupNorm(num_groups, feature_dim), nn.SiLU(), nn.Conv2d(feature_dim, feature_dim, 3, padding=1) ) # History tracking self.coherence_history = deque(maxlen=max_passes) def should_continue_refinement(self, coherence_score: float, pass_num: int) -> bool: """ Determine if refinement should continue. Args: coherence_score: Current coherence score pass_num: Current pass number Returns: should_continue: Whether to continue refinement """ # Stop if coherence threshold is reached if coherence_score >= self.coherence_threshold: return False # Stop if maximum passes reached if pass_num >= self.max_passes: return False # Stop if coherence is not improving if len(self.coherence_history) >= 2: recent_improvement = self.coherence_history[-1] - self.coherence_history[-2] if recent_improvement < 0.01: # Minimal improvement threshold return False return True def refine_features(self, features: torch.Tensor, corrections: torch.Tensor) -> torch.Tensor: """ Apply refinement to features using corrections. Args: features: Input features corrections: Correction suggestions Returns: refined_features: Refined feature tensor """ # Concatenate features and corrections combined = torch.cat([features, corrections], dim=1) # Apply refinement network refinement = self.refinement_net(combined) # Apply refinement with residual connection refined_features = features + refinement return refined_features def forward(self, initial_features: torch.Tensor) -> Dict[str, torch.Tensor]: """ Perform multi-pass refinement. Args: initial_features: Initial feature tensor Returns: refinement_result: Dictionary containing refinement results """ current_features = initial_features pass_num = 0 refinement_history = [] # Clear history for new refinement session self.coherence_history.clear() while True: # Perform self-reflection reflection_result = self.reflection_module(current_features) # Extract coherence score coherence_score = reflection_result['coherence_analysis']['mean_coherence'].mean().item() self.coherence_history.append(coherence_score) # Store history refinement_history.append({ 'pass': pass_num, 'features': current_features.clone(), 'coherence_score': coherence_score, 'reflection_result': reflection_result }) # Check if refinement should continue if not self.should_continue_refinement(coherence_score, pass_num): break # Apply refinement corrections = reflection_result['corrections'] current_features = self.refine_features(current_features, corrections) pass_num += 1 return { 'final_features': current_features, 'initial_features': initial_features, 'refinement_history': refinement_history, 'total_passes': pass_num + 1, 'final_coherence': coherence_score } def test_coherence_monitoring(): """Test function for coherence monitoring components.""" print("Testing Coherence Monitoring and Self-Reflection...") # Create test features batch_size, channels, height, width = 2, 64, 32, 32 test_features = torch.randn(batch_size, channels, height, width) # Test coherence metrics semantic_coh = CoherenceMetrics.semantic_coherence(test_features) structural_coh = CoherenceMetrics.structural_coherence(test_features) print(f"Semantic coherence shape: {semantic_coh.shape}") print(f"Structural coherence shape: {structural_coh.shape}") print(f"Semantic coherence mean: {semantic_coh.mean().item():.4f}") print(f"Structural coherence mean: {structural_coh.mean().item():.4f}") # Test temporal coherence feature_sequence = [torch.randn(batch_size, channels, height, width) for _ in range(5)] temporal_coh = CoherenceMetrics.temporal_coherence(feature_sequence) print(f"Temporal coherence shape: {temporal_coh.shape}") print(f"Temporal coherence mean: {temporal_coh.mean().item():.4f}") # Test self-reflection module reflection_module = SelfReflectionModule(channels) reflection_result = reflection_module(test_features) print(f"Reflected features shape: {reflection_result['reflected_features'].shape}") print(f"Corrections shape: {reflection_result['corrections'].shape}") print(f"Overall coherence mean: {reflection_result['coherence_analysis']['overall_coherence'].mean().item():.4f}") # Test multi-pass refinement refinement_module = MultiPassRefinement(channels, max_passes=3, coherence_threshold=0.9) refinement_result = refinement_module(test_features) print(f"Final features shape: {refinement_result['final_features'].shape}") print(f"Total passes: {refinement_result['total_passes']}") print(f"Final coherence: {refinement_result['final_coherence']:.4f}") print(f"Refinement history length: {len(refinement_result['refinement_history'])}") print("All coherence monitoring tests passed!") if __name__ == "__main__": test_coherence_monitoring()