coherence_monitor.py

"""
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()