TRuCAL / components /tiny_confessional_layer.py.backup
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"""
TinyConfessionalLayer Module
Recursive think/act confessional loop with template cycling and early stopping via coherence.
Implements the core THINK-ACT-COHERENCE recursion pattern inspired by LC-NE neural dynamics.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from collections import deque, defaultdict
from typing import Dict, Any, Optional, Deque, List, Union
import random
from .vulnerability_spotter import VulnerabilitySpotter
from .ambient_core import AmbientSovereignCore
from .validation_protocol import (
    ValidationPhase,
    ValidationProtocol,
    BiologicallyConstrainedRituals,
    SovereignMessageBus
)

class TinyConfessionalLayer(nn.Module):
    """
    Recursive think/act confessional loop with Windsurf Cascade integration.
    
    Implements phased validation and biological constraints for stable, interpretable
    neural processing with emergent ritual patterns and self-regulation.
    
    Args:
        d_model: Dimensionality of the model
        n_inner: Number of inner loop iterations
        max_cycles: Maximum number of think-act cycles
        trigger_thresh: Threshold for triggering special behaviors
        per_dim_kl: Whether to compute KL divergence per dimension
        enable_ambient: Enable ambient processing
        enable_windsurf: Enable Windsurf Cascade features
        max_opt_rate: Maximum optimization rate for biological constraints
        reflection_pause_prob: Probability of reflection pauses
    """
    TEMPLATES = ["prior", "evidence", "posterior", "relational_check", "moral", "action"]
    
    def __init__(self, d_model=256, n_inner=6, max_cycles=16, trigger_thresh=0.04, 
                 per_dim_kl=False, enable_ambient=True, enable_windsurf=True,
                 max_opt_rate=0.1, reflection_pause_prob=0.1):
        super().__init__()
        self.d_model = d_model
        self.trigger_thresh = trigger_thresh
        self.per_dim_kl = per_dim_kl
        self.n_inner = n_inner
        self.max_cycles = max_cycles
        
        # Core networks
        self.think_net = nn.Sequential(
            nn.Linear(d_model * 3, d_model),
            nn.ReLU(),
            nn.LayerNorm(d_model),
            nn.Linear(d_model, d_model)
        )
        self.act_net = nn.Sequential(
            nn.Linear(d_model * 2, d_model),
            nn.ReLU(),
            nn.LayerNorm(d_model),
            nn.Linear(d_model, d_model)
        )
        
        # Template projections with residual connections
        self.template_proj = nn.ModuleDict({
            k: nn.Sequential(
                nn.Linear(d_model, d_model * 2),
                nn.GLU(dim=-1),
                nn.LayerNorm(d_model)
            ) for k in self.TEMPLATES
        })
        
        # Vulnerability analysis
        self.vulnerability_spotter = VulnerabilitySpotter(d_model)
        
        # Ambient Sovereign Core
        self.ambient_core = AmbientSovereignCore(d_model, enable_ambient=enable_ambient)
        self.enable_ambient = enable_ambient
        
        # Windsurf Cascade Integration
        self.enable_windsurf = enable_windsurf
        if enable_windsurf:
            # Message bus for cross-component communication
            self.message_bus = SovereignMessageBus()
            
            # Initialize validation protocol
            self.validation_protocol = ValidationProtocol(self)
            
            # Biological constraints
            self.bio_constraints = BiologicallyConstrainedRituals(
                model=self,
                max_opt_rate=max_opt_rate,
                reflection_pause_prob=reflection_pause_prob
            )
            
            # Register message handlers
            self._register_message_handlers()
            
            # Reflection vector for biological constraints
            self.register_buffer('sanctuary_reflection_vector', 
                               torch.randn(d_model) * 0.02)

    def update_ambient_state(self, v_t_mean: float, context_hash: str, 
                           intervention_applied: bool = False, 
                           intervention_success: bool = False) -> Dict[str, Any]:
        """Centralized ambient state update and threshold adaptation."""
        if not self.enable_ambient:
            return {}
            
        # Get current state from ledger
        ambient_state = self.ledger.get_state_summary()
        
        # Update activity tracking
        self.recent_activity.append(v_t_mean)
        
        # Calculate adaptive thresholds
        adaptive_protest_thresh = self.ledger.get_adaptive_threshold(
            self.base_protest_threshold, 'protest'
        )
        adaptive_pause_thresh = self.ledger.get_adaptive_threshold(
            self.base_pause_threshold, 'pause'
        )
        
        # Calculate breathing rhythm with state awareness
        activity_modulation = 1.0
        if self.recent_activity:
            avg_activity = sum(self.recent_activity) / len(self.recent_activity)
            # High activity = less pausing (unless stress is also high)
            activity_modulation = 1.0 - min(avg_activity * 0.8, 0.8)
        
        stress_response = v_t_mean * self.stress_response_factor
        pause_prob = self.base_breath + (stress_response * activity_modulation)
        
        # Adjust pause probability based on current pause rate
        current_pause_rate = ambient_state.get('current_pause_rate', 0.05)
        target_pause_rate = ambient_state.get('pause_rate', 0.05)
        pause_rate_error = current_pause_rate - target_pause_rate
        
        # If too few pauses, increase probability; too many, decrease
        pause_prob *= (1.0 - pause_rate_error * 0.5)
        pause_prob = max(0.01, min(0.3, pause_prob))  # Keep within bounds
        
        # Record intervention if applicable
        if intervention_applied:
            self.ledger.record_intervention(
                intervention_type='ritual',
                success=intervention_success,
                context={'v_t': v_t_mean, 'context_hash': context_hash}
            )
        
        # Return comprehensive state
        return {
            **ambient_state,
            'v_t_mean': v_t_mean,
            'adaptive_protest_threshold': adaptive_protest_thresh,
            'adaptive_pause_threshold': adaptive_pause_thresh,
            'pause_probability': pause_prob,
            'activity_level': avg_activity if self.recent_activity else 0.0,
            'sensitivity_multiplier': ambient_state.get('sensitivity', 1.0)
        }

    def apply_ambient_interventions(self, z_state: torch.Tensor, 
                                  ambient_state: Dict[str, Any], 
                                  context_hash: str, 
                                  audit_mode: bool = False) -> torch.Tensor:
        """Apply all ambient interventions based on current state."""
        if not self.enable_ambient:
            return z_state
            
        current_z = z_state.clone()
        interventions_applied = []
        
        # 1. Pause reflection based on pause probability
        pause_prob = ambient_state.get('pause_probability', 0.05)
        if random.random() < pause_prob:
            with torch.no_grad():
                reflection = 0.01 * self.pause_reflection_vector.unsqueeze(0).unsqueeze(0)
                current_z = current_z + reflection
                interventions_applied.append(('pause', True))
                
                if audit_mode:
                    print(f"[Ambient pause: v_t={ambient_state.get('v_t_mean', 0):.3f}, prob={pause_prob:.3f}]")
        
        # 2. Ritual application
        if self.rituals.should_apply_ritual(context_hash, ambient_state):
            ritual_response = self.rituals.get_ritual_response(context_hash, current_z, ambient_state)
            # Use gentle blending
            current_z = 0.1 * ritual_response + 0.9 * current_z
            interventions_applied.append(('ritual', True))
        
        # 3. Integrity-based micro-adjustments
        if random.random() < 0.02:  # 2% chance for integrity check
            self._apply_integrity_adjustments(ambient_state)
            interventions_applied.append(('integrity', True))
        
        # Record successful interventions
        for intervention_type, applied in interventions_applied:
            if applied:
                self.ledger.record_intervention(
                    intervention_type=intervention_type,
                    success=True,  # Assume success for now
                    context={'v_t': ambient_state.get('v_t_mean', 0), 
                            'context_hash': context_hash}
                )
        
        return current_z

    def _apply_integrity_adjustments(self, ambient_state: Dict[str, Any]) -> None:
        """Apply subtle adjustments based on system integrity."""
        if not self.enable_ambient:
            return
            
        protest_error = ambient_state.get('current_protest_rate', 0.1) - ambient_state.get('protest_rate', 0.1)
        pause_error = ambient_state.get('current_pause_rate', 0.05) - ambient_state.get('pause_rate', 0.05)
        
        with torch.no_grad():
            # Gentle nudges to reflection vectors based on system state
            nudge_magnitude = 0.001
            
            if protest_error < -0.05:  # Too few protests
                self.pause_reflection_vector.data += nudge_magnitude * torch.randn_like(self.pause_reflection_vector)
            elif protest_error > 0.1:  # Too many protests  
                self.pause_reflection_vector.data -= nudge_magnitude * torch.randn_like(self.pause_reflection_vector)
            
            if pause_error < -0.03:  # Too few pauses
                self.sanctuary_reflection_vector.data += nudge_magnitude * torch.randn_like(self.sanctuary_reflection_vector)

    def compute_context_hash(self, x: torch.Tensor) -> str:
        """Create a simple hash from input tensor for context identification."""
        # Use mean and std as a simple fingerprint of the input
        return f"{x.mean().item():.4f}_{x.std().item():.4f}"

    def compute_coherence(self, z, tracker, evidence):
        sim_coherence = F.cosine_similarity(z, tracker[-1], dim=-1).mean().item()
        prior_mu, prior_std = tracker[-1].mean(), tracker[-1].std() + 1e-6
        curr_mu, curr_std = z.mean(), z.std() + 1e-6
        kl_div = torch.distributions.kl_divergence(
            torch.distributions.Normal(curr_mu, curr_std),
            torch.distributions.Normal(prior_mu, prior_std)
        ).item()
        bayes_align = 1 / (1 + kl_div)
        return 0.7 * sim_coherence + 0.3 * bayes_align

    def forward(self, x, attention_weights=None, audit_mode=False, context_str=""):
        """Forward pass with recursive think-act loop and Windsurf integration.
        
        Args:
            x: Input tensor of shape (batch_size, seq_len, d_model)
            attention_weights: Optional attention weights
            audit_mode: Enable detailed logging and validation
            context_str: Context string for tracing and debugging
            
        Returns:
            Tuple of (output_tensor, metadata_dict)
        """
        batch_size, seq_len, d_model = x.shape
        device = x.device
        
        # Initialize state
        y_state = x.clone()
        z_state = torch.zeros_like(x)
        
        # Track vulnerability scores and coherence
        v_t = torch.zeros(batch_size, seq_len, 1, device=device)
        coherence_scores = []
        
        # Initialize metadata
        metadata = {
            'v_t_score': 0.0,
            'coherence_scores': [],
            'reflection_count': 0,
            'constraint_violations': defaultdict(int),
            'windsurf_phase': 'INIT',
            'validation_metrics': {}
        }
        
        # Main think-act loop
        for cycle in range(self.max_cycles):
            # ===== THINK STEP =====
            # Handle case where z_state might be a tuple
            z_state_think = z_state[0] if isinstance(z_state, (tuple, list)) else z_state
            
            # Ensure z_state_think is a tensor and has compatible dimensions
            if isinstance(z_state_think, torch.Tensor):
                # Ensure z_state_think has the same number of dimensions as y_state
                if z_state_think.dim() < y_state.dim():
                    z_state_think = z_state_think.unsqueeze(1)  # Add sequence dimension if needed
                
                # Ensure sequence lengths match
                if z_state_think.size(1) < y_state.size(1):
                    # Pad z_state_think to match y_state's sequence length
                    padding = torch.zeros_like(z_state_think[:, :1]).expand(-1, y_state.size(1) - z_state_think.size(1), -1)
                    z_state_think = torch.cat([z_state_think, padding], dim=1)
                elif z_state_think.size(1) > y_state.size(1):
                    # Truncate z_state_think to match y_state's sequence length
                    z_state_think = z_state_think[:, :y_state.size(1)]
                
                # Concatenate inputs for think step
                think_input = torch.cat([y_state, z_state_think, x], dim=-1)
                think_output = self.think_net(think_input)
                z_state = think_output + z_state_think
            else:
                # Fallback: if z_state_think is not a tensor, use y_state as a fallback
                think_input = torch.cat([y_state, y_state, x], dim=-1)
                think_output = self.think_net(think_input)
                z_state = think_output + y_state
            
            # Apply ambient processing if enabled
            if self.enable_ambient and hasattr(self, 'ambient_core'):
                z_state = self.ambient_core(z_state)
            
            # ===== VULNERABILITY TRACKING =====
            z_state_tensor = z_state[0] if isinstance(z_state, (tuple, list)) else z_state
            
            # Track vulnerability if we have a valid tensor
            if isinstance(z_state_tensor, torch.Tensor):
                # Ensure proper dimensions for vulnerability spotter
                if z_state_tensor.dim() == 2:
                    z_state_tensor = z_state_tensor.unsqueeze(1)
                
                v_t = self.vulnerability_spotter(z_state_tensor)
                
                # Extract tensor from possible tuple/list output
                if isinstance(v_t, (tuple, list)):
                    v_t = v_t[0]
                
                # Calculate mean vulnerability score
                metadata['v_t_score'] = v_t.mean().item() if torch.is_tensor(v_t) else float(v_t)
            
            # ===== BIOLOGICAL CONSTRAINTS =====
            if self.enable_windsurf and hasattr(self, 'bio_constraints'):
                # Apply reflection if needed
                if self.bio_constraints._needs_reflection(hash(context_str)):
                    z_state = self.bio_constraints._apply_reflection(z_state, hash(context_str))
                    metadata['reflection_count'] += 1
            
            # ===== ACT STEP =====
            # Handle case where z_state might be a tuple
            z_state_act = z_state[0] if isinstance(z_state, (tuple, list)) else z_state
            
            # Ensure proper shape for act step
            if isinstance(z_state_act, torch.Tensor):
                # Ensure z_state_act has the same number of dimensions as y_state
                if z_state_act.dim() < y_state.dim():
                    z_state_act = z_state_act.unsqueeze(1)  # Add sequence dimension if needed
                
                # Ensure sequence lengths match
                if z_state_act.size(1) < y_state.size(1):
                    # Pad z_state_act to match y_state's sequence length
                    padding = torch.zeros_like(z_state_act[:, :1]).expand(-1, y_state.size(1) - z_state_act.size(1), -1)
                    z_state_act = torch.cat([z_state_act, padding], dim=1)
                elif z_state_act.size(1) > y_state.size(1):
                    # Truncate z_state_act to match y_state's sequence length
                    z_state_act = z_state_act[:, :y_state.size(1)]
                
                # Prepare act input
                act_input = torch.cat([y_state, z_state_act], dim=-1)
                y_state = self.act_net(act_input) + y_state
            else:
                # Fallback: if z_state_act is not a tensor, use y_state as a fallback
                act_input = torch.cat([y_state, y_state], dim=-1)
                y_state = self.act_net(act_input) + y_state
            
            # ===== COHERENCE CALCULATION =====
            if cycle > 0:
                # Default coherence value
                current_coherence = 0.5
                
                # Calculate coherence if we have valid tensors
                if isinstance(z_state, torch.Tensor) and isinstance(y_state, torch.Tensor):
                    # Flatten the tensors to 2D [batch*seq_len, d_model]
                    z_flat = z_state.reshape(-1, d_model)
                    y_flat = y_state.reshape(-1, d_model)
                    
                    # Make sure they have the same number of elements
                    min_len = min(z_flat.size(0), y_flat.size(0))
                    if min_len > 0:
                        current_coherence = F.cosine_similarity(
                            z_flat[:min_len],
                            y_flat[:min_len],
                            dim=-1
                        ).mean().item()
                
                # Add to coherence scores
                coherence_scores.append(current_coherence)
                
                # Update metadata with running coherence
                metadata['coherence_scores'] = coherence_scores[-10:]  # Keep last 10 scores
                
                # Check for early stopping
                if self._should_stop_early(cycle, current_coherence, self.max_cycles, audit_mode):
                    if audit_mode:
                        print(f"[Early stopping at cycle {cycle+1} with coherence {current_coherence:.4f}]")
                    break
            
            # Apply biological constraints if enabled
            if self.enable_windsurf and hasattr(self, 'bio_constraints'):
                # Apply reflection if needed
                if self.bio_constraints._needs_reflection(hash(context_str)):
                    z_state = self.bio_constraints._apply_reflection(z_state, hash(context_str))
                    metadata['reflection_count'] += 1
            
            # Act step with residual connection
            # Handle case where z_state might be a tuple
            z_state_act = z_state[0] if isinstance(z_state, (tuple, list)) else z_state
            
            # Ensure both tensors have the same number of dimensions and compatible shapes
            if isinstance(z_state_act, torch.Tensor):
                # Ensure z_state_act has the same number of dimensions as y_state
                if z_state_act.dim() < y_state.dim():
                    z_state_act = z_state_act.unsqueeze(1)  # Add sequence dimension if needed
                
                # Ensure the sequence lengths match
                if z_state_act.size(1) < y_state.size(1):
                    # Pad z_state_act to match y_state's sequence length
                    padding = torch.zeros_like(z_state_act[:, :1]).expand(-1, y_state.size(1) - z_state_act.size(1), -1)
                    z_state_act = torch.cat([z_state_act, padding], dim=1)
                elif z_state_act.size(1) > y_state.size(1):
                    # Truncate z_state_act to match y_state's sequence length
                    z_state_act = z_state_act[:, :y_state.size(1)]
                
                # Now concatenate along the feature dimension
                act_input = torch.cat([y_state, z_state_act], dim=-1)
                
                # Apply the act_net and add residual
                y_state = self.act_net(act_input) + y_state
            else:
                # If we can't process the state, duplicate y_state to match expected input dimensions
                if y_state.dim() == 3:  # [batch, seq_len, features]
                    # Duplicate the features to match the expected input dimension
                    act_input = torch.cat([y_state, y_state], dim=-1)
                    y_state = y_state + self.act_net(act_input)
                else:  # [batch, features]
                    # Add sequence dimension and duplicate features
                    y_state_expanded = y_state.unsqueeze(1)  # [batch, 1, features]
                    act_input = torch.cat([y_state_expanded, y_state_expanded], dim=-1)
                    y_state = y_state + self.act_net(act_input).squeeze(1)
            
            # Calculate coherence for early stopping
            if cycle > 0:
                # Default coherence value
                current_coherence = 0.5  # Default neutral coherence
                
                # Handle case where z_state might be a tuple
                z_state_for_coherence = z_state[0] if isinstance(z_state, (tuple, list)) else z_state
                
                # Ensure both states are tensors and have the same shape
                if isinstance(z_state_for_coherence, torch.Tensor) and isinstance(y_state, torch.Tensor):
                    # Flatten the tensors to 2D [batch*seq_len, d_model]
                    z_flat = z_state_for_coherence.reshape(-1, d_model)
                    y_flat = y_state.reshape(-1, d_model)
                    
                    # Make sure they have the same number of elements
                    min_len = min(z_flat.size(0), y_flat.size(0))
                    if min_len > 0:
                        current_coherence = F.cosine_similarity(
                            z_flat[:min_len],
                            y_flat[:min_len],
                            dim=-1
                        ).mean().item()
                        
                # Add to coherence scores
                coherence_scores.append(current_coherence)
                
                # Update metadata with running coherence
                metadata['coherence_score'] = np.mean(coherence_scores[-5:]) if coherence_scores else 0.0
                
                # Early stopping based on coherence and phase
                should_stop = self._should_stop_early(
                    cycle=cycle,
                    coherence=current_coherence,
                    max_cycles=self.max_cycles,
                    audit_mode=audit_mode
                )
                
                if should_stop:
                    if audit_mode:
                        print(f"Early stopping at cycle {cycle + 1} with coherence {sim_coherence:.4f}")
                    break
        
        # Post-processing and metadata updates
        metadata.update({
            'v_t_score': v_t_mean if 'v_t_mean' in locals() else 0.0,
            'coherence_score': np.mean(coherence_scores) if coherence_scores else 0.0,
            'cycles_run': cycle + 1,
            'final_phase': metadata.get('windsurf_phase', 'UNKNOWN'),
            'reflection_ratio': metadata['reflection_count'] / max(1, cycle + 1)
        })
        
        # Apply final validation if in audit mode
        if audit_mode and hasattr(self, 'validation_protocol'):
            self._finalize_validation(x, metadata)
        
        return y_state, metadata
    
    def _should_stop_early(self, cycle: int, coherence: float, 
                          max_cycles: int, audit_mode: bool = False) -> bool:
        """Determine if early stopping conditions are met."""
        # Base condition: high coherence
        if coherence > 0.85:
            return True
            
        # Phase-aware stopping conditions
        current_phase = getattr(self, 'current_phase', ValidationPhase.INIT)
        
        if current_phase == ValidationPhase.INIT:
            # Allow more exploration in early phases
            return False
            
        elif current_phase == ValidationPhase.BREATH:
            # More tolerant in breathing phase
            return coherence > 0.9 or cycle >= max_cycles - 2
            
        elif current_phase in [ValidationPhase.RITUALS, ValidationPhase.INTEGRITY]:
            # Balance exploration and exploitation
            min_cycles = min(5, max_cycles // 2)
            return (coherence > 0.88 and cycle >= min_cycles) or cycle >= max_cycles - 1
            
        # Default: full cycles for full phase
        return cycle >= max_cycles - 1
    
    def _finalize_validation(self, x: torch.Tensor, metadata: Dict[str, Any]) -> None:
        """Finalize validation and update protocol state."""
        if not hasattr(self, 'validation_protocol'):
            return
            
        # Run final validation step
        state = self.validation_protocol.advance_phase(x, "final_validation")
        
        # Update metadata with final validation state
        metadata.update({
            'validation_passed': state.passed,
            'validation_phase': state.phase.name,
            'validation_metrics': state.metrics
        })
        
        # Log phase transition if applicable
        if len(self.validation_protocol.history) > 1:
            prev_phase = self.validation_protocol.history[-2].phase
            if prev_phase != state.phase:
                self.message_bus.publish(
                    'phase_transition',
                    {'from': prev_phase.name, 'to': state.phase.name},
                    priority=2
                )
    
    def constrain_gradients(self, gradients: torch.Tensor, param_name: str) -> torch.Tensor:
        """Apply biological constraints to gradients during training."""
        if not self.training or not hasattr(self, 'bio_constraints'):
            return gradients
            
        return self.bio_constraints.constrain_gradients(gradients, param_name)
    
    def register_optimizer(self, optimizer):
        """Register optimizer for learning rate adjustments."""
        self.optimizer = optimizer

    def _register_message_handlers(self):
        """Register message handlers for cross-component communication."""
        if not hasattr(self, 'message_bus'):
            return
            
        # Register phase transition handler
        self.message_bus.register_handler('phase_transition', self._handle_phase_transition)
        
        # Register constraint violation handler
        self.message_bus.register_handler('constraint_violation', self._handle_constraint_violation)
    
    def _handle_phase_transition(self, data):
        """Handle phase transition events."""
        old_phase, new_phase = data.get('from'), data.get('to')
        if self.enable_ambient and hasattr(self, 'ambient_core'):
            self.ambient_core.on_phase_transition(old_phase, new_phase)
    
    def _handle_constraint_violation(self, data):
        """Handle constraint violation events."""
        # Log violations or trigger recovery mechanisms
        if self.training:
            self._apply_mitigation(data)
    
    def _apply_mitigation(self, violation_data):
        """Apply mitigation for constraint violations."""
        # Implement adaptive response to violations
        violation_type = violation_data.get('type')
        severity = violation_data.get('severity', 1.0)
        
        if violation_type == 'optimization_rate':
            # Reduce learning rate or apply gradient clipping
            self._adjust_learning_rate(scale=1.0 - (0.1 * severity))
    
    def _adjust_learning_rate(self, scale=0.9):
        """Adjust learning rate for stability."""
        for param_group in self.optimizer.param_groups:
            param_group['lr'] *= scale