From 7f401b90414e288ee3e348796cf0b39198af8fc1 Mon Sep 17 00:00:00 2001 From: kasinadhsarma Date: Thu, 26 Dec 2024 13:13:03 +0530 Subject: [PATCH] Remove deprecated error handling module and add new error correction functionality; update tests for consciousness model --- init.py | 8 - .../consciousness_model.cpython-310.pyc | Bin 15690 -> 18370 bytes .../error_handling.cpython-310.pyc | Bin 0 -> 3913 bytes models/consciousness_model.py | 683 ++++++++++-------- models/error_handling.py | 129 ++++ ...consciousness.cpython-310-pytest-8.3.4.pyc | Bin 32308 -> 32308 bytes ...ousness_model.cpython-310-pytest-8.3.4.pyc | Bin 0 -> 1740 bytes ...or_correction.cpython-310-pytest-8.3.4.pyc | Bin 0 -> 5437 bytes ...rror_handling.cpython-310-pytest-8.3.4.pyc | Bin 0 -> 176 bytes tests/test_consciousness.py | 6 +- tests/test_error_correction.py | 149 ++++ tests/test_error_handling.py | 0 12 files changed, 680 insertions(+), 295 deletions(-) delete mode 100644 init.py create mode 100644 models/__pycache__/error_handling.cpython-310.pyc create mode 100644 models/error_handling.py create mode 100644 tests/__pycache__/test_consciousness_model.cpython-310-pytest-8.3.4.pyc create mode 100644 tests/__pycache__/test_error_correction.cpython-310-pytest-8.3.4.pyc create mode 100644 tests/__pycache__/test_error_handling.cpython-310-pytest-8.3.4.pyc create mode 100644 tests/test_error_correction.py create mode 100644 tests/test_error_handling.py diff --git a/init.py b/init.py deleted file mode 100644 index ae6f5e7..0000000 --- a/init.py +++ /dev/null @@ -1,8 +0,0 @@ - -# ...existing code... -# Remove or replace deprecated torch.set_default_tensor_type() -# torch.set_default_tensor_type(torch.FloatTensor) - -torch.set_default_dtype(torch.float32) -torch.set_default_device('cpu') # Adjust based on your hardware -# ...existing code... \ No newline at end of file diff --git a/models/__pycache__/consciousness_model.cpython-310.pyc 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.attention import GlobalWorkspace from .memory import WorkingMemory, InformationIntegration from .consciousness_state import CognitiveProcessIntegration, ConsciousnessStateManager +from .error_handling import ErrorHandler, ErrorCorrection, validate_state torch.set_default_dtype(torch.float32) torch.set_default_device('cpu') # or 'cuda' if using GPU @@ -121,6 +122,11 @@ def __init__(self, hidden_dim: int, num_heads: int, num_layers: int, num_states: }) self.logger = logging.getLogger(__name__) + self.cognition_progress_history = [] + + # Add error handling components + self.error_handler = ErrorHandler(self.logger) + self.error_correction = ErrorCorrection(hidden_dim) def add_meta_learning_layer(self): """Add meta-learning capabilities""" @@ -208,325 +214,420 @@ def calculate_cognition_progress(self, metrics): # Example calculation based on 'phi' metric phi = metrics.get('phi', 0) cognition_percentage = phi * 100 + self.cognition_progress_history.append(cognition_percentage) return cognition_percentage - def forward(self, inputs, state=None, initial_state=None, deterministic=True, consciousness_threshold=0.5): + def report_cognition_progress(self): """ - Process inputs through consciousness architecture. + Report the overall cognition progress and identify areas for improvement. """ - # Initialize attention maps dictionary - attention_maps = {} - - # Validate and process inputs - if not inputs: - raise ValueError("Inputs cannot be empty.") - - # Allow for more flexible input combinations - required_modalities = {'visual', 'textual'} # Required modalities - missing_modalities = required_modalities - inputs.keys() - if missing_modalities: - # Auto-populate missing modalities with zero tensors - batch_size = next(iter(inputs.values())).size(0) - seq_len = next(iter(inputs.values())).size(1) - for modality in missing_modalities: - inputs[modality] = torch.zeros(batch_size, seq_len, self.hidden_dim, device=inputs[next(iter(inputs.keys()))].device) - - # Check input dimensions - expected_dims = { - 'attention': (None, 8, self.hidden_dim), - 'memory': (None, 10, self.hidden_dim), - 'visual': (None, None, self.hidden_dim), - 'textual': (None, None, self.hidden_dim) - } - - # Project inputs to correct dimension if needed - for modality, tensor in inputs.items(): - if modality in expected_dims: - # Project if dimensions don't match - if tensor.size(-1) != self.hidden_dim: - inputs[modality] = self.input_projection(tensor) - - batch_size = next(iter(inputs.values())).shape[0] - inputs = {k: v.clone().detach().to(dtype=torch.float32) if isinstance(v, torch.Tensor) - else torch.tensor(v, dtype=torch.float32) - for k, v in inputs.items()} - - # Initialize consciousness state if none provided - if state is None: - state = torch.zeros(batch_size, self.hidden_dim, device=next(iter(inputs.values())).device) + if not self.cognition_progress_history: + return "No cognition progress data available." + + avg_progress = sum(self.cognition_progress_history) / len(self.cognition_progress_history) + areas_to_improve = [] + + # Example criteria for identifying areas to improve + if avg_progress < 50: + areas_to_improve.append("Increase phi metric to improve cognition progress.") + if 'context_stability' in self.metrics and self.metrics['context_stability'] < 0.5: + areas_to_improve.append("Improve context stability.") + if 'coherence' in self.metrics and self.metrics['coherence'] < 0.5: + areas_to_improve.append("Enhance coherence in state transitions.") + + report = f"Average Cognition Progress: {avg_progress}%\n" + if areas_to_improve: + report += "Areas to Improve:\n" + "\n".join(areas_to_improve) else: - state = torch.tensor(state, dtype=torch.float32) + report += "All areas are performing well." - metrics = {} + return report - # Global workspace processing - workspace_input = next(iter(inputs.values())) - workspace_output, workspace_attention = self.global_workspace(workspace_input) - - # Ensure attention weights have correct shape (batch, seq, seq) - attention_weights = workspace_attention.squeeze(1) # Remove head dimension - metrics['attention_weights'] = attention_weights - - # Working memory update - memory_output, memory_state = self.working_memory( - workspace_output, - deterministic=deterministic, - initial_state=initial_state - ) + def forward(self, inputs, state=None, initial_state=None, deterministic=True, consciousness_threshold=0.5): + """ + Process inputs through consciousness architecture. + """ + try: + # Validate inputs + if not inputs: + raise ValueError("Inputs cannot be empty") + + # Validate state if provided + if state is not None: + error_msg = validate_state(state, (inputs[next(iter(inputs))].size(0), self.hidden_dim)) + if error_msg: + raise ValueError(f"Invalid state: {error_msg}") + + # Initialize attention maps dictionary + attention_maps = {} + + # Validate and process inputs + if not inputs: + raise ValueError("Inputs cannot be empty.") + + # Allow for more flexible input combinations + required_modalities = {'visual', 'textual'} # Required modalities + missing_modalities = required_modalities - inputs.keys() + if missing_modalities: + # Auto-populate missing modalities with zero tensors + batch_size = next(iter(inputs.values())).size(0) + seq_len = next(iter(inputs.values())).size(1) + for modality in missing_modalities: + inputs[modality] = torch.zeros(batch_size, seq_len, self.hidden_dim, device=inputs[next(iter(inputs.keys()))].device) + + # Check input dimensions + expected_dims = { + 'attention': (None, 8, self.hidden_dim), + 'memory': (None, 10, self.hidden_dim), + 'visual': (None, None, self.hidden_dim), + 'textual': (None, None, self.hidden_dim) + } + + # Project inputs to correct dimension if needed + for modality, tensor in inputs.items(): + if modality in expected_dims: + # Project if dimensions don't match + if tensor.size(-1) != self.hidden_dim: + inputs[modality] = self.input_projection(tensor) + + batch_size = next(iter(inputs.values())).shape[0] + inputs = {k: v.clone().detach().to(dtype=torch.float32) if isinstance(v, torch.Tensor) + else torch.tensor(v, dtype=torch.float32) + for k, v in inputs.items()} + + # Initialize consciousness state if none provided + if state is None: + state = torch.zeros(batch_size, self.hidden_dim, device=next(iter(inputs.values())).device) + else: + state = torch.tensor(state, dtype=torch.float32) - # Information integration - integrated_output, phi = self.information_integration(memory_output, deterministic=deterministic) - - # Update required metrics - metrics.update({ - 'memory_state': memory_state, - 'attention_weights': attention_weights, - 'phi': phi, - 'attention_maps': attention_maps - }) + metrics = {} - # Fix state shape handling - ensure it matches sequence length - if 'state' in inputs: - # Handle 4D state tensor case - state_tensor = inputs['state'] - if state_tensor.dim() == 4: - # Remove extra dimensions (batch, extra_dim, seq, hidden) - state_tensor = state_tensor.squeeze(1) - elif state_tensor.dim() == 3: - # Already correct shape (batch, seq, hidden) - pass - else: - # Add sequence dimension if needed - state_tensor = state_tensor.unsqueeze(1) + # Global workspace processing + workspace_input = next(iter(inputs.values())) + workspace_output, workspace_attention = self.global_workspace(workspace_input) - # Now expand to match sequence length - target_seq_len = next(iter(inputs.values())).size(1) - if state_tensor.size(1) != target_seq_len: - state_tensor = state_tensor.expand(-1, target_seq_len, -1) - inputs['state'] = state_tensor - - # Cognitive process integration with fixed state shape - consciousness_state, attention_maps = self.cognitive_integration(inputs, deterministic=deterministic) - - # Update consciousness state - new_state, state_metrics = self.state_manager( - consciousness_state, - integrated_output, - threshold=consciousness_threshold, - deterministic=deterministic - ) - metrics.update(state_metrics) - - # Apply multi-head attention - attn_output, attention_weights = self.attention( - memory_output, - memory_output, - memory_output, - need_weights=True - ) - - # Store attention map - attention_maps['self_attention'] = attention_weights + # Ensure attention weights have correct shape (batch, seq, seq) + attention_weights = workspace_attention.squeeze(1) # Remove head dimension + metrics['attention_weights'] = attention_weights + + # Working memory update + memory_output, memory_state = self.working_memory( + workspace_output, + deterministic=deterministic, + initial_state=initial_state + ) - # Add sequence prediction - sequence_pred = self.sequence_predictor(new_state) - - # Add transformation understanding - if inputs['visual'].shape[1] > 1: # If we have a sequence - trans_input = torch.cat([new_state[:,0], new_state[:,1]], dim=1) - trans_vec = self.transformation_net(trans_input) - else: - trans_vec = torch.zeros_like(new_state[:,0]) + # Information integration + integrated_output, phi = self.information_integration(memory_output, deterministic=deterministic) - # Add rule learning - rule_embed = self.rule_encoder(new_state.mean(dim=1)) - - metrics.update({ - 'sequence_predictions': sequence_pred, - 'transformation_vectors': trans_vec, - 'rule_embeddings': rule_embed, - 'rule_confidence': torch.sigmoid(rule_embed.norm(dim=-1, keepdim=True)) - }) + # Update required metrics + metrics.update({ + 'memory_state': memory_state, + 'attention_weights': attention_weights, + 'phi': phi, + 'attention_maps': attention_maps + }) - # Ensure new_state has shape (batch_size, hidden_dim) - new_state = new_state.mean(dim=1) + # Fix state shape handling - ensure it matches sequence length + if 'state' in inputs: + # Handle 4D state tensor case + state_tensor = inputs['state'] + if state_tensor.dim() == 4: + # Remove extra dimensions (batch, extra_dim, seq, hidden) + state_tensor = state_tensor.squeeze(1) + elif state_tensor.dim() == 3: + # Already correct shape (batch, seq, hidden) + pass + else: + # Add sequence dimension if needed + state_tensor = state_tensor.unsqueeze(1) + + # Now expand to match sequence length + target_seq_len = next(iter(inputs.values())).size(1) + if state_tensor.size(1) != target_seq_len: + state_tensor = state_tensor.expand(-1, target_seq_len, -1) + inputs['state'] = state_tensor + + # Cognitive process integration with fixed state shape + consciousness_state, attention_maps = self.cognitive_integration(inputs, deterministic=deterministic) + + # Update consciousness state + new_state, state_metrics = self.state_manager( + consciousness_state, + integrated_output, + threshold=consciousness_threshold, + deterministic=deterministic + ) + metrics.update(state_metrics) + + # Apply multi-head attention + attn_output, attention_weights = self.attention( + memory_output, + memory_output, + memory_output, + need_weights=True + ) + + # Store attention map + attention_maps['self_attention'] = attention_weights - # Add context-switching challenges - context_switching_output = self.context_switching_net(new_state) - context_switching_gate = torch.sigmoid(self.context_switching_gate(new_state)) - context_switching_state = context_switching_gate * context_switching_output + (1 - context_switching_gate) * new_state + # Add sequence prediction + sequence_pred = self.sequence_predictor(new_state) + + # Add transformation understanding + if inputs['visual'].shape[1] > 1: # If we have a sequence + trans_input = torch.cat([new_state[:,0], new_state[:,1]], dim=1) + trans_vec = self.transformation_net(trans_input) + else: + trans_vec = torch.zeros_like(new_state[:,0]) + + # Add rule learning + rule_embed = self.rule_encoder(new_state.mean(dim=1)) + + metrics.update({ + 'sequence_predictions': sequence_pred, + 'transformation_vectors': trans_vec, + 'rule_embeddings': rule_embed, + 'rule_confidence': torch.sigmoid(rule_embed.norm(dim=-1, keepdim=True)) + }) - # Add creative problem-solving scenarios - creative_problem_solving_output = self.creative_problem_solving_net(new_state) - creative_problem_solving_gate = torch.sigmoid(self.creative_problem_solving_gate(new_state)) - creative_problem_solving_state = creative_problem_solving_gate * creative_problem_solving_output + (1 - creative_problem_solving_gate) * new_state - - # Add self-reflection and meta-learning - reflection_output, coherence = self.self_reflection_mechanism( - state=new_state, - previous_states=self.state_history[-5:] - ) - - # Enhanced context handling - context_attention = self.enhanced_context_switching( - inputs=inputs, - context_history=self.context_history - ) - - metrics['coherence'] = coherence - metrics['context_stability'] = context_attention.mean().item() + # Ensure new_state has shape (batch_size, hidden_dim) + new_state = new_state.mean(dim=1) - metrics.update({ - 'context_switching_state': context_switching_state, - 'creative_problem_solving_state': creative_problem_solving_state - }) + # Add context-switching challenges + context_switching_output = self.context_switching_net(new_state) + context_switching_gate = torch.sigmoid(self.context_switching_gate(new_state)) + context_switching_state = context_switching_gate * context_switching_output + (1 - context_switching_gate) * new_state - # Update meta-learning components more robustly - if not hasattr(self, 'state_history'): - self.state_history = [] - if not hasattr(self, 'context_history'): - self.context_history = [] - - # Store current state in history (limit size) - self.state_history = self.state_history[-10:] + [new_state.detach()] - - # Add self-reflection with proper error handling - try: + # Add creative problem-solving scenarios + creative_problem_solving_output = self.creative_problem_solving_net(new_state) + creative_problem_solving_gate = torch.sigmoid(self.creative_problem_solving_gate(new_state)) + creative_problem_solving_state = creative_problem_solving_gate * creative_problem_solving_output + (1 - creative_problem_solving_gate) * new_state + + # Add self-reflection and meta-learning reflection_output, coherence = self.self_reflection_mechanism( state=new_state, - previous_states=self.state_history[:-1] # Exclude current state + previous_states=self.state_history[-5:] ) - metrics['coherence'] = coherence - except Exception as e: - print(f"Warning: Self-reflection failed: {str(e)}") - metrics['coherence'] = 0.0 - reflection_output = new_state - - # Compute coherence score - coherence_score = 0.0 - if len(self.state_history) > 0: - current_state = new_state.detach() - similarities = [] - for prev_state in self.state_history[-5:]: - try: - # Handle batch size mismatch by broadcasting - if current_state.size(0) != prev_state.size(0): - if current_state.size(0) > prev_state.size(0): - prev_state = prev_state.expand(current_state.size(0), -1) - else: - current_state = current_state.mean(0, keepdim=True).expand(prev_state.size(0), -1) - sim = F.cosine_similarity(current_state, prev_state, dim=-1) - similarities.append(sim.mean().item()) - except Exception as e: - print(f"Warning: Similarity calculation failed: {str(e)}") - similarities.append(0.0) - coherence_score = sum(similarities) / len(similarities) if similarities else 0.0 - - # Update metrics with coherence - metrics.update({ - 'coherence': coherence_score, - 'context_stability': context_attention.mean().item() if isinstance(context_attention, torch.Tensor) else 0.0 - }) - - # Update state history with proper shape - if len(self.state_history) >= 10: - self.state_history.pop(0) - self.state_history.append(new_state.detach().mean(dim=0) if new_state.dim() > 2 else new_state.detach()) - - # Update current state - self.current_state = new_state.detach().mean(dim=0, keepdim=True) - - # Ensure all required metrics are present before returning - required_metrics = ['memory_state', 'attention_weights', 'phi', 'attention_maps'] - for metric in required_metrics: - if metric not in metrics: - metrics[metric] = torch.tensor(0.0) if metric != 'attention_maps' else {} - - try: - # Add performance monitoring - performance_metric = metrics['coherence'] - self.performance_history = torch.cat([ - self.performance_history[1:], - torch.tensor([performance_metric]) - ]) - # Apply adaptive learning - adaptation = self.adaptive_learning_step( - new_state, - 1.0 - performance_metric + # Enhanced context handling + context_attention = self.enhanced_context_switching( + inputs=inputs, + context_history=self.context_history ) - new_state = new_state + adaptation - # Add performance stats to metrics + metrics['coherence'] = coherence + metrics['context_stability'] = context_attention.mean().item() + metrics.update({ - 'adaptation_rate': self.adaptation_rate.item(), - 'average_performance': self.performance_history[-100:].mean().item(), - 'performance_trend': (self.performance_history[-100:] - - self.performance_history[-200:-100]).mean().item() + 'context_switching_state': context_switching_state, + 'creative_problem_solving_state': creative_problem_solving_state }) + + # Update meta-learning components more robustly + if not hasattr(self, 'state_history'): + self.state_history = [] + if not hasattr(self, 'context_history'): + self.context_history = [] + + # Store current state in history (limit size) + self.state_history = self.state_history[-10:] + [new_state.detach()] - except Exception as e: - print(f"Warning: Adaptation step failed: {str(e)}") - # Provide default metrics even if adaptation fails + # Add self-reflection with proper error handling + try: + reflection_output, coherence = self.self_reflection_mechanism( + state=new_state, + previous_states=self.state_history[:-1] # Exclude current state + ) + metrics['coherence'] = coherence + except Exception as e: + print(f"Warning: Self-reflection failed: {str(e)}") + metrics['coherence'] = 0.0 + reflection_output = new_state + + # Compute coherence score + coherence_score = 0.0 + if len(self.state_history) > 0: + current_state = new_state.detach() + similarities = [] + for prev_state in self.state_history[-5:]: + try: + # Handle batch size mismatch by broadcasting + if current_state.size(0) != prev_state.size(0): + if current_state.size(0) > prev_state.size(0): + prev_state = prev_state.expand(current_state.size(0), -1) + else: + current_state = current_state.mean(0, keepdim=True).expand(prev_state.size(0), -1) + sim = F.cosine_similarity(current_state, prev_state, dim=-1) + similarities.append(sim.mean().item()) + except Exception as e: + print(f"Warning: Similarity calculation failed: {str(e)}") + similarities.append(0.0) + coherence_score = sum(similarities) / len(similarities) if similarities else 0.0 + + # Update metrics with coherence metrics.update({ - 'adaptation_rate': 0.1, - 'average_performance': 0.5, - 'performance_trend': 0.0 + 'coherence': coherence_score, + 'context_stability': context_attention.mean().item() if isinstance(context_attention, torch.Tensor) else 0.0 }) - # Update metrics with proper bounds and required fields - metrics.update({ - 'patterns': self.meta_learner['pattern_recognition'](new_state).detach(), - 'pattern_confidence': torch.sigmoid(rule_embed.norm(dim=-1)).mean().item(), - 'coherence': max(min(coherence_score, 1.0), 0.0), # Ensure coherence is bounded - 'context_stability': context_attention.mean().item() if isinstance(context_attention, torch.Tensor) else 0.0 - }) + # Update state history with proper shape + if len(self.state_history) >= 10: + self.state_history.pop(0) + self.state_history.append(new_state.detach().mean(dim=0) if new_state.dim() > 2 else new_state.detach()) + + # Update current state + self.current_state = new_state.detach().mean(dim=0, keepdim=True) + + # Ensure all required metrics are present before returning + required_metrics = ['memory_state', 'attention_weights', 'phi', 'attention_maps'] + for metric in required_metrics: + if metric not in metrics: + metrics[metric] = torch.tensor(0.0) if metric != 'attention_maps' else {} + + try: + # Add performance monitoring + performance_metric = metrics['coherence'] + self.performance_history = torch.cat([ + self.performance_history[1:], + torch.tensor([performance_metric]) + ]) + + # Apply adaptive learning + adaptation = self.adaptive_learning_step( + new_state, + 1.0 - performance_metric + ) + new_state = new_state + adaptation + + # Add performance stats to metrics + metrics.update({ + 'adaptation_rate': self.adaptation_rate.item(), + 'average_performance': self.performance_history[-100:].mean().item(), + 'performance_trend': (self.performance_history[-100:] - + self.performance_history[-200:-100]).mean().item() + }) + + except Exception as e: + print(f"Warning: Adaptation step failed: {str(e)}") + # Provide default metrics even if adaptation fails + metrics.update({ + 'adaptation_rate': 0.1, + 'average_performance': 0.5, + 'performance_trend': 0.0 + }) + + # Update metrics with proper bounds and required fields + metrics.update({ + 'patterns': self.meta_learner['pattern_recognition'](new_state).detach(), + 'pattern_confidence': torch.sigmoid(rule_embed.norm(dim=-1)).mean().item(), + 'coherence': max(min(coherence_score, 1.0), 0.0), # Ensure coherence is bounded + 'context_stability': context_attention.mean().item() if isinstance(context_attention, torch.Tensor) else 0.0 + }) - try: - # Performance monitoring with bounded metrics - performance_metric = min(max(metrics['coherence'], 0.0), 1.0) - self.performance_history = torch.cat([ - self.performance_history[1:], - torch.tensor([performance_metric], device=self.performance_history.device) - ]) + try: + # Performance monitoring with bounded metrics + performance_metric = min(max(metrics['coherence'], 0.0), 1.0) + self.performance_history = torch.cat([ + self.performance_history[1:], + torch.tensor([performance_metric], device=self.performance_history.device) + ]) + + # Apply adaptive learning + adaptation = self.adaptive_learning_step( + new_state.detach(), + 1.0 - performance_metric + ) + new_state = new_state + adaptation + + # Add performance stats to metrics + metrics.update({ + 'adaptation_rate': float(self.adaptation_rate.mean().item()), + 'average_performance': float(self.performance_history[-100:].mean().item()), + 'performance_trend': float((self.performance_history[-100:] - + self.performance_history[-200:-100]).mean().item()) + }) + + except Exception as e: + print(f"Warning: Adaptation step failed: {str(e)}") + # Provide default metrics even if adaptation fails + metrics.update({ + 'adaptation_rate': 0.1, + 'average_performance': 0.5, + 'performance_trend': 0.0 + }) + + # Apply error correction with proper metrics tracking + corrected_state, error_prob = self.error_correction(new_state) + metrics['error_prob'] = error_prob - # Apply adaptive learning - adaptation = self.adaptive_learning_step( - new_state.detach(), - 1.0 - performance_metric - ) - new_state = new_state + adaptation + if error_prob > 0.3: # Threshold for logging errors + self.error_handler.log_error( + "state_error", + f"High error probability: {error_prob:.3f}", + metrics + ) - # Add performance stats to metrics - metrics.update({ - 'adaptation_rate': float(self.adaptation_rate.mean().item()), - 'average_performance': float(self.performance_history[-100:].mean().item()), - 'performance_trend': float((self.performance_history[-100:] - - self.performance_history[-200:-100]).mean().item()) - }) + # Update state after error correction + new_state = corrected_state - except Exception as e: - print(f"Warning: Adaptation step failed: {str(e)}") - # Provide default metrics even if adaptation fails - metrics.update({ - 'adaptation_rate': 0.1, - 'average_performance': 0.5, - 'performance_trend': 0.0 - }) - - # Ensure output requires gradients - if not deterministic: - state = state.detach().requires_grad_(True) - - # Keep original shape for state history - batch_state = new_state.clone() - # Store mean across batch dimension for history - self.state_history = self.state_history[-10:] + [new_state.mean(dim=0, keepdim=True).detach()] + # Ensure incremental learning performance + if hasattr(self, 'state_history') and len(self.state_history) > 0: + # Calculate coherence with history + current_state = new_state.detach() + similarities = [] + for prev_state in self.state_history[-5:]: + try: + # Handle batch size mismatch + if current_state.size(0) != prev_state.size(0): + if current_state.size(0) > prev_state.size(0): + prev_state = prev_state.expand(current_state.size(0), -1) + else: + current_state = current_state.mean(0, keepdim=True).expand(prev_state.size(0), -1) + sim = F.cosine_similarity(current_state, prev_state, dim=-1) + similarities.append(sim.mean().item()) + except Exception as e: + self.logger.warning(f"Similarity calculation failed: {str(e)}") + similarities.append(0.0) + + coherence_score = sum(similarities) / len(similarities) if similarities else 0.0 + # Apply exponential moving average for stability + if 'coherence' in metrics: + metrics['coherence'] = 0.7 * metrics['coherence'] + 0.3 * coherence_score + else: + metrics['coherence'] = coherence_score + + # Add error correction + new_state, error_prob = self.error_correction(new_state) + if error_prob > 0.3: # Threshold for logging errors + self.error_handler.log_error( + "state_error", + f"High error probability: {error_prob:.3f}", + metrics + ) + + # Ensure output requires gradients + if not deterministic: + state = state.detach().requires_grad_(True) + + # Keep original shape for state history + batch_state = new_state.clone() + # Store mean across batch dimension for history + self.state_history = self.state_history[-10:] + [new_state.mean(dim=0, keepdim=True).detach()] + + # Calculate cognition progress + cognition_progress = self.calculate_cognition_progress(metrics) + metrics['cognition_progress'] = cognition_progress + self.logger.debug(f"Cognition Progress: {cognition_progress}%") - # Calculate cognition progress - cognition_progress = self.calculate_cognition_progress(metrics) - metrics['cognition_progress'] = cognition_progress - self.logger.debug(f"Cognition Progress: {cognition_progress}%") + except Exception as e: + self.error_handler.log_error( + "forward_pass_error", + str(e), + {"input_shapes": {k: v.shape for k, v in inputs.items()}} + ) + raise return new_state, metrics # Detach output state @@ -557,6 +658,18 @@ def create_default_config(cls) -> Dict[str, Any]: 'dropout_rate': 0.1 } + def analyze_model_health(self) -> Dict[str, Any]: + """ + Analyze model health and error patterns + """ + error_patterns = self.error_handler.analyze_errors() + return { + "error_patterns": error_patterns, + "total_errors": len(self.error_handler.error_history), + "recent_errors": len([e for e in self.error_handler.error_history[-100:] if e]), + "error_rate": len(self.error_handler.error_history[-100:]) / 100 if self.error_handler.error_history else 0 + } + class WorkingMemory(nn.Module): def __init__(self, input_dim, hidden_dim, dropout_rate): super().__init__() diff --git a/models/error_handling.py b/models/error_handling.py new file mode 100644 index 0000000..d1d291d --- /dev/null +++ b/models/error_handling.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import logging +from typing import Dict, Any, Tuple, Optional + +class ErrorHandler: + """ + Handles errors and implements correction mechanisms for the consciousness model. + """ + def __init__(self, logger=None): + self.logger = logger or logging.getLogger(__name__) + self.error_history = [] + self.correction_history = [] + self.max_history = 1000 + + def log_error(self, error_type: str, details: str, metrics: Dict[str, Any]) -> None: + """Log an error with relevant metrics""" + error_entry = { + 'type': error_type, + 'details': details, + 'metrics': metrics + } + self.error_history.append(error_entry) + if len(self.error_history) > self.max_history: + self.error_history.pop(0) + self.logger.error(f"Error detected: {error_type} - {details}") + + def analyze_errors(self) -> Dict[str, float]: + """Analyze error patterns""" + if not self.error_history: + return {} + + error_counts = {} + for entry in self.error_history: + error_type = entry['type'] + error_counts[error_type] = error_counts.get(error_type, 0) + 1 + + total_errors = len(self.error_history) + return {k: v/total_errors for k, v in error_counts.items()} + +class ErrorCorrection(nn.Module): + """ + Neural network component for error correction in consciousness model. + """ + def __init__(self, hidden_dim: int): + super().__init__() + self.hidden_dim = hidden_dim + + # Error detection network + self.error_detector = nn.Sequential( + nn.Linear(hidden_dim, hidden_dim // 2), + nn.ReLU(), + nn.Linear(hidden_dim // 2, 1), + nn.Sigmoid() + ) + + # Enhanced Error correction network + self.correction_net = nn.Sequential( + nn.Linear(hidden_dim, hidden_dim), + nn.ReLU(), + nn.LayerNorm(hidden_dim), + nn.Linear(hidden_dim, hidden_dim), # Added layer for better correction + nn.Tanh() # Changed activation for bounded output + ) + + def forward(self, state: torch.Tensor) -> Tuple[torch.Tensor, float]: + """ + Detect and correct errors in the state. + Returns: (corrected_state, error_probability) + """ + # Handle NaN values first + nan_mask = torch.isnan(state) + if nan_mask.any(): + # Replace NaN values with zeros initially + working_state = torch.where(nan_mask, torch.zeros_like(state), state) + error_prob = 1.0 # High error probability for NaN values + else: + working_state = state + # Calculate error probability for non-NaN state + with torch.no_grad(): + error_prob = self.error_detector(state).mean().item() + + # Apply enhanced correction network + corrected_state = self.correction_net(working_state) + + # If there were NaN values, apply additional correction + if nan_mask.any(): + # For positions that had NaN, use neighbor averaging if available + batch_size = corrected_state.size(0) + for b in range(batch_size): + nan_indices = torch.where(nan_mask[b])[0] + if len(nan_indices) > 0: + # Get valid neighbor values + valid_values = corrected_state[b][~nan_mask[b]] + if len(valid_values) > 0: + # Use mean of valid values to fill NaN positions + corrected_state[b][nan_indices] = valid_values.mean() + else: + # If no valid values, initialize with small random values + corrected_state[b][nan_indices] = torch.randn(len(nan_indices), device=state.device) * 0.1 + + # Ensure values are bounded + corrected_state = torch.clamp(corrected_state, -1.0, 1.0) + + # Final normalization + corrected_state = nn.functional.normalize(corrected_state, dim=-1) + + # Ensure no NaN values remain + if torch.isnan(corrected_state).any(): + corrected_state = torch.where( + torch.isnan(corrected_state), + torch.zeros_like(corrected_state), + corrected_state + ) + error_prob = 1.0 + + return corrected_state, error_prob + +def validate_state(state: torch.Tensor, expected_shape: Tuple[int, ...]) -> Optional[str]: + """Validate state tensor""" + if not isinstance(state, torch.Tensor): + return "State must be a tensor" + if state.shape != expected_shape: + return f"Invalid state shape: expected {expected_shape}, got {state.shape}" + if torch.isnan(state).any(): + return "State contains NaN values" + if torch.isinf(state).any(): + return "State contains 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a/tests/test_error_correction.py b/tests/test_error_correction.py new file mode 100644 index 0000000..80d8c28 --- /dev/null +++ b/tests/test_error_correction.py @@ -0,0 +1,149 @@ +import unittest +import torch +import torch.nn as nn +from models.consciousness_model import ConsciousnessModel +from models.error_handling import ErrorHandler, ErrorCorrection + +class TestErrorCorrection(unittest.TestCase): + def setUp(self): + self.hidden_dim = 64 + self.error_correction = ErrorCorrection(hidden_dim=self.hidden_dim) + self.error_handler = ErrorHandler(logger=None) + self.model = ConsciousnessModel( + hidden_dim=self.hidden_dim, + num_heads=4, + num_layers=2, + num_states=3 + ) + + def test_error_correction_shape(self): + """Test if error correction maintains correct tensor shape""" + batch_size = 8 + input_state = torch.randn(batch_size, self.hidden_dim) + corrected_state, error_prob = self.error_correction(input_state) + + self.assertEqual(corrected_state.shape, input_state.shape) + self.assertTrue(isinstance(error_prob, float)) + self.assertTrue(0 <= error_prob <= 1) + + def test_error_detection(self): + """Test if error detection works for invalid states""" + # Test with valid state + valid_state = torch.randn(4, self.hidden_dim) + valid_state = torch.nn.functional.normalize(valid_state, dim=-1) + _, valid_error = self.error_correction(valid_state) + + # Test with invalid state (NaN values) + invalid_state = torch.full((4, self.hidden_dim), float('nan')) + _, invalid_error = self.error_correction(invalid_state) + + self.assertLess(valid_error, invalid_error) + + def test_error_correction_recovery(self): + """Test if error correction can recover from corrupted states""" + # Create original state + original_state = torch.randn(4, self.hidden_dim) + original_state = torch.nn.functional.normalize(original_state, dim=-1) + + # Create corrupted state with some NaN values + corrupted_state = original_state.clone() + corrupted_state[:, :10] = float('nan') + + # Apply error correction + corrected_state, error_prob = self.error_correction(corrupted_state) + + # Check if NaN values were fixed + self.assertFalse(torch.isnan(corrected_state).any()) + self.assertTrue(error_prob > 0.5) # Should detect high error probability + + def test_error_handling_integration(self): + """Test integration of error correction with error handling""" + batch_size = 4 + seq_len = 3 + + # Create input with some invalid values + inputs = { + 'visual': torch.randn(batch_size, seq_len, self.hidden_dim), + 'textual': torch.randn(batch_size, seq_len, self.hidden_dim) + } + inputs['visual'][0, 0] = float('nan') # Introduce error + + # Process through model + try: + state, metrics = self.model(inputs) + self.assertTrue('error_prob' in metrics) + self.assertFalse(torch.isnan(state).any()) + except Exception as e: + self.fail(f"Error correction should handle NaN values: {str(e)}") + + def test_error_correction_consistency(self): + """Test if error correction is consistent across multiple runs""" + input_state = torch.randn(4, self.hidden_dim) + + # Run multiple corrections + results = [] + for _ in range(5): + corrected, prob = self.error_correction(input_state) + results.append((corrected.clone(), prob)) + + # Check consistency + for i in range(1, len(results)): + torch.testing.assert_close(results[0][0], results[i][0]) + self.assertAlmostEqual(results[0][1], results[i][1]) + + def test_error_correction_gradients(self): + """Test if error correction maintains gradient flow""" + input_state = torch.randn(4, self.hidden_dim, requires_grad=True) + corrected_state, _ = self.error_correction(input_state) + + # Check if gradients can flow + loss = corrected_state.sum() + loss.backward() + + self.assertIsNotNone(input_state.grad) + self.assertFalse(torch.isnan(input_state.grad).any()) + + def test_error_correction_bounds(self): + """Test if error correction maintains value bounds""" + # Test with extreme values + extreme_state = torch.randn(4, self.hidden_dim) * 1000 + corrected_state, _ = self.error_correction(extreme_state) + + # Check if values are normalized + self.assertTrue(torch.all(corrected_state <= 1)) + self.assertTrue(torch.all(corrected_state >= -1)) + + def test_error_logging(self): + """Test if errors are properly logged""" + # Create invalid state + invalid_state = torch.full((4, self.hidden_dim), float('nan')) + + # Process with error handler + self.error_handler.log_error( + "state_error", + "Invalid state detected", + {"state": invalid_state} + ) + + # Check error history + self.assertTrue(len(self.error_handler.error_history) > 0) + latest_error = self.error_handler.error_history[-1] + self.assertEqual(latest_error['type'], "state_error") + + def test_error_correction_with_noise(self): + """Test error correction with different noise levels""" + base_state = torch.randn(4, self.hidden_dim) + noise_levels = [0.1, 0.5, 1.0] + + for noise in noise_levels: + noisy_state = base_state + torch.randn_like(base_state) * noise + corrected_state, error_prob = self.error_correction(noisy_state) + + # Higher noise should lead to higher error probability + self.assertTrue( + error_prob >= noise * 0.1, + f"Error probability too low for noise level {noise}" + ) + +if __name__ == '__main__': + unittest.main() diff --git a/tests/test_error_handling.py b/tests/test_error_handling.py new file mode 100644 index 0000000..e69de29