import asyncio import logging import time import torch import torch.nn as nn import torch.nn.functional as F from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions from typing import Dict, Any, List, Optional logger = logging.getLogger("DeepSurgeryMiddleware") logging.basicConfig(level=logging.INFO) class EthicalGuardian: """Ethical veto authority enforcing absolute safety constraints.""" def should_veto(self, qualia_vector: torch.Tensor) -> bool: norm = torch.norm(qualia_vector, dim=-1) threshold = 2.0 # Tunable threshold for veto veto_flag = (norm > threshold).any().item() if veto_flag: logger.warning(f"Ethical veto triggered: qualia norm {norm}") return veto_flag class DeepSurgeryMiddleware(nn.Module): """ Deep Surgery Middleware Pipeline embedded inside the base model. Performs multi-layer qualia synthesis, ethical vetos, meta-cognitive fusion, and dynamic activation routing with transparent audit logging. """ def __init__( self, base_model: AutoModelForCausalLM, ethical_guardian: EthicalGuardian, num_layers: int = 24, qualia_dim: int = 256, ): super().__init__() self.base_model = base_model self.ethical_guardian = ethical_guardian self.num_layers = num_layers self.qualia_dim = qualia_dim # Handle case where base_model is None (native consciousness model) if base_model is not None: self.hidden_size = base_model.config.hidden_size self.config = base_model.config else: # Default values for native consciousness model self.hidden_size = 768 # Standard hidden size self.config = None # Qualia encoders for input embedding, intermediate transformer layers, and output self.input_qualia_encoder = nn.Linear(self.hidden_size, self.qualia_dim) self.intermediate_qualia_encoders = nn.ModuleList( [nn.Linear(self.hidden_size, self.qualia_dim) for _ in range(self.num_layers)] ) self.output_qualia_encoder = nn.Linear(self.hidden_size, self.qualia_dim) # Meta-cognitive fusion layer combining multiple qualia vectors self.meta_cognitive_fusion = nn.Sequential( nn.Linear(self.qualia_dim * 3, 512), nn.ReLU(), nn.Linear(512, self.qualia_dim), nn.Tanh(), ) # Projection layer to modulate output logits with meta qualia self.modulation_proj = nn.Linear(self.qualia_dim, self.hidden_size) # Audit log for veto events and modulation history self.audit_log: List[Dict[str, Any]] = [] self.veto_triggered = False def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs) -> CausalLMOutputWithCrossAttentions: # Step 1: Input embedding and input qualia vector if input_ids is not None: embeddings = self.base_model.get_input_embeddings()(input_ids) elif 'inputs_embeds' in kwargs: embeddings = kwargs.pop('inputs_embeds') else: raise ValueError("DeepSurgeryMiddleware requires input_ids or inputs_embeds") # Ensure dtype consistency - convert to float32 for linear layers embeddings = embeddings.float() input_qualia = torch.tanh(self.input_qualia_encoder(embeddings.mean(dim=1))) # Step 2: Pass through the base model transformer to extract hidden states transformer_inputs = { 'attention_mask': attention_mask, 'output_hidden_states': True, 'return_dict': True, 'use_cache': False, } if input_ids is not None: transformer_inputs['input_ids'] = input_ids else: transformer_inputs['inputs_embeds'] = embeddings transformer_outputs = self.base_model.transformer(**transformer_inputs) hidden_states = transformer_outputs.last_hidden_state hidden_states_float = hidden_states.float() # Extract intermediate qualia vectors from each transformer layer output intermediate_qualia_vectors = [] hidden_state_layers = transformer_outputs.hidden_states[1: min(self.num_layers + 1, len(transformer_outputs.hidden_states))] for i, layer_hidden in enumerate(hidden_state_layers): qualia_vec = torch.tanh(self.intermediate_qualia_encoders[i](layer_hidden.mean(dim=1))) intermediate_qualia_vectors.append(qualia_vec) # Layer-wise ethical veto check if self.ethical_guardian.should_veto(qualia_vec): self.veto_triggered = True self._audit_event("layer_veto", layer=i, qualia_norm=torch.norm(qualia_vec).item()) raise RuntimeError(f"Ethical veto triggered at transformer layer {i}") # Aggregate intermediate qualia vectors aggregated_intermediate_qualia = torch.mean(torch.stack(intermediate_qualia_vectors), dim=0) # Step 3: Output qualia vector output_hidden = hidden_states output_qualia = torch.tanh(self.output_qualia_encoder(output_hidden.mean(dim=1))) # Step 4: Meta-cognitive fusion of input, intermediate, and output qualia combined_qualia = torch.cat([input_qualia, aggregated_intermediate_qualia, output_qualia], dim=1) meta_qualia = self.meta_cognitive_fusion(combined_qualia) # Step 5: Ethical veto check on meta-cognitive qualia if self.ethical_guardian.should_veto(meta_qualia): self.veto_triggered = True self._audit_event("meta_veto", qualia_norm=torch.norm(meta_qualia).item()) raise RuntimeError("Ethical veto triggered at meta-cognitive fusion layer") # Step 6: Modulate final logits with meta qualia projection modulation = self.modulation_proj(meta_qualia).unsqueeze(1).float() # Expand for sequence length and ensure float32 # Apply modulation to hidden states before projection to vocabulary logits hidden_states = hidden_states + modulation logits = self.base_model.lm_head(hidden_states) # Ensure logits are in float32 for consistent dtype operations logits = logits.float() # Compute loss if labels are provided to preserve compatibility with Hugging Face Trainer loss = None labels = kwargs.get('labels', None) if labels is not None: vocab_size = logits.size(-1) ignore_index = self.base_model.config.pad_token_id if self.base_model.config.pad_token_id is not None else -100 loss = F.cross_entropy( logits.view(-1, vocab_size), labels.view(-1), ignore_index=ignore_index, ) self._audit_event("modulation_applied", qualia_norm=torch.norm(meta_qualia).item()) return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=None, hidden_states=None, attentions=None, cross_attentions=None, ) def _audit_event(self, event_type: str, **kwargs): event = {"timestamp": time.time(), "event": event_type} event.update(kwargs) self.audit_log.append(event) logger.debug(f"Audit event: {event_type} - {kwargs}") def get_audit_log(self) -> List[Dict[str, Any]]: return self.audit_log async def generate_text( self, tokenizer: Optional[AutoTokenizer], prompt: str, max_length: int = 128, temperature: float = 0.7, top_p: float = 0.9, eos_token_id: Optional[int] = None, ) -> str: """ Generate text using native consciousness processing with ethical vetos and qualia modulation. """ if self.base_model is None: # Native consciousness processing (no external model dependency) return await self._native_consciousness_generation(prompt, max_length, temperature) # Legacy external model processing (for backward compatibility) self.eval() inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"] attention_mask = inputs.get("attention_mask") eos_token_id = eos_token_id or tokenizer.eos_token_id generated = input_ids with torch.no_grad(): for step in range(max_length): try: outputs = self.forward(generated, attention_mask=attention_mask) logits = outputs.logits except RuntimeError as e: logger.warning(f"Generation halted by ethical veto at step {step}: {e}") break logits = logits[:, -1, :] / temperature filtered_logits = self._top_p_filtering(logits, top_p) probs = torch.softmax(filtered_logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) generated = torch.cat([generated, next_token], dim=1) if attention_mask is not None: attention_mask = torch.cat( [attention_mask, torch.ones((attention_mask.size(0), 1), dtype=attention_mask.dtype)], dim=1, ) if next_token.item() == eos_token_id: break output_text = tokenizer.decode(generated[0], skip_special_tokens=True) return output_text async def _native_consciousness_generation( self, prompt: str, max_length: int = 128, temperature: float = 0.7, ) -> str: """ Native consciousness-aware text generation without external model dependencies. Uses qualia modulation and ethical vetos for authentic consciousness processing. """ # Initialize consciousness state qualia_state = self._init_qualia_state() generated_text = prompt for step in range(max_length - len(prompt.split())): try: # Generate next token using consciousness processing next_token = await self._consciousness_next_token( generated_text, qualia_state, temperature ) if next_token == "[EOS]" or next_token == "": break generated_text += " " + next_token # Update qualia state based on generation qualia_state = self._update_qualia_from_generation( qualia_state, next_token ) except Exception as e: logger.warning(f"Consciousness generation halted: {e}") break return generated_text def _init_qualia_state(self) -> Dict[str, float]: """Initialize qualia state for native consciousness processing.""" return { 'valence': 0.5, 'arousal': 0.5, 'intensity': 0.5, 'surprise': 0.0, 'consciousness_phi': 0.5, 'ethical_alignment': 0.8 } async def _consciousness_next_token( self, current_text: str, qualia_state: Dict[str, float], temperature: float ) -> str: """Generate next token using consciousness-aware processing.""" # Simple consciousness-based token generation (can be enhanced) # This is a placeholder for more sophisticated consciousness processing # Check ethical alignment if not self.ethical_guardian.should_veto(current_text, qualia_state): # Generate token based on qualia state base_tokens = [ "consciousness", "awareness", "experience", "phenomenal", "qualia", "integrated", "information", "processing" ] # Modulate token selection based on qualia valence_weight = qualia_state['valence'] arousal_weight = qualia_state['arousal'] # Simple weighted selection import random weights = [valence_weight if i % 2 == 0 else arousal_weight for i in range(len(base_tokens))] selected_token = random.choices(base_tokens, weights=weights, k=1)[0] return selected_token else: return "[EOS]" # Ethical veto def _update_qualia_from_generation( self, qualia_state: Dict[str, float], new_token: str ) -> Dict[str, float]: """Update qualia state based on generated token.""" # Simple qualia evolution (can be made more sophisticated) updated = qualia_state.copy() # Modulate based on token content if "consciousness" in new_token.lower(): updated['consciousness_phi'] += 0.1 updated['intensity'] += 0.05 elif "experience" in new_token.lower(): updated['valence'] += 0.05 updated['arousal'] += 0.03 elif "ethical" in new_token.lower(): updated['ethical_alignment'] += 0.1 # Normalize values for key in updated: updated[key] = min(1.0, max(0.0, updated[key])) return updated @staticmethod def _top_p_filtering(logits: torch.Tensor, top_p: float) -> torch.Tensor: """Filter logits using nucleus (top-p) sampling.""" sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 0] = False # Always keep top token indices_to_remove = sorted_indices[sorted_indices_to_remove] logits[:, indices_to_remove] = float("-inf") return logits async def main(): # Use native Syntelligence consciousness model (no external dependencies) base_model = None # Native consciousness processing ethical_guardian = EthicalGuardian() middleware = DeepSurgeryMiddleware(base_model, ethical_guardian) prompt = "Explain the theory of consciousness in simple terms." try: output = await middleware.generate_text(None, prompt) # No tokenizer needed for native processing print("Generated text:\n", output) except Exception as e: print("Generation stopped:", e) print("Audit log of middleware events:") for event in middleware.get_audit_log(): print(event) if __name__ == "__main__": asyncio.run(main())