| #!/usr/bin/env python3 | |
| """ | |
| Nova PyTorch Runner (V0) | |
| This script implements the core Soul+Mask+Fast-Weights architecture for the Nova LLM. | |
| It serves as a standalone PyTorch service for inference, masked updates, and guards. | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import mlflow | |
| import mlflow.pytorch | |
| # --- Configuration Placeholders --- | |
| MODEL_NAME = "Qwen/Qwen3-8B-Instruct" # Placeholder: Will be replaced with Qwen3-32B or similar | |
| SOUL_DIM = 512 # Dimension of the soul vector | |
| MASK_PCT = 0.05 # Percentage of parameters in the plasticity mask | |
| FAST_WEIGHT_HALF_LIFE_SEC = 120 # Half-life for fast-weights decay | |
| # --- Nova Core Model (Conceptual) --- | |
| class NovaCore(nn.Module): | |
| def __init__(self, vocab_size, d_model): | |
| super().__init__() | |
| # Placeholder for actual Transformer blocks | |
| self.transformer = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| # 1. Soul Injection (Conceptual) | |
| self.soul_vector = nn.Parameter(torch.randn(SOUL_DIM)) | |
| # Conceptual soul gates: a simple linear layer that would process soul_vector | |
| # and apply it to some part of the transformer's internal representation. | |
| # In a real model, this would be integrated within transformer blocks. | |
| self.soul_gates = nn.Linear(SOUL_DIM, d_model) # d_model is conceptual output dim | |
| # 2. Plasticity Mask (Conceptual) | |
| self.plastic_params = [] | |
| # Conceptual selection of plastic parameters: | |
| # In a real scenario, this would involve careful selection, e.g., | |
| # for name, param in self.transformer.named_parameters(): | |
| # if "bias" in name or "layer_norm" in name: # Example criteria | |
| # self.plastic_params.append(param) | |
| # For this conceptual scaffold, we'll just add a dummy parameter to illustrate. | |
| self.dummy_plastic_param = nn.Parameter(torch.randn(10)) | |
| self.plastic_params.append(self.dummy_plastic_param) | |
| # 3. Fast-Weights (Conceptual) | |
| self.fast_weights_cache = {} | |
| def forward(self, input_ids): | |
| # Conceptual application of soul_vector | |
| # In a real model, soul_vector would modulate internal activations. | |
| # Here, we just show it's processed. | |
| soul_modulation = self.soul_gates(self.soul_vector) | |
| # This modulation would then be applied within the transformer's layers. | |
| output = self.transformer(input_ids) | |
| return output | |
| # --- Turn Loop (Conceptual) --- | |
| def nova_turn_loop(model: NovaCore, tokenizer: AutoTokenizer, input_text: str): | |
| # 1) Infer | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| model_output = model(input_ids) | |
| # 2) Score (identity/utility/self-sup) - Placeholder | |
| identity_score = 0.0 | |
| utility_score = 0.0 | |
| # 3) Imprint fast-weights - Placeholder | |
| # Update model.fast_weights_cache based on current turn | |
| # 4) Masked SGD step (clip, EMA, EWC, orth-grad, ΔW caps) - Conceptual | |
| # In a real scenario, this would involve a tiny optimizer step on model.plastic_params | |
| # For conceptual illustration: | |
| if model.plastic_params: | |
| # Simulate a conceptual loss dependent on a plastic parameter | |
| # This ensures gradients are computed for plastic_params | |
| conceptual_loss = (model.dummy_plastic_param ** 2).sum() # Example: simple squared sum loss | |
| conceptual_loss.backward() # Simulate backward pass | |
| # Simulate a conceptual optimizer step on plastic_params | |
| # In reality, this would be a proper optimizer (e.g., Adam, SGD) | |
| with torch.no_grad(): | |
| for param in model.plastic_params: | |
| if param.grad is not None: | |
| param.data -= 1e-5 * param.grad # Conceptual tiny update | |
| param.grad.zero_() # Zero out gradients after update (like a real optimizer) | |
| # 5) Guards (rollback on 2σ drop) - Placeholder | |
| # Check identity_score and potentially rollback | |
| # 6) Log - Placeholder | |
| print(f"Turn processed. Identity Score: {identity_score}, Utility Score: {utility_score}") | |
| # MLflow: Log metrics per turn (conceptual) | |
| mlflow.log_metric("identity_score", identity_score) | |
| mlflow.log_metric("utility_score", utility_score) | |
| # --- Main Execution (Conceptual) --- | |
| if __name__ == "__main__": | |
| # MLflow: Start an MLflow run | |
| with mlflow.start_run(): | |
| # Placeholder for actual model loading and setup | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = NovaCore(tokenizer.vocab_size, d_model=768) # d_model is a placeholder | |
| print("Nova PyTorch Runner (V0) initialized.") | |
| print("This is a conceptual scaffold. Actual implementation will follow.") | |
| # MLflow: Log parameters (conceptual) | |
| mlflow.log_param("model_name", MODEL_NAME) | |
| mlflow.log_param("soul_dim", SOUL_DIM) | |
| mlflow.log_param("mask_pct", MASK_PCT) | |
| mlflow.log_param("fast_weight_half_life_sec", FAST_WEIGHT_HALF_LIFE_SEC) | |
| # Example turn loop execution | |
| # nova_turn_loop(model, tokenizer, "Hello, Nova. How are you today?") | |
| # MLflow: Log the model (conceptual) | |
| # mlflow.pytorch.log_model(model, "nova_model") | |