# Conceptual pseudo-code for adaptive mixed-precision assignment def assign_bit_widths_adaptively(model, calibration_data, target_accuracy_drop): """ Assigns bit-widths per layer based on sensitivity. This is a simplified conceptual approach. """ layer_sensitivities = {} # 1. Evaluate baseline full-precision accuracy baseline_accuracy = evaluate_model(model, calibration_data) # 2. Iterate through layers to determine sensitivity for layer_name, layer in model.named_layers(): # Temporarily quantize layer to a very low bit-width (e.g., 2-bit) # This is a proxy for maximum impact temp_quantized_model = quantize_layer_temporarily(model, layer_name, 2) temp_accuracy = evaluate_model(temp_quantized_model, calibration_data) layer_sensitivities[layer_name] = baseline_accuracy - temp_accuracy # 3. Sort layers by sensitivity and assign bit-widths sorted_layers = sorted(layer_sensitivities.items(), key=lambda item: item[1], reverse=True) assigned_bit_widths = {} for layer_name, _ in sorted_layers: # Start with a default lower bit-width, e.g., 1-bit or 0.5-bit # Gradually increase for more sensitive layers until target accuracy drop is met. current_bit_width = 1 # Or 0.5 for the most aggressive # This loop would involve iteratively trying different bit-widths # and re-evaluating, which is computationally expensive for a real system. # A more practical approach might use a pre-defined budget or a more complex heuristic. while current_bit_width < 4: # Assume max 4-bit for highly sensitive trial_model = assign_specific_bit_width(model, assigned_bit_widths, layer_name, current_bit_width) trial_accuracy = evaluate_model(trial_model, calibration_data) if (baseline_accuracy - trial_accuracy) < target_accuracy_drop: assigned_bit_widths[layer_name] = current_bit_width break current_bit_width += 1 # Or other discrete steps else: # If still too sensitive after trying all, assign highest allowed assigned_bit_widths[layer_name] = 4 return assigned_bit_widths