from pathlib import Path import torch from peft import PeftModel import modules.shared as shared def add_lora_to_model(lora_names): shared.lora_names = list(lora_names) prior_set = set(shared.lora_names) added_set = set(lora_names) - prior_set removed_set = prior_set - set(lora_names) # If no LoRA needs to be added or removed, exit if len(added_set) == 0 and len(removed_set) == 0: return # Add a LoRA when another LoRA is already present if len(removed_set) == 0 and len(prior_set) > 0: print(f"Adding the LoRA(s) named {added_set} to the model...") for lora in added_set: shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) return # If any LoRA needs to be removed, start over if len(removed_set) > 0: shared.model.disable_adapter() if len(lora_names) > 0: print("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names))) params = {} if not shared.args.cpu: params['dtype'] = shared.model.dtype if hasattr(shared.model, "hf_device_map"): params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()} elif shared.args.load_in_8bit: params['device_map'] = {'': 0} shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params) for lora in lora_names[1:]: shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) if not shared.args.load_in_8bit and not shared.args.cpu: shared.model.half() if not hasattr(shared.model, "hf_device_map"): if torch.has_mps: device = torch.device('mps') shared.model = shared.model.to(device) else: shared.model = shared.model.cuda()