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| import logging | |
| from pathlib import Path | |
| import torch | |
| from peft import PeftModel | |
| import modules.shared as shared | |
| def add_lora_to_model(lora_names): | |
| prior_set = set(shared.lora_names) | |
| added_set = set(lora_names) - prior_set | |
| removed_set = prior_set - set(lora_names) | |
| shared.lora_names = list(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: | |
| logging.info(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() | |
| shared.model = shared.model.base_model.model | |
| if len(lora_names) > 0: | |
| logging.info("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() | |