from pathlib import Path import torch from peft import PeftModel import modules.shared as shared from modules.logging_colors import logger from modules.models import reload_model def add_lora_to_model(lora_names): if 'GPTQForCausalLM' in shared.model.__class__.__name__: add_lora_autogptq(lora_names) elif shared.model.__class__.__name__ in ['ExllamaModel', 'ExllamaHF']: add_lora_exllama(lora_names) else: add_lora_transformers(lora_names) def add_lora_exllama(lora_names): try: from exllama.lora import ExLlamaLora except: try: from repositories.exllama.lora import ExLlamaLora except: logger.error("Could not find the file repositories/exllama/lora.py. Make sure that exllama is cloned inside repositories/ and is up to date.") return if len(lora_names) == 0: if shared.model.__class__.__name__ == 'ExllamaModel': shared.model.generator.lora = None else: shared.model.lora = None shared.lora_names = [] return else: if len(lora_names) > 1: logger.warning('ExLlama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.') lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}") lora_config_path = lora_path / "adapter_config.json" lora_adapter_path = lora_path / "adapter_model.bin" logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]]))) if shared.model.__class__.__name__ == 'ExllamaModel': lora = ExLlamaLora(shared.model.model, str(lora_config_path), str(lora_adapter_path)) shared.model.generator.lora = lora else: lora = ExLlamaLora(shared.model.ex_model, str(lora_config_path), str(lora_adapter_path)) shared.model.lora = lora shared.lora_names = [lora_names[0]] return # Adapted from https://github.com/Ph0rk0z/text-generation-webui-testing def add_lora_autogptq(lora_names): try: from auto_gptq import get_gptq_peft_model from auto_gptq.utils.peft_utils import GPTQLoraConfig except: logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.") return if len(lora_names) == 0: if len(shared.lora_names) > 0: reload_model() shared.lora_names = [] return else: if len(lora_names) > 1: logger.warning('AutoGPTQ can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.') peft_config = GPTQLoraConfig( inference_mode=True, ) lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}") logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]]))) shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path) shared.lora_names = [lora_names[0]] return def add_lora_transformers(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: logger.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 may no longer be PeftModel if hasattr(shared.model, 'disable_adapter'): shared.model.disable_adapter() shared.model = shared.model.base_model.model if len(lora_names) > 0: 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} logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names))) shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params) for lora in lora_names[1:]: shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) shared.lora_names = lora_names 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()