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| 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__ or shared.args.loader == 'AutoGPTQ': | |
| add_lora_autogptq(lora_names) | |
| elif shared.model.__class__.__name__ in ['ExllamaModel', 'ExllamaHF'] or shared.args.loader == 'ExLlama': | |
| 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: | |
| 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.') | |
| if not shared.args.no_inject_fused_attention: | |
| logger.warning('Fused Atttention + AutoGPTQ may break Lora loading. Disable it.') | |
| 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: | |
| if shared.args.load_in_4bit or shared.args.load_in_8bit: | |
| params['peft_type'] = shared.model.dtype | |
| else: | |
| 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()} | |
| 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.backends.mps.is_available(): | |
| device = torch.device('mps') | |
| shared.model = shared.model.to(device) | |
| else: | |
| shared.model = shared.model.cuda() | |