mirror of
https://github.com/oobabooga/text-generation-webui.git
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112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
from pathlib import Path
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import modules.shared as shared
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from modules.logging_colors import logger
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from modules.models import get_device
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def add_lora_to_model(lora_names):
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if shared.model.__class__.__name__ in ['Exllamav2Model', 'Exllamav2HF'] or shared.args.loader in ['ExLlamav2', 'ExLlamav2_HF']:
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add_lora_exllamav2(lora_names)
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else:
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add_lora_transformers(lora_names)
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def get_lora_path(lora_name):
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p = Path(lora_name)
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if p.exists():
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lora_name = p.parts[-1]
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return Path(f"{shared.args.lora_dir}/{lora_name}")
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def add_lora_exllamav2(lora_names):
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from exllamav2 import ExLlamaV2Lora
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if isinstance(shared.model.loras, list):
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for lora in shared.model.loras:
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lora.unload()
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if len(lora_names) > 0:
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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shared.model.loras = []
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for lora_name in lora_names:
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lora_path = get_lora_path(lora_name)
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if shared.model.__class__.__name__ == 'Exllamav2Model':
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lora = ExLlamaV2Lora.from_directory(shared.model.model, str(lora_path))
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else:
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lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path))
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shared.model.loras.append(lora)
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shared.lora_names = lora_names
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else:
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shared.lora_names = []
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shared.model.loras = None
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def add_lora_transformers(lora_names):
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from peft import PeftModel
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prior_set = set(shared.lora_names)
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added_set = set(lora_names) - prior_set
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removed_set = prior_set - set(lora_names)
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# If no LoRA needs to be added or removed, exit
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if len(added_set) == 0 and len(removed_set) == 0:
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return
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# Add a LoRA when another LoRA is already present
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if len(removed_set) == 0 and len(prior_set) > 0 and "__merged" not in shared.model.peft_config.keys():
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logger.info(f"Adding the LoRA(s) named {added_set} to the model")
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for lora in added_set:
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shared.model.load_adapter(get_lora_path(lora), lora)
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if len(lora_names) > 1:
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merge_loras()
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shared.lora_names = lora_names
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return
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# If any LoRA needs to be removed, start over
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if len(removed_set) > 0:
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shared.model = shared.model.unload()
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if len(lora_names) > 0:
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params = {}
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if not shared.args.cpu:
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if shared.args.load_in_4bit or shared.args.load_in_8bit:
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params['peft_type'] = shared.model.dtype
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else:
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params['dtype'] = shared.model.dtype
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if hasattr(shared.model, "hf_device_map"):
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params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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shared.model = PeftModel.from_pretrained(shared.model, get_lora_path(lora_names[0]), adapter_name=lora_names[0], **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(get_lora_path(lora), lora)
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if len(lora_names) > 1:
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merge_loras()
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if not shared.args.load_in_8bit and not shared.args.cpu:
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shared.model.half()
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if not hasattr(shared.model, "hf_device_map"):
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device = get_device()
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if device:
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shared.model = shared.model.to(device)
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shared.lora_names = lora_names
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def merge_loras():
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if len(list({shared.model.peft_config[adapter].r for adapter in shared.model.peft_config.keys()})) > 1:
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logger.warning("The loaded LoRAs cannot be merged, as they have dissimilar ranks. Only the first one will be active.")
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return
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shared.model.add_weighted_adapter(shared.lora_names, [1] * len(shared.lora_names), "__merged")
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shared.model.set_adapter("__merged")
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