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56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
import logging
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from pathlib import Path
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import torch
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from peft import PeftModel
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import modules.shared as shared
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def add_lora_to_model(lora_names):
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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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shared.lora_names = list(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:
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logging.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(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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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.disable_adapter()
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shared.model = shared.model.base_model.model
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if len(lora_names) > 0:
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logging.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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params = {}
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if not shared.args.cpu:
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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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elif shared.args.load_in_8bit:
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params['device_map'] = {'': 0}
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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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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if torch.has_mps:
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device = torch.device('mps')
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shared.model = shared.model.to(device)
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else:
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shared.model = shared.model.cuda()
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