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36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
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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from modules.models import reload_model
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def add_lora_to_model(lora_name):
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# If a LoRA had been previously loaded, or if we want
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# to unload a LoRA, reload the model
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if shared.lora_name not in ['None', ''] or lora_name in ['None', '']:
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reload_model()
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shared.lora_name = lora_name
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if lora_name not in ['None', '']:
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print(f"Adding the LoRA {lora_name} to the model...")
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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_name}"), **params)
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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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