60 lines
1.8 KiB
Python
Executable File
60 lines
1.8 KiB
Python
Executable File
import argparse
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import torch
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import torch.nn
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from .data import get_phone_symmap
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from .engines import load_engines
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from .config import cfg
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from .models.lora import lora_get_state_dict
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def extract_lora( state_dict, config = None, save_path = None, dtype = None ):
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if dtype is None:
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dtype = cfg.inference.dtype
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lora = state_dict["lora"] if "lora" in state_dict else None
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# should always be included, but just in case
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if lora is None and "module" in state_dict:
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lora, module = lora_get_state_dict( state_dict["module"], split = True )
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state_dict["module"] = module
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state_dict["lora"] = lora
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# should raise an exception since there's nothing to extract, or at least a warning
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if not lora:
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return state_dict
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# save lora specifically
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# should probably export other attributes, similar to what SD LoRAs do
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save_path = save_path.parent / "lora.pth"
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torch.save( {
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"module": lora,
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"config": cfg.lora.__dict__ if cfg.lora is not None else None,
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}, save_path )
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return state_dict
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def main():
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parser = argparse.ArgumentParser("Save trained model to path.")
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parser.add_argument("--module-only", action='store_true')
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parser.add_argument("--lora", action='store_true', default=None) # exports LoRA
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parser.add_argument("--dtype", type=str, default="auto") # set target dtype to export to
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args, unknown = parser.parse_known_args()
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if args.module_only:
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cfg.trainer.load_module_only = True
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callback = None
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if args.lora:
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callback = extract_lora
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if args.dtype != "auto":
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cfg.trainer.weight_dtype = args.dtype
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# necessary to ensure we are actually exporting the weights right
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cfg.inference.backend if inferencing else cfg.trainer.backend
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engines = load_engines(training=False)
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engines.export(userdata={"symmap": get_phone_symmap()}, callback=callback)
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if __name__ == "__main__":
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main() |