# ------------------------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
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# ------------------------------------------------------------------------------------------
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import torch
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import torch.nn as nn
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from typing import Dict
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from .layers import LoRALayer
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def mark_only_lora_as_trainable(model: nn.Module, bias: str = 'none') -> None:
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for n, p in model.named_parameters():
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if 'lora_' not in n and 'cif' not in n:
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p.requires_grad = False
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if bias == 'none':
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return
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elif bias == 'all':
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for n, p in model.named_parameters():
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if 'bias' in n:
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p.requires_grad = True
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elif bias == 'lora_only':
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for m in model.modules():
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if isinstance(m, LoRALayer) and \
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hasattr(m, 'bias') and \
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m.bias is not None:
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m.bias.requires_grad = True
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else:
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raise NotImplementedError
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def lora_state_dict(model: nn.Module, bias: str = 'none') -> Dict[str, torch.Tensor]:
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my_state_dict = model.state_dict()
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if bias == 'none':
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return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k}
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elif bias == 'all':
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return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k or 'bias' in k}
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elif bias == 'lora_only':
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to_return = {}
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for k in my_state_dict:
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if 'lora_' in k:
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to_return[k] = my_state_dict[k]
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bias_name = k.split('lora_')[0]+'bias'
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if bias_name in my_state_dict:
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to_return[bias_name] = my_state_dict[bias_name]
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return to_return
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else:
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raise NotImplementedError
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