46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
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# QRotaryTraining - A novel method for fully training all parameters of large
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# language models (llms) while using less device memory than traditional methods.
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# Copyright (C) 2024 Carl Philipp Klemm
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#
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# This file is part of QRotaryTraining.
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#
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# QRotaryTraining is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# QRotaryTraining is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with QRotaryTraining. If not, see <http://www.gnu.org/licenses/>.
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from peft.utils import _get_submodules
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import torch
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def replace_module(model, key: str, module: torch.nn.Module):
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parent, target, target_name = _get_submodules(model, key)
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setattr(parent, target_name, module)
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def find_all_linear_module_names(model) -> list[str]:
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module_names = set()
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear):
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module_names.add(name)
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if 'lm_head' in module_names: # needed for 16-bit
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module_names.remove('lm_head')
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return list(module_names)
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def find_all_outher_module_names(model) -> list[str]:
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module_names = set()
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for name, module in model.named_modules():
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if not isinstance(module, torch.nn.Linear):
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module_names.add(name)
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return list(module_names)
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