QRotaryTraining/utils.py
2024-07-20 22:57:16 +02:00

47 lines
1.6 KiB
Python

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