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| # Copyright 2023 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| PEFT utilities: Utilities related to peft library | |
| """ | |
| from .import_utils import is_torch_available | |
| if is_torch_available(): | |
| import torch | |
| def recurse_remove_peft_layers(model): | |
| r""" | |
| Recursively replace all instances of `LoraLayer` with corresponding new layers in `model`. | |
| """ | |
| from peft.tuners.lora import LoraLayer | |
| for name, module in model.named_children(): | |
| if len(list(module.children())) > 0: | |
| ## compound module, go inside it | |
| recurse_remove_peft_layers(module) | |
| module_replaced = False | |
| if isinstance(module, LoraLayer) and isinstance(module, torch.nn.Linear): | |
| new_module = torch.nn.Linear(module.in_features, module.out_features, bias=module.bias is not None).to( | |
| module.weight.device | |
| ) | |
| new_module.weight = module.weight | |
| if module.bias is not None: | |
| new_module.bias = module.bias | |
| module_replaced = True | |
| elif isinstance(module, LoraLayer) and isinstance(module, torch.nn.Conv2d): | |
| new_module = torch.nn.Conv2d( | |
| module.in_channels, | |
| module.out_channels, | |
| module.kernel_size, | |
| module.stride, | |
| module.padding, | |
| module.dilation, | |
| module.groups, | |
| module.bias, | |
| ).to(module.weight.device) | |
| new_module.weight = module.weight | |
| if module.bias is not None: | |
| new_module.bias = module.bias | |
| module_replaced = True | |
| if module_replaced: | |
| setattr(model, name, new_module) | |
| del module | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| return model | |