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# Copyright 2023-present the HuggingFace Inc. team. | |
# | |
# 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. | |
# The implementation is based on "Parameter-Efficient Orthogonal Finetuning | |
# via Butterfly Factorization" (https://arxiv.org/abs/2311.06243) in ICLR 2024. | |
import warnings | |
from dataclasses import asdict | |
from enum import Enum | |
from typing import List, Optional | |
import torch | |
from torch import nn | |
from tqdm import tqdm | |
from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists | |
from peft.utils import ( | |
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING, | |
ModulesToSaveWrapper, | |
_get_submodules, | |
) | |
from .config import BOFTConfig | |
from .layer import BOFTLayer, Conv2d, Linear | |
class BOFTModel(BaseTuner): | |
""" | |
Creates BOFT and OFT model from a pretrained transformers model. Paper: https://arxiv.org/abs/2311.06243 | |
https://arxiv.org/abs/2306.07280 | |
Args: | |
model ([`transformers.PreTrainedModel`]): The model to be adapted. | |
config ([`BOFTConfig`]): The configuration of the BOFT model. | |
adapter_name (`str`): The name of the adapter, defaults to `"default"`. | |
Returns: | |
`torch.nn.Module`: The BOFT model. | |
Example:: | |
>>> import transformers >>> from transformers import AutoModelForSeq2SeqLM, BOFTConfig >>> from peft import | |
BOFTConfig, get_peft_model | |
>>> config = BOFTConfig( ... boft_block_size=8, ... boft_n_butterfly_factor=1, ... target_modules=["query", | |
"value", "key", "output.dense", "mlp.fc1", "mlp.fc2"], ... boft_dropout=0.1, ... bias="boft_only", ... | |
modules_to_save=["classifier"], ... ) | |
>>> model = transformers.Dinov2ForImageClassification.from_pretrained( ... "facebook/dinov2-large", ... | |
num_labels=100, ... ) >>> boft_model = get_peft_model(model, config) | |
**Attributes**: | |
- **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted. | |
- **peft_config** ([`BOFTConfig`]): The configuration of the BOFT model. | |
""" | |
prefix: str = "boft_" | |
def __init__(self, model, config, adapter_name) -> None: | |
super().__init__(model, config, adapter_name) | |
def _check_new_adapter_config(self, config: BOFTConfig) -> None: | |
""" | |
A helper method to check the config when a new adapter is being added. | |
Raise a ValueError if there is something wrong with the config or if it conflicts with existing adapters. | |
""" | |
# TODO: there should be a check if any of the existing adapters actually has bias != "none", or else the check | |
# does not fully correspond to the error message. | |
if (len(self.peft_config) > 1) and (config.bias != "none"): | |
raise ValueError( | |
f"{self.__class__.__name__} supports only 1 adapter with bias. When using multiple adapters, " | |
"set bias to 'none' for all adapters." | |
) | |
def _check_target_module_exists(boft_config, key): | |
return check_target_module_exists(boft_config, key) | |
def _create_and_replace( | |
self, | |
boft_config, | |
adapter_name, | |
target, | |
target_name, | |
parent, | |
current_key, | |
**optional_kwargs, | |
): | |
if current_key is None: | |
raise ValueError("Current Key shouldn't be `None`") | |
bias = hasattr(target, "bias") and target.bias is not None | |
kwargs = { | |
"boft_block_size": boft_config.boft_block_size, | |
"boft_block_num": boft_config.boft_block_num, | |
"boft_n_butterfly_factor": boft_config.boft_n_butterfly_factor, | |
"boft_dropout": boft_config.boft_dropout, | |
"fan_in_fan_out": boft_config.fan_in_fan_out, | |
"init_weights": boft_config.init_weights, | |
} | |
kwargs["bias"] = bias | |
# If it is not a BOFTLayer, create a new module, else update it with new adapters | |
if not isinstance(target, BOFTLayer): | |
new_module = self._create_new_module(boft_config, adapter_name, target, **kwargs) | |
if adapter_name not in self.active_adapters: | |
# adding an additional adapter: it is not automatically trainable | |
new_module.requires_grad_(False) | |
self._replace_module(parent, target_name, new_module, target) | |
else: | |
target.update_layer( | |
adapter_name, | |
boft_block_size=boft_config.boft_block_size, | |
boft_block_num=boft_config.boft_block_num, | |
boft_n_butterfly_factor=boft_config.boft_n_butterfly_factor, | |
boft_dropout=boft_config.boft_dropout, | |
init_weights=boft_config.init_weights, | |
) | |
def _replace_module(self, parent, child_name, new_module, child): | |
setattr(parent, child_name, new_module) | |
# It's not necessary to set requires_grad here, as that is handled by | |
# _mark_only_adapters_as_trainable | |
# child layer wraps the original module, unpack it | |
if hasattr(child, "base_layer"): | |
child = child.base_layer | |
if not hasattr(new_module, "base_layer"): | |
new_module.weight = child.weight | |
if hasattr(child, "bias"): | |
new_module.bias = child.bias | |
if getattr(child, "state", None) is not None: | |
if hasattr(new_module, "base_layer"): | |
new_module.base_layer.state = child.state | |
else: | |
new_module.state = child.state | |
new_module.to(child.weight.device) | |
# dispatch to correct device | |
for name, module in new_module.named_modules(): | |
if self.prefix in name: | |
module.to(child.weight.device) | |
def _mark_only_adapters_as_trainable(self, model: nn.Module) -> None: | |
for n, p in model.named_parameters(): | |
if self.prefix not in n: | |
p.requires_grad = False | |
for active_adapter in self.active_adapters: | |
bias = self.peft_config[active_adapter].bias | |
if bias == "none": | |
continue | |
if bias == "all": | |
for n, p in model.named_parameters(): | |
if "bias" in n: | |
p.requires_grad = True | |
elif bias == "boft_only": | |
for name, m in model.named_modules(): | |
if isinstance(m, BOFTLayer) and hasattr(m, "bias") and m.bias is not None: | |
m.bias.requires_grad = True | |
else: | |
raise NotImplementedError(f"Requested bias: {bias}, is not implemented.") | |
def _create_new_module(boft_config, adapter_name, target, **kwargs): | |
if isinstance(target, BaseTunerLayer): | |
target_base_layer = target.get_base_layer() | |
else: | |
target_base_layer = target | |
if isinstance(target_base_layer, torch.nn.Linear): | |
if kwargs["fan_in_fan_out"]: | |
warnings.warn( | |
"fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. " | |
"Setting fan_in_fan_out to False." | |
) | |
kwargs["fan_in_fan_out"] = boft_config.fan_in_fan_out = False | |
new_module = Linear(target, adapter_name, **kwargs) | |
elif isinstance(target_base_layer, torch.nn.Conv2d): | |
new_module = Conv2d(target, adapter_name, **kwargs) | |
else: | |
raise ValueError( | |
f"Target module {target} is not supported. " | |
"Currently, only `torch.nn.Linear` and `torch.nn.Conv2d` are supported." | |
) | |
return new_module | |
def __getattr__(self, name: str): | |
"""Forward missing attributes to the wrapped module.""" | |
try: | |
return super().__getattr__(name) # defer to nn.Module's logic | |
except AttributeError: | |
return getattr(self.model, name) | |
def get_peft_config_as_dict(self, inference: bool = False): | |
config_dict = {} | |
for key, value in self.peft_config.items(): | |
config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()} | |
if inference: | |
config["inference_mode"] = True | |
config_dict[key] = config | |
return config | |
def _set_adapter_layers(self, enabled=True): | |
for module in self.model.modules(): | |
if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)): | |
module.enable_adapters(enabled) | |
def enable_adapter_layers(self): | |
self._set_adapter_layers(enabled=True) | |
def disable_adapter_layers(self): | |
for active_adapter in self.active_adapters: | |
val = self.peft_config[active_adapter].bias | |
if val != "none": | |
msg = ( | |
f"Careful, disabling adapter layers with bias configured to be '{val}' does not produce the same " | |
"output as the the base model would without adaption." | |
) | |
warnings.warn(msg) | |
self._set_adapter_layers(enabled=False) | |
def set_adapter(self, adapter_name): | |
for module in self.model.modules(): | |
if isinstance(module, BOFTLayer): | |
if module.merged: | |
warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.") | |
module.unmerge() | |
module.set_adapter(adapter_name) | |
self.active_adapter = adapter_name | |
def _prepare_adapter_config(peft_config, model_config): | |
if peft_config.target_modules is None: | |
if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING: | |
raise ValueError("Please specify `target_modules` in `peft_config`") | |
peft_config.target_modules = set( | |
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]] | |
) | |
return peft_config | |
def _unload_and_optionally_merge( | |
self, | |
merge=True, | |
progressbar: bool = False, | |
safe_merge: bool = False, | |
adapter_names: Optional[List[str]] = None, | |
): | |
self._unloading_checks(adapter_names) | |
key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key] | |
desc = "Unloading " + ("and merging " if merge else "") + "model" | |
for key in tqdm(key_list, disable=not progressbar, desc=desc): | |
try: | |
parent, target, target_name = _get_submodules(self.model, key) | |
except AttributeError: | |
continue | |
if hasattr(target, "base_layer"): | |
if merge: | |
target.merge(safe_merge=safe_merge, adapter_names=adapter_names) | |
self._replace_module(parent, target_name, target.get_base_layer(), target) | |
elif isinstance(target, ModulesToSaveWrapper): | |
# save any additional trainable modules part of `modules_to_save` | |
setattr(parent, target_name, target.modules_to_save[target.active_adapter]) | |
return self.model | |
def delete_adapter(self, adapter_name: str) -> None: | |
""" | |
Deletes an existing adapter. | |
Args: | |
adapter_name (str): Name of the adapter to be deleted. | |
""" | |
if adapter_name not in list(self.peft_config.keys()): | |
raise ValueError(f"Adapter {adapter_name} does not exist") | |
del self.peft_config[adapter_name] | |
key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key] | |
new_adapter = None | |
for key in key_list: | |
_, target, _ = _get_submodules(self.model, key) | |
if isinstance(target, BOFTLayer): | |
target.delete_adapter(adapter_name) | |
if new_adapter is None: | |
new_adapter = target.active_adapters[:] | |
self.active_adapter = new_adapter or [] | |
def merge_and_unload( | |
self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[List[str]] = None | |
) -> torch.nn.Module: | |
r""" | |
This method merges the BOFT layers into the base model. This is needed if someone wants to use the base model | |
as a standalone model. | |
Args: | |
progressbar (`bool`): | |
whether to show a progressbar indicating the unload and merge process | |
safe_merge (`bool`): | |
whether to activate the safe merging check to check if there is any potential Nan in the adapter | |
weights | |
adapter_names (`List[str]`, *optional*): | |
The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults | |
to `None`. | |
""" | |
return self._unload_and_optionally_merge( | |
progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names | |
) | |
def unload(self) -> torch.nn.Module: | |
""" | |
Gets back the base model by removing all the boft modules without merging. This gives back the original base | |
model. | |
""" | |
return self._unload_and_optionally_merge(merge=False) | |