# 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. from __future__ import annotations import re import warnings from dataclasses import replace import torch from transformers.pytorch_utils import Conv1D from peft.import_utils import is_bnb_4bit_available, is_bnb_available from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer from peft.utils import ( TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING, TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING, ModulesToSaveWrapper, _freeze_adapter, _get_submodules, ) from .layer import Conv2d, Conv3d, IA3Layer, Linear class IA3Model(BaseTuner): """ Creates a Infused Adapter by Inhibiting and Amplifying Inner Activations ((IA)^3) model from a pretrained transformers model. The method is described in detail in https://huggingface.co/papers/2205.05638 Args: model ([`~transformers.PreTrainedModel`]): The model to be adapted. config ([`IA3Config`]): The configuration of the (IA)^3 model. adapter_name (`str`): The name of the adapter, defaults to `"default"`. low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the loading process. Returns: `torch.nn.Module`: The (IA)^3 model. Example: ```py >>> from transformers import AutoModelForSeq2SeqLM, ia3Config >>> from peft import IA3Model, IA3Config >>> config = IA3Config( ... peft_type="IA3", ... task_type="SEQ_2_SEQ_LM", ... target_modules=["k", "v", "w0"], ... feedforward_modules=["w0"], ... ) >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> ia3_model = IA3Model(config, model) ``` **Attributes**: - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted. - **peft_config** ([`ia3Config`]): The configuration of the (IA)^3 model. """ prefix: str = "ia3_" tuner_layer_cls = IA3Layer @staticmethod def _create_new_module(ia3_config, adapter_name, target, **kwargs): # avoid eager bnb import if is_bnb_available(): import bitsandbytes as bnb from .bnb import Linear8bitLt if is_bnb_4bit_available(): from .bnb import Linear4bit loaded_in_8bit = kwargs.pop("loaded_in_8bit", False) loaded_in_4bit = kwargs.pop("loaded_in_4bit", False) is_feedforward = kwargs.pop("is_feedforward", False) if isinstance(target, BaseTunerLayer): target_base_layer = target.get_base_layer() else: target_base_layer = target if loaded_in_8bit and isinstance(target_base_layer, bnb.nn.Linear8bitLt): eightbit_kwargs = kwargs.copy() eightbit_kwargs.update( { "has_fp16_weights": target_base_layer.state.has_fp16_weights, "threshold": target_base_layer.state.threshold, "index": target_base_layer.index, } ) new_module = Linear8bitLt(target, adapter_name, is_feedforward=is_feedforward, **eightbit_kwargs) elif loaded_in_4bit and isinstance(target_base_layer, bnb.nn.Linear4bit): fourbit_kwargs = kwargs.copy() fourbit_kwargs.update( { "compute_dtype": target_base_layer.compute_dtype, "compress_statistics": target_base_layer.weight.compress_statistics, "quant_type": target_base_layer.weight.quant_type, } ) new_module = Linear4bit(target, adapter_name, is_feedforward=is_feedforward, **fourbit_kwargs) elif isinstance(target, torch.nn.Conv2d): new_module = Conv2d(target, adapter_name, is_feedforward=is_feedforward, **kwargs) elif isinstance(target, torch.nn.Conv3d): new_module = Conv3d(target, adapter_name, is_feedforward=is_feedforward, **kwargs) elif 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"] = ia3_config.fan_in_fan_out = False new_module = Linear(target, adapter_name, is_feedforward=is_feedforward, **kwargs) elif isinstance(target_base_layer, Conv1D): if not kwargs["fan_in_fan_out"]: warnings.warn( "fan_in_fan_out is set to False but the target module is `Conv1D`. Setting fan_in_fan_out to True." ) kwargs["fan_in_fan_out"] = ia3_config.fan_in_fan_out = True new_module = Linear( target, adapter_name, is_feedforward=is_feedforward, is_target_conv_1d_layer=True, **kwargs ) else: raise ValueError( f"Target module {target} is not supported. " f"Currently, only `torch.nn.Linear`, `torch.nn.Conv2d`, and `Conv1D` are supported." ) return new_module def _create_and_replace( self, ia3_config, adapter_name, target, target_name, parent, current_key, ): # check if target module is in feedforward_modules is_feedforward = self._check_target_module_feedforward(ia3_config, current_key) kwargs = { "fan_in_fan_out": ia3_config.fan_in_fan_out, "init_ia3_weights": ia3_config.init_ia3_weights, "is_feedforward": is_feedforward, "loaded_in_8bit": getattr(self.model, "is_loaded_in_8bit", False), "loaded_in_4bit": getattr(self.model, "is_loaded_in_4bit", False), } if isinstance(target, IA3Layer): target.update_layer( adapter_name, ia3_config.init_ia3_weights, ) else: new_module = self._create_new_module(ia3_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) @staticmethod def _check_target_module_feedforward(ia3_config, key) -> bool: """ A helper private method that checks if the target module `key` matches with a feedforward module specified in `ia3_config` """ if isinstance(ia3_config.feedforward_modules, str): is_feedforward = bool(re.fullmatch(ia3_config.feedforward_modules, key)) else: is_feedforward = any(key.endswith(target_key) for target_key in ia3_config.feedforward_modules) return is_feedforward @staticmethod 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_IA3_TARGET_MODULES_MAPPING: raise ValueError("Please specify `target_modules` in `peft_config`") peft_config.target_modules = set( TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING[model_config["model_type"]] ) if peft_config.feedforward_modules is None: if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING: raise ValueError("Please specify `feedforward_modules` in `peft_config`") peft_config.feedforward_modules = set( TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING[model_config["model_type"]] ) return peft_config def _unload_and_optionally_merge(self, *args, **kwargs): r""" This method merges the (IA)^3 layers into the base model. This is needed if someone wants to use the base model as a standalone model. Args: safe_merge (`bool`, `optional`, defaults to `False`): If True, the merge operation will be performed in a copy of the original weights and check for NaNs before merging the weights. This is useful if you want to check if the merge operation will produce NaNs. Defaults to `False`. 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`. """ if getattr(self.model, "is_loaded_in_8bit", False): raise ValueError("Cannot merge ia3 layers when the model is loaded in 8-bit mode") if getattr(self.model, "is_loaded_in_4bit", False): raise ValueError("Cannot merge ia3 layers when the model is loaded in 4-bit mode") return super()._unload_and_optionally_merge(*args, **kwargs) def _check_add_weighted_adapter(self, adapters: list[str]) -> tuple[str, str]: """ Helper function to check if the arguments to add_weighted_adapter are valid and compatible with the underlying model. """ # Validate existence of adapters for adapter in adapters: if adapter not in self.peft_config: raise ValueError(f"Adapter {adapter} does not exist") # Check for conflicting modules_to_save modules_to_save_wrappers = [module for module in self.modules() if isinstance(module, ModulesToSaveWrapper)] if any( sum(adapter in wrapper.modules_to_save for adapter in adapters) > 1 for wrapper in modules_to_save_wrappers ): raise ValueError("Cannot add weighted adapters targeting the same module with modules_to_save.") # Ensure all adapters have compatible target and feedforward module types target_module_types = {type(self.peft_config[adapter].target_modules) for adapter in adapters} feedforward_module_types = {type(self.peft_config[adapter].feedforward_modules) for adapter in adapters} if len(target_module_types) > 1 or len(feedforward_module_types) > 1: raise ValueError("All adapter configs should have the same type for target and feedforward modules.") # Combine target and feedforward modules if str in target_module_types: new_target_modules = "|".join(f"({self.peft_config[adapter].target_modules})" for adapter in adapters) else: new_target_modules = set.union(*(self.peft_config[adapter].target_modules for adapter in adapters)) if str in feedforward_module_types: new_feedforward_modules = "|".join( f"({self.peft_config[adapter].feedforward_modules})" for adapter in adapters ) else: new_feedforward_modules = set.union( *(self.peft_config[adapter].feedforward_modules for adapter in adapters) ) return new_target_modules, new_feedforward_modules def add_weighted_adapter( self, adapters: list[str], weights: list[float], adapter_name: str, ) -> None: """ This method adds a new adapter by merging the given adapters with the given weights. Args: adapters (`list`): List of adapter names to be merged. weights (`list`): List of weights for each adapter. adapter_name (`str`): Name of the new adapter. """ if adapter_name in list(self.peft_config.keys()): return new_target_modules, new_feedforward_modules = self._check_add_weighted_adapter( adapters=adapters, ) self.peft_config[adapter_name] = replace( self.peft_config[adapters[0]], target_modules=new_target_modules, feedforward_modules=new_feedforward_modules, ) self.inject_adapter(self.model, adapter_name) # Do we really need that? _freeze_adapter(self.model, adapter_name) key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key] for key in key_list: _, target, _ = _get_submodules(self.model, key) if isinstance(target, IA3Layer): if adapter_name in target.ia3_l: target_ia3_l = target.ia3_l[adapter_name] else: continue target_ia3_l.data = target_ia3_l.data.zero_() for adapter, weight in zip(adapters, weights): if adapter in target.ia3_l: current_adapter_ia3_l = target.ia3_l[adapter] else: continue target_ia3_l.data += current_adapter_ia3_l.data * weight