# 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 math import warnings from dataclasses import asdict from enum import Enum from typing import Optional, Union import torch import torch.nn as nn from torch.nn.init import _calculate_correct_fan from tqdm import tqdm from transformers.pytorch_utils import Conv1D from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists from peft.utils import ( TRANSFORMERS_MODELS_TO_VERA_TARGET_MODULES_MAPPING, ModulesToSaveWrapper, _get_submodules, ) from ..tuners_utils import _maybe_include_all_linear_layers from .buffer_dict import BufferDict from .config import VeraConfig from .layer import Linear, VeraLayer def _kaiming_init( tensor_or_shape: Union[torch.Tensor, tuple[int, ...]], generator: torch.Generator, ) -> torch.Tensor: """ Kaiming Uniform Initialisation adapted to accept a `torch.Generator` object for PRNG. Args: tensor_or_shape (`Union[torch.Tensor, tuple[int, ...]]`): Tensor to initialise, or shape of new tensor to create and then initialise. generator: (`torch.Generator`): Generator object that manages the state of the PRNG algorithm in use. Returns: `torch.Tensor`: The initialised tensor. """ if isinstance(tensor_or_shape, tuple): tensor = torch.empty(tensor_or_shape) else: tensor = tensor_or_shape fan = _calculate_correct_fan(tensor, "fan_in") gain = math.sqrt(2) std = gain / math.sqrt(fan) bound = math.sqrt(3.0) * std with torch.no_grad(): return tensor.uniform_(-bound, bound, generator=generator) class VeraModel(BaseTuner): """ Creates Vector-based Random Matrix Adaptation (Vera) model from a pretrained transformers model. Args: model ([`~transformers.PreTrainedModel`]): The model to be adapted. config ([`VeraConfig`]): The configuration of the Vera model. adapter_name (`str`): The name of the adapter, defaults to `"default"`. Returns: `torch.nn.Module`: The Vera model. Example: ```py >>> from transformers import AutoModelForCausalLM >>> from peft import VeraConfig, get_peft_model >>> base_model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") >>> config = VeraConfig(r=128) >>> model = get_peft_model(base_model, config) ``` **Attributes**: - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted. - **peft_config** ([`VeraConfig`]): The configuration of the Vera model. """ prefix: str = "vera_lambda" def __init__(self, model, config, adapter_name) -> None: super().__init__(model, config, adapter_name) def _find_first_dim(self, config) -> tuple[int, int]: """ Finds the first linear layer that has been wrapped with Vera, and extract the input and output dimension. This will be used for determining the size of the shared vera_A and vera_B matrices. This will throw an error if there are multiple layers of the same type with different shapes. """ model_config = getattr(self.model, "config", {"model_type": "custom"}) if hasattr(model_config, "to_dict"): model_config = model_config.to_dict() peft_config = self._prepare_adapter_config(config, model_config) peft_config = _maybe_include_all_linear_layers(peft_config, self.model) first_shape = None for key, module in self.model.named_modules(): if not self._check_target_module_exists(peft_config, key): continue if isinstance(module, (nn.Linear, Conv1D)): module_shape = tuple(module.weight.shape) if isinstance(module, Conv1D): module_shape = module_shape[::-1] else: continue if first_shape is None: first_shape = module_shape continue if module_shape != first_shape: raise ValueError( "Multiple target layers with different dimensions were specified. VeRA only supports a " f"single dimension size. Expected shape {first_shape}, got {module_shape}." ) if first_shape is None: msg = "No layers types compatible with VeRA were found. Please check `peft_config.target_modules`." raise ValueError(msg) return first_shape def _init_vera_A_vera_B(self, config: VeraConfig, adapter_name: str) -> None: first_linear_out_dim, first_linear_in_dim = self._find_first_dim(config) # use of persistent to exclude vera_A and vera_B from the state dict if we choose not to save them. self.vera_A = BufferDict({}, persistent=config.save_projection) self.vera_B = BufferDict({}, persistent=config.save_projection) # deterministic init of vera_A and vera_B if we know the key generator = torch.Generator(device="cpu").manual_seed(config.projection_prng_key) vera_A = _kaiming_init((config.r, first_linear_in_dim), generator=generator) vera_B = _kaiming_init((first_linear_out_dim, config.r), generator=generator) self.vera_A[adapter_name] = vera_A self.vera_B[adapter_name] = vera_B def _pre_injection_hook(self, model: nn.Module, config: VeraConfig, adapter_name: str) -> None: self._init_vera_A_vera_B(config, adapter_name) def _check_new_adapter_config(self, config: VeraConfig) -> 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. """ # the below todo is copied from LoRA # 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." ) for existing_config in self.peft_config.values(): if existing_config is config: # skip the current config continue if existing_config.projection_prng_key != config.projection_prng_key: raise ValueError( f"Vera PRNG initialisation key must be the same for all adapters. Got {config.projection_prng_key=} but " f"previous config had {existing_config.projection_prng_key}." ) save_project_unique_values = sorted({config.save_projection for config in self.peft_config.values()}) if len(save_project_unique_values) > 1: raise ValueError( "VeRA projection weights must be saved for all adapters or none, but got multiple different values: " f"{save_project_unique_values}" ) @staticmethod def _check_target_module_exists(vera_config, key): return check_target_module_exists(vera_config, key) def _create_and_replace( self, vera_config, adapter_name, target, target_name, parent, current_key, **optional_kwargs, ): if current_key is None: raise ValueError("Current Key shouldn't be `None`") r = vera_config.r bias = hasattr(target, "bias") and target.bias is not None kwargs = { "r": r, "vera_dropout": vera_config.vera_dropout, "fan_in_fan_out": vera_config.fan_in_fan_out, "init_weights": vera_config.init_weights, } kwargs["bias"] = bias # TODO: add quantization support if isinstance(target, Linear): target.update_layer( adapter_name, self.vera_A, self.vera_B, r, vera_config.vera_dropout, vera_config.init_weights, d_initial=vera_config.d_initial, ) else: new_module = self._create_new_module(vera_config, self.vera_A, self.vera_B, adapter_name, target, **kwargs) if adapter_name not in self.active_adapter: # 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 _replace_module(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 "vera_" 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 == "vera_only": for m in model.modules(): if isinstance(m, VeraLayer) 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.") @staticmethod def _create_new_module(vera_config, vera_A, vera_B, adapter_name, target, **kwargs): bias = kwargs.pop("bias", False) 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"] = vera_config.fan_in_fan_out = False elif isinstance(target_base_layer, Conv1D): kwargs["is_target_conv_1d_layer"] = True 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"] = vera_config.fan_in_fan_out = True else: raise ValueError( f"Target module {target} is not supported. Currently, only the following modules are supported: " "`torch.nn.Linear`, `transformers.pytorch_utils.Conv1D`." ) new_module = Linear( target, vera_A, vera_B, adapter_name, bias=bias, d_initial=vera_config.d_initial, **kwargs, ) 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, VeraLayer): 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 @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_VERA_TARGET_MODULES_MAPPING: raise ValueError("Please specify `target_modules` in `peft_config`") peft_config.target_modules = set( TRANSFORMERS_MODELS_TO_VERA_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, ): # we cannot use self.prefix as we want to include non-trainable vera parameters key_list = [key for key, _ in self.model.named_modules() if "vera" 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): """ 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] # we cannot use self.prefix as we want to include non-trainable vera parameters key_list = [key for key, _ in self.model.named_modules() if "vera" not in key] new_adapter = None for key in key_list: _, target, _ = _get_submodules(self.model, key) if isinstance(target, VeraLayer): target.delete_adapter(adapter_name) if new_adapter is None: new_adapter = target.active_adapter[:] self.active_adapter = new_adapter or [] def merge_and_unload( self, progressbar: bool = False, safe_merge: bool = False, adapter_names: Optional[list[str]] = None ): r""" This method merges the Vera 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`. Example: ```py >>> from transformers import AutoModelForCausalLM >>> from peft import PeftModel >>> base_model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-40b") >>> peft_model_id = "smangrul/falcon-40B-int4-peft-lora-sfttrainer-sample" >>> model = PeftModel.from_pretrained(base_model, peft_model_id) >>> merged_model = model.merge_and_unload() ``` """ return self._unload_and_optionally_merge( progressbar=progressbar, safe_merge=safe_merge, adapter_names=adapter_names ) def unload(self): """ Gets back the base model by removing all the Vera modules without merging. This gives back the original base model. """ return self._unload_and_optionally_merge(merge=False)