import math import os from functools import partial from typing import Iterator, Optional, Tuple, Union import torch import torch.nn.utils.parametrize as parametrize from torch import nn from torch.nn import Parameter from transformers import PretrainedConfig from .modeling_xlm_roberta import ( XLMRobertaFlashConfig, XLMRobertaModel, ) def initialized_weights( shape: Tuple[int], num_adaptations: int, init: str = "kaiming" ) -> torch.Tensor: weight_data = [] for _ in range(num_adaptations): new_adaption = torch.zeros(shape) if init == "kaiming": nn.init.kaiming_uniform_(new_adaption, a=math.sqrt(5)) elif init == "normal": nn.init.normal_(new_adaption) else: raise NotImplementedError weight_data.append(new_adaption) return torch.stack(weight_data, dim=0) class LoRAParametrization(nn.Module): """ This LoRA implementation was inspired by https://github.com/cccntu/minLoRA The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ def __init__( self, fan_in: int, fan_out: int, layer_type: str = "linear", num_adaptations: int = 1, rank: int = 4, dropout_p: float = 0.0, alpha: float = 1, ): super().__init__() # if weight is stored as (fan_out, fan_in), the memory layout of A & B follows (W + BA)x # otherwise, it's x(W + AB). This allows us to tie the weights between linear layers and embeddings fan_in_fan_out = layer_type == "embedding" self.swap = (lambda x: (x[1], x[0])) if fan_in_fan_out else (lambda x: x) if layer_type == "linear": self.lora_A = nn.Parameter( initialized_weights((rank, fan_in), num_adaptations, init="kaiming") ) self.lora_B = nn.Parameter(torch.zeros((num_adaptations, fan_out, rank))) elif layer_type == "embedding": self.lora_A = nn.Parameter(torch.zeros((num_adaptations, fan_in, rank))) self.lora_B = nn.Parameter( initialized_weights( (rank, fan_out), num_adaptations=num_adaptations, init="normal" ) ) else: raise NotImplementedError self.lora_alpha, self.rank = alpha, rank self.scaling = alpha / rank self.lora_dropout = nn.Dropout(p=dropout_p) if dropout_p > 0 else lambda x: x self.dropout_fn = self._dropout if dropout_p > 0 else lambda x: x self.register_buffer( "lora_dropout_mask", torch.ones(self.swap((1, fan_in)), dtype=self.lora_A.dtype), persistent=False, ) self.forward_fn = lambda x: x self.current_task = None def _dropout(self, A): # to mimic the original implementation: A @ dropout(x), we do (A * dropout(ones)) @ x return A * self.lora_dropout(self.lora_dropout_mask) def lora_forward(self, X): assert self.current_task is not None return ( X + torch.matmul( *self.swap( ( self.lora_B[self.current_task], self.dropout_fn(self.lora_A[self.current_task]), ) ) ).view(X.shape) * self.scaling ) def forward(self, X): return self.forward_fn(X) @property def current_task(self): return self._current_task @current_task.setter def current_task(self, task: Union[None, int]): self._current_task = task if task is None: self.forward_fn = lambda x: x else: self.forward_fn = self.lora_forward @classmethod def from_linear( cls, layer: nn.Module, num_adaptations: int, rank: int, dropout_p: float, alpha: float, ): assert isinstance(layer, nn.Linear) fan_out, fan_in = layer.weight.shape return cls( fan_in, fan_out, num_adaptations=num_adaptations, layer_type="linear", rank=rank, dropout_p=dropout_p, alpha=alpha, ) @classmethod def from_embedding( cls, layer: nn.Module, num_adaptations: int, rank: int, dropout_p: float, alpha: float, ): assert isinstance(layer, nn.Embedding) fan_in, fan_out = layer.weight.shape return cls( fan_in, fan_out, num_adaptations=num_adaptations, layer_type="embedding", rank=rank, dropout_p=dropout_p, alpha=alpha, ) @classmethod def add_to_layer( cls, layer: nn.Module, num_adaptations: int, rank: int, dropout_p: float, alpha: float, ): if isinstance(layer, nn.Linear): parametrize.register_parametrization( layer, "weight", cls.from_linear( layer, num_adaptations=num_adaptations, rank=rank, dropout_p=dropout_p, alpha=alpha, ), ) elif isinstance(layer, nn.Embedding): parametrize.register_parametrization( layer, "weight", cls.from_embedding( layer, num_adaptations=num_adaptations, rank=rank, dropout_p=dropout_p, alpha=alpha, ), ) @staticmethod def select_task_for_layer(layer: nn.Module, task_idx: Optional[int] = None): if isinstance(layer, LoRAParametrization): layer.current_task = task_idx class XLMRobertaLoRA(XLMRobertaModel): def __init__( self, config: XLMRobertaFlashConfig, ): super().__init__(config) self._num_adaptations = len(config.lora_adaptations) self._rank = config.lora_rank self._dropout_p = config.lora_dropout_p self._alpha = config.lora_alpha self._register_lora( num_adaptations=self._num_adaptations, rank=self._rank, dropout_p=self._dropout_p, alpha=self._alpha, ) self.main_params_trainable = False self._task_idx = None # By default, we select the first LoRA self.current_task = 0 @property def main_params_trainable(self): return self._main_params_trainable @main_params_trainable.setter def main_params_trainable(self, val: bool): """Whether the main parameters (i.e. those that are not LoRA) should be trainable. This method sets the `requires_grad_` attribute of the main weights and controls which parameters are returned in `self.parameters()`. :param val: Whether or not to make the parameters trainable. :return: None """ self._main_params_trainable = val for name, param in super().named_parameters(): if "lora" not in name: param.requires_grad_(val) @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None, cache_dir: Optional[Union[str, os.PathLike]] = None, ignore_mismatched_sizes: bool = False, force_download: bool = False, local_files_only: bool = False, token: Optional[Union[str, bool]] = None, revision: str = "main", use_safetensors: bool = None, **kwargs, ): config = XLMRobertaFlashConfig.from_pretrained( pretrained_model_name_or_path, *model_args, **kwargs ) if config.load_trained_adapters: return super().from_pretrained( pretrained_model_name_or_path, *model_args, **kwargs ) else: torch.set_default_dtype(torch.float16) return cls(config) def _register_lora(self, num_adaptations, rank, dropout_p, alpha): self.apply( partial( LoRAParametrization.add_to_layer, num_adaptations=num_adaptations, rank=rank, dropout_p=dropout_p, alpha=alpha, ) ) @property def current_task(self): """Which LoRA is currently selected :return: Integer or None (when LoRA is disabled) """ return self._task_idx @current_task.setter def current_task(self, task_idx: Union[None, int]): """Set the LoRA that is to be used. The LoRA is specified by `task_idx`, which may be an integer >= 0, indexing the available LoRAs. If it is None, no LoRA is used. :param task_idx: Which LoRA to use :return: """ assert task_idx is None or 0 <= task_idx < self._num_adaptations if self._task_idx != task_idx: # In this case, we need to update the LoRAs everywhere self._task_idx = task_idx self.apply( partial(LoRAParametrization.select_task_for_layer, task_idx=task_idx) ) def forward(self, *args, lora_adaptation: Union[None, int] = -1, **kwargs): if lora_adaptation is None or lora_adaptation >= 0: self.current_task = lora_adaptation return super().forward(*args, **kwargs) def parameters(self, recurse: bool = True) -> Iterator[Parameter]: for _, param in self.named_parameters(recurse=recurse): yield param def named_parameters( self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True ) -> Iterator[Tuple[str, Parameter]]: for name, param in super().named_parameters( prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate ): if "lora" in name or self.main_params_trainable: yield name, param