import math import os import warnings from functools import partial from typing import Iterator, List, Optional, Tuple, Union import numpy as np 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, XLMRobertaPreTrainedModel 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(XLMRobertaPreTrainedModel): def __init__( self, config: XLMRobertaFlashConfig, roberta: Optional[XLMRobertaModel] = None ): super().__init__(config) if roberta is None: self.roberta = XLMRobertaModel(config) else: self.roberta = roberta self._lora_adaptations = config.lora_adaptations if ( not isinstance(self._lora_adaptations, list) or len(self._lora_adaptations) < 1 ): raise ValueError( f'`lora_adaptations` must be a list and contain at least one element' ) self._lora_prompts = config.lora_prompts if ( not isinstance(self._lora_prompts, dict) or len(self._lora_prompts) != len(self._lora_adaptations) ): raise ValueError( f'`lora_prompts` must be a dict and contain the same number of elements as `lora_adaptations`' ) self._adaptation_map = { name: idx for idx, name in enumerate(self._lora_adaptations) } self._rank = config.lora_rank self._dropout_p = config.lora_dropout_p self._alpha = config.lora_alpha self._register_lora( num_adaptations=len(self._lora_adaptations), rank=self._rank, dropout_p=self._dropout_p, alpha=self._alpha, ) self.main_params_trainable = config.lora_main_params_trainable self._task_idx = None # By default, disable LoRA until it's specified which adapter/task to use self.current_task = None @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: roberta = XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) return cls(config, roberta=roberta) 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_name: Union[None, str]): """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_name: Which LoRA to use :return: """ if task_name and task_name not in self._lora_adaptations: raise ValueError( f"Unsupported task '{task_name}'. " f"Supported tasks are: {', '.join(self.config.lora_adaptations)}." f"Alternatively, set `task` to `None` if you want to disable LoRA." ) task_idx = self._adaptation_map[task_name] if task_name else None 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, task_type: Union[str, None] = None, **kwargs): if task_type: self.current_task = task_type else: input_ids = kwargs["input_ids"] input_text = self.roberta.tokenizer.decode(input_ids[0], skip_special_tokens=True) for task_name, prompt in self._lora_prompts.items(): if input_text.startswith(prompt): self.current_task = task_name break else: self.current_task = None # No task-specific adapter is found, just use the general-purpose weights return self.roberta(*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 @torch.inference_mode() def encode( self, *args, task_type: Union[str, None] = None, **kwargs, ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]: """ Computes sentence embeddings task(`str`, *optional*, defaults to `LORA_NO_UPDATE`): Specifies the task for which the encoding is intended. This parameter controls the use of specialized LoRA adapters that are tuned for specific tasks. If `task` is set to `None`, all LoRA adapters are disabled, and the model reverts to its original, general-purpose weights. If `task` is set to a specific LoRA adaptation, that adaptation is activated. """ if task_type: self.current_task = task_type else: # infer the task from the input text input_text = args[0][0] if isinstance(args[0], list) else args[0] # take only the first sentence for task_name, prompt in self._lora_prompts.items(): if input_text.startswith(prompt): self.current_task = task_name break else: warnings.warn( f"Task-specific embeddings are disabled. To enable, specify the `task` " f"argument with one of the supported tasks: {', '.join(self.config.lora_adaptations)}", category=UserWarning, ) self.current_task = None # No task-specific adapter is found, just use the general-purpose weights return self.roberta.encode(*args, **kwargs)