import math import os 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 torch.nn import functional as F 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, ) 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, current_task): return ( X + torch.matmul( *self.swap( ( self.lora_B[current_task], self.dropout_fn(self.lora_A[current_task]), ) ) ).view(X.shape) * self.scaling ) def forward(self, X): return X @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, ), ) def new_forward(self, input, task_id=None, residual=False): if task_id is not None: weights = self.parametrizations.weight[0].lora_forward( self.weight, current_task=task_id ) else: weights = self.weight out = F.linear(input, weights, self.bias) if residual: return out, input return out layer.forward = new_forward.__get__(layer, layer.__class__) 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, ), ) def new_forward(self, input, task_id=None): if task_id is not None: weights = self.parametrizations.weight[0].lora_forward( self.weight, current_task=task_id ) else: weights = self.weight out = F.embedding( input, weights, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse, ) return out layer.forward = new_forward.__get__(layer, layer.__class__) 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) or not all([v in self._lora_adaptations for v in self._lora_prompts.keys()]) ): raise ValueError( f"`lora_prompts` must be a dict and contain the same number of elements " f"as `lora_adaptations` with all keys in `lora_prompts` present in `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 @property def rotary_emb_base(self): return self.roberta.rotary_emb_base @rotary_emb_base.setter def rotary_emb_base(self, base): self.roberta.rotary_emb_base = base @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, ) ) def forward(self, *args, **kwargs): 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, sentences: Union[str, List[str]], *args, task_type: Optional[str] = None, **kwargs, ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]: """ Computes sentence embeddings task_type(`str`, *optional*, defaults to `None`): Specifies the task for which the encoding is intended. If `task_type` is not provide, all LoRA adapters are disabled, and the model reverts to its original, general-purpose weights. """ if task_type and task_type not in self._lora_adaptations: raise ValueError( f"Unsupported task '{task_type}'. " f"Supported tasks are: {', '.join(self.config.lora_adaptations)}." f"Alternatively, don't pass the `task_type` argument to disable LoRA." ) adapter_mask = None if task_type: task_id = self._adaptation_map[task_type] num_examples = 1 if isinstance(sentences, str) else len(sentences) adapter_mask = torch.full( (num_examples,), task_id, dtype=torch.int32, device=self.device ) return self.roberta.encode( sentences, *args, adapter_mask=adapter_mask, **kwargs )