152 lines
6.6 KiB
Python
152 lines
6.6 KiB
Python
from typing import Optional, Union
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from opendelta.utils.signature import get_arg_names, get_arg_names_inside_func
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from opendelta.utils.name_based_addressing import *
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from opendelta.basemodel import DeltaBase
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from transformers.models.t5 import T5ForConditionalGeneration
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import torch.nn as nn
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from opendelta import BaseDeltaConfig
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import math
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class LowRankLinear(nn.Module):
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# ------------------------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
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# ------------------------------------------------------------------------------------------
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# copy from loralib and do some refactor
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def __init__(self,
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in_features,
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out_features,
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weight,
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r=8,
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lora_alpha=16,
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lora_dropout=0.0,
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):
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super().__init__()
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self.r = r
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self.lora_alpha = lora_alpha
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self.lora_dropout = lora_dropout
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if lora_dropout > 0.:
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self.lora_dropout = nn.Dropout(p=lora_dropout)
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else:
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self.lora_dropout = lambda x: x
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if r > 0:
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self.lora_A = nn.Parameter(weight.new_zeros((r, in_features)))
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self.lora_B = nn.Parameter(weight.new_zeros((out_features, r)))
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self.scaling = self.lora_alpha / self.r
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nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
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nn.init.zeros_(self.lora_B)
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def forward(self, x):
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return (self.lora_dropout(x) @ self.lora_A.T @ self.lora_B.T) * self.scaling
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class LoraConfig(BaseDeltaConfig):
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r"""
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This is the configuration class to store the configuration of a :py:class:`~LoraModel`
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"""
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def __init__(
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self,
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lora_r=8,
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lora_alpha=16,
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lora_dropout=0.0,
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**kwargs
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):
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super().__init__(**kwargs)
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arg_names = get_arg_names_inside_func(self.__init__)
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for arg_name in arg_names:
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if not hasattr(self, arg_name): # the arg has not been registered in parent config
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setattr(self, arg_name, locals()[arg_name])
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class LoraModel(DeltaBase):
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r""" The implementation of `LoRA: Low-Rank Adaptation of Large Language Models <https://arxiv.org/abs/2106.09685>`_ .
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Thanks for their `loralib <https://github.com/microsoft/LoRA/tree/main/loralib>`_.
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.. note::
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In our implementation, we did not use loralib.linear to replace the linear layer of the backbone model.
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Instead, we insert a parallel module into the backbone.
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In other words, we treat :math:`(W + A^TB) X` as :math:`WX+ A^TBX`, and insert the :math:`A^TBX` as a parallel insertion module.
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If you want to use the original implementation, please refer to `lora_old.py`
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class attributes:
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- default_modified_modules = ['attn.q', 'attn.v'] According to the paper, they modify q and v matrix in the
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attention layer. However, other linears can also be modified, and may lead to better performance.
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.. note::
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modified_modules should point to linear layer. We currently don't support broadcast to all linears in
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a module's child modules.
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- delta_type = "lora"
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Args:
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backbone_model (:obj:`transformers.PretrainedModels`): The backbone model to be modified.
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lora_r (:obj:`int`, *optional*): the rank of the lora parameters. The smaller lora_r is , the fewer parameters lora has.
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lora_alpha (:obj:`int`, *optional*): A hyper-parameter to control the init scale of loralib.linear .
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lora_dropout (:obj:`float`, *optional*): The dropout rate in lora.linear.
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modified_modules (:obj:`List[str]`): For prefix tuning, the it must refer to an attention layer (Currently, only
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the implemented ones)
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unfrozen_modules (:obj:`List[str]`, *optional*, default to :obj:`None`): The modules that should be unfrozen
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together with the prefix parameters.
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common_structure (:obj:`bool`): whether using name-based addressing with a common structure mapping.
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"""
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config_class = LoraConfig
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delta_type = "lora"
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default_modified_modules = ['attn.q', 'attn.v']
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def __init__(self,
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backbone_model: nn.Module,
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lora_r=8,
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lora_alpha=16,
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lora_dropout=0.0,
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modified_modules: Optional[List[str]] = None,
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unfrozen_modules: Optional[List[str]] = None,
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exclude_modules: Optional[List[str]] = None,
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common_structure: Optional[bool] = None,
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interactive_modify: Optional[Union[bool, int]] = False,
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):
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DeltaBase.__init__(self,
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backbone_model,
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modified_modules=modified_modules,
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unfrozen_modules=unfrozen_modules,
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common_structure=common_structure,
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interactive_modify=interactive_modify,
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)
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arg_names = get_arg_names_inside_func(self.__init__)
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for arg_name in arg_names:
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if not hasattr(self, arg_name): # not registered in parent class
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setattr(self, arg_name, locals()[arg_name])
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self.delta_modules = nn.ModuleList()
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self.add_all_delta_to_backbone(self.backbone_model,
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self.modified_modules,
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)
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def update_module(self, module: nn.Module, key: str):
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parent_ref, child_name, child_ref = self.find_module(module, key)
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parallel_module = self.new_module_like(child_module=child_ref)
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self.insert_parallel_module(child_ref, delta_module=parallel_module, delta_name="lora")
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def _pseudo_data_to_instantiate(self, module):
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# no need to pass pseudo input, so overwrite it
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pass
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def new_module_like(self, child_module):
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if isinstance(child_module, nn.Linear):
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in_features, out_features = child_module.in_features, child_module.out_features
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new_module = LowRankLinear(in_features = in_features,
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out_features = out_features,
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weight = child_module.weight,
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r=self.lora_r,
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lora_alpha=self.lora_alpha,
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lora_dropout=self.lora_dropout)
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self.delta_modules.append(new_module)
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else:
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raise NotImplementedError
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return new_module
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