# LoRA implemented in a dense layer def __init__( self, in_features: int, out_features: int, r: int = 0, lora_alpha: int = 1, lora_dropout: float = 0., fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) mer...
Lora implemented in a dense layer,Linear LoRA implemented in a Embedding layer,Embedding Lora implemented in a conv2d layer, Conv2d 4-2、loralib的lora实现 除了PEFT的库可以实现LoRA,微软loralib也可以实现LoRA github地址: github.com/microsoft/Lo 假设我们需要对attention层中的query_key_value添加LoRA,...
classLinear(nn.Linear,LoraLayer):# LoRA implemented in a dense layerdef__init__(self,in_features:int,out_features:int,r:int=0,lora_alpha:int=1,lora_dropout:float=0.,fan_in_fan_out:bool=False,# Set this to True if the layer to replace stores weight like (fan_in, fan_out)merge_w...
假设 是一个线性层(LinearLayer),我们一起来看看对其应用 LoRA 是如何实现的。 (麻烦认真看下代码中的注释,谢谢~) classMergedLinear(nn.Linear,LoraLayer):#Loraimplementedinadenselayerdef__init__( self, in_features:int, out_features:int, r:int=0, lora_alpha:int=1, lora_dropout:float=0.0, enable...
# LoRA implemented in a dense layer def __init__( self, in_features: int, out_features: int, r: int = 0, lora_alpha: int = 1, lora_dropout: float = 0., fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) ...
Linear, LoRALayer): # LoRA implemented in a dense layer def __init__( self, in_features: int, out_features: int, r: int = 0, lora_alpha: int = 1, lora_dropout: float = 0., enable_lora: List[bool] = [False], fan_in_fan_out: bool = False, merge_weights: bool = True, ...
然后就要讲到上文中所提到的Linear类,也就是Lora的具体实现,它同时继承了nn.Linear和LoraLayer。 classLinear(nn.Linear,LoraLayer):# Lora implemented in a dense layerdef__init__(self,in_features:int,out_features:int,r:int=0,lora_alpha:int=1,lora_dropout:float=0.0,fan_in_fan_out:bool=False,...
class Linear(nn.Linear, LoRALayer): # LoRA implemented in a dense layer def __init__( self, in_features: int, out_features: int, r: int = 0, lora_alpha: int = 1, lora_dropout: float = 0., fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores...
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假设在神经网络中有一个 MxM 的预训练密集层(pre-trained dense layer)(权重矩阵)W。 例如,这个 Keras 模型有 3 个 size 为 512x512 的密集层(dense layers): 然后再初始化两个密集层 A 和 B,它们的 shapes 分别为 M x R 和 R x M。