Transformer架构是大模型的“核心骨架”，而LoRA是工业界主流的高效微调技术，二者紧密相关。LoRA的核心建立在Transformer架构之上，通过对Transformer架构进行微调操作来生效。理解Transformer架构是掌握LoRA微调核心逻辑的基础，因为要进行LoRA调参实战，需要明白Transformer架构这一底层原理，才能够从“凭感觉试”转变为“有依据地调” [^1][^2]。 ### 相关代码示例 旨在展示在处理文本数据时，如何将Transformer架构与LoRA技术相结合，实现文本的编码与解码。这里假设使用PyTorch作为深度学习框架，并考虑融入Transformer和LoRA的关键元素。 ```python import torch import torch.nn as nn # 定义Transformer编码器层 class TransformerEncoderLayer(nn.Module): def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1): super(TransformerEncoderLayer, self).__init__() self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) self.linear1 = nn.Linear(d_model, dim_feedforward) self.dropout = nn.Dropout(dropout) self.linear2 = nn.Linear(dim_feedforward, d_model) self.norm1 = nn.LayerNorm(d_model) self.norm2 = nn.LayerNorm(d_model) self.dropout1 = nn.Dropout(dropout) self.dropout2 = nn.Dropout(dropout) def forward(self, src, src_mask=None, src_key_padding_mask=None): src2 = self.self_attn(src, src, src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0] src = src + self.dropout1(src2) src = self.norm1(src) src2 = self.linear2(self.dropout(torch.relu(self.linear1(src)))) src = src + self.dropout2(src2) src = self.norm2(src) return src # 定义Transformer编码器 class TransformerEncoder(nn.Module): def __init__(self, encoder_layer, num_layers): super(TransformerEncoder, self).__init__() self.layers = nn.ModuleList([encoder_layer for _ in range(num_layers)]) def forward(self, src, mask=None, src_key_padding_mask=None): output = src for mod in self.layers: output = mod(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask) return output # 定义LoRA模块 class LoRA(nn.Module): def __init__(self, in_features, out_features, rank=8): super(LoRA, self).__init__() self.A = nn.Parameter(torch.randn(in_features, rank)) self.B = nn.Parameter(torch.randn(rank, out_features)) self.scaling = nn.Parameter(torch.ones(1)) def forward(self, x): lora_output = torch.matmul(x, torch.matmul(self.A, self.B)) * self.scaling return lora_output # 结合Transformer和LoRA class TransformerWithLoRA(nn.Module): def __init__(self, d_model, nhead, num_layers, rank=8): super(TransformerWithLoRA, self).__init__() encoder_layer = TransformerEncoderLayer(d_model, nhead) self.transformer_encoder = TransformerEncoder(encoder_layer, num_layers) self.lora = LoRA(d_model, d_model, rank) def forward(self, src): transformer_output = self.transformer_encoder(src) lora_output = self.lora(transformer_output) final_output = transformer_output + lora_output return final_output # 示例使用 d_model = 512 nhead = 8 num_layers = 6 rank = 8 model = TransformerWithLoRA(d_model, nhead, num_layers, rank) src = torch.randn(10, 32, d_model) # 输入数据 output = model(src) print(output.shape) ```