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汽车网站怎么做,网文推广怎么做,怎样维护网站,网站免费高清素材软件小游戏1--相关讲解 LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS LoRA 在 Stable Diffusion 中的三种应用：原理讲解与代码示例 PEFT-LoRA 2--基本原理固定原始层，通过添加和训练两个低秩矩阵，达到微调模型的效果； 3--简单代…

1--相关讲解

LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

LoRA 在 Stable Diffusion 中的三种应用：原理讲解与代码示例

PEFT-LoRA

2--基本原理

固定原始层，通过添加和训练两个低秩矩阵，达到微调模型的效果；

3--简单代码

import torch
import torch.nn as nn
from peft import LoraConfig, get_peft_model, LoraModel
from peft.utils import get_peft_model_state_dict# 创建模型
class Simple_Model(nn.Module):def __init__(self):super().__init__()self.linear1 = nn.Linear(64, 128)self.linear2 = nn.Linear(128, 256)def forward(self, x: torch.Tensor):x = self.linear1(x)x = self.linear2(x)return xif __name__ == "__main__":# 初始化原始模型origin_model = Simple_Model()# 配置lora configmodel_lora_config = LoraConfig(r = 32, lora_alpha = 32, # scaling = lora_alpha / r 一般来说，lora_alpha的参数初始化为与r相同，即scale=1init_lora_weights = "gaussian", # 参数初始化方式target_modules = ["linear1", "linear2"], # 对应层添加lora层lora_dropout = 0.1)# Test datainput_data = torch.rand(2, 64)origin_output = origin_model(input_data)# 原始模型的权重参数origin_state_dict = origin_model.state_dict() # 两种方式生成对应的lora模型，调用后会更改原始的模型new_model1 = get_peft_model(origin_model, model_lora_config)new_model2 = LoraModel(origin_model, model_lora_config, "default")output1 = new_model1(input_data)output2 = new_model2(input_data)# 初始化时，lora_B矩阵会初始化为全0，因此最初 y = WX + (alpha/r) * BA * X == WX# origin_output == output1 == output2# 获取lora权重参数，两者在key_name上会有区别new_model1_lora_state_dict = get_peft_model_state_dict(new_model1)new_model2_lora_state_dict = get_peft_model_state_dict(new_model2)# origin_state_dict['linear1.weight'].shape -> [output_dim, input_dim]# new_model1_lora_state_dict['base_model.model.linear1.lora_A.weight'].shape -> [r, input_dim]# new_model1_lora_state_dict['base_model.model.linear1.lora_B.weight'].shape -> [output_dim, r]print("All Done!")

4--权重保存和合并

核心公式是：new_weights = origin_weights + alpha* (BA)

    # 借助diffuser的save_lora_weights保存模型权重from diffusers import StableDiffusionPipelinesave_path = "./"global_step = 0StableDiffusionPipeline.save_lora_weights(save_directory = save_path,unet_lora_layers = new_model1_lora_state_dict,safe_serialization = True,weight_name = f"checkpoint-{global_step}.safetensors",)# 加载lora模型权重(参考Stable Diffusion)，其实可以重写一个简单的版本from safetensors import safe_openalpha = 1. # 参数融合因子lora_path = "./" + f"checkpoint-{global_step}.safetensors"state_dict = {}with safe_open(lora_path, framework="pt", device="cpu") as f:for key in f.keys():state_dict[key] = f.get_tensor(key)all_lora_weights = []for idx,key in enumerate(state_dict):# only process lora down keyif "lora_B." in key: continueup_key    = key.replace(".lora_A.", ".lora_B.") # 通过lora_A直接获取lora_B的键名model_key = key.replace("unet.", "").replace("lora_A.", "").replace("lora_B.", "")layer_infos = model_key.split(".")[:-1]curr_layer = new_model1while len(layer_infos) > 0:temp_name = layer_infos.pop(0)curr_layer = curr_layer.__getattr__(temp_name)weight_down = state_dict[key].to(curr_layer.weight.data.device)weight_up   = state_dict[up_key].to(curr_layer.weight.data.device)# 将lora参数合并到原模型参数中 -> new_W = origin_W + alpha*(BA)curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).to(curr_layer.weight.data.device)all_lora_weights.append([model_key, torch.mm(weight_up, weight_down).t()])print('Load Lora Done')