Bert模型冻结指定参数进行训练

最新推荐文章于 2024-11-08 11:12:59 发布

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最新推荐文章于 2024-11-08 11:12:59 发布

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分类专栏： # 预训练模型

本文链接：https://blog.youkuaiyun.com/HUSTHY/article/details/104006106

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本文详细介绍了BERT模型的参数结构，并提供了如何在微调过程中冻结特定层参数的实战代码，以提高训练效率。

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由于bert模型具有12层，参数量达一亿，bert模型做微调有的时候就需要只训练部分参数，那么就需要把其他的参数冻结掉，固定住，又能微调bert模型，还能提高模型训练的效率。这个就需要用到parameter的requires_grad的属性，来冻结和放开参数。

首先我们看看bert模型的具体参数有那些：

bert.embeddings.word_embeddings.weight torch.Size([21128, 768])
bert.embeddings.position_embeddings.weight torch.Size([512, 768])
bert.embeddings.token_type_embeddings.weight torch.Size([2, 768])
bert.embeddings.LayerNorm.weight torch.Size([768])
bert.embeddings.LayerNorm.bias torch.Size([768])

bert.encoder.layer.0.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.query.bias torch.Size([768])
bert.encoder.layer.0.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.key.bias torch.Size([768])
bert.encoder.layer.0.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.self.value.bias torch.Size([768])
bert.encoder.layer.0.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.0.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.0.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.0.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.0.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.0.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.0.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.0.output.dense.bias torch.Size([768])
bert.encoder.layer.0.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.0.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.1.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.query.bias torch.Size([768])
bert.encoder.layer.1.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.key.bias torch.Size([768])
bert.encoder.layer.1.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.self.value.bias torch.Size([768])
bert.encoder.layer.1.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.1.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.1.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.1.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.1.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.1.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.1.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.1.output.dense.bias torch.Size([768])
bert.encoder.layer.1.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.1.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.2.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.query.bias torch.Size([768])
bert.encoder.layer.2.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.key.bias torch.Size([768])
bert.encoder.layer.2.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.self.value.bias torch.Size([768])
bert.encoder.layer.2.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.2.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.2.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.2.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.2.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.2.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.2.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.2.output.dense.bias torch.Size([768])
bert.encoder.layer.2.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.2.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.3.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.query.bias torch.Size([768])
bert.encoder.layer.3.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.key.bias torch.Size([768])
bert.encoder.layer.3.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.self.value.bias torch.Size([768])
bert.encoder.layer.3.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.3.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.3.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.3.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.3.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.3.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.3.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.3.output.dense.bias torch.Size([768])
bert.encoder.layer.3.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.3.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.4.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.query.bias torch.Size([768])
bert.encoder.layer.4.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.key.bias torch.Size([768])
bert.encoder.layer.4.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.self.value.bias torch.Size([768])
bert.encoder.layer.4.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.4.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.4.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.4.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.4.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.4.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.4.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.4.output.dense.bias torch.Size([768])
bert.encoder.layer.4.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.4.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.5.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.query.bias torch.Size([768])
bert.encoder.layer.5.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.key.bias torch.Size([768])
bert.encoder.layer.5.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.self.value.bias torch.Size([768])
bert.encoder.layer.5.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.5.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.5.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.5.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.5.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.5.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.5.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.5.output.dense.bias torch.Size([768])
bert.encoder.layer.5.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.5.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.6.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.query.bias torch.Size([768])
bert.encoder.layer.6.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.key.bias torch.Size([768])
bert.encoder.layer.6.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.self.value.bias torch.Size([768])
bert.encoder.layer.6.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.6.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.6.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.6.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.6.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.6.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.6.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.6.output.dense.bias torch.Size([768])
bert.encoder.layer.6.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.6.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.7.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.query.bias torch.Size([768])
bert.encoder.layer.7.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.key.bias torch.Size([768])
bert.encoder.layer.7.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.self.value.bias torch.Size([768])
bert.encoder.layer.7.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.7.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.7.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.7.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.7.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.7.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.7.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.7.output.dense.bias torch.Size([768])
bert.encoder.layer.7.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.7.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.8.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.query.bias torch.Size([768])
bert.encoder.layer.8.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.key.bias torch.Size([768])
bert.encoder.layer.8.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.self.value.bias torch.Size([768])
bert.encoder.layer.8.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.8.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.8.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.8.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.8.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.8.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.8.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.8.output.dense.bias torch.Size([768])
bert.encoder.layer.8.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.8.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.9.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.query.bias torch.Size([768])
bert.encoder.layer.9.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.key.bias torch.Size([768])
bert.encoder.layer.9.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.self.value.bias torch.Size([768])
bert.encoder.layer.9.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.9.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.9.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.9.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.9.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.9.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.9.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.9.output.dense.bias torch.Size([768])
bert.encoder.layer.9.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.9.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.10.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.query.bias torch.Size([768])
bert.encoder.layer.10.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.key.bias torch.Size([768])
bert.encoder.layer.10.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.self.value.bias torch.Size([768])
bert.encoder.layer.10.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.10.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.10.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.10.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.10.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.10.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.10.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.10.output.dense.bias torch.Size([768])
bert.encoder.layer.10.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.10.output.LayerNorm.bias torch.Size([768])

bert.encoder.layer.11.attention.self.query.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.query.bias torch.Size([768])
bert.encoder.layer.11.attention.self.key.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.key.bias torch.Size([768])
bert.encoder.layer.11.attention.self.value.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.self.value.bias torch.Size([768])
bert.encoder.layer.11.attention.output.dense.weight torch.Size([768, 768])
bert.encoder.layer.11.attention.output.dense.bias torch.Size([768])
bert.encoder.layer.11.attention.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.11.attention.output.LayerNorm.bias torch.Size([768])
bert.encoder.layer.11.intermediate.dense.weight torch.Size([3072, 768])
bert.encoder.layer.11.intermediate.dense.bias torch.Size([3072])
bert.encoder.layer.11.output.dense.weight torch.Size([768, 3072])
bert.encoder.layer.11.output.dense.bias torch.Size([768])
bert.encoder.layer.11.output.LayerNorm.weight torch.Size([768])
bert.encoder.layer.11.output.LayerNorm.bias torch.Size([768])

bert.pooler.dense.weight torch.Size([768, 768])
bert.pooler.dense.bias torch.Size([768])
out.weight torch.Size([2, 768])
out.bias torch.Size([2])

比如说现在我们要放开第11和12层以及bert.pooler和out层参数，冻结其他的参数，怎么实现呢？

pytorch中有 model.named_parameters() 和 requires_grad，直接写一个遍历然后设置就好。具体实现代码：

import torch.nn as nn
from transformers import BertModel
import torch

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.bert = BertModel.from_pretrained('pretrained_models/Chinese-BERT-wwm')
        self.out = nn.Linear(768,2)

    def forward(self):
        out = self.bert()
        return out

if __name__ == '__main__':
    model = Model()
    unfreeze_layers = ['layer.10','layer.11','bert.pooler','out.']

    for name, param in model.named_parameters():
        print(name,param.size())

    print("*"*30)
    print('\n')

    for name ,param in model.named_parameters():
        param.requires_grad = False
        for ele in unfreeze_layers:
            if ele in name:
                param.requires_grad = True
                break
    #验证一下
    for name, param in model.named_parameters():
        if param.requires_grad:
            print(name,param.size())

    #过滤掉requires_grad = False的参数
    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=0.00001)

最后在做训练的时候，优化器中一定要添加过滤器filter把requires_grad = False的参数过滤掉，在训练的时候，不会更新这些参数。

参考文章：

Pytorch 如何精确的冻结我想冻结的预训练模型的某一层，有什么命令吗？