bert-pytorch版源码详细解读

前言

bert作为当下最火的NLP模型（或者说该类型的模型，包括AlBert，XLNet等）。对于志在NLP的同学，有必要对其原理和代码都进行比较深入的了解。废话不多说，进入正题。
PS：1.这里的代码有些参数传入是阉割过的，而且代码版本也是比较老版的，但更容易理解，更详细的还是参考：https://huggingface.co/transformers/
2.关键的注解都在代码的注释里。

主要代码

1.主函数入口

class BertModel(nn.Module):def __init__(self, config: BertConfig):super(BertModel, self).__init__()self.embeddings = BERTEmbeddings(config)self.encoder = BERTEncoder(config)self.pooler = BERTPooler(config)def forward(self, input_ids, token_type_ids=None, attention_mask=None):if attention_mask is None:attention_mask = torch.ones_like(input_ids)if token_type_ids is None:token_type_ids = torch.zeros_like(input_ids)# attention_mask的维度应保持和多头的hidden_states一致#!!!个人感觉这里extended_attention_mask 还应该扩展一下，感觉这个维度不太对!extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)extended_attention_mask = extended_attention_mask.float()# mask部分token的权重直接给-10000，使其在self-att的时候基本不起作用。extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0#根据input_ids, token_type_ids以及position_ids来确定初始embeddingsembedding_output = self.embeddings(input_ids, token_type_ids)#核心层，由以多层self_attention为主的神经网络构成all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)#最后一层隐藏层sequence_output = all_encoder_layers[-1]#取出最后一层隐藏层的[cls]的表征，经过网络层(self.pooler)后得到pooled_outputpooled_output = self.pooler(sequence_output)return all_encoder_layers, pooled_output

大致讲一下吧：
一般必传的三个参数input_idx，token_type_ids，attention_mask。
维度均为（batch_size, max_sent_length）

input_idx就是每个token对应的idx，对应关系在预训练模型文件集的vocab.txt里
token_type_ids有两种取值(0对应sentenceA,1对应sentenceB）该tensor会在self.embeddings的时候和input_iput生成的embedding相加生成初始的embeddings。
attention_mask有两种取值（1代表非mask词，0代表mask掉的词）一般来说在finetune阶段，我们会把padding部分都设成mask掉的词。

其他基本也都注释了。

2.BertEmbedding层

class BERTEmbeddings(nn.Module):def __init__(self, config):super(BERTEmbeddings, self).__init__()self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)self.LayerNorm = BERTLayerNorm(config)self.dropout = nn.Dropout(config.hidden_dropout_prob)def forward(self, input_ids, token_type_ids=None):#根据每个token的位置生成position_ids，很直观seq_length = input_ids.size(1)position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)position_ids = position_ids.unsqueeze(0).expand_as(input_ids)if token_type_ids is None:token_type_ids = torch.zeros_like(input_ids)#这三个embeddings相信大家可以参见下图就一目了然了words_embeddings = self.word_embeddings(input_ids)position_embeddings = self.position_embeddings(position_ids)token_type_embeddings = self.token_type_embeddings(token_type_ids)embeddings = words_embeddings + position_embeddings + token_type_embeddings#最后过一个layerNorm和dropout层embeddings = self.LayerNorm(embeddings)embeddings = self.dropout(embeddings)return embeddings

3.BertEnocder层

class BERTEncoder(nn.Module):def __init__(self, config):super(BERTEncoder, self).__init__()layer = BERTLayer(config)self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])    def forward(self, hidden_states, attention_mask):all_encoder_layers = []for layer_module in self.layer:hidden_states = layer_module(hidden_states, attention_mask)all_encoder_layers.append(hidden_states)return all_encoder_layersclass BERTLayer(nn.Module):def __init__(self, config):super(BERTLayer, self).__init__()self.attention = BERTAttention(config)self.intermediate = BERTIntermediate(config)self.output = BERTOutput(config)def forward(self, hidden_states, attention_mask):attention_output = self.attention(hidden_states, attention_mask)intermediate_output = self.intermediate(attention_output)layer_output = self.output(intermediate_output, attention_output)return layer_output

BertEncoder层实质上就是由多个（num_hidden_layers）BertLayer层堆叠而成。
而BertLayer又由attention，intermediate和output三部分组成，下面分别来看。

3.1BertTAttention

重头戏开始！详见注释，看完你会发现很简单。

class BERTAttention(nn.Module):def __init__(self, config):super(BERTAttention, self).__init__()self.self = BERTSelfAttention(config)self.output = BERTSelfOutput(config)def forward(self, input_tensor, attention_mask):self_output = self.self(input_tensor, attention_mask)attention_output = self.output(self_output, input_tensor)return attention_outputclass BERTSelfAttention(nn.Module):def __init__(self, config):super(BERTSelfAttention, self).__init__()if config.hidden_size % config.num_attention_heads != 0:raise ValueError("The hidden size (%d) is not a multiple of the number of attention""heads (%d)" % (config.hidden_size, config.num_attention_heads))self.num_attention_heads = config.num_attention_heads#多头self_attentionself.attention_head_size = int(config.hidden_size /config.num_attention_heads)#每个头的维度，一般是768/12=64self.all_head_size = self.num_attention_heads * self.attention_head_sizeself.query = nn.Linear(config.hidden_size, self.all_head_size)self.key = nn.Linear(config.hidden_size, self.all_head_size)self.value = nn.Linear(config.hidden_size, self.all_head_size)self.dropout = nn.Dropout(config.attention_probs_dropout_prob)def transpose_for_scores(self, x):new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)x = x.view(*new_x_shape)return x.permute(0, 2, 1, 3)def forward(self, hidden_states, attention_mask):#经典生成QKV#(batch_size, max_sen_length, hidden_size)->(batch_size, max_sen_length, hidden_size)#(8, 512, 768)->(8, 512, 768)mixed_query_layer = self.query(hidden_states)mixed_key_layer = self.key(hidden_states)mixed_value_layer = self.value(hidden_states)#改变维度，形成多头，记住是在生成QKV之后才干的事#(batch_size, max_sen_length, hidden_size)->(batch_size, num_attention_heads, max_sen_length, attention_head_size)#(8, 512, 768)->(8, 12, 512, 64)query_layer = self.transpose_for_scores(mixed_query_layer)key_layer = self.transpose_for_scores(mixed_key_layer)value_layer = self.transpose_for_scores(mixed_value_layer)#QK tensor相乘，只对最后两维做矩阵乘法#(batch_size, num_attention_heads, max_sen_length, attention_head_size)*(batch_size, num_attention_heads, attention_head_size, max_sen_length)->(batch_size, num_attention_heads, max_sen_length, max_sen_length)#(8, 12, 512, 64)*(8, 12, 64, 512)->(8, 12, 512, 512)attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))#除以维度的开方，这是为了使QV的结果方差变为1，使得sortmax后不会发生梯度消失。attention_scores = attention_scores / math.sqrt(self.attention_head_size)#之前传的attention_mask在此刻发挥它的作用了！把mask掉的词的“权重”变成-10000，softmax后就基本等于0。attention_scores = attention_scores + attention_mask# softmax加一个dropout, 这也没啥好说的attention_probs = nn.Softmax(dim=-1)(attention_scores)attention_probs = self.dropout(attention_probs)# 最后再和V相乘,至此就完成了经典的softmax(QK/sqrt(dk))*V的操作！#(8, 12, 512, 512)*(8, 12, 512, 64)->(8, 12, 512, 64)context_layer = torch.matmul(attention_probs, value_layer)#之后就是把维度进行还原#(8, 12, 512, 64)->(8, 512,12 ,64)->(8, 512, 768)context_layer = context_layer.permute(0, 2, 1, 3).contiguous()new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)context_layer = context_layer.view(*new_context_layer_shape)return context_layerclass BERTSelfOutput(nn.Module):def __init__(self, config):super(BERTSelfOutput, self).__init__()self.dense = nn.Linear(config.hidden_size, config.hidden_size)self.LayerNorm = BERTLayerNorm(config)self.dropout = nn.Dropout(config.hidden_dropout_prob)def forward(self, hidden_states, input_tensor):#很平淡的全连接层加上dropout和LayerNormhidden_states = self.dense(hidden_states)hidden_states = self.dropout(hidden_states)hidden_states = self.LayerNorm(hidden_states + input_tensor)return hidden_states

3.2 BertIntermediate&& BertOutput

class BERTIntermediate(nn.Module):def __init__(self, config):super(BERTIntermediate, self).__init__()#之前一直不清楚这个intermediate_size是干嘛的，原来是self_attention后还跟了BERTIntermediate和BERTOutput2个全连接层。self.dense = nn.Linear(config.hidden_size, config.intermediate_size)self.intermediate_act_fn = geludef forward(self, hidden_states):hidden_states = self.dense(hidden_states)hidden_states = self.intermediate_act_fn(hidden_states)return hidden_statesclass BERTOutput(nn.Module):def __init__(self, config):super(BERTOutput, self).__init__()self.dense = nn.Linear(config.intermediate_size, config.hidden_size)self.LayerNorm = BERTLayerNorm(config)self.dropout = nn.Dropout(config.hidden_dropout_prob)def forward(self, hidden_states, input_tensor):hidden_states = self.dense(hidden_states)hidden_states = self.dropout(hidden_states)hidden_states = self.LayerNorm(hidden_states + input_tensor)return hidden_states

！！！这个和我之前看的transformers的残差连接层差别还挺大的，所以并不完全和transformers的encoder部分结构一致。
这之后就是主函数里的几步骤收尾工作了，这里也不再赘述。

4.补充

下面补充一下中途涉及到的相关类（LayerNorm）的代码

4.1 BertLayerNorm

class BERTLayerNorm(nn.Module):def __init__(self, config, variance_epsilon=1e-12):"""Construct a layernorm module in the TF style (epsilon inside the square root)."""super(BERTLayerNorm, self).__init__()self.gamma = nn.Parameter(torch.ones(config.hidden_size))self.beta = nn.Parameter(torch.zeros(config.hidden_size))self.variance_epsilon = variance_epsilondef forward(self, x):u = x.mean(-1, keepdim=True)s = (x - u).pow(2).mean(-1, keepdim=True)x = (x - u) / torch.sqrt(s + self.variance_epsilon)return self.gamma * x + self.beta

1.batchNorm是对多个样本进行标准化，而layerNorm是对单样本标准化。
2.BertLayerNorm除了标准化以外还加上了gamma和beta的变化。

4.2 BertPooler

class BERTPooler(nn.Module):def __init__(self, config):super(BERTPooler, self).__init__()self.dense = nn.Linear(config.hidden_size, config.hidden_size)self.activation = nn.Tanh()def forward(self, hidden_states):#取出[cls]后过一个全连接层和激活函数。first_token_tensor = hidden_states[:, 0]pooled_output = self.dense(first_token_tensor)pooled_output = self.activation(pooled_output)return pooled_output

上文也提到了，BertPooler就是专门为[cls]设计的

4.3 gelu


def gelu(x):"""Implementation of the gelu activation function."""return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))

4.4 transpose_for_scores

    def transpose_for_scores(self, x):new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)x = x.view(*new_x_shape)return x.permute(0, 2, 1, 3)

总结

到此基本就结束了，整体流程看下来其实很快，关键是理清里面每一步的维度的变换和几个核心的类就行。希望能对大家有所帮助。
代码参考来自于：https://github.com/DA-southampton/Read_Bert_Code