本篇文章我们将拆分Bert，细究Bert的结构以及每一层的参数个数
我们以bert-base为例（768维）：
普通bert：
bert的模型如下（省略多层）：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Input-Token (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
Input-Segment (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
Embedding-Token (Embedding) multiple 16226304 Input-Token[0][0]
MLM-Norm[0][0]
__________________________________________________________________________________________________
Embedding-Segment (Embedding) (None, None, 768) 1536 Input-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Token-Segment (Add) (None, None, 768) 0 Embedding-Token[0][0]
Embedding-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Position (PositionEmb (None, None, 768) 393216 Embedding-Token-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Norm (LayerNormalizat (None, None, 768) 1536 Embedding-Position[0][0]
__________________________________________________________________________________________________
Embedding-Dropout (Dropout) (None, None, 768) 0 Embedding-Norm[0][0]
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 2362368 Embedding-Dropout[0][0]
Embedding-Dropout[0][0]
Embedding-Dropout[0][0]
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 0 Transformer-0-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 0 Embedding-Dropout[0][0]
Transformer-0-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 1536 Transformer-0-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-0-FeedForward (Feed (None, None, 768) 4722432 Transformer-0-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-0-FeedForward-Dropo (None, None, 768) 0 Transformer-0-FeedForward[0][0]
__________________________________________________________________________________________________
Transformer-0-FeedForward-Add ( (None, None, 768) 0 Transformer-0-MultiHeadSelfAttent
Transformer-0-FeedForward-Dropout
__________________________________________________________________________________________________
Transformer-0-FeedForward-Norm (None, None, 768) 1536 Transformer-0-FeedForward-Add[0][
__________________________________________________________________________________________________
Transformer-1-MultiHeadSelfAtte (None, None, 768) 2362368 Transformer-0-FeedForward-Norm[0]
Transformer-0-FeedForward-Norm[0]
Transformer-0-FeedForward-Norm[0]
__________________________________________________________________________________________________
Transformer-1-MultiHeadSelfAtte (None, None, 768) 0 Transformer-1-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-1-MultiHeadSelfAtte (None, None, 768) 0 Transformer-0-FeedForward-Norm[0]
Transformer-1-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-1-MultiHeadSelfAtte (None, None, 768) 1536 Transformer-1-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-1-FeedForward (Feed (None, None, 768) 4722432 Transformer-1-MultiHeadSelfAttent
__________________________________________________________________________________________________
Transformer-1-FeedForward-Dropo (None, None, 768) 0 Transformer-1-FeedForward[0][0]
__________________________________________________________________________________________________
Transformer-1-FeedForward-Add ( (None, None, 768) 0 Transformer-1-MultiHeadSelfAttent
Transformer-1-FeedForward-Dropout
__________________________________________________________________________________________________
Transformer-1-FeedForward-Norm (None, None, 768) 1536 Transformer-1-FeedForward-Add[0][
__________________________________________________________________________________________________
下面我们简单剖析一下各部分参数：
首先是输入：
embedding部分，bert使用了embedding、token type（用来区分两个句子）和position embedding三部分。
embedding就是 (这里以词典大小21128为例) ：
voab size * embedding size = 21128*768=16226304。
__________________________________________________________________________________________________
Embedding-Token (Embedding) multiple 16226304 Input-Token[0][0]
MLM-Norm[0][0]
token type：
使用0和1标记句子（比如NSP任务时区分两个句子）：
768*2=1536。
__________________________________________________________________________________________________
Embedding-Segment (Embedding) (None, None, 768) 1536 Input-Segment[0][0]
position embedding：
max length * embedding size = 512*768=393216
__________________________________________________________________________________________________
Embedding-Position (PositionEmb (None, None, 768) 393216 Embedding-Token-Segment[0][0]
_
然后Bert在embedding部分还有一个layer Normalization，因此还要有768*2个参数（ α 和 β ）
__________________________________________________________________________________________________
Embedding-Norm (LayerNormalizat (None, None, 768) 1536 Embedding-Position[0][0]
搞懂了embedding的参数，下面就是Transformer的参数，为了简便，这里只介绍一层。
首先是多头注意力：
bert base使用了12头注意力机制，QKV维度为64维度，同时最后还需要一个O矩阵，将12头注意力结合。
因此总参数就是： embedding size* head nub * qkv size * len(qkv)[三个映射矩阵] + （head nub* qkv size）* embedding size[多头结果拼接后处理] + qkvo bias= 768*12*64*3 + 12*64*768+ 768*4 =2362368
（这里最后的768*4 分别为Q矩阵、K矩阵、V矩阵的偏置以及最后的O矩阵的偏置。）
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 2362368 Embedding-Dropout[0][0]
Embedding-Dropout[0][0]
Embedding-Dropout[0][0]
随后是多头注意力机制的LN：768*2=1536
__________________________________________________________________________________________________
Transformer-0-MultiHeadSelfAtte (None, None, 768) 1536 Transformer-0-MultiHeadSelfAttent
随后，是全连接层：
Bert中隐藏层个数采用了传统的4*input的大小，因此为4*768=3072。
因此，这部分参数为：embedding size * hidden size + bias + hidden size * embedding size + bias = 768*3072+3072+3072*768+768=4722432
__________________________________________________________________________________________________
Transformer-0-FeedForward (Feed (None, None, 768) 4722432 Transformer-0-MultiHeadSelfAttent
然后是LN：768*2=1536
__________________________________________________________________________________________________
Transformer-0-FeedForward-Norm (None, None, 768) 1536 Transformer-0-FeedForward-Add[0][
然后就是下一层Transformer 以此类推。
bert的Conditional Layer Normalization：
使用了Conditional Layer Normalization后，bert的LayerNormalizattion变为198144个。
__________________________________________________________________________________________________
Embedding-Norm (LayerNormalizat (None, None, 768) 198144 Embedding-Position[0][0]
reshape[0][0]
由于 β、γ 没有任何变化，还是1536个参数，我们可以分析一下这多出来的196608是从哪来的。
由于我们需要对 β、γ 进行相同的变换，因此参数个数也是相同的，我们分析的参数格式可以进一步缩小为98304个。
而我们之前提到，需要将c的128维度升到768维，如果不考虑bias偏置，只做矩阵变换（没有偏置的单层神经网络实际上就是矩阵变换），恰好是768*128=98304个参数。
详见 Conditional Layer Normalization。
bert的mlm任务：
__________________________________________________________________________________________________
Transformer-11-FeedForward-Norm (None, None, 768) 1536 Transformer-11-FeedForward-Add[0]
__________________________________________________________________________________________________
MLM-Dense (Dense) (None, None, 768) 590592 Transformer-11-FeedForward-Norm[0
__________________________________________________________________________________________________
MLM-Norm (LayerNormalization) (None, None, 768) 1536 MLM-Dense[0][0]
__________________________________________________________________________________________________
MLM-Bias (BiasAdd) (None, None, 21128) 21128 Embedding-Token[1][0]
__________________________________________________________________________________________________
MLM-Activation (Activation) (None, None, 21128) 0 MLM-Bias[0][0]
__________________________________________________________________________________________________
cross_entropy (CrossEntropy) (None, None, 21128) 0 Input-Token[0][0]
MLM-Activation[0][0]
==================================================================================================
这里主要多了MLM-Dense、MLM-Norm 、MLM-Bias几处。
MLM-Dense为768*768+768=590592。
其他两个就没啥可说的了。
bert的mlm任务实现方式