transformer t5代码解读4(主要内容bert4keras实现t5模型)

作者：Cpp五条 | 2024-04-01 10:38:12

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t5代码解读

继续解读t5代码之中源码的内容

回到t5的整体结构之中

回到t5模型的整体的结构之中

(0): T5LayerSelfAttention(
            (SelfAttention): T5Attention(
              (q): Linear(in_features=512, out_features=512, bias=False)
              (k): Linear(in_features=512, out_features=512, bias=False)
              (v): Linear(in_features=512, out_features=512, bias=False)
              (o): Linear(in_features=512, out_features=512, bias=False)
              (relative_attention_bias): Embedding(32, 8)
            )
            (layer_norm): T5LayerNorm()
            (dropout): Dropout(p=0.1, inplace=False)
          )
          (1): T5LayerFF(
            (DenseReluDense): T5DenseReluDense(
              (wi): Linear(in_features=512, out_features=2048, bias=False)
              (wo): Linear(in_features=2048, out_features=512, bias=False)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (layer_norm): T5LayerNorm()
            (dropout): Dropout(p=0.1, inplace=False)
          )
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这里我们接着查看一下T5DenseReluDense模型的结构特点

class T5DenseReluDense(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, hidden_states):
        hidden_states = self.wi(hidden_states)
        hidden_states = nn.functional.relu(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.wo(hidden_states)
        return hidden_states
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这里内容的参数之中，

config.d_model = 512
config.d_ff = 2048
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这里还需要注意一下T5LayerNorm网络层结构的实现
先copy一下T5LayerNorm整个部分的源代码

class T5LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Construct a layernorm module in the T5 style No bias and no subtraction of mean.
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        # layer norm should always be calculated in float32
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into float16 if necessary
        if self.weight.dtype == torch.float16:
            hidden_states = hidden_states.to(torch.float16)
        return self.weight * hidden_states
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常规的layernormalization的计算公式为
$\frac{x-\mu}{\sqrt{Var(x)+\epsilon}}$
这里的 $\mu$ 为期望， $\epsilon$ 为方差
而新更新的layernormalization的计算公式为
$weight*\frac{x}{\sqrt{Var(x)+\epsilon}}$
这里对应的weight参数初始化的时候全为1

self.weight = nn.Parameter(torch.ones(hidden_size))
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比如对应的一个tensor的内容

tensor = torch.FloatTensor([[1,2,4,1],
                            [6,3,2,4],
                            [2,4,6,1]])
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这里对最后一个维度的数值计算期望的方差的内容

E(x) = [2.0,3.75,3.25]
V(x) = [1.5,2.18,3.68]
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然后对于当中的每个数值，计算
$\frac{x-\mu}{\sqrt{Var(x)+\epsilon}}$
这里就是改成
$weight*\frac{x}{\sqrt{Var(x)+\epsilon}}$

hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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这里的x去除掉了减去 $\mu$ 期望的内容，最后一波直接乘上self.weight初始化为1的参数
另外需要注意的一点是，T5LayerFF调用了残差连接的内容

class T5LayerFF(nn.Module):
	......
	def forward(self,hidden_states):
		forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states
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T5CrossAttention网络层结构的调用

这里我们再返回查看一下T5decoder部分模型的整个的内容

(0): T5LayerSelfAttention(
  (SelfAttention): T5Attention(
    (q): Linear(in_features=512, out_features=512, bias=False)
    (k): Linear(in_features=512, out_features=512, bias=False)
    (v): Linear(in_features=512, out_features=512, bias=False)
    (o): Linear(in_features=512, out_features=512, bias=False)
    (relative_attention_bias): Embedding(32, 8)
  )
  (layer_norm): T5LayerNorm()
  (dropout): Dropout(p=0.1, inplace=False)
)
(1): T5LayerCrossAttention(
  (EncDecAttention): T5Attention(
    (q): Linear(in_features=512, out_features=512, bias=False)
    (k): Linear(in_features=512, out_features=512, bias=False)
    (v): Linear(in_features=512, out_features=512, bias=False)
    (o): Linear(in_features=512, out_features=512, bias=False)
  )
  (layer_norm): T5LayerNorm()
  (dropout): Dropout(p=0.1, inplace=False)
)
(2): T5LayerFF(
  (DenseReluDense): T5DenseReluDense(
    (wi): Linear(in_features=512, out_features=2048, bias=False)
    (wo): Linear(in_features=2048, out_features=512, bias=False)
    (dropout): Dropout(p=0.1, inplace=False)
  )
  (layer_norm): T5LayerNorm()
  (dropout): Dropout(p=0.1, inplace=False)
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首先放出T5LayerSelfAttention和T5LayerCrossAttention网络层结构的内容，这里目前暂时没有看出来有什么差异之处
(从上面来看，t5多了一个relative_attention_bias)
查看一下t5的Decoder部分，能看出来也是多了一个a_bias的内容

t5的Encoder和Decoder内容比对

t5的Encoder部分内容

x = self.apply(
    inputs=[x, x, x, position_bias],
    layer=MultiHeadAttention,
    arguments={'p_bias': 't5_relative'},
    heads=self.num_attention_heads,
    head_size=self.attention_head_size,
    out_dim=self.hidden_size,
    key_size=self.attention_key_size,
    use_bias=False,
    attention_scale=False,
    kernel_initializer=self.initializer,
    name=attention_name
)
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t5的Decoder部分的内容

x = self.apply(
    inputs=[x, x, x, attention_mask, position_bias[0]],
    layer=MultiHeadAttention,
    arguments={
        'a_bias': True,
        'p_bias': 't5_relative'
    },
    heads=self.num_attention_heads,
    head_size=self.attention_head_size,
    out_dim=self.hidden_size,
    key_size=self.attention_key_size,
    use_bias=False,
    attention_scale=False,
    kernel_initializer=self.initializer,
    name=self_attention_name
)
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可以看出来上面的t5decoder部分多了一个a_bias的内容

class T5LayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        position_bias=None,
        layer_head_mask=None,
        past_key_value=None,
        use_cache=False,
        output_attentions=False,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.SelfAttention(
            normed_hidden_states,
            mask=attention_mask,
            position_bias=position_bias,
            layer_head_mask=layer_head_mask,
            past_key_value=past_key_value,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
        return outputs


class T5LayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.EncDecAttention = T5Attention(config, has_relative_attention_bias=False)
        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(
        self,
        hidden_states,
        key_value_states,
        attention_mask=None,
        position_bias=None,
        layer_head_mask=None,
        past_key_value=None,
        use_cache=False,
        query_length=None,
        output_attentions=False,
    ):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.EncDecAttention(
            normed_hidden_states,
            mask=attention_mask,
            key_value_states=key_value_states,
            position_bias=position_bias,
            layer_head_mask=layer_head_mask,
            past_key_value=past_key_value,
            use_cache=use_cache,
            query_length=query_length,
            output_attentions=output_attentions,
        )
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
        return outputs
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bert4keras调用t5模型之中的encoder部分和decoder部分

这里先查看一下bert4keras中t5模型encoder与decoder部分的调用
encoder部分调用attention

x = self.apply(
    inputs=[x, x, x, position_bias],
    layer=MultiHeadAttention,
    arguments={'p_bias': 't5_relative'},
    heads=self.num_attention_heads,
    head_size=self.attention_head_size,
    out_dim=self.hidden_size,
    key_size=self.attention_key_size,
    use_bias=False,
    attention_scale=False,
    kernel_initializer=self.initializer,
    name=attention_name
)
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decoder部分调用attention(分为两部分：内容1、内容2)

x = self.apply(
    inputs=[x, x, x, attention_mask, position_bias[0]],
    layer=MultiHeadAttention,
    arguments={
        'a_bias': True,
        'p_bias': 't5_relative'
    },
    heads=self.num_attention_heads,
    head_size=self.attention_head_size,
    out_dim=self.hidden_size,
    key_size=self.attention_key_size,
    use_bias=False,
    attention_scale=False,
    kernel_initializer=self.initializer,
    name=self_attention_name
)
............
............
x = self.apply(
inputs=[x, c, c, position_bias[1]],
layer=MultiHeadAttention,
arguments={
    'a_bias': None,
    'p_bias': 't5_relative'
},
heads=self.num_attention_heads,
head_size=self.attention_head_size,
out_dim=self.hidden_size,
key_size=self.attention_key_size,
use_bias=False,
attention_scale=False,
kernel_initializer=self.initializer,
name=cross_attention_name
)
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从苏神的代码来看，上面的decoder部分的后一部分和encoder的中的attention网络结构内容相同
(transformer中的代码感觉全都相似。)
另外一个区别就是transformer每次encoder开始和decoder开始都会调用一个embedding网络层

(0): T5LayerSelfAttention(
            (SelfAttention): T5Attention(
              (q): Linear(in_features=512, out_features=512, bias=False)
              (k): Linear(in_features=512, out_features=512, bias=False)
              (v): Linear(in_features=512, out_features=512, bias=False)
              (o): Linear(in_features=512, out_features=512, bias=False)
              (relative_attention_bias): Embedding(32, 8)
            )
            (layer_norm): T5LayerNorm()
            (dropout): Dropout(p=0.1, inplace=False)
          )
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将T5LayerCrossAttention部分改为T5LayerSelfAttention部分，发现了报错，终于看出来了两者的不同之处
仔细查看transformer之中T5LayerCrossAttention网络层和T5LayerSelfAttention网络层的不同之处

class T5LayerSelfAttention(nn.Module):
	......
   def forward(
      self,
      hidden_states,
      attention_mask=None,
      position_bias=None,
      layer_head_mask=None,
      past_key_value=None,
      use_cache=False,
      output_attentions=False,
  ):
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class T5LayerCrossAttention(nn.Module):
	......
   def forward(
      self,
      hidden_states,
      key_value_states,
      attention_mask=None,
      position_bias=None,
      layer_head_mask=None,
      past_key_value=None,
      use_cache=False,
      query_length=None,
      output_attentions=False,
  ):
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发现T5LayerCrossAttention比T5LayerSelfAttention多了两个参数key_value_states以及query_length
其他的区域部分保持一致
回到bert4keras中的模型
发现

x = self.apply(
    inputs=[x, x, x, attention_mask, position_bias[0]],
    layer=MultiHeadAttention,
    arguments={
        'a_bias': True,
        'p_bias': 't5_relative'
    },
    heads=self.num_attention_heads,
    head_size=self.attention_head_size,
    out_dim=self.hidden_size,
    key_size=self.attention_key_size,
    use_bias=False,
    attention_scale=False,
    kernel_initializer=self.initializer,
    name=self_attention_name
)
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这里的a_bias本质上就是下三角的mask内容

class LM_Mask(object):
    """定义下三角Attention Mask（语言模型用）
    """
    def compute_attention_bias(self, inputs=None):
        """通过idxs序列的比较来得到对应的mask
        """
        if self.attention_bias is None:

            def lm_mask(s):
                seq_len = K.shape(s)[1]
                idxs = K.arange(0, seq_len)
                mask = idxs[None, :] <= idxs[:, None]
                mask = K.cast(mask, K.floatx())
                return -(1 - mask[None, None]) * 1e12

            self.attention_bias = self.apply(
                inputs=self.inputs[0],
                layer=Lambda,
                function=lm_mask,
                name='Attention-LM-Mask'
            )

        return self.attention_bias
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如果a_bias == True的情况下，这里先加上LM_Mask(下三角掩码)，再加上相对位置编码，否则直接加上相对位置编码

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