attention终极版本multhead-self-attention，代码实现（直接复制粘贴，可以直接运行）_tf.keras.layers.multiheadattention 怎么用

multhead-self-attention终于实现了，下面附上可以直接运行的代码。

# 构造mutil head attention层
class MutilHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MutilHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        
        # d_model 必须可以正确分为各个头
        assert d_model % num_heads == 0
        # 分头后的维度
        self.depth = d_model // num_heads
        
        self.wq = tf.keras.layers.Dense(d_model)
        self.wk = tf.keras.layers.Dense(d_model)
        self.wv = tf.keras.layers.Dense(d_model)
        
        self.dense = tf.keras.layers.Dense(d_model)
        
    def split_heads(self, x, batch_size):
        # 分头, 将头个数的维度 放到 seq_len 前面
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])
    
    def call(self, v, k, q, mask):
        batch_size = tf.shape(q)[0]
        
        # 分头前的前向网络，获取q、k、v语义
        q = self.wq(q)  # (batch_size, seq_len, d_model)
        k = self.wk(k)
        v = self.wv(v)
        
        # 分头
        q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)
        k = self.split_heads(k, batch_size)
        v = self.split_heads(v, batch_size)
        # scaled_attention.shape == (batch_size, num_heads, seq_len_v, depth)
        # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
        
        # 通过缩放点积注意力层
        scaled_attention, attention_weights = scaled_dot_product_attention(
        q, k, v, mask)
        # 把多头维度后移
        scaled_attention = tf.transpose(scaled_attention, [0, 2, 1, 3]) # (batch_size, seq_len_v, num_heads, depth)
 
        # 合并多头
        concat_attention = tf.reshape(scaled_attention, 
                                      (batch_size, -1, self.d_model))
        
        # 全连接重塑
        output = self.dense(concat_attention)
        return output, attention_weights
 
        
        
        

然后是这个类里面用到的一个函数

def scaled_dot_product_attention(q, k, v, mask):
    # query key 相乘获取匹配关系
    matmul_qk = tf.matmul(q, k, transpose_b=True)
    
    # 使用dk进行缩放
    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    
    # 掩码
    if mask is not None:
        scaled_attention_logits += (mask * -1e9)
        
    # 通过softmax获取attention权重
    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
    
    # attention 乘上value
    output = tf.matmul(attention_weights, v) # （.., seq_len_v, depth）
    
    return output, attention_weights
    

然后是一个调用函数（这个是我自己写的，上面的类和函数都是tensorflow官网的，大家按照自己需求改造）

def attention(y):
    temp_mha = MutilHeadAttention(d_model=300, num_heads=6)
    output, att = temp_mha(y, k=y, q=y, mask=None)
    print(output.shape, att.shape)
y = tf.random.uniform((20, 80, 300))
attention(y)