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_headsself.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_headsself.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valueoutput = 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)