tf的多头自注意力代码

以下是 TensorFlow 中实现多头自注意力机制的代码示例：

import tensorflow as tf

class MultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        
        assert d_model % self.num_heads == 0
        
        self.depth = d_model // self.num_heads
        
        self.query_dense = tf.keras.layers.Dense(units=d_model)
        self.key_dense = tf.keras.layers.Dense(units=d_model)
        self.value_dense = tf.keras.layers.Dense(units=d_model)
        
        self.dense = tf.keras.layers.Dense(units=d_model)
        
    def split_heads(self, inputs, batch_size):
        inputs = tf.reshape(inputs, shape=(batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(inputs, perm=[0, 2, 1, 3])
    
    def call(self, inputs):
        query, key, value, mask = inputs['query'], inputs['key'], inputs['value'], inputs['mask']
        batch_size = tf.shape(query)[0]
        
        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_dense(value)
        
        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)
        
        scaled_attention_logits = tf.matmul(query, key, transpose_b=True)
        scaled_attention_logits /= tf.math.sqrt(tf.cast(self.depth, tf.float32))
        
        if mask is not None:
            scaled_attention_logits += (mask * -1e9)
        
        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
        output = tf.matmul(attention_weights, value)
        output = tf.transpose(output, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(output, shape=(batch_size, -1, self.d_model))
        output = self.dense(concat_attention)
        
        return output

这个自注意力层的输入包括一个字典，包含查询、键、值和遮罩矩阵。在这里，我们使用了一个 Dense 层来对查询、键和值进行线性变换，并将它们拆成多个头。在计算注意力权重时，我们首先将查询和键进行点积，然后除以根号下单个头的深度，最后加上一个可选的遮罩矩阵。接着使用 softmax 函数来计算注意力权重，再将权重与值矩阵相乘，最后将多头的输出拼接起来并通过一个全连接层进行线性变换。