用tensorflow实现一个self-attention

这里提供一个使用 TensorFlow 实现的自注意力机制（self-attention）的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Layer

class SelfAttention(Layer):
    def __init__(self, hidden_size, num_heads, **kwargs):
        super(SelfAttention, self).__init__(**kwargs)
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_size = hidden_size // num_heads

        self.query = tf.keras.layers.Dense(hidden_size, use_bias=False)
        self.key = tf.keras.layers.Dense(hidden_size, use_bias=False)
        self.value = tf.keras.layers.Dense(hidden_size, use_bias=False)

        self.softmax = tf.keras.layers.Softmax(axis=-1)

    def call(self, x):
        batch_size, seq_len, hidden_size = tf.shape(x)[0], tf.shape(x)[1], tf.shape(x)[2]
        residual = x

        # Project inputs to Q, K, V
        q = self.query(x)  # (batch_size, seq_len, hidden_size)
        k = self.key(x)  # (batch_size, seq_len, hidden_size)
        v = self.value(x)  # (batch_size, seq_len, hidden_size)

        # Reshape Q, K, V for multi-head attention
        q = tf.reshape(q, (batch_size, seq_len, self.num_heads, self.head_size))
        k = tf.reshape(k, (batch_size, seq_len, self.num_heads, self.head_size))
        v = tf.reshape(v, (batch_size, seq_len, self.num_heads, self.head_size))
        q = tf.transpose(q, perm=[0, 2, 1, 3])
        k = tf.transpose(k, perm=[0, 2, 1, 3])
        v = tf.transpose(v, perm=[0, 2, 1, 3])

        # Compute dot product attention
        attention_scores = tf.matmul(q, k, transpose_b=True) / tf.math.sqrt(tf.cast(self.head_size, tf.float32))  # (batch_size, num_heads, seq_len, seq_len)
        attention_probs = self.softmax(attention_scores)

        # Apply attention weights to V
        attention_output = tf.matmul(attention_probs, v)  # (batch_size, num_heads, seq_len, head_size)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
        attention_output = tf.reshape(attention_output, (batch_size, seq_len, self.hidden_size))

        # Add residual connection
        output = attention_output + residual

        return output

这个自注意力机制的实现与 PyTorch 中的实现类似，也包含了查询（query）、键（key）和值（value）三个线性变换，以及一个 Softmax 操作。不同之处在于 TensorFlow 中需要明确指定多头注意力的具体维度变换方式，同时也需要注意一些维度的 reshape 和 transpose 操作。