attention.py

import tensorflow as tf


def scaled_dot_product_attention(query, key, value, mask):
    matmul_qk = tf.matmul(query, key, transpose_b=True)

    dk = tf.cast(tf.shape(key)[-1], tf.float32)
    scores = matmul_qk / tf.math.sqrt(dk)

    if mask is not None:
        scores += (mask * -1e9)

    attention_weights = tf.nn.softmax(scores, axis=-1)

    return tf.matmul(attention_weights, value), attention_weights


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

        # assume d_v always equals d_k
        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):
        """Split the last dimension into (num_heads, dk).
        Transpose the result's shape to (batch_size, num_heads, seq_len, dk)
        """
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, query, key, value, mask):
        batch_size = tf.shape(query)[0]

        query = self.wq(query)
        key = self.wk(key)
        value = self.wv(value)

        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        scaled_attention, attention_weights = scaled_dot_product_attention(query, key, value, mask)

        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])

        concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))

        output = self.dense(concat_attention)

        return output, attention_weights