text: #!/usr/bin/env python
  #-*- coding:utf-8 -*-
  # author: Zhen YANG
  # created at:17/11/2020 4:19 PM
  # contact: zhyang8-c@my.cityu.edu.hk
  
  import tensorflow as tf
  from modules.TransformerUtils import TransformerUtils as utils
  
  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.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):
          """分拆最后一个维度到 (num_heads, depth).
          转置结果使得形状为 (batch_size, num_heads, seq_len, depth)
          """
          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]
          q = self.wq(q)  # (batch_size, seq_len, d_model)
          k = self.wk(k)  # (batch_size, seq_len, d_model)
          v = self.wv(v)  # (batch_size, seq_len, d_model)
  
          q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)
          k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)
          v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)
  
          # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)
          # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
  
          scaled_attention, attention_weights = utils.scaled_dot_product_attention(q, k, v, mask)
  
          scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)
  
          concat_attention = tf.reshape(scaled_attention,
                                        (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)
  
          output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)
  
          return output, attention_weights
  
  if __name__ =="__main__":
      gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
      for gpu in gpus:
          tf.config.experimental.set_memory_growth(gpu, True)
      temp_mha = MultiHeadAttention(d_model=256, num_heads=8)
      y = tf.random.uniform((1, 60, 512))  # (batch_size, encoder_sequence, d_model)
      out, attn = temp_mha(y, k=y, q=y, mask=None)
  