scale_dot_product_attention and multi_head_attention tf2.x

Transformer用到了两个attention模块：一个模块被用于encoder，一个模块位于decoder。encoder中的attention叫做self-attention，此时QKV分别为这个模块的输入（第一层为词嵌入，第二层及以后为上一次层的输出）分别乘上三个矩阵得到的结果分别为QKV，这三个矩阵是在训练的时候学习。decoder中的attention叫做encoder-decoder attention，这个attention的KV来自encoder的最后一层输出，继续乘以不同的矩阵。至于Q就是decoder上一层的输出乘以一个矩阵。

import matplotlib as mpl
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)


# 缩放点积注意力
def scaled_dot_product_attention(q ,k ,v ,mask):
    '''
    Args:
    -q : shape==(...,seq_len_q,depth)
    -k : shape==(...,seq_len_k,depth)
    -v : shape==(...,seq_len_v,depth_v)
    - seq_len_k = seq_len_v
    - mask: shape == (...,seq_len_q,seq_len_k) 点积
    return:
    output:weighted sum
    attention_weights:weights of attention
    '''
    # shape == (...,seq_len_q,seq_len_k)
    # embedding 向量算法内积
    matmul_qk =tf.matmul(q, k, transpose_b=True)
    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    if mask is not None:
        # 10的负九次方比较大，会使得需要掩盖的数据在softmax的时候趋近0
        scaled_attention_logits += (mask * -1e9)
    # shape == (...,seq_len_q,seq_len_k)
    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
    # shape==(...,seq_len_q,depth_v)
    output = tf.matmul(attention_weights, v)
    return output, attention_weights


def print_scaled_dot_attention(q, k, v):
    temp_out, temp_att = scaled_dot_product_attention(q, k, v, None)
    print("Attention weights are:")
    print(temp_att)
    print("Outputs are:")
    print(temp_out)

![在这里插入图片描述](https://img-blog.csdnimg.cn/20210511203439170.jpg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dpdGh1Yl8zOTYwNTI4NA==,size_16,color_FFFFFF,t_70#pic_center)



# 测试代码
# self attention attention
np.set_printoptions(suppress=True)  # 使得小数结果压缩


# 多头注意力机制的实现
#     '''
#     理论上
#     x->Wq0->q0
#     x->Wk0->k0
#     x->Wv0->v0
#     实战中
#     q->Wq0->q0
#     k->Wk0->k0
#     v->Wv0->v0
#     技巧
#     q->Wq->Q->split->q0,q1,...
#     '''
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
        # 三个神经网络，对同一输入进行三次不同变换，生成了Q，K，V 
        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 = 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


temp_mha = MultiHeadAttention(d_model=512, 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)
out.shape, attn.shape

深度学习attention机制中的Q,K,V分别是从哪来的？
各种版本的解释：

q：query代表的是当前单词
k：key代表的是每个单词
v: value代表的也是当前单词