decoder.py

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import pandas as pd
import numpy as np
import tensorflow as tf


import time
import re
import pickle
 
import math

from multiHead import MultiHeadAttention
from utils import point_wise_feed_forward_network, positional_encoding


class DecoderLayer(tf.keras.layers.Layer):
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(DecoderLayer, self).__init__()
        
        #note for first multihead attention, masking is already taken care off inside the class
        self.mha1 = MultiHeadAttention(d_model, num_heads)
        self.mha2 = MultiHeadAttention(d_model, num_heads)
        
        self.ffn = point_wise_feed_forward_network(d_model, dff)
        
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)        
        self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)        
        
        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)
        self.dropout3 = tf.keras.layers.Dropout(rate)
        
    def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
        attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask)
        attn1 = self.dropout1(attn1, training=training)
        out1 = self.layernorm1(attn1 + x)
        
        attn2, attn_weights_block2 = self.mha2(enc_output, enc_output, out1, padding_mask)
        attn2 = self.dropout2(attn2, training=training)
        out2 = self.layernorm2(attn2 + out1)
        
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout3(ffn_output, training=training)
        
        out3 = self.layernorm3(ffn_output + out2)
        
        return out3, attn_weights_block1, attn_weights_block2

#consisting of multiple (N_x) DecoderLayers, alongside with input embeddings
class Decoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size, maximum_position_encoding, rate=0.1):
        super(Decoder, self).__init__()
        
        self.d_model = d_model
        self.num_layers = num_layers
        
        self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)
        
        self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]
        self.dropout = tf.keras.layers.Dropout(rate)
        
    def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
        seq_len = tf.shape(x)[1]
        attention_weights = {}
        
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        
        x = self.dropout(x, training=training)
        
        for i in range(self.num_layers):
            x, block1, block2 = self.dec_layers[i](x, enc_output, training, look_ahead_mask, padding_mask)
            
            attention_weights['decoder_layer{}_block1'.format(i+1)] = block1
            attention_weights['decoder_layer{}_block2'.format(i+1)] = block2
            
        return x, attention_weights