TensorFlow: import tensorflow as tf
from d2l import tensorflow as d2l
class Seq2SeqAttentionDecoder(AttentionDecoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super().__init__(**kwargs)
        self.attention = d2l.AdditiveAttention(num_hiddens, num_hiddens, num_hiddens, dropout)
        self.embedding = tf.keras.layers.Embedding(vocab_size, embed_size)
        self.rnn = tf.keras.layers.RNN(tf.keras.layers.StackedRNNCells([tf.keras.layers.GRUCell(num_hiddens, dropout=dropout) for _ in range(num_layers)]), return_sequences=True, return_state=True)
        self.dense = tf.keras.layers.Dense(vocab_size)
    def init_state(self, enc_outputs, enc_valid_lens, *args):
        outputs, hidden_state = enc_outputs
        return (outputs, hidden_state, enc_valid_lens)
    def call(self, X, state, **kwargs):
        enc_outputs, hidden_state, enc_valid_lens = state
        X = self.embedding(X)
        X = tf.transpose(X, perm=(1, 0, 2))
        outputs, self._attention_weights = [], []
        for x in X:
            query = tf.expand_dims(hidden_state[-1], axis=1)
            context = self.attention(query, enc_outputs, enc_outputs, enc_valid_lens, **kwargs)
            x = tf.concat((context, tf.expand_dims(x, axis=1)), axis=-1)
            out = self.rnn(x, hidden_state, **kwargs)
            hidden_state = out[1:]
            outputs.append(out[0])
            self._attention_weights.append(self.attention.attention_weights)
        outputs = self.dense(tf.concat(outputs, axis=1))
        return outputs, [enc_outputs, hidden_state, enc_valid_lens]
    @property
    def attention_weights(self):
        return self._attention_weights
encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
X = tf.zeros((4, 7))
state = decoder.init_state(encoder(X, training=False), None)
output, state = decoder(X, state, training=False)
output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape
engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .']
fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .']
for eng, fra in zip(engs, fras):
    translation, dec_attention_weight_seq = d2l.predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps, True)
    print(f'{eng} => {translation}, ', f'bleu {d2l.bleu(translation, fra, k=2):.3f}')
attention_weights = tf.reshape(tf.concat([step[0][0][0] for step in dec_attention_weight_seq], 0), (1, 1, -1, num_steps))
d2l.show_heatmaps(attention_weights[:, :, :, :len(engs[-1].split()) + 1], xlabel='Key posistions', ylabel='Query posistions')

PyTorch: import torch
from torch import nn
from d2l import torch as d2l
class Seq2SeqAttentionDecoder(AttentionDecoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)
        self.attention = d2l.AdditiveAttention(num_hiddens, num_hiddens, num_hiddens, dropout)
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.GRU(embed_size + num_hiddens, num_hiddens, num_layers, dropout=dropout)
        self.dense = nn.Linear(num_hiddens, vocab_size)
    def init_state(self, enc_outputs, enc_valid_lens, *args):
        outputs, hidden_state = enc_outputs
        return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)
    def forward(self, X, state):
        enc_outputs, hidden_state, enc_valid_lens = state
        X = self.embedding(X).permute(1, 0, 2)
        outputs, self._attention_weights = [], []
        for x in X:
            query = torch.unsqueeze(hidden_state[-1], dim=1)
            context = self.attention(query, enc_outputs, enc_outputs, enc_valid_lens)
            x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)
            out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)
            outputs.append(out)
            self._attention_weights.append(self.attention.attention_weights)
        outputs = self.dense(torch.cat(outputs, dim=0))
        return outputs.permute(1, 0, 2), [enc_outputs, hidden_state, enc_valid_lens]
    @property
    def attention_weights(self):
        return self._attention_weights
encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
encoder.eval()
decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
decoder.eval()
X = torch.zeros((4, 7), dtype=torch.long)
state = decoder.init_state(encoder(X), None)
output, state = decoder(X, state)
output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape
engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .']
fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .']
for eng, fra in zip(engs, fras):
    translation, dec_attention_weight_seq = d2l.predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps, device, True)
    print(f'{eng} => {translation}, ', f'bleu {d2l.bleu(translation, fra, k=2):.3f}')
attention_weights = torch.cat([step[0][0][0] for step in dec_attention_weight_seq], 0).reshape((
    1, 1, -1, num_steps))
d2l.show_heatmaps(attention_weights[:, :, :, :len(engs[-1].split()) + 1].cpu(), xlabel='Key positions', ylabel='Query positions')