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
attention_weights = torch.cat([step[0][0][0] for step in dec_attention_weight_seq], 0).reshape((
    1, 1, -1, num_steps))


Paddle: import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
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, bias_ih_attr=True, time_major=True, 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.transpose((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).transpose((1, 0, 2))
        outputs, self._attention_weights = [], []
        for x in X:
            query = paddle.unsqueeze(hidden_state[-1], axis=1)
            context = self.attention(query, enc_outputs, enc_outputs, enc_valid_lens)
            x = paddle.concat((context, paddle.unsqueeze(x, axis=1)), axis=-1)
            out, hidden_state = self.rnn(x.transpose((1, 0, 2)), hidden_state)
            outputs.append(out)
            self._attention_weights.append(self.attention.attention_weights)
        outputs = self.dense(paddle.concat(outputs, axis=0))
        return outputs.transpose((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 = paddle.zeros((4, 7), dtype='int64')
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
attention_weights = paddle.concat([step[0][0][0] for step in dec_attention_weight_seq], 0).reshape(
    (1, 1, -1, num_steps))