TensorFlow: import collections
import math
import tensorflow as tf
from d2l import tensorflow as d2l
class Seq2SeqEncoder(d2l.Encoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super().__init__(*kwargs)
        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)
    def call(self, X, *args, **kwargs):
        X = self.embedding(X)
        output = self.rnn(X, **kwargs)
        state = output[1:]
        return output[0], state
encoder = Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
X = tf.zeros((4, 7))
output, state = encoder(X, training=False)
output.shape
len(state), [element.shape for element in state]
class Seq2SeqDecoder(d2l.Decoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super().__init__(**kwargs)
        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, *args):
        return enc_outputs[1]
    def call(self, X, state, **kwargs):
        X = self.embedding(X)
        context = tf.repeat(tf.expand_dims(state[-1], axis=1), repeats=X.shape[1], axis=1)
        X_and_context = tf.concat((X, context), axis=2)
        rnn_output = self.rnn(X_and_context, state, **kwargs)
        output = self.dense(rnn_output[0])
        return output, rnn_output[1:]
decoder = Seq2SeqDecoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
state = decoder.init_state(encoder(X))
output, state = decoder(X, state, training=False)
output.shape, len(state), state[0].shape
def sequence_mask(X, valid_len, value=0):
    maxlen = X.shape[1]
    mask = tf.range(start=0, limit=maxlen, dtype=tf.float32)[None, :] < tf.cast(valid_len[:, None], dtype=tf.float32)
    if len(X.shape) == 3:
        return tf.where(tf.expand_dims(mask, axis=-1), X, value)
    else:
        return tf.where(mask, X, value)
X = tf.constant([[1, 2, 3], [4, 5, 6]])
sequence_mask(X, tf.constant([1, 2]))
X = tf.ones((2,3,4))
sequence_mask(X, tf.constant([1, 2]), value=-1)
class MaskedSoftmaxCELoss(tf.keras.losses.Loss):
    def __init__(self, valid_len):
        super().__init__(reduction='none')
        self.valid_len = valid_len

    def call(self, label, pred):
        weights = tf.ones_like(label, dtype=tf.float32)
        weights = sequence_mask(weights, self.valid_len)
        label_one_hot = tf.one_hot(label, depth=pred.shape[-1])
        unweighted_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True, reduction='none')(label_one_hot, pred)
        weighted_loss = tf.reduce_mean((unweighted_loss*weights), axis=1)
        return weighted_loss
loss = MaskedSoftmaxCELoss(tf.constant([4, 2, 0]))
loss(tf.ones((3,4), dtype = tf.int32), tf.ones((3, 4, 10))).numpy()
def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device):
    optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
    animator = d2l.Animator(xlabel="epoch", ylabel="loss", xlim=[10, num_epochs])
    for epoch in range(num_epochs):
        timer = d2l.Timer()
        metric = d2l.Accumulator(2)
        for batch in data_iter:
            X, X_valid_len, Y, Y_valid_len = [x for x in batch]
            bos = tf.reshape(tf.constant([tgt_vocab['<bos>']] * Y.shape[0]), shape=(-1, 1))
            dec_input = tf.concat([bos, Y[:, :-1]], 1)
            with tf.GradientTape() as tape:
                Y_hat, _ = net(X, dec_input, X_valid_len, training=True)
                l = MaskedSoftmaxCELoss(Y_valid_len)(Y, Y_hat)
            gradients = tape.gradient(l, net.trainable_variables)
            gradients = d2l.grad_clipping(gradients, 1)
            optimizer.apply_gradients(zip(gradients, net.trainable_variables))
            num_tokens = tf.reduce_sum(Y_valid_len).numpy()
            metric.add(tf.reduce_sum(l), num_tokens)
        if (epoch + 1) % 10 == 0:
            animator.add(epoch + 1, (metric[0] / metric[1],))
def predict_seq2seq(net, src_sentence, src_vocab, tgt_vocab, num_steps, save_attention_weights=False):
    src_tokens = src_vocab[src_sentence.lower().split(' ')] + [src_vocab['<eos>']]
    enc_valid_len = tf.constant([len(src_tokens)])
    src_tokens = d2l.truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
    enc_X = tf.expand_dims(src_tokens, axis=0)
    enc_outputs = net.encoder(enc_X, enc_valid_len, training=False)
    dec_state = net.decoder.init_state(enc_outputs, enc_valid_len)
    dec_X = tf.expand_dims(tf.constant([tgt_vocab['<bos>']]), axis=0)
    output_seq, attention_weight_seq = [], []
    for _ in range(num_steps):
        Y, dec_state = net.decoder(dec_X, dec_state, training=False)
        dec_X = tf.argmax(Y, axis=2)
        pred = tf.squeeze(dec_X, axis=0)
        if save_attention_weights:
            attention_weight_seq.append(net.decoder.attention_weights)
        if pred == tgt_vocab['<eos>']:
            break
        output_seq.append(pred.numpy())
    return ' '.join(tgt_vocab.to_tokens(tf.reshape(output_seq, shape = -1).numpy().tolist())), attention_weight_seq
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, attention_weight_seq = predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps)
    print(f'{eng} => {translation}, bleu {bleu(translation, fra, k=2):.3f}')

PyTorch: import collections
import math
import torch
from torch import nn
from d2l import torch as d2l
class Seq2SeqEncoder(d2l.Encoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super(Seq2SeqEncoder, self).__init__(**kwargs)
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.GRU(embed_size, num_hiddens, num_layers, dropout=dropout)
    def forward(self, X, *args):
        X = self.embedding(X)
        X = X.permute(1, 0, 2)
        output, state = self.rnn(X)
        return output, state
encoder = Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
encoder.eval()
X = torch.zeros((4, 7), dtype=torch.long)
output, state = encoder(X)
output.shape
state.shape
class Seq2SeqDecoder(d2l.Decoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super(Seq2SeqDecoder, self).__init__(**kwargs)
        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, *args):
        return enc_outputs[1]
    def forward(self, X, state):
        X = self.embedding(X).permute(1, 0, 2)
        context = state[-1].repeat(X.shape[0], 1, 1)
        X_and_context = torch.cat((X, context), 2)
        output, state = self.rnn(X_and_context, state)
        output = self.dense(output).permute(1, 0, 2)
        return output, state
decoder = Seq2SeqDecoder(vocab_size=10, embed_size=8, num_hiddens=16, num_layers=2)
decoder.eval()
state = decoder.init_state(encoder(X))
output, state = decoder(X, state)
output.shape, state.shape
def sequence_mask(X, valid_len, value=0):
    maxlen = X.size(1)
    mask = torch.arange((maxlen), dtype=torch.float32, device=X.device)[None, :] < valid_len[:, None]
    X[~mask] = value
    return X
X = torch.tensor([[1, 2, 3], [4, 5, 6]])
sequence_mask(X, torch.tensor([1, 2]))
X = torch.ones(2, 3, 4)
sequence_mask(X, torch.tensor([1, 2]), value=-1)
class MaskedSoftmaxCELoss(nn.CrossEntropyLoss):
    def forward(self, pred, label, valid_len):
        weights = torch.ones_like(label)
        weights = sequence_mask(weights, valid_len)
        self.reduction='none'
        unweighted_loss = super(MaskedSoftmaxCELoss, self).forward(pred.permute(0, 2, 1), label)
        weighted_loss = (unweighted_loss * weights).mean(dim=1)
        return weighted_loss
loss = MaskedSoftmaxCELoss()
loss(torch.ones(3, 4, 10), torch.ones((3, 4), dtype=torch.long), torch.tensor([4, 2, 0]))
def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device):
    def xavier_init_weights(m):
        if type(m) == nn.Linear:
            nn.init.xavier_uniform_(m.weight)
        if type(m) == nn.GRU:
            for param in m._flat_weights_names:
                if "weight" in param:
                    nn.init.xavier_uniform_(m._parameters[param])
    net.apply(xavier_init_weights)
    net.to(device)
    optimizer = torch.optim.Adam(net.parameters(), lr=lr)
    loss = MaskedSoftmaxCELoss()
    net.train()
    animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[10, num_epochs])
    for epoch in range(num_epochs):
        timer = d2l.Timer()
        metric = d2l.Accumulator(2)
        for batch in data_iter:
            optimizer.zero_grad()
            X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch]
            bos = torch.tensor([tgt_vocab['<bos>']] * Y.shape[0], device=device).reshape(-1, 1)
            dec_input = torch.cat([bos, Y[:, :-1]], 1)
            Y_hat, _ = net(X, dec_input, X_valid_len)
            l = loss(Y_hat, Y, Y_valid_len)
            l.sum().backward()
            d2l.grad_clipping(net, 1)
            num_tokens = Y_valid_len.sum()
            optimizer.step()
            with torch.no_grad():
                metric.add(l.sum(), num_tokens)
        if (epoch + 1) % 10 == 0:
            animator.add(epoch + 1, (metric[0] / metric[1],))
def predict_seq2seq(net, src_sentence, src_vocab, tgt_vocab, num_steps, device, save_attention_weights=False):
    net.eval()
    src_tokens = src_vocab[src_sentence.lower().split(' ')] + [src_vocab['<eos>']]
    enc_valid_len = torch.tensor([len(src_tokens)], device=device)
    src_tokens = d2l.truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
    enc_X = torch.unsqueeze(torch.tensor(src_tokens, dtype=torch.long, device=device), dim=0)
    enc_outputs = net.encoder(enc_X, enc_valid_len)
    dec_state = net.decoder.init_state(enc_outputs, enc_valid_len)
    dec_X = torch.unsqueeze(torch.tensor([tgt_vocab['<bos>']], dtype=torch.long, device=device), dim=0)
    output_seq, attention_weight_seq = [], []
    for _ in range(num_steps):
        Y, dec_state = net.decoder(dec_X, dec_state)
        dec_X = Y.argmax(dim=2)
        pred = dec_X.squeeze(dim=0).type(torch.int32).item()
        if save_attention_weights:
            attention_weight_seq.append(net.decoder.attention_weights)
        if pred == tgt_vocab['<eos>']:
            break
        output_seq.append(pred)
    return ' '.join(tgt_vocab.to_tokens(output_seq)), attention_weight_seq
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, attention_weight_seq = predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps, device)
    print(f'{eng} => {translation}, bleu {bleu(translation, fra, k=2):.3f}')