Seq2Seq.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''
@Project :Awesome-DL-Models 
@File    :Seq2Seq.py
@Author  :JackHCC
@Date    :2022/3/14 19:37 
@Desc    :

'''
import torch
from torch import nn
import math
import collections

from Encoder_Decoder import Encoder, Decoder, EncoderDecoder
from _utils import Timer, Accumulator, grad_clipping


class Seq2SeqEncoder(Encoder):
    """用于序列到序列学习的循环神经网络编码器"""

    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):
        super(Seq2SeqEncoder, self).__init__(**kwargs)
        # 嵌入层
        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):
        # 输出'X'的形状：(batch_size,num_steps,embed_size)
        X = self.embedding(X)
        # 在循环神经网络模型中，第一个轴对应于时间步
        X = X.permute(1, 0, 2)
        # 如果未提及状态，则默认为0
        output, state = self.rnn(X)
        # output的形状:(num_steps,batch_size,num_hiddens)
        # state[0]的形状:(num_layers,batch_size,num_hiddens)
        return output, state


class Seq2SeqDecoder(Decoder):
    """用于序列到序列学习的循环神经网络解码器"""

    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):
        # 输出'X'的形状：(batch_size,num_steps,embed_size)
        X = self.embedding(X).permute(1, 0, 2)
        # 广播context，使其具有与X相同的num_steps
        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)
        # output的形状:(batch_size,num_steps,vocab_size)
        # state[0]的形状:(num_layers,batch_size,num_hiddens)
        return output, state


def sequence_mask(X, valid_len, value=0):
    """在序列中屏蔽不相关的项"""
    maxlen = X.size(1)
    mask = torch.arange((maxlen), dtype=torch.float32,
                        device=X.device)[None, :] < valid_len[:, None]
    X[~mask] = value
    return X


class MaskedSoftmaxCELoss(nn.CrossEntropyLoss):
    """带遮蔽的softmax交叉熵损失函数"""

    # pred的形状：(batch_size,num_steps,vocab_size)
    # label的形状：(batch_size,num_steps)
    # valid_len的形状：(batch_size,)
    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


def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device):
    """训练序列到序列模型"""

    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()
    for epoch in range(num_epochs):
        timer = Timer()
        metric = Accumulator(2)  # 训练损失总和，词元数量
        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)  # 强制教学
            Y_hat, _ = net(X, dec_input, X_valid_len)
            l = loss(Y_hat, Y, Y_valid_len)
            l.sum().backward()  # 损失函数的标量进行“反向传播”
            grad_clipping(net, 1)
            num_tokens = Y_valid_len.sum()
            optimizer.step()
            with torch.no_grad():
                metric.add(l.sum(), num_tokens)
    print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} '
          f'tokens/sec on {str(device)}')


def truncate_pad(line, num_steps, padding_token):
    """Truncate or pad sequences.

    Defined in :numref:`sec_machine_translation`"""
    if len(line) > num_steps:
        return line[:num_steps]  # Truncate
    return line + [padding_token] * (num_steps - len(line))  # Pad


def predict_seq2seq(net, src_sentence, src_vocab, tgt_vocab, num_steps, device, save_attention_weights=False):
    """序列到序列模型的预测"""
    # 在预测时将net设置为评估模式
    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 = truncate_pad(src_tokens, num_steps, src_vocab['<pad>'])
    # 添加批量轴
    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)
    # 添加批量轴
    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)
        # 我们使用具有预测最高可能性的词元，作为解码器在下一时间步的输入
        dec_X = Y.argmax(dim=2)
        pred = dec_X.squeeze(dim=0).type(torch.int32).item()
        # 保存注意力权重（稍后讨论）
        if save_attention_weights:
            attention_weight_seq.append(net.decoder.attention_weights)
        # 一旦序列结束词元被预测，输出序列的生成就完成了
        if pred == tgt_vocab['<eos>']:
            break
        output_seq.append(pred)
    return ' '.join(tgt_vocab.to_tokens(output_seq)), attention_weight_seq


def bleu(pred_seq, label_seq, k):
    """计算BLEU"""
    pred_tokens, label_tokens = pred_seq.split(' '), label_seq.split(' ')
    len_pred, len_label = len(pred_tokens), len(label_tokens)
    score = math.exp(min(0, 1 - len_label / len_pred))
    for n in range(1, k + 1):
        num_matches, label_subs = 0, collections.defaultdict(int)
        for i in range(len_label - n + 1):
            label_subs[' '.join(label_tokens[i: i + n])] += 1
        for i in range(len_pred - n + 1):
            if label_subs[' '.join(pred_tokens[i: i + n])] > 0:
                num_matches += 1
                label_subs[' '.join(pred_tokens[i: i + n])] -= 1
        score *= math.pow(num_matches / (len_pred - n + 1), math.pow(0.5, n))
    return score


if __name__ == "__main__":
    # Test Encoder
    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)
    print(output.shape, state.shape)

    # Test Decoder
    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)
    print(output.shape, state.shape)


    # Test Seq2Seq
    embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1
    batch_size, num_steps = 64, 10
    lr, num_epochs, device = 0.005, 300, torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # load_data_nmt 是加载机器翻译数据集的顶层函数，需要自己实现
    train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size, num_steps)
    encoder = Seq2SeqEncoder(len(src_vocab), embed_size, num_hiddens, num_layers,
                             dropout)
    decoder = Seq2SeqDecoder(len(tgt_vocab), embed_size, num_hiddens, num_layers,
                             dropout)
    net = EncoderDecoder(encoder, decoder)
    train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)