# Seq2seq # 进行机器翻译 import collections import math import os import torch from torch import nn from d2l import torch as d2l import matplotlib.pyplot as plt class Seq2SeqEncoder(d2l.Encoder): def __init__(self, vocab_size, embed_size, num_hidden, num_layers, dropout=0, **kwargs): super(Seq2SeqEncoder, self).__init__(**kwargs) self.embedding = nn.Embedding(vocab_size, embed_size) # 将文本数据转化成标记关系 # 是为了将离散的词汇信息表示为连续的、连贯的向量空间中的点，以便模型能够更好地理解单词之间的语义关系。 self.rnn = nn.GRU(embed_size, num_hidden, num_layers, dropout=dropout) # 创造一个rnn层负责进行编码 def forward(self, x, *args): x = self.embedding(x) # 创建一个嵌入层，用于将输入的整数标记（词汇表中的单词索引）转换为密集的词嵌入。这是将词汇信息转化为连续的向量表示的一种常用方法。 # 类似于one_hot x = x.permute(1, 0, 2) # 对x进行维度置换，将批量大小放在第二维，序列长度放在第一维。这是因为RNN层通常期望输入的维度顺序是(序列长度, 批量大小, 特征维度)。 output, state = self.rnn(x) # 将处理后的输入x传递给GRU层进行编码。output包含了每个时间步的隐藏状态，state包含了GRU最后一个时间步的隐藏状态。 return output, state encoder = Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hidden=16, num_layers=2) encoder.eval() # 让dropout=0 x = torch.zeros((4, 7), dtype=torch.long) # batch_size为4,序列长度为7的一个矩阵 output, state = encoder(x) print(output.shape) # 在编码器中将x矩阵转置,序列长度与批量大小转置,方便进行rnn处理 print(state.shape) # state中的2是层数,每一层在最后时刻的输出,4为批量大小,16为num_hidden class Seq2SeqDecoder(d2l.Decoder): def __init__(self, vocab_size, embed_size, num_hidden, 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_hidden, num_hidden, num_layers, dropout=dropout) # 创建了一个GRU层，用于对输入序列进行解码。GRU的输入维度是 embed_size + num_hidden， # 其中 embed_size 是嵌入层输出的维度，num_hidden 是上一个时间步的隐藏状态的维度。这个GRU层有 num_layers 层，用于对序列进行建模。 self.dense = nn.Linear(num_hidden, vocab_size) # 创建了一个全连接层，用于将GRU的输出转换为词汇表大小的向量，以便生成模型的输出。这一层的输出维度等于词汇表的大小 # 输出函数 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) # 广播context，使其具有与X相同的num_steps # 将编码器的最后一个隐藏状态 state[-1] 广播到与 x 相同的时间步数，用于上下文信息。 x_and_context = torch.cat((x, context), 2) output, state = self.rnn(x_and_context, state) # 将连接后的输入和隐藏状态传递给GRU层，计算解码器的输出和新的隐藏状态。 output = self.dense(output).permute(1, 0, 2) # 通过全连接层将GRU的输出转换为模型的最终输出，然后进行维度置换以匹配预期的输出形状。 # output的形状:(batch_size,num_steps,vocab_size) # state的形状:(num_layers,batch_size,num_hidden) return output, state decoder = Seq2SeqDecoder(vocab_size=10, embed_size=8, num_hidden=16, num_layers=2) decoder.eval() state = decoder.init_state(encoder(x)) # 对编码器的最后状态进行初始化 output, state = decoder(x, state) # 获取输出和状态 print(output.shape, state.shape) # ""在序列中屏蔽不相关的项""" def sequence_mask(x, valid_len, value=0): max_len = x.size(1) mask = torch.arange((max_len), dtype=torch.float32, device=x.device)[None, :] < valid_len[:, None] # 定义一个筛选的函数,只保留从 0 到valid长度的值,其余的全部赋值成value x[~mask] = value return x x = torch.tensor([[1, 2, 3], [4, 5, 6]]) print(sequence_mask(x, torch.tensor([1, 2]))) # 作用是对于一维只保留前一个,二维只保留前两个,根据valid的长度来实现 # 通过扩展softmax交叉熵函数来屏蔽不相关 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 # 训练 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]) # 构建图像 for epoch in range(num_epochs): timer = d2l.Timer() metric = d2l.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) 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” 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],)) print(f'loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} ' f'tokens/sec on {str(device)}') plt.show() embed_size, num_hidden, num_layers, dropout = 32, 32, 2, 0.1 batch_size, num_steps = 64, 10 lr, num_epochs, device = 0.005, 300, d2l.try_gpu() # def read_data_nmt(): # """载入⼊“英语－法语”数据集。""" # data_dir = d2l.download_extract('fra-eng') # with open(os.path.join(data_dir, 'fra.txt'), 'r',encoding='UTF-8') as f: # return f.read() # raw_text = read_data_nmt() train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps) encoder = Seq2SeqEncoder(len(src_vocab), embed_size, num_hidden, num_layers, dropout) decoder = Seq2SeqDecoder(len(tgt_vocab), embed_size, num_hidden, num_layers, dropout) net = d2l.EncoderDecoder(encoder, decoder) train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device) # 预测 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 = d2l.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_X是把句子中的 之前添加的pad和bos删除 enc_outputs = net.encoder(enc_x, enc_valid_len) dec_state = net.decoder.init_state(enc_outputs, enc_valid_len) # 拿出编码器的输出和最后状态 # 添加批量轴 dec_x = torch.unsqueeze(torch.tensor( [tgt_vocab['<bos>']], dtype=torch.long, device=device), dim=0) # 没有bos的序列句子 output_seq, attention_weight_seq = [], [] for _ in range(num_steps): # 取出 输出和状态,作为下一步的输入 和 更新状态 y, dec_state = net.decoder(dec_x, dec_state) dec_x = y.argmax(dim=2) # 出去输出,即最大预测概率 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): # @save """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 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}')