动手学深度学习 Pytorch版 104 Bahdanau注意力
终极管理员 知识笔记 81阅读
Bahdanau 等人提出了一个没有严格单向对齐限制的可微注意力模型。在预测词元时如果不是所有输入词元都相关模型将仅对齐或参与输入序列中与当前预测相关的部分。这是通过将上下文变量视为注意力集中的输出来实现的。
新的基于注意力的模型与 9.7 节中的模型相同只不过 9.7 节中的上下文变量 c \boldsymbol{c} c 在任何解码时间步 t ′ \boldsymbol{t} t′ 都会被 c t ′ \boldsymbol{c}_{t} ct′ 替换。假设输入序列中有 T \boldsymbol{T} T 个词元解码时间步 t ′ \boldsymbol{t} t′ 的上下文变量是注意力集中的输出
c t ′ ∑ t 1 T α ( s t ′ − 1 , h t ) h t \boldsymbol{c}_{t}\sum^T_{t1}{\alpha{(\boldsymbol{s}_{t-1},\boldsymbol{h}_t)\boldsymbol{h}_t}} ct′t1∑Tα(st′−1,ht)ht
参数字典
遵循与 9.7 节中的相同符号表达
时间步 t ′ − 1 \boldsymbol{t-1} t′−1 时的解码器隐状态 s t ′ − 1 \boldsymbol{s}_{t-1} st′−1 是查询
编码器隐状态 h t \boldsymbol{h}_t ht 既是键也是值
注意力权重 α \alpha α 是使用上节所定义的加性注意力打分函数计算的
从图中可以看到加入注意力机制后
将编码器对每次词的输出作为 key 和 value
将解码器对上一个词的输出作为 querry
将注意力的输出和下一个词的词嵌入合并作为解码器输入
import torchfrom torch import nnfrom d2l import torch as d2lAttentionDecoder 类定义了带有注意力机制解码器的基本接口
#saveclass AttentionDecoder(d2l.Decoder): 带有注意力机制解码器的基本接口 def __init__(self, **kwargs): super(AttentionDecoder, self).__init__(**kwargs) property def attention_weights(self): raise NotImplementedError在 Seq2SeqAttentionDecoder 类中实现带有 Bahdanau 注意力的循环神经网络解码器。初始化解码器的状态需要下面的输入
编码器在所有时间步的最终层隐状态将作为注意力的键和值
上一时间步的编码器全层隐状态将作为初始化解码器的隐状态
编码器有效长度排除在注意力池中填充词元。
class Seq2SeqAttentionDecoder(AttentionDecoder): def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout0, **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, dropoutdropout) self.dense nn.Linear(num_hiddens, vocab_size) def init_state(self, enc_outputs, enc_valid_lens, *args): # 新增 enc_valid_lens 表示有效长度 # outputs的形状为(batch_sizenum_stepsnum_hiddens). # hidden_state的形状为(num_layersbatch_sizenum_hiddens) outputs, hidden_state enc_outputs return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens) def forward(self, X, state): # enc_outputs的形状为(batch_size,num_steps,num_hiddens). # hidden_state的形状为(num_layers,batch_size,num_hiddens) enc_outputs, hidden_state, enc_valid_lens state # 输出X的形状为(num_steps,batch_size,embed_size) X self.embedding(X).permute(1, 0, 2) outputs, self._attention_weights [], [] for x in X: # query的形状为(batch_size,1,num_hiddens) query torch.unsqueeze(hidden_state[-1], dim1) # 解码器最终隐藏层的上一个输出添加querry个数的维度后作为querry # context的形状为(batch_size,1,num_hiddens) context self.attention( query, enc_outputs, enc_outputs, enc_valid_lens) # 编码器的输出作为key和value # 在特征维度上连结 x torch.cat((context, torch.unsqueeze(x, dim1)), dim-1) # 并起来当解码器输入 # 将x变形为(1,batch_size,embed_sizenum_hiddens) out, hidden_state self.rnn(x.permute(1, 0, 2), hidden_state) outputs.append(out) self._attention_weights.append(self.attention.attention_weights) # 存一下注意力权重 # 全连接层变换后outputs的形状为 (num_steps,batch_size,vocab_size) outputs self.dense(torch.cat(outputs, dim0)) return outputs.permute(1, 0, 2), [enc_outputs, hidden_state, enc_valid_lens] property def attention_weights(self): return self._attention_weightsencoder d2l.Seq2SeqEncoder(vocab_size10, embed_size8, num_hiddens16, num_layers2)encoder.eval()decoder Seq2SeqAttentionDecoder(vocab_size10, embed_size8, num_hiddens16, num_layers2)decoder.eval()X torch.zeros((4, 7), dtypetorch.long) # (batch_size,num_steps)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(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))embed_size, num_hiddens, num_layers, dropout 32, 32, 2, 0.1batch_size, num_steps 64, 10lr, num_epochs, device 0.005, 250, d2l.try_gpu()train_iter, src_vocab, tgt_vocab d2l.load_data_nmt(batch_size, num_steps)encoder d2l.Seq2SeqEncoder( len(src_vocab), embed_size, num_hiddens, num_layers, dropout)decoder Seq2SeqAttentionDecoder( len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)net d2l.EncoderDecoder(encoder, decoder)d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)loss 0.020, 7252.9 tokens/sec on cuda:0engs [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}, , fbleu {d2l.bleu(translation, fra, k2):.3f})go . > va !, bleu 1.000i lost . > jai perdu ., bleu 1.000hes calm . > il est mouillé ., bleu 0.658im home . > je suis chez moi ., bleu 1.000训练结束后下面通过可视化注意力权重会发现每个查询都会在键值对上分配不同的权重这说明在每个解码步中输入序列的不同部分被选择性地聚集在注意力池中。
attention_weights torch.cat([step[0][0][0] for step in dec_attention_weight_seq], 0).reshape(( 1, 1, -1, num_steps))# 加上一个包含序列结束词元d2l.show_heatmaps( attention_weights[:, :, :, :len(engs[-1].split()) 1].cpu(), xlabelKey positions, ylabelQuery positions)
练习1在实验中用LSTM替换GRU。
class Seq2SeqEncoder_LSTM(d2l.Encoder): def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout0, **kwargs): super(Seq2SeqEncoder_LSTM, self).__init__(**kwargs) self.embedding nn.Embedding(vocab_size, embed_size) self.lstm nn.LSTM(embed_size, num_hiddens, num_layers, # 更换为 LSTM dropoutdropout) def forward(self, X, *args): X self.embedding(X) X X.permute(1, 0, 2) output, state self.lstm(X) return output, stateclass Seq2SeqAttentionDecoder_LSTM(AttentionDecoder): def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout0, **kwargs): super(Seq2SeqAttentionDecoder_LSTM, 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.LSTM( embed_size num_hiddens, num_hiddens, num_layers, dropoutdropout) 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][0], dim1) # 解码器最终隐藏层的上一个输出添加querry个数的维度后作为querry context self.attention( query, enc_outputs, enc_outputs, enc_valid_lens) x torch.cat((context, torch.unsqueeze(x, dim1)), 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, dim0)) return outputs.permute(1, 0, 2), [enc_outputs, hidden_state, enc_valid_lens] property def attention_weights(self): return self._attention_weightsembed_size_LSTM, num_hiddens_LSTM, num_layers_LSTM, dropout_LSTM 32, 32, 2, 0.1batch_size_LSTM, num_steps_LSTM 64, 10lr_LSTM, num_epochs_LSTM, device_LSTM 0.005, 250, d2l.try_gpu()train_iter_LSTM, src_vocab_LSTM, tgt_vocab_LSTM d2l.load_data_nmt(batch_size_LSTM, num_steps_LSTM)encoder_LSTM Seq2SeqEncoder_LSTM( len(src_vocab_LSTM), embed_size_LSTM, num_hiddens_LSTM, num_layers_LSTM, dropout_LSTM)decoder_LSTM Seq2SeqAttentionDecoder_LSTM( len(tgt_vocab_LSTM), embed_size_LSTM, num_hiddens_LSTM, num_layers_LSTM, dropout_LSTM)net_LSTM d2l.EncoderDecoder(encoder_LSTM, decoder_LSTM)d2l.train_seq2seq(net_LSTM, train_iter_LSTM, lr_LSTM, num_epochs_LSTM, tgt_vocab_LSTM, device_LSTM)loss 0.021, 7280.8 tokens/sec on cuda:0engs [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_LSTM d2l.predict_seq2seq( net_LSTM, eng, src_vocab_LSTM, tgt_vocab_LSTM, num_steps_LSTM, device_LSTM, True) print(f{eng} > {translation}, , fbleu {d2l.bleu(translation, fra, k2):.3f})go . > va !, bleu 1.000i lost . > jai perdu ., bleu 1.000hes calm . > puis-je <unk> <unk> ., bleu 0.000im home . > je suis chez moi ., bleu 1.000attention_weights_LSTM torch.cat([step[0][0][0] for step in dec_attention_weight_seq_LSTM], 0).reshape(( 1, 1, -1, num_steps_LSTM))# 加上一个包含序列结束词元d2l.show_heatmaps( attention_weights_LSTM[:, :, :, :len(engs[-1].split()) 1].cpu(), xlabelKey positions, ylabelQuery positions)
2修改实验以将加性注意力打分函数替换为缩放点积注意力它如何影响训练效率
class Seq2SeqAttentionDecoder_Dot(AttentionDecoder): def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout0, **kwargs): super(Seq2SeqAttentionDecoder, self).__init__(**kwargs) self.attention d2l.DotProductAttention( # 替换为缩放点积注意力 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, dropoutdropout) 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], dim1) context self.attention( query, enc_outputs, enc_outputs, enc_valid_lens) x torch.cat((context, torch.unsqueeze(x, dim1)), 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, dim0)) return outputs.permute(1, 0, 2), [enc_outputs, hidden_state, enc_valid_lens] property def attention_weights(self): return self._attention_weightsembed_size_Dot, num_hiddens_Dot, num_layers_Dot, dropout_Dot 32, 32, 2, 0.1batch_size_Dot, num_steps_Dot 64, 10lr_Dot, num_epochs_Dot, device_Dot 0.005, 250, d2l.try_gpu()train_iter_Dot, src_vocab_Dot, tgt_vocab_Dot d2l.load_data_nmt(batch_size_Dot, num_steps_Dot)encoder_Dot Seq2SeqEncoder_LSTM( len(src_vocab_Dot), embed_size_LSTM, num_hiddens_Dot, num_layers_Dot, dropout_Dot)decoder_Dot Seq2SeqAttentionDecoder_LSTM( len(tgt_vocab_Dot), embed_size_Dot, num_hiddens_Dot, num_layers_Dot, dropout_Dot)net_Dot d2l.EncoderDecoder(encoder_Dot, decoder_Dot)d2l.train_seq2seq(net_Dot, train_iter_Dot, lr_Dot, num_epochs_Dot, tgt_vocab_Dot, device_Dot)loss 0.021, 7038.8 tokens/sec on cuda:0engs [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_Dot d2l.predict_seq2seq( net_Dot, eng, src_vocab_Dot, tgt_vocab_Dot, num_steps_Dot, device_Dot, True) print(f{eng} > {translation}, , fbleu {d2l.bleu(translation, fra, k2):.3f})go . > va !, bleu 1.000i lost . > jai perdu ., bleu 1.000hes calm . > il est riche ., bleu 0.658im home . > je suis chez moi ., bleu 1.000attention_weights_Dot torch.cat([step[0][0][0] for step in dec_attention_weight_seq_Dot], 0).reshape(( 1, 1, -1, num_steps_Dot))# 加上一个包含序列结束词元d2l.show_heatmaps( attention_weights_Dot[:, :, :, :len(engs[-1].split()) 1].cpu(), xlabelKey positions, ylabelQuery positions)