47-注意力机制-Bahdanau注意力-自然语言处理-pytorch.ipynb

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"
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   "cell_type": "markdown",
   "id": "a45f95bc",
   "metadata": {},
   "source": [
    "1. Bahdanau注意力\n",
    "![image.png](attachment:image.png)\n",
    "2. 一个带有Bahdanau注意力的循环神经网络编码器-解码器模型\n",
    "![image-2.png](attachment:image-2.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ca909c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import dltools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0764bc30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义注意力解码器\n",
    "class AttentionDecoder(dltools.Decoder):\n",
    "    \"\"\"带有注意力机制解码器的基本接口\"\"\"\n",
    "    def __init__(self, **kwargs):\n",
    "        super(AttentionDecoder, self).__init__(**kwargs)\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f05238ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加入bahdanau注意力\n",
    "    #1. 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；\n",
    "    #2. 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；\n",
    "    #3. 编码器有效长度（排除在注意力池中填充词元）。\n",
    "    \n",
    "class Seq2SeqAttentionDecoder(AttentionDecoder):\n",
    "    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,\n",
    "                 dropout=0, **kwargs):\n",
    "        super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)\n",
    "        self.attention = dltools.AdditiveAttention(num_hiddens, num_hiddens, num_hiddens, dropout)\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
    "        self.rnn = nn.GRU(embed_size + num_hiddens, num_hiddens, num_layers,dropout=dropout)\n",
    "        self.dense = nn.Linear(num_hiddens, vocab_size) # 7 4 16 → 7 4 10\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
    "        # outputs的形状为(batch_size，num_steps，num_hiddens).\n",
    "        # hidden_state的形状为(num_layers，batch_size，num_hiddens)\n",
    "        outputs, hidden_state = enc_outputs\n",
    "        print(outputs.shape,hidden_state.shape)\n",
    "        return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        # enc_outputs的形状为(batch_size,num_steps,num_hiddens)\n",
    "        # hidden_state的形状为(num_layers,batch_size,num_hiddens)\n",
    "        enc_outputs, hidden_state, enc_valid_lens = state\n",
    "        # 输出X的形状为(num_steps,batch_size,embed_size)\n",
    "        print(X.shape)\n",
    "        X = self.embedding(X).permute(1, 0, 2)\n",
    "        print(X.shape)\n",
    "        outputs, self._attention_weights = [], []\n",
    "        for x in X:\n",
    "            # query的形状为(batch_size,1,num_hiddens)\n",
    "            print(\"hidden_state[-1]的形状：\")\n",
    "            print(hidden_state[-1].shape)\n",
    "            query = torch.unsqueeze(hidden_state[-1], dim=1) # 4 x 16\n",
    "            print(\"query形状:\")\n",
    "            print(query.shape)\n",
    "            # context的形状为(batch_size,1,num_hiddens)\n",
    "            context = self.attention(query, enc_outputs, enc_outputs, enc_valid_lens) #编码器的输出作为attention的k v\n",
    "            # 在特征维度上连结\n",
    "            x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1) # x由4 x 8 → 4 1 8 与 4 1 16 cat 变成 4 1 24\n",
    "            print(\"x的形状：\")\n",
    "            print(x.shape)\n",
    "            # 将x变形为(1,batch_size,embed_size+num_hiddens) 1 4 24\n",
    "            out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)\n",
    "            print(\"解码器输出形状\")\n",
    "            print(out.shape) # 1 4 16\n",
    "            outputs.append(out)\n",
    "            self._attention_weights.append(self.attention.attention_weights)\n",
    "        # 全连接层变换后，outputs的形状为\n",
    "        # (num_steps,batch_size,vocab_size) 7 4 10\n",
    "        outputs = self.dense(torch.cat(outputs, dim=0)) # 将outputs列表第一维度连接再一起 7 4 16 → 7 4 10\n",
    "        print(\"解码器最终输出形状\")\n",
    "        print(outputs.shape)\n",
    "        return outputs.permute(1, 0, 2), [enc_outputs, hidden_state,enc_valid_lens]\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68085c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([7, 4, 16]) torch.Size([2, 4, 16])\n",
      "torch.Size([4, 7])\n",
      "torch.Size([7, 4, 8])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([4, 16])\n",
      "query形状:\n",
      "torch.Size([4, 1, 16])\n",
      "x的形状：\n",
      "torch.Size([4, 1, 24])\n",
      "解码器输出形状\n",
      "torch.Size([1, 4, 16])\n",
      "解码器最终输出形状\n",
      "torch.Size([7, 4, 10])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用包含7个时间步的4个序列输入的小批量测试Bahdanau注意力解码器。\n",
    "encoder = dltools.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,num_layers=2)\n",
    "encoder.eval()\n",
    "decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,num_layers=2)\n",
    "decoder.eval()\n",
    "X = torch.zeros((4, 7), dtype=torch.long)  # (batch_size,num_steps)\n",
    "state = decoder.init_state(encoder(X), None)\n",
    "output, state = decoder(X, state)\n",
    "output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a57ac53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "loss 0.061, 1890.6 tokens/sec on cpu\n"
     ]
    },
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   ],
   "source": [
    "# 训练阶段\n",
    "embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1\n",
    "batch_size, num_steps = 64, 10\n",
    "lr, num_epochs, device = 0.005, 50, dltools.try_gpu()\n",
    "\n",
    "train_iter, src_vocab, tgt_vocab = dltools.load_data_nmt(batch_size, num_steps)\n",
    "encoder = dltools.Seq2SeqEncoder(len(src_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
    "decoder = Seq2SeqAttentionDecoder(len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
    "net = dltools.EncoderDecoder(encoder, decoder)\n",
    "dltools.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b9f3c72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "torch.Size([10, 1, 32]) torch.Size([2, 1, 32])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "go . => va !,  bleu 1.000\n",
      "torch.Size([10, 1, 32]) torch.Size([2, 1, 32])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "i lost . => je m'en suis <unk> .,  bleu 0.000\n",
      "torch.Size([10, 1, 32]) torch.Size([2, 1, 32])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "he's calm . => il est mouillé .,  bleu 0.658\n",
      "torch.Size([10, 1, 32]) torch.Size([2, 1, 32])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 1])\n",
      "torch.Size([1, 1, 32])\n",
      "hidden_state[-1]的形状：\n",
      "torch.Size([1, 32])\n",
      "query形状:\n",
      "torch.Size([1, 1, 32])\n",
      "x的形状：\n",
      "torch.Size([1, 1, 64])\n",
      "解码器输出形状\n",
      "torch.Size([1, 1, 32])\n",
      "解码器最终输出形状\n",
      "torch.Size([1, 1, 201])\n",
      "i'm home . => je suis calme .,  bleu 0.512\n"
     ]
    }
   ],
   "source": [
    "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "for eng, fra in zip(engs, fras):\n",
    "    translation, dec_attention_weight_seq = dltools.predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
    "    print(f'{eng} => {translation}, ',f'bleu {dltools.bleu(translation, fra, k=2):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0ec37bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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