chapter_transformer/2_bahadanau_attention_mechanism.ipynb

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      "cell_type": "markdown",
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/ashkan-mokarian/d2l-pytorch/blob/main/chapter_transformer/2_bahadanau_attention_mechanism.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ],
      "metadata": {
        "id": "mYIfDMY2a5m9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install d2l==1.0.0a0"
      ],
      "metadata": {
        "id": "v10cmyaIbCwW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from d2l import torch as d2l"
      ],
      "metadata": {
        "id": "0HTOZK9_dxNg"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bahadanau Attention Mechanism\n",
        "Here, instead of keeping all the history of the sequence in a fixed context variable, depending on the decoder state, the valuable encoder states are attended to accordingly to generate the next token.\n",
        "\n",
        "Original text/input sequence: $h_t$: Encoder hidden states**s**\n",
        "\n",
        "Text that was already generated: $s_{t'-1}$: decoder hidden state**s**\n",
        "\n",
        "This yields context $c'_t = \\sum_{t=1}^{T} α(s_{t'-1}, h_t)h_t$ where the output of the previous decoder state is used as query and all encoder hidden states are used as key-value pairs for the input text. Note that $c'_t$ is used to generate both the next decoder hidden state $s_{t'}$ and to generate a new token as output."
      ],
      "metadata": {
        "id": "HndAGejKbAKu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1Vn2CI2nazOI"
      },
      "outputs": [],
      "source": [
        "class Seq2SeqAttentionDecoder(d2l.Decoder):\n",
        "    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0):\n",
        "        super().__init__()\n",
        "        self.attention = d2l.AdditiveAttention(num_hiddens, dropout)\n",
        "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
        "        self.rnn = nn.GRU(\n",
        "            embed_size + num_hiddens, num_hiddens, num_layers, dropout=dropout)\n",
        "        self.dense = nn.LazyLinear(vocab_size)\n",
        "        self.apply(d2l.init_seq2seq)\n",
        "    \n",
        "    def init_state(self, enc_outputs, enc_valid_lens):\n",
        "        \"\"\"Initializing the state of the decoder.\n",
        "\n",
        "        The state of the decoder is initialized with:\n",
        "        (i) the hidden states of the last layer of the encoder at all time steps\n",
        "          used as keys and values for attention\n",
        "        (ii) The hidden state of the encoder at all layers of the final step used\n",
        "          to initialize the state of the decoder\n",
        "        (iii) Valid_lens of the encoder to exclude padding tokens at attention pooling\n",
        "        \"\"\"\n",
        "        # shape of outputs: (num_steps, batch_size, num_hiddens)\n",
        "        # shape of hidden_state: (num_layers, batch_size, num_hiddens)\n",
        "        outputs, hidden_state = enc_outputs\n",
        "        return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)\n",
        "    \n",
        "    def forward(self, X, state):\n",
        "        enc_outputs, hidden_state, enc_valid_lens = state\n",
        "        # shape of output X: (num_steps, batch_size, embed_size)\n",
        "        X = self.embedding(X).permute(1, 0, 2)\n",
        "        outputs, self._attention_weights = [], []\n",
        "        \n",
        "        for x in X:\n",
        "            # Query is $s_{t'-1}$ as defined above\n",
        "            # shape of query is: (batch_size, 1, num_hiddens)\n",
        "            query = torch.unsqueeze(hidden_state[-1], dim=1)\n",
        "            # shape of context: (batch_size, 1, num_hiddens)\n",
        "            context = self.attention(\n",
        "                query, enc_outputs, enc_outputs, enc_valid_lens)\n",
        "            # concatenate context to feature dimension. As mentioned above, both\n",
        "            # embed(x) and context are used to predict the output and next decoder\n",
        "            # state\n",
        "            x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)\n",
        "            # x needs to be reshaped to have shape (1, batch_size, num_hiddens)\n",
        "            out, hidden_state = self.rnn(x.permute(1,0,2), hidden_state)\n",
        "            outputs.append(out)\n",
        "            self._attention_weights.append(self.attention.attention_weights)\n",
        "        # shape of outputs: (num_steps, batch_size, vocab_size)\n",
        "        outputs = self.dense(torch.cat(outputs, dim=0))\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",
      "source": [
        "vocab_size, embed_size, num_hiddens, num_layers = 10, 8, 16, 2\n",
        "batch_size, num_steps = 4, 7\n",
        "encoder = d2l.Seq2SeqEncoder(vocab_size, embed_size, num_hiddens, num_layers)\n",
        "decoder = Seq2SeqAttentionDecoder(vocab_size, embed_size, num_hiddens,\n",
        "                                  num_layers)\n",
        "X = torch.zeros((batch_size, num_steps), dtype=torch.long)\n",
        "state = decoder.init_state(encoder(X), None)\n",
        "output, state = decoder(X, state)\n",
        "d2l.check_shape(output, (batch_size, num_steps, vocab_size))\n",
        "d2l.check_shape(state[0], (batch_size, num_steps, num_hiddens))\n",
        "d2l.check_shape(state[1][0], (batch_size, num_hiddens))"
      ],
      "metadata": {
        "id": "tvlpgXHZjXPm"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = d2l.MTFraEng(batch_size=128)\n",
        "embed_size, num_hiddens, num_layers, dropout = 256, 256, 2, 0.2\n",
        "encoder = d2l.Seq2SeqEncoder(\n",
        "    len(data.src_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
        "decoder = Seq2SeqAttentionDecoder(\n",
        "    len(data.tgt_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
        "model = d2l.Seq2Seq(encoder, decoder, tgt_pad=data.tgt_vocab['<pad>'],\n",
        "                    lr=0.005)\n",
        "trainer = d2l.Trainer(max_epochs=30, gradient_clip_val=1, num_gpus=1)\n",
        "trainer.fit(model, data)"
      ],
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        "id": "DPskrB1kjZMv",
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      "execution_count": 7,
      "outputs": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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",
        "preds, _ = model.predict_step(\n",
        "    data.build(engs, fras), d2l.try_gpu(), data.num_steps)\n",
        "for en, fr, p in zip(engs, fras, preds):\n",
        "    translation = []\n",
        "    for token in data.tgt_vocab.to_tokens(p):\n",
        "        if token == '<eos>':\n",
        "            break\n",
        "        translation.append(token)\n",
        "    print(f'{en} => {translation}, bleu,'\n",
        "          f'{d2l.bleu(\" \".join(translation), fr, k=2):.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXOu7aGSl1J-",
        "outputId": "fecbf5d0-cc85-43a0-a816-4134aca46d42"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "go . => ['va', '!'], bleu,1.000\n",
            "i lost . => [\"j'ai\", 'perdu', '.'], bleu,1.000\n",
            "he's calm . => [\"j'en\", 'est', 'parti', '.'], bleu,0.000\n",
            "i'm home . => ['je', 'suis', 'chez', 'moi', '.'], bleu,1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, dec_attention_weights = model.predict_step(\n",
        "    data.build([engs[-1]], [fras[-1]]), d2l.try_gpu(), data.num_steps, True)\n",
        "attention_weights = torch.cat(\n",
        "    [step[0][0][0] for step in dec_attention_weights], 0)\n",
        "attention_weights = attention_weights.reshape((1, 1, -1, data.num_steps))\n",
        "\n",
        "# Plus one to include the end-of-sequence token\n",
        "d2l.show_heatmaps(\n",
        "    attention_weights[:, :, :, :len(engs[-1].split()) + 1].cpu(),\n",
        "    xlabel='Key positions', ylabel='Query positions')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "4HoL2Q6Ll7nf",
        "outputId": "6c0ae60f-fabb-4127-f5a2-4a8319df74f8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 250x250 with 2 Axes>"
            ],
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