Mecanismos_de_atenção_e_Transformers.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nascimento-luciano/Artificial-Intelligence/blob/master/Mecanismos_de_aten%C3%A7%C3%A3o_e_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UNIVERSIDADE FEDERAL DO AGRESTE DE PERNAMBUCO**\n",
        "\n",
        "**Redes Neurais [MÓDULO 02] - Mecanismos de Atenção - Prof. Dr. Luis F. Alves Pereira**\n",
        "\n",
        "---\n",
        "**Conteúdo:**\n",
        "\n",
        "* Pooling de atenção\n",
        "* Visualização de coeficientes de atenção\n",
        "* Pooling de atenção Nadaraya-Watson\n",
        "  - Pooling Nadaraya-Watson paramétrico\n",
        "* Funções para nível de atenção\n",
        "* Atenção de Bahdanau\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "#1. Pooling de atenção\n",
        "\n",
        "O pooling de atenção proposto baseia-se na teoria clássica da psicologia em que percepções **não intencionais** e demandas **intencionais** do sujeito são processados em conjunto para definir o seu foco de atenção.\n",
        "\n",
        "No modelo ilustrado a seguir, as percpções não intencionais são denominadas **keys**, as demandas intencionais são denominadas **queries**, e as entradas sensoriais que são objeto de foco da atenção são denominadas **values**.\n",
        "\n"
      ],
      "metadata": {
        "id": "rUiLsPEBX42C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![pooling_atencao.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "vt0epuvU1E0A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Visualização de coeficientes de atenção\n",
        "\n",
        "No código que segue implementamos função auxiliar para visualizar futuras iterações entre **queries** e **keys**. "
      ],
      "metadata": {
        "id": "O-26KXao1KO-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matplotlib==2.2.3\n",
        "!pip install -U d2l\n",
        "import math\n",
        "import tensorflow as tf\n",
        "from d2l import tensorflow as d2l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Fa1Cy_MtFBHU",
        "outputId": "70236e35-7701-40e2-af62-8b32f11666d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting matplotlib==2.2.3\n",
            "  Using cached matplotlib-2.2.3-cp37-cp37m-manylinux1_x86_64.whl (12.6 MB)\n",
            "Requirement already satisfied: numpy>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (1.21.5)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (1.15.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (3.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (1.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==2.2.3) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib==2.2.3) (3.10.0.2)\n",
            "Installing collected packages: matplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.5.1\n",
            "    Uninstalling matplotlib-3.5.1:\n",
            "      Successfully uninstalled matplotlib-3.5.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "plotnine 0.6.0 requires matplotlib>=3.1.1, but you have matplotlib 2.2.3 which is incompatible.\n",
            "mizani 0.6.0 requires matplotlib>=3.1.1, but you have matplotlib 2.2.3 which is incompatible.\n",
            "d2l 0.17.5 requires matplotlib==3.5.1, but you have matplotlib 2.2.3 which is incompatible.\n",
            "arviz 0.11.4 requires matplotlib>=3.0, but you have matplotlib 2.2.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-2.2.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: d2l in /usr/local/lib/python3.7/dist-packages (0.17.5)\n",
            "Requirement already satisfied: requests==2.25.1 in /usr/local/lib/python3.7/dist-packages (from d2l) (2.25.1)\n",
            "Collecting matplotlib==3.5.1\n",
            "  Using cached matplotlib-3.5.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (11.2 MB)\n",
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            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter==1.0.0->d2l) (4.1.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter==1.0.0->d2l) (0.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->jupyter==1.0.0->d2l) (1.5.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->jupyter==1.0.0->d2l) (0.5.1)\n",
            "Requirement already satisfied: qtpy in /usr/local/lib/python3.7/dist-packages (from qtconsole->jupyter==1.0.0->d2l) (2.0.1)\n",
            "Installing collected packages: matplotlib\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 2.2.3\n",
            "    Uninstalling matplotlib-2.2.3:\n",
            "      Successfully uninstalled matplotlib-2.2.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed matplotlib-3.5.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_heatmaps(matrices, xlabel, ylabel, titles=None, figsize=(2.5, 2.5),\n",
        "                  cmap='Reds'):\n",
        "    \"\"\"Show heatmaps of matrices.\"\"\"\n",
        "    d2l.use_svg_display()\n",
        "    num_rows, num_cols = matrices.shape[0], matrices.shape[1]\n",
        "    fig, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize,\n",
        "                                 sharex=True, sharey=True, squeeze=False)\n",
        "    for i, (row_axes, row_matrices) in enumerate(zip(axes, matrices)):\n",
        "        for j, (ax, matrix) in enumerate(zip(row_axes, row_matrices)):\n",
        "            pcm = ax.imshow(matrix.numpy(), cmap=cmap)\n",
        "            if i == num_rows - 1:\n",
        "                ax.set_xlabel(xlabel)\n",
        "            if j == 0:\n",
        "                ax.set_ylabel(ylabel)\n",
        "            if titles:\n",
        "                ax.set_title(titles[j])\n",
        "    fig.colorbar(pcm, ax=axes, shrink=0.6);\n",
        "\n",
        "attention_weights = tf.reshape(tf.eye(10), (1, 1, 10, 10))\n",
        "show_heatmaps(attention_weights, xlabel='Keys', ylabel='Queries')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "vSdipqiOEVap",
        "outputId": "d6f7eb50-2d20-4581-f93d-75490c83a778"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 2 Axes>"
            ],
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        "#3. Pooling de atenção Nadaraya-Watson\n",
        "\n",
        "Para um determinado conjunto de treino $\\{(x_1,y_1), (x_2,y_2), \\cdots\\}$, a regressão proposta por Nadaraya e Watson, em 1964, é composta pela soma ponderada das saídas $y_i$ em **função da distância** de $i$ para o ponto de interesse da regressão (i.e., **query**) $x$:\n",
        "\n",
        "\n",
        "$f(x)= \\sum_{i=1}^n\\frac{K(x-x_i)}{\\sum_{j=1}^n K(x-x_j)}y_i $\n",
        "\n",
        "onde $K(\\cdot,\\cdot)$ é um kernel. Tal estimador pode ser visto como:\n",
        "\n",
        "$f(x) = \\sum_{i=1}^n \\alpha(x,x_i)y_i$\n",
        "\n",
        "A luz da teoria da atenção, $x$ pode ser entendida como **query**, $x_i$ como **key** e $y_i$ como **value**. Dessa forma, $\\alpha(x,x_i)$ é um **coeficiente de atenção** associado ao value $y_i$ gerada a partir da interação entre a **query** $x$ e a **key** $x_i$.  \n",
        "\n",
        "Por fim, note que todos os **coeficiente de atenção** $\\alpha(x,x_i) = \\frac{K(x-x_i)}{\\sum_{j=1}^n K(x-x_j)}$ formam uma distribuição de probabilidade (valores não-negativos somando 1).\n",
        "\n",
        "Ao utilizar o kernel gaussiano $K(u) = \\frac{1}{\\sqrt{2\\pi}}exp\\left(-\\frac{u^2}{2}\\right)$, temos:\n",
        "\n",
        "\n",
        "$f(x)= \\sum_{i=1}^n\\alpha(x,x_i)y_i$\n",
        "\n",
        "$f(x)= \\sum_{i=1}^n\\frac{exp\\left(-\\frac{1}{2}(x-x_i)^2\\right)}{\\sum_{j=1}^n exp\\left(-\\frac{1}{2}(x-x_j)^2\\right)}y_i$\n",
        "\n",
        "$f(x)=\\sum_{i=1}^n softmax\\left(-\\frac{1}{2}(x-x_i)^2\\right)y_i$\n",
        "\n",
        "Para avaliar o pooling de atenção proposto, vamos criar a seguinte base de dados sintética:"
      ],
      "metadata": {
        "id": "neTF1iWuGWzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_train = 50\n",
        "x_train = tf.sort(tf.random.uniform(shape=(n_train,), maxval=5))\n",
        "\n",
        "def f(x):\n",
        "    return 2 * tf.sin(x) + x**0.8\n",
        "\n",
        "y_train = f(x_train) + tf.random.normal((n_train,), 0.0, 0.5)  # Training outputs\n",
        "x_test = tf.range(0, 5, 0.1)  # Testing examples\n",
        "y_truth = f(x_test)  # Ground-truth outputs for the testing examples\n",
        "n_test = len(x_test)  # No. of testing examples\n"
      ],
      "metadata": {
        "id": "W3vyulck-Uxo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "E a seguinte funcionalidade para visualização:"
      ],
      "metadata": {
        "id": "Bi22bZuN-T_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_kernel_reg(y_hat):\n",
        "    d2l.plot(x_test, [y_truth, y_hat], 'x', 'y', legend=['Truth', 'Pred'],\n",
        "             xlim=[0, 5], ylim=[-1, 5])\n",
        "    d2l.plt.plot(x_train, y_train, 'o', alpha=0.5);"
      ],
      "metadata": {
        "id": "Kdm1xZzT-rKG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aplicando agora o pooling de atenção Nadaraya-Watson com kernel Gaussiano como estimador para os pontos da base, temos:"
      ],
      "metadata": {
        "id": "GXvJsPtA-tR4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of `X_repeat`: (`n_test`, `n_train`), where each row contains the\n",
        "# same testing inputs (i.e., same queries)\n",
        "X_repeat = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_train, axis=0)\n",
        "# Note that `x_train` contains the keys. Shape of `attention_weights`:\n",
        "# (`n_test`, `n_train`), where each row contains attention weights to be\n",
        "# assigned among the values (`y_train`) given each query\n",
        "attention_weights = tf.nn.softmax(-(X_repeat - tf.expand_dims(x_train, axis=1))**2/2, axis=1)\n",
        "# Each element of `y_hat` is weighted average of values, where weights are attention weights\n",
        "y_hat = tf.matmul(attention_weights, tf.expand_dims(y_train, axis=1))\n",
        "plot_kernel_reg(y_hat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "ayvFw4G4-0mM",
        "outputId": "458b0571-ccd3-43c9-e248-c654438433a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Por fim, visualizamos que a atenção do modelo esteve em volta das **keys** mais próximas de cada **query** dada."
      ],
      "metadata": {
        "id": "u9HnLR_J_Y0g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.show_heatmaps(tf.expand_dims(tf.expand_dims(attention_weights, axis=0), axis=0),\n",
        "                  xlabel='Sorted training inputs (keys)',\n",
        "                  ylabel='Sorted testing inputs (Queries)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "F8GOaUMa_ZBC",
        "outputId": "cac8b2d2-1167-45c1-da64-f06efa9206b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 2 Axes>"
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    {
      "cell_type": "markdown",
      "source": [
        "##3.1. Pooling Nadaraya-Watson paramétrico\n",
        "\n",
        "Nesse exemplo, adicionamos um parâmetro treinável $\\omega$ ao estimador descrito anteriormente. Assim temos:\n",
        "\n",
        "$f(x)=\\sum_{i=1}^n softmax\\left(-\\frac{1}{2}((x-x_i)\\omega)^2\\right)y_i$"
      ],
      "metadata": {
        "id": "JYeSJ1mBUXjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NWKernelRegression(tf.keras.layers.Layer):\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.w = tf.Variable(initial_value=tf.random.uniform(shape=(1,)))\n",
        "\n",
        "    def call(self, queries, keys, values, **kwargs):\n",
        "        # For training queries are `x_train`. Keys are distance of taining data for each point. Values are `y_train`.\n",
        "        # Shape of the output `queries` and `attention_weights`: (no. of queries, no. of key-value pairs)\n",
        "        queries = tf.repeat(tf.expand_dims(queries, axis=1), repeats=keys.shape[1], axis=1)\n",
        "        self.attention_weights = tf.nn.softmax(-((queries - keys) * self.w)**2 /2, axis =1)\n",
        "        # Shape of `values`: (no. of queries, no. of key-value pairs)\n",
        "        return tf.squeeze(tf.matmul(tf.expand_dims(self.attention_weights, axis=1), tf.expand_dims(values, axis=-1)))"
      ],
      "metadata": {
        "id": "JmyZM6MSV9fp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usamos os dados em *x_train* e *y_train* para treinar o modelo:"
      ],
      "metadata": {
        "id": "CetWl6JXWQ4E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of `X_tile`: (`n_train`, `n_train`), where each column contains the\n",
        "# same training inputs\n",
        "X_tile = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_train, axis=0)\n",
        "# Shape of `Y_tile`: (`n_train`, `n_train`), where each column contains the\n",
        "# same training outputs\n",
        "Y_tile = tf.repeat(tf.expand_dims(y_train, axis=0), repeats=n_train, axis=0)\n",
        "# Shape of `keys`: ('n_train', 'n_train' - 1)\n",
        "keys = tf.reshape(X_tile[tf.cast(1 - tf.eye(n_train), dtype=tf.bool)], shape=(n_train, -1))\n",
        "# Shape of `values`: ('n_train', 'n_train' - 1)\n",
        "values = tf.reshape(Y_tile[tf.cast(1 - tf.eye(n_train), dtype=tf.bool)], shape=(n_train, -1))\n",
        "\n",
        "net = NWKernelRegression()\n",
        "loss_object = tf.keras.losses.MeanSquaredError()\n",
        "optimizer = tf.keras.optimizers.SGD(learning_rate=0.5)\n",
        "animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])\n",
        "\n",
        "\n",
        "for epoch in range(5):\n",
        "    with tf.GradientTape() as t:\n",
        "        loss = loss_object(y_train, net(x_train, keys, values)) * len(y_train)\n",
        "    grads = t.gradient(loss, net.trainable_variables)\n",
        "    optimizer.apply_gradients(zip(grads, net.trainable_variables))\n",
        "    print(f'epoch {epoch + 1}, loss {float(loss):.6f}')\n",
        "    animator.add(epoch + 1, float(loss))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "8iseR8kFWc4n",
        "outputId": "52a3b576-f9c9-414f-a327-ae289911b83b"
      },
      "execution_count": null,
      "outputs": [
        {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos então visualizar a predição do modelo treinado para a base de treinamento:"
      ],
      "metadata": {
        "id": "3waDZu13W_SA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of `keys`: (`n_test`, `n_train`), where each column contains the same\n",
        "# training inputs (i.e., same keys)\n",
        "keys = tf.repeat(tf.expand_dims(x_train, axis=0), repeats=n_test, axis=0)\n",
        "# Shape of `value`: (`n_test`, `n_train`)\n",
        "values = tf.repeat(tf.expand_dims(y_train, axis=0), repeats=n_test, axis=0)\n",
        "y_hat = net(x_test, keys, values)\n",
        "plot_kernel_reg(y_hat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "P6aqME8gXJJE",
        "outputId": "79e3117b-a6f3-41b7-de87-7199401b3e20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Por fim, vamos observar os coeficientes de atenção do modelo Nadaraya-Watson paramétrico. Perceba que o foco de atenção está muito mais concentrado nas **keys** muito próximas a cada uma das **queries**."
      ],
      "metadata": {
        "id": "miKV-01aXS-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d2l.show_heatmaps(tf.expand_dims(tf.expand_dims(net.attention_weights, axis=0), axis=0),\n",
        "                  xlabel='Sorted training inputs (Keys)',\n",
        "                  ylabel='Sorted testing inputs (Queries)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "7c3sMEDrXeS-",
        "outputId": "49e3f6f3-5c5d-47e8-cbe4-0295b28fa037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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      "cell_type": "markdown",
      "source": [
        "#4. Funções para nível de atenção\n",
        "\n",
        "A arquitetura que implementa o pooling de atenção discutido anteriormente é representado a seguir:"
      ],
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)"
      ],
      "metadata": {
        "id": "OyzcHpKbgy9e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vê-se claramente que a saída do pooling de atenção $f(\\cdot)$ é dada pela soma das values ponderadas pelos coeficientes de atenção $\\alpha(\\cdot,\\cdot)$ conforme explicitado a seguir: "
      ],
      "metadata": {
        "id": "sjTbZ0hqmvRD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "rjfGYaEFmzIz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Além disso, o coeficiente de atenção é dado em função de função de nível de atenção $a(\\cdot,\\cdot)$. Percebe-se portanto que funções $a$ diferentes de $a = -\\frac{1}{2}(\\mathbf{q}-\\mathbf{k_i})^2$ irão gerar poolings de atenção $f(\\cdot)$ com novos comportamentos.\n",
        "\n",
        "Uma alternativa para a medida de atenção $a(\\cdot,\\cdot)$ é dada pela **atenção aditiva** que segue:\n",
        "\n",
        "\n",
        "$a(\\mathbf{q},\\mathbf{k}) = \\mathbf{w}^T_v tanh(\\mathbf{W}_{\\frac{h}{2}q}\\mathbf{q}+ \\mathbf{W}_{\\frac{h}{2}k}\\mathbf{k}) \\in \\mathbb{R}, \\mathbf{w}_v \\in \\mathbb{R}^h$\n",
        "\n",
        "Através do código a seguir, demonstra-se a operação desse novo pooling de atenção."
      ],
      "metadata": {
        "id": "xe8__y4hmzUP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "\"As we just mentioned, a softmax operation is used to output a probability distribution as attention weights. \n",
        "In some cases, not all the values should be fed into attention pooling. For instance, for efficient minibatch \n",
        "processing in Section 9.5, some text sequences are padded with special tokens that do not carry meaning. To get \n",
        "an attention pooling over only meaningful tokens as values, we can specify a valid sequence length (in number of tokens) \n",
        "to filter out those beyond this specified range when computing softmax. In this way, we can implement such a masked softmax\n",
        "operation in the following masked_softmax function, where any value beyond the valid length is masked as zero.\"\n",
        "'''\n",
        "\n",
        "def masked_softmax(X, valid_lens):\n",
        "    \"\"\"Ignora dimensoes selecionadas para o calculo do softamax\"\"\"\n",
        "    # `X`: 3D tensor, `valid_lens`: 1D or 2D tensor\n",
        "    if valid_lens is None:\n",
        "        return tf.nn.softmax(X, axis=-1)\n",
        "    else:\n",
        "        shape = X.shape\n",
        "        if len(valid_lens.shape) == 1:\n",
        "            valid_lens = tf.repeat(valid_lens, repeats=shape[1])\n",
        "\n",
        "        else:\n",
        "            valid_lens = tf.reshape(valid_lens, shape=-1)\n",
        "        # On the last axis, replace masked elements with a very large negative\n",
        "        # value, whose exponentiation outputs 0\n",
        "        X = d2l.sequence_mask(tf.reshape(X, shape=(-1, shape[-1])), valid_lens, value=-1e6)\n",
        "        return tf.nn.softmax(tf.reshape(X, shape=shape), axis=-1)\n",
        "\n",
        "class AdditiveAttention(tf.keras.layers.Layer):\n",
        "    \"\"\"Additive attention.\"\"\"\n",
        "    def __init__(self, key_size, query_size, num_hiddens, dropout, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.W_k = tf.keras.layers.Dense(num_hiddens, use_bias=False)\n",
        "        self.W_q = tf.keras.layers.Dense(num_hiddens, use_bias=False)\n",
        "        self.w_v = tf.keras.layers.Dense(1, use_bias=False)\n",
        "        self.dropout = tf.keras.layers.Dropout(dropout)\n",
        "\n",
        "    def call(self, queries, keys, values, valid_lens, **kwargs):\n",
        "        queries, keys = self.W_q(queries), self.W_k(keys)\n",
        "        features = tf.expand_dims(queries, axis=2) + tf.expand_dims(keys, axis=1) # queries (`batch_size`, no. of queries, 1, `num_hiddens`) and keys  (`batch_size`, 1, no. of key-value pairs, `num_hiddens`)\n",
        "        features = tf.nn.tanh(features)\n",
        "        scores = tf.squeeze(self.w_v(features), axis=-1) #score/medida de arencao\n",
        "        self.attention_weights = masked_softmax(scores, valid_lens) #apenas os coeficientes de atencao\n",
        "        #valor final do pooling de atenção: values ponderados pelos coeficientes de atenção\n",
        "        return tf.matmul(self.dropout(self.attention_weights, **kwargs), values) #values (`batch_size`, no. of key-value pairs, value dimension)\n",
        "\n",
        "\n",
        "queries, keys = tf.random.normal(shape=(2, 1, 20)), tf.ones((2, 10, 2))\n",
        "values = tf.repeat(tf.reshape(tf.range(40, dtype=tf.float32), shape=(1, 10, 4)), repeats=2, axis=0)\n",
        "\n",
        "print(\"Shape Queries (Q):\",queries.shape)\n",
        "print(\"Shape Keys (K):\",keys.shape)\n",
        "print(\"Shape Values (V):\", values.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tppeuAcJhI7h",
        "outputId": "a1900c51-f4e1-4a7c-f8a1-871dab231947"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape Queries (Q): (2, 1, 20)\n",
            "Shape Keys (K): (2, 10, 2)\n",
            "Shape Values (V): (2, 10, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A cardinalidade dos vetores $Q$, $K$, e $V$ pode ser compreendida pelo esquemático que segue"
      ],
      "metadata": {
        "id": "Yc521hK1dOOZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "BLRcx3F8ddHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "valid_lens = tf.constant([2, 6])\n",
        "attention = AdditiveAttention(key_size=2, query_size=20, num_hiddens=8,dropout=0.1)\n",
        "output = attention(queries, keys, values, valid_lens, training=False)\n",
        "\n",
        "print(\"Shape output (f): \", output.shape)\n",
        "print(\"Shape coeficientes de atencao (alfa): \", attention.attention_weights.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EN4ofC27zG22",
        "outputId": "55452f08-81cf-428f-e3a7-3ae211e37547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape output (f):  (2, 1, 4)\n",
            "Shape coeficientes de atencao (alfa):  (2, 1, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A medida de **atenção aditiva** recebe este nome pois o processamento de **queries** e **keys** de diferentes tamanhos é possível \"somando-se\" suas dimensões em um estado escondido de tamanho fixo (que é atingido através de camadas dense).\n",
        "\n",
        "Nos casos em que as **queries** e **keys** possuem o mesmo tamanho $d$, é possível utilizar uma medida de atenção baseada no produto interno normalizado entre $q$ e $k$; *i.e.*,  $a(\\mathbf{q},\\mathbf{t}) = \\frac{\\mathbf{q^Tk}}{\\sqrt{d}}$.\n",
        "\n",
        "O codigo exemplo implementa esta segunda medida de atenção:"
      ],
      "metadata": {
        "id": "kg3E3VuOegZ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DotProductAttention(tf.keras.layers.Layer):\n",
        "    \"\"\"Scaled dot product attention.\"\"\"\n",
        "    def __init__(self, dropout, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.dropout = tf.keras.layers.Dropout(dropout)\n",
        "\n",
        "    # Shape of `queries`: (`batch_size`, no. of queries, `d`)\n",
        "    # Shape of `keys`: (`batch_size`, no. of key-value pairs, `d`)\n",
        "    # Shape of `values`: (`batch_size`, no. of key-value pairs, value\n",
        "    # dimension)\n",
        "    # Shape of `valid_lens`: (`batch_size`,) or (`batch_size`, no. of queries)\n",
        "    def call(self, queries, keys, values, valid_lens, **kwargs):\n",
        "        d = queries.shape[-1]\n",
        "        scores = tf.matmul(queries, keys, transpose_b=True)/tf.math.sqrt(\n",
        "            tf.cast(d, dtype=tf.float32))\n",
        "        self.attention_weights = masked_softmax(scores, valid_lens)\n",
        "        return tf.matmul(self.dropout(self.attention_weights, **kwargs), values)\n",
        "\n",
        "queries = tf.random.normal(shape=(2, 1, 2))\n",
        "attention = DotProductAttention(dropout=0.5)\n",
        "attention(queries, keys, values, valid_lens, training=False)\n",
        "\n",
        "print(\"Shape Queries (Q):\",queries.shape)\n",
        "print(\"Shape Keys (K):\",keys.shape)\n",
        "print(\"Shape Values (V):\", values.shape)\n",
        "print(\"Shape output (f): \", output.shape)\n",
        "print(\"Shape coeficientes de atencao (alfa): \", attention.attention_weights.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCbqV_5pgiPI",
        "outputId": "b1b47ffa-b48f-46f1-ff56-77e6213577e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape Queries (Q): (2, 1, 2)\n",
            "Shape Keys (K): (2, 10, 2)\n",
            "Shape Values (V): (2, 10, 4)\n",
            "Shape output (f):  (2, 1, 4)\n",
            "Shape coeficientes de atencao (alfa):  (2, 1, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Atenção de Bahdanau\n",
        "\n"
      ],
      "metadata": {
        "id": "VWaQ8wWxghsA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![bahdanau.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ZWLbjYyogfpg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Seq2SeqEncoder(d2l.Encoder):\n",
        "    \"\"\"The RNN encoder for sequence to sequence learning.\"\"\"\n",
        "    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers, dropout=0, **kwargs):\n",
        "        super().__init__(*kwargs)\n",
        "        # Embedding layer\n",
        "        self.embedding = tf.keras.layers.Embedding(vocab_size, embed_size)\n",
        "        self.rnn = tf.keras.layers.RNN(tf.keras.layers.StackedRNNCells(\n",
        "            [tf.keras.layers.GRUCell(num_hiddens, dropout=dropout) for _ in range(num_layers)]),\n",
        "             return_sequences=True, return_state=True)\n",
        "\n",
        "    def call(self, X, *args, **kwargs):\n",
        "        # The input `X` shape: (`batch_size`, `num_steps`)\n",
        "        # The output `X` shape: (`batch_size`, `num_steps`, `embed_size`)\n",
        "        X = self.embedding(X)\n",
        "        output = self.rnn(X, **kwargs)\n",
        "        state = output[1:]\n",
        "        return output[0], state\n",
        "\n",
        "class AttentionDecoder(d2l.Decoder):\n",
        "    \"\"\"The base attention-based decoder interface.\"\"\"\n",
        "    def __init__(self, **kwargs):\n",
        "        super(AttentionDecoder, self).__init__(**kwargs)\n",
        "\n",
        "    @property\n",
        "    def attention_weights(self):\n",
        "        raise NotImplementedError\n",
        "\n",
        "class Seq2SeqAttentionDecoder(AttentionDecoder):\n",
        "    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,\n",
        "                 dropout=0, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.attention = d2l.AdditiveAttention(num_hiddens, num_hiddens,\n",
        "                                               num_hiddens, dropout)\n",
        "        self.embedding = tf.keras.layers.Embedding(vocab_size, embed_size)\n",
        "        self.rnn = tf.keras.layers.RNN(tf.keras.layers.StackedRNNCells(\n",
        "            [tf.keras.layers.GRUCell(num_hiddens, dropout=dropout)\n",
        "             for _ in range(num_layers)]),\n",
        "                                      return_sequences=True, return_state=True)\n",
        "        self.dense = tf.keras.layers.Dense(vocab_size)\n",
        "\n",
        "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
        "        # Shape of `outputs`: (`batch_size`, `num_steps`, `num_hiddens`).\n",
        "        # Shape of `hidden_state[0]`: (`num_layers`, `batch_size`, `num_hiddens`)\n",
        "        outputs, hidden_state = enc_outputs\n",
        "        return (outputs, hidden_state, enc_valid_lens)\n",
        "\n",
        "    def call(self, X, state, **kwargs):\n",
        "        # Shape of `enc_outputs`: (`batch_size`, `num_steps`, `num_hiddens`).\n",
        "        # Shape of `hidden_state[0]`: (`num_layers`, `batch_size`, `num_hiddens`)\n",
        "        enc_outputs, hidden_state, enc_valid_lens = state\n",
        "        # Shape of the output `X`: (`num_steps`, `batch_size`, `embed_size`)\n",
        "        X = self.embedding(X) # Input `X` has shape: (`batch_size`, `num_steps`)\n",
        "        X = tf.transpose(X, perm=(1, 0, 2))\n",
        "        outputs, self._attention_weights = [], []\n",
        "        for x in X:\n",
        "            # Shape of `query`: (`batch_size`, 1, `num_hiddens`)\n",
        "            query = tf.expand_dims(hidden_state[-1], axis=1)\n",
        "            # Shape of `context`: (`batch_size, 1, `num_hiddens`)\n",
        "            context = self.attention(query, enc_outputs, enc_outputs,\n",
        "                                     enc_valid_lens, **kwargs)\n",
        "            # Concatenate on the feature dimension\n",
        "            x = tf.concat((context, tf.expand_dims(x, axis=1)), axis=-1)\n",
        "            out = self.rnn(x, hidden_state, **kwargs)\n",
        "            hidden_state = out[1:]\n",
        "            outputs.append(out[0])\n",
        "            self._attention_weights.append(self.attention.attention_weights)\n",
        "        # After fully-connected layer transformation, shape of `outputs`:\n",
        "        # (`batch_size`, `num_steps`, `vocab_size`)\n",
        "        outputs = self.dense(tf.concat(outputs, axis=1))\n",
        "        return outputs, [enc_outputs, hidden_state, enc_valid_lens]\n",
        "\n",
        "    @property\n",
        "    def attention_weights(self):\n",
        "        return self._attention_weights"
      ],
      "metadata": {
        "id": "UDgxoWwArFVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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, 250, d2l.try_gpu()\n",
        "\n",
        "train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
        "encoder = d2l.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 = d2l.EncoderDecoder(encoder, decoder)\n",
        "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "dMZqzyuYJfjv",
        "outputId": "ba21b586-a1e7-4f26-e0a0-375a131da724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.026, 774.0 tokens/sec on <tensorflow.python.eager.context._EagerDeviceContext object at 0x7f1d7056f730>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
            ],
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .', 'I am student .']\n",
        "\n",
        "for eng in engs:\n",
        "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(net, eng, src_vocab, tgt_vocab, num_steps, True)\n",
        "    print(f'{eng} => {translation}, ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlcyKJi8e8bs",
        "outputId": "45d9305d-e187-41a7-9ce6-d66680c999f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "go . => va !, \n",
            "i lost . => j'ai perdu ., \n",
            "he's calm . => il est mouillé ., \n",
            "i'm home . => je suis chez moi ., \n",
            "I am student . => je suis <unk> ., \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attention_weights = tf.reshape(tf.concat([step[0][0][0] for step in dec_attention_weight_seq], 0), (1, 1, -1, num_steps))\n",
        "\n",
        "d2l.show_heatmaps(attention_weights[:, :, :, :len(engs[-1].split()) + 1], xlabel='Key posistions', ylabel='Query posistions')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "JEK7SlAfIsWi",
        "outputId": "3b4435d4-5027-46eb-b269-7575e91d3f34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 2 Axes>"
            ],
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