NLP/Notebooks/8_Seq_2_Seq_Attention.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Seq-2-Seq-Attention.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KVA36WnFgjB"
      },
      "source": [
        "# Seq 2 Seq con Atención para traducción automática\n",
        "\n",
        "[[Paper](https://arxiv.org/pdf/1409.0473.pdf)]\n",
        "\n",
        "![3-min.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vvC9hKUGIwI",
        "outputId": "6e8f3a2c-3ddf-48f8-ce34-b79770777341"
      },
      "source": [
        "!pip install torchtext\n",
        "!python -m spacy download de\n",
        "!python -m spacy download en"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchtext in /usr/local/lib/python3.7/dist-packages (0.10.0)\n",
            "Requirement already satisfied: torch==1.9.0 in /usr/local/lib/python3.7/dist-packages (from torchtext) (1.9.0+cu102)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torchtext) (4.41.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchtext) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchtext) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.0->torchtext) (3.7.4.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torchtext) (3.0.4)\n",
            "Collecting de_core_news_sm==2.2.5\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz (14.9MB)\n",
            "\u001b[K     |████████████████████████████████| 14.9MB 6.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from de_core_news_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (57.0.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (4.6.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.4.1)\n",
            "Building wheels for collected packages: de-core-news-sm\n",
            "  Building wheel for de-core-news-sm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for de-core-news-sm: filename=de_core_news_sm-2.2.5-cp37-none-any.whl size=14907055 sha256=471c68e39d58869d0788ce776eba0b9a271bf72e70bce3247acd3d65669f1c02\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3zi5jlz9/wheels/ba/3f/ed/d4aa8e45e7191b7f32db4bfad565e7da1edbf05c916ca7a1ca\n",
            "Successfully built de-core-news-sm\n",
            "Installing collected packages: de-core-news-sm\n",
            "Successfully installed de-core-news-sm-2.2.5\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('de_core_news_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/de_core_news_sm -->\n",
            "/usr/local/lib/python3.7/dist-packages/spacy/data/de\n",
            "You can now load the model via spacy.load('de')\n",
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.7/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.2)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (57.0.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (4.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.7.4.3)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/en_core_web_sm -->\n",
            "/usr/local/lib/python3.7/dist-packages/spacy/data/en\n",
            "You can now load the model via spacy.load('en')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOK2WG31FRiv"
      },
      "source": [
        "import random\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "import spacy\n",
        "from torchtext.data.metrics import bleu_score\n",
        "import sys\n",
        "from torch.utils.tensorboard import SummaryWriter  # to print to tensorboard\n",
        "from torchtext.legacy.datasets import Multi30k\n",
        "from torchtext.legacy.data import Field, BucketIterator"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpu0yPKXGZs5"
      },
      "source": [
        "def translate_sentence(model, sentence, german, english, device, max_length=50):\n",
        "    # Load german tokenizer\n",
        "    spacy_ger = spacy.load(\"de\")\n",
        "\n",
        "    # Create tokens using spacy and everything in lower case (which is what our vocab is)\n",
        "    if type(sentence) == str:\n",
        "        tokens = [token.text.lower() for token in spacy_ger(sentence)]\n",
        "    else:\n",
        "        tokens = [token.lower() for token in sentence]\n",
        "\n",
        "    # Add <SOS> and <EOS> in beginning and end respectively\n",
        "    tokens.insert(0, german.init_token)\n",
        "    tokens.append(german.eos_token)\n",
        "\n",
        "    # Go through each german token and convert to an index\n",
        "    text_to_indices = [german.vocab.stoi[token] for token in tokens]\n",
        "\n",
        "    # Convert to Tensor\n",
        "    sentence_tensor = torch.LongTensor(text_to_indices).unsqueeze(1).to(device)\n",
        "\n",
        "    # Build encoder hidden, cell state\n",
        "    with torch.no_grad():\n",
        "        outputs_encoder, hiddens, cells = model.encoder(sentence_tensor)\n",
        "\n",
        "    outputs = [english.vocab.stoi[\"<sos>\"]]\n",
        "\n",
        "    for _ in range(max_length):\n",
        "        previous_word = torch.LongTensor([outputs[-1]]).to(device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            output, hiddens, cells = model.decoder(\n",
        "                previous_word, outputs_encoder, hiddens, cells\n",
        "            )\n",
        "            best_guess = output.argmax(1).item()\n",
        "\n",
        "        outputs.append(best_guess)\n",
        "\n",
        "        # Model predicts it's the end of the sentence\n",
        "        if output.argmax(1).item() == english.vocab.stoi[\"<eos>\"]:\n",
        "            break\n",
        "\n",
        "    translated_sentence = [english.vocab.itos[idx] for idx in outputs]\n",
        "\n",
        "    # remove start token\n",
        "    return translated_sentence[1:]\n",
        "\n",
        "\n",
        "def bleu(data, model, german, english, device):\n",
        "    targets = []\n",
        "    outputs = []\n",
        "\n",
        "    for example in data:\n",
        "        src = vars(example)[\"src\"]\n",
        "        trg = vars(example)[\"trg\"]\n",
        "\n",
        "        prediction = translate_sentence(model, src, german, english, device)\n",
        "        prediction = prediction[:-1]  # remove <eos> token\n",
        "\n",
        "        targets.append([trg])\n",
        "        outputs.append(prediction)\n",
        "\n",
        "    return bleu_score(outputs, targets)\n",
        "\n",
        "\n",
        "def save_checkpoint(state, filename=\"my_checkpoint.pth.tar\"):\n",
        "    print(\"=> Saving checkpoint\")\n",
        "    torch.save(state, filename)\n",
        "\n",
        "\n",
        "def load_checkpoint(checkpoint, model, optimizer):\n",
        "    print(\"=> Loading checkpoint\")\n",
        "    model.load_state_dict(checkpoint[\"state_dict\"])\n",
        "    optimizer.load_state_dict(checkpoint[\"optimizer\"])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HM--3u-0F13f",
        "outputId": "19e809dd-e90d-4c6b-a05d-d0a34c49b92c"
      },
      "source": [
        "\"\"\"\n",
        "To install spacy languages do:\n",
        "python -m spacy download en\n",
        "python -m spacy download de\n",
        "\"\"\"\n",
        "spacy_ger = spacy.load(\"de\")\n",
        "spacy_eng = spacy.load(\"en\")\n",
        "\n",
        "\n",
        "def tokenize_ger(text):\n",
        "    return [tok.text for tok in spacy_ger.tokenizer(text)]\n",
        "\n",
        "\n",
        "def tokenize_eng(text):\n",
        "    return [tok.text for tok in spacy_eng.tokenizer(text)]\n",
        "\n",
        "\n",
        "german = Field(tokenize=tokenize_ger, lower=True, init_token=\"<sos>\", eos_token=\"<eos>\")\n",
        "\n",
        "english = Field(\n",
        "    tokenize=tokenize_eng, lower=True, init_token=\"<sos>\", eos_token=\"<eos>\"\n",
        ")\n",
        "\n",
        "train_data, valid_data, test_data = Multi30k.splits(\n",
        "    exts=(\".de\", \".en\"), fields=(german, english)\n",
        ")\n",
        "\n",
        "german.build_vocab(train_data, max_size=10000, min_freq=2)\n",
        "english.build_vocab(train_data, max_size=10000, min_freq=2)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "downloading training.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "training.tar.gz: 100%|██████████| 1.21M/1.21M [00:01<00:00, 1.14MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "downloading validation.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "validation.tar.gz: 100%|██████████| 46.3k/46.3k [00:00<00:00, 170kB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "downloading mmt_task1_test2016.tar.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "mmt_task1_test2016.tar.gz: 100%|██████████| 66.2k/66.2k [00:00<00:00, 169kB/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGwz5MHXGO-w"
      },
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(self, input_size, embedding_size, hidden_size, num_layers, p):\n",
        "        super(Encoder, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.embedding = nn.Embedding(input_size, embedding_size)\n",
        "        self.rnn = nn.LSTM(embedding_size, hidden_size, num_layers, bidirectional=True)\n",
        "\n",
        "        self.fc_hidden = nn.Linear(hidden_size * 2, hidden_size)\n",
        "        self.fc_cell = nn.Linear(hidden_size * 2, hidden_size)\n",
        "        self.dropout = nn.Dropout(p)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x: (seq_length, N) where N is batch size\n",
        "\n",
        "        embedding = self.dropout(self.embedding(x))\n",
        "        # embedding shape: (seq_length, N, embedding_size)\n",
        "\n",
        "        encoder_states, (hidden, cell) = self.rnn(embedding)\n",
        "        # outputs shape: (seq_length, N, hidden_size)\n",
        "\n",
        "        # Use forward, backward cells and hidden through a linear layer\n",
        "        # so that it can be input to the decoder which is not bidirectional\n",
        "        # Also using index slicing ([idx:idx+1]) to keep the dimension\n",
        "        hidden = self.fc_hidden(torch.cat((hidden[0:1], hidden[1:2]), dim=2))\n",
        "        cell = self.fc_cell(torch.cat((cell[0:1], cell[1:2]), dim=2))\n",
        "\n",
        "        return encoder_states, hidden, cell\n",
        "\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(\n",
        "        self, input_size, embedding_size, hidden_size, output_size, num_layers, p\n",
        "    ):\n",
        "        super(Decoder, self).__init__()\n",
        "        self.hidden_size = hidden_size\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.embedding = nn.Embedding(input_size, embedding_size)\n",
        "        self.rnn = nn.LSTM(hidden_size * 2 + embedding_size, hidden_size, num_layers)\n",
        "\n",
        "        self.energy = nn.Linear(hidden_size * 3, 1)\n",
        "        self.fc = nn.Linear(hidden_size, output_size)\n",
        "        self.dropout = nn.Dropout(p)\n",
        "        self.softmax = nn.Softmax(dim=0)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, x, encoder_states, hidden, cell):\n",
        "        x = x.unsqueeze(0)\n",
        "        # x: (1, N) where N is the batch size\n",
        "\n",
        "        embedding = self.dropout(self.embedding(x))\n",
        "        # embedding shape: (1, N, embedding_size)\n",
        "\n",
        "        sequence_length = encoder_states.shape[0]\n",
        "        h_reshaped = hidden.repeat(sequence_length, 1, 1)\n",
        "        # h_reshaped: (seq_length, N, hidden_size*2)\n",
        "\n",
        "        energy = self.relu(self.energy(torch.cat((h_reshaped, encoder_states), dim=2)))\n",
        "        # energy: (seq_length, N, 1)\n",
        "\n",
        "        attention = self.softmax(energy)\n",
        "        # attention: (seq_length, N, 1)\n",
        "\n",
        "        # attention: (seq_length, N, 1), snk\n",
        "        # encoder_states: (seq_length, N, hidden_size*2), snl\n",
        "        # we want context_vector: (1, N, hidden_size*2), i.e knl\n",
        "        context_vector = torch.einsum(\"snk,snl->knl\", attention, encoder_states)\n",
        "\n",
        "        rnn_input = torch.cat((context_vector, embedding), dim=2)\n",
        "        # rnn_input: (1, N, hidden_size*2 + embedding_size)\n",
        "\n",
        "        outputs, (hidden, cell) = self.rnn(rnn_input, (hidden, cell))\n",
        "        # outputs shape: (1, N, hidden_size)\n",
        "\n",
        "        predictions = self.fc(outputs).squeeze(0)\n",
        "        # predictions: (N, hidden_size)\n",
        "\n",
        "        return predictions, hidden, cell\n",
        "\n",
        "\n",
        "class Seq2Seq(nn.Module):\n",
        "    def __init__(self, encoder, decoder):\n",
        "        super(Seq2Seq, self).__init__()\n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "\n",
        "    def forward(self, source, target, teacher_force_ratio=0.5):\n",
        "        batch_size = source.shape[1]\n",
        "        target_len = target.shape[0]\n",
        "        target_vocab_size = len(english.vocab)\n",
        "\n",
        "        outputs = torch.zeros(target_len, batch_size, target_vocab_size).to(device)\n",
        "        encoder_states, hidden, cell = self.encoder(source)\n",
        "\n",
        "        # First input will be <SOS> token\n",
        "        x = target[0]\n",
        "\n",
        "        for t in range(1, target_len):\n",
        "            # At every time step use encoder_states and update hidden, cell\n",
        "            output, hidden, cell = self.decoder(x, encoder_states, hidden, cell)\n",
        "\n",
        "            # Store prediction for current time step\n",
        "            outputs[t] = output\n",
        "\n",
        "            # Get the best word the Decoder predicted (index in the vocabulary)\n",
        "            best_guess = output.argmax(1)\n",
        "\n",
        "            # With probability of teacher_force_ratio we take the actual next word\n",
        "            # otherwise we take the word that the Decoder predicted it to be.\n",
        "            # Teacher Forcing is used so that the model gets used to seeing\n",
        "            # similar inputs at training and testing time, if teacher forcing is 1\n",
        "            # then inputs at test time might be completely different than what the\n",
        "            # network is used to. This was a long comment.\n",
        "            x = target[t] if random.random() < teacher_force_ratio else best_guess\n",
        "\n",
        "        return outputs"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hm4q8oFRGUrp"
      },
      "source": [
        "### We're ready to define everything we need for training our Seq2Seq model ###\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "load_model = False\n",
        "save_model = True\n",
        "\n",
        "# Training hyperparameters\n",
        "num_epochs = 100\n",
        "learning_rate = 3e-4\n",
        "batch_size = 32\n",
        "\n",
        "# Model hyperparameters\n",
        "input_size_encoder = len(german.vocab)\n",
        "input_size_decoder = len(english.vocab)\n",
        "output_size = len(english.vocab)\n",
        "encoder_embedding_size = 300\n",
        "decoder_embedding_size = 300\n",
        "hidden_size = 1024\n",
        "num_layers = 1\n",
        "enc_dropout = 0.0\n",
        "dec_dropout = 0.0\n",
        "\n",
        "# Tensorboard to get nice loss plot\n",
        "writer = SummaryWriter(f\"runs/loss_plot\")\n",
        "step = 0\n",
        "\n",
        "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
        "    (train_data, valid_data, test_data),\n",
        "    batch_size=batch_size,\n",
        "    sort_within_batch=True,\n",
        "    sort_key=lambda x: len(x.src),\n",
        "    device=device,\n",
        ")\n",
        "\n",
        "encoder_net = Encoder(\n",
        "    input_size_encoder, encoder_embedding_size, hidden_size, num_layers, enc_dropout\n",
        ").to(device)\n",
        "\n",
        "decoder_net = Decoder(\n",
        "    input_size_decoder,\n",
        "    decoder_embedding_size,\n",
        "    hidden_size,\n",
        "    output_size,\n",
        "    num_layers,\n",
        "    dec_dropout,\n",
        ").to(device)\n",
        "\n",
        "model = Seq2Seq(encoder_net, decoder_net).to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "pad_idx = english.vocab.stoi[\"<pad>\"]\n",
        "criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)\n",
        "\n",
        "if load_model:\n",
        "    load_checkpoint(torch.load(\"my_checkpoint.pth.tar\"), model, optimizer)\n",
        "\n",
        "'''\n",
        "# un barco con varios hombres es arrastrado a tierra por un gran equipo de caballos\n",
        "# a boat with several men on it is pulled ashore by a large team of horses.\n",
        "sentence = \"ein boot mit mehreren männern darauf wird von einem großen pferdegespann ans ufer gezogen.\"\n",
        "'''\n",
        "\n",
        "sentence = (\n",
        "    \"ein boot mit mehreren männern darauf wird von einem großen\"\n",
        "    \"pferdegespann ans ufer gezogen.\"\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "UrQcUc_5GTqx",
        "outputId": "5aa56a48-5a7d-49cd-8cae-88caee30b966"
      },
      "source": [
        "for epoch in range(num_epochs):\n",
        "    print(f\"[Epoch {epoch} / {num_epochs}]\")\n",
        "\n",
        "    if save_model:\n",
        "        checkpoint = {\n",
        "            \"state_dict\": model.state_dict(),\n",
        "            \"optimizer\": optimizer.state_dict(),\n",
        "        }\n",
        "        save_checkpoint(checkpoint)\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    translated_sentence = translate_sentence(\n",
        "        model, sentence, german, english, device, max_length=50\n",
        "    )\n",
        "\n",
        "    print(f\"Translated example sentence: \\n {translated_sentence}\")\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    for batch_idx, batch in enumerate(train_iterator):\n",
        "        # Get input and targets and get to cuda\n",
        "        inp_data = batch.src.to(device)\n",
        "        target = batch.trg.to(device)\n",
        "\n",
        "        # Forward prop\n",
        "        output = model(inp_data, target)\n",
        "\n",
        "        # Output is of shape (trg_len, batch_size, output_dim) but Cross Entropy Loss\n",
        "        # doesn't take input in that form. For example if we have MNIST we want to have\n",
        "        # output to be: (N, 10) and targets just (N). Here we can view it in a similar\n",
        "        # way that we have output_words * batch_size that we want to send in into\n",
        "        # our cost function, so we need to do some reshapin. While we're at it\n",
        "        # Let's also remove the start token while we're at it\n",
        "        output = output[1:].reshape(-1, output.shape[2])\n",
        "        target = target[1:].reshape(-1)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(output, target)\n",
        "\n",
        "        # Back prop\n",
        "        loss.backward()\n",
        "\n",
        "        # Clip to avoid exploding gradient issues, makes sure grads are\n",
        "        # within a healthy range\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)\n",
        "\n",
        "        # Gradient descent step\n",
        "        optimizer.step()\n",
        "\n",
        "        # Plot to tensorboard\n",
        "        writer.add_scalar(\"Training loss\", loss, global_step=step)\n",
        "        step += 1\n",
        "\n",
        "# running on entire test data takes a while\n",
        "score = bleu(test_data[1:100], model, german, english, device)\n",
        "print(f\"Bleu score {score * 100:.2f}\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Epoch 0 / 100]\n",
            "=> Saving checkpoint\n",
            "Translated example sentence: \n",
            " ['preparation', 'bartender', 'split', 'enthusiasm', 'enthusiasm', 'country', 'cartwheel', 'patient', 'patient', 'patient', 'caged', 'ahead', 'blocking', 'blocked', 'cable', 'pedestrian', 'only', 'cola', 'cola', 'barricade', 'batman', 'instructions', 'brazil', 'little', 'sexy', 'maintenance', 'intersection', 'smooth', 'array', 'brook', 'heartbeat', 'reception', 'roots', 'flood', 'nibbles', 'wading', 'juggler', 'specialized', 'scrabble', 'buckets', 'decorations', 'iced', 'merry', 'cutting', \"'\", 'spending', 'seashore', 'early', 'hook', 'flooded']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-19e40e9e5335>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;31m# Back prop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;31m# Clip to avoid exploding gradient issues, makes sure grads are\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    253\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 255\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    147\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    148\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SyJ0xRWGtm6"
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs/loss_plot"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}