nmt_follow_along.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ejmejm/multilingual-nmt-mt5/blob/main/nmt_follow_along.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_uBnCuyadg6"
      },
      "source": [
        "## Model: mt5\n",
        "attention based model\n",
        "\n",
        "\"Rick is cool, I like the way he looks.\" -> MODEL -> sijfiosdjfiosj"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrHARGS2dlK2",
        "outputId": "05abb501-4fde-4062-c432-b9bfb7ba77bf"
      },
      "source": [
        "!pip install transformers sentencepiece datasets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d8/b2/57495b5309f09fa501866e225c84532d1fd89536ea62406b2181933fb418/transformers-4.5.1-py3-none-any.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 5.6MB/s \n",
            "\u001b[?25hCollecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 53.1MB/s \n",
            "\u001b[?25hCollecting datasets\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/54/90/43b396481a8298c6010afb93b3c1e71d4ba6f8c10797a7da8eb005e45081/datasets-1.5.0-py3-none-any.whl (192kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 54.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3MB 49.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 52.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.10.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.1.5)\n",
            "Collecting huggingface-hub<0.1.0\n",
            "  Downloading https://files.pythonhosted.org/packages/a1/88/7b1e45720ecf59c6c6737ff332f41c955963090a18e72acbcbeac6b25e86/huggingface_hub-0.0.8-py3-none-any.whl\n",
            "Requirement already satisfied: pyarrow>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (3.0.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.3)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.11.1)\n",
            "Collecting fsspec\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/62/11/f7689b996f85e45f718745c899f6747ee5edb4878cadac0a41ab146828fa/fsspec-0.9.0-py3-none-any.whl (107kB)\n",
            "\u001b[K     |████████████████████████████████| 112kB 45.9MB/s \n",
            "\u001b[?25hCollecting xxhash\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/4f/0a862cad26aa2ed7a7cd87178cbbfa824fc1383e472d63596a0d018374e7/xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 48.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (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->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.1)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.44-cp37-none-any.whl size=886084 sha256=f91dd457ceeef3b3d72a933164ab32f951a98edb078da8b45844ea1d0f510e39\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers, sentencepiece, huggingface-hub, fsspec, xxhash, datasets\n",
            "Successfully installed datasets-1.5.0 fsspec-0.9.0 huggingface-hub-0.0.8 sacremoses-0.0.44 sentencepiece-0.1.95 tokenizers-0.10.2 transformers-4.5.1 xxhash-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAyTW5Ipe1tW",
        "outputId": "6703172a-d511-49b5-c205-e0bf40cb5529"
      },
      "source": [
        "from datasets import load_dataset\n",
        "from google.colab import drive\n",
        "from IPython.display import display\n",
        "from IPython.html import widgets\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import torch\n",
        "from torch import optim\n",
        "from torch.nn import functional as F\n",
        "from transformers import AdamW, AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "from transformers import get_linear_schedule_with_warmup\n",
        "from tqdm import tqdm_notebook\n",
        "\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
            "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OF0NdpIzeb95"
      },
      "source": [
        "model_repo = 'google/mt5-small'\n",
        "max_seq_len = model.config.max_length"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "colab": {
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          "height": 214,
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        "id": "Ikvs8z6LevIv",
        "outputId": "851575d9-5162-4180-836f-27eb25d594d7"
      },
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_repo)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a255ab4dfc4243478d928e9bbe955ed2",
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          "name": "stdout"
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          "name": "stdout"
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        {
          "output_type": "stream",
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          ],
          "name": "stdout"
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "f0339ed6207649eb89465d26fed5f85b",
            "283226edbbaf48aab56c8b9f065d7c98",
            "fb5918c6645d4b78aaf0ecbb4a24ca14",
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        "id": "tVWi5VV2fEvX",
        "outputId": "ac66ccb2-549f-4e2a-c08f-e6adfcdc0d92"
      },
      "source": [
        "# Model description: https://huggingface.co/google/mt5-base\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_repo)\n",
        "model = model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0339ed6207649eb89465d26fed5f85b",
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYNukljQfT8r",
        "outputId": "19fe8cbe-d82a-40cd-dca8-c92c281bcd4a"
      },
      "source": [
        "input_sent = 'Here is our test sentence!'\n",
        "token_ids = tokenizer.encode(input_sent, return_tensors='pt').cuda()\n",
        "token_ids\n",
        "\n",
        "model_out = model.generate(token_ids)\n",
        "print(model_out)\n",
        "\n",
        "output_text = tokenizer.convert_tokens_to_string(\n",
        "    tokenizer.convert_ids_to_tokens(model_out[0]))\n",
        "print(output_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[     0, 250099,      1]], device='cuda:0')\n",
            "<pad> <extra_id_0></s>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llpdLex8f6tX"
      },
      "source": [
        "# Steps\n",
        "1. Load the pretrained model and tokenizer\n",
        "2. Load in the dataset\n",
        "3. Transform the dataset into input\n",
        "4. Train/finetune the model on our dataset\n",
        "5. Test the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yO6hKdD3gS0M",
        "outputId": "ccdad67c-0659-4796-ac19-d73443799bd8"
      },
      "source": [
        "example_input_str = '<jp>This is a test nbuig.'\n",
        "input_ids = tokenizer.encode(example_input_str, return_tensors='pt')\n",
        "print('Input IDs:', input_ids)\n",
        "\n",
        "tokens = tokenizer.convert_ids_to_tokens(input_ids[0])\n",
        "print('Tokens:', tokens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input IDs: tensor([[ 1042,  3889,   669, 13673,   339,   259,   262,  2978,   259,   272,\n",
            "          1982,  1315,   260,     1]])\n",
            "Tokens: ['▁<', 'jp', '>', 'This', '▁is', '▁', 'a', '▁test', '▁', 'n', 'bu', 'ig', '.', '</s>']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLFLtliphr-o"
      },
      "source": [
        "# sorted(tokenizer.vocab.items(), key=lambda x: x[1])"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "id": "jNPKnrJCiiDm",
        "outputId": "b7bc009a-24be-4b93-da7b-47999dcdba49"
      },
      "source": [
        "# Source: https://huggingface.co/datasets/alt\n",
        "dataset = load_dataset('alt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "No config specified, defaulting to: alt/alt-parallel\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset alt/alt-parallel (download: 20.30 MiB, generated: 72.47 MiB, post-processed: Unknown size, total: 92.77 MiB) to /root/.cache/huggingface/datasets/alt/alt-parallel/1.0.0/70124cadb11e926eb17904d6edf5f79041aee772bab75a7ef37cb25633fed98f...\n"
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        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        },
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          ],
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        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stdout"
        },
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            "\r"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset alt downloaded and prepared to /root/.cache/huggingface/datasets/alt/alt-parallel/1.0.0/70124cadb11e926eb17904d6edf5f79041aee772bab75a7ef37cb25633fed98f. Subsequent calls will reuse this data.\n"
          ],
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNj7pQl9isLn"
      },
      "source": [
        "train_dataset = dataset['train']\n",
        "test_dataset = dataset['test']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpE4tzDYiwmW",
        "outputId": "10b37a34-3a7f-4ce6-ee8f-9257a710b2d0"
      },
      "source": [
        "train_dataset[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'SNT.URLID': '80188',\n",
              " 'SNT.URLID.SNTID': '1',\n",
              " 'translation': {'bg': 'ফ্রান্সের প্যারিসের পার্ক দি প্রিন্সেস-এ হওয়া ২০০৭-এর রাগবি বিশ্বকাপের পুল সি-তে ইটালি পর্তুগালকে ৩১-৫ গোলে হারিয়েছে।',\n",
              "  'en': 'Italy have defeated Portugal 31-5 in Pool C of the 2007 Rugby World Cup at Parc des Princes, Paris, France.',\n",
              "  'en_tok': 'Italy have defeated Portugal 31-5 in Pool C of the 2007 Rugby World Cup at Parc des Princes , Paris , France .',\n",
              "  'fil': 'Natalo ng Italya ang Portugal sa puntos na 31-5 sa Grupong C noong 2007 sa Pandaigdigang laro ng Ragbi sa Parc des Princes, Paris, France.',\n",
              "  'hi': '2007 में फ़्रांस, पेरिस के पार्क डेस प्रिंसेस में हुए रग्बी विश्व कप के पूल C में इटली ने पुर्तगाल को 31-5 से हराया।',\n",
              "  'id': 'Italia berhasil mengalahkan Portugal 31-5 di grup C dalam Piala Dunia Rugby 2007 di Parc des Princes, Paris, Perancis.',\n",
              "  'ja': 'フランスのパリ、パルク・デ・プランスで行われた2007年ラグビーワールドカップのプールCで、イタリアは31対5でポルトガルを下した。',\n",
              "  'khm': 'អ៊ីតាលីបានឈ្នះលើព័រទុយហ្គាល់ 31-5 ក្នុងប៉ូលCនៃពីធីប្រកួតពានរង្វាន់ពិភពលោកនៃកីឡាបាល់ឱបឆ្នាំ2007ដែលប្រព្រឹត្តនៅប៉ាសឌេសប្រីន ក្រុងប៉ារីស បារាំង។',\n",
              "  'lo': 'ອິຕາລີໄດ້ເສຍໃຫ້ປ໊ອກຕຸຍການ 31 ຕໍ່ 5 ໃນພູລ C ຂອງ ການແຂ່ງຂັນຣັກບີ້ລະດັບໂລກປີ 2007 ທີ່ ປາກເດແພຣັງ ປາຣີ ປະເທດຝຣັ່ງ.',\n",
              "  'ms': 'Itali telah mengalahkan Portugal 31-5 dalam Pool C pada Piala Dunia Ragbi 2007 di Parc des Princes, Paris, Perancis.',\n",
              "  'my': 'ပြင်သစ်နိုင်ငံ ပါရီမြို့ ပါ့ဒက်စ် ပရင့်စက် ၌ ၂၀၀၇ခုနှစ် ရပ်ဘီ ကမ္ဘာ့ ဖလား တွင် အီတလီ သည် ပေါ်တူဂီ ကို ၃၁-၅ ဂိုး ဖြင့် ရေကူးကန် စီ တွင် ရှုံးနိမ့်သွားပါသည် ။',\n",
              "  'th': 'อิตาลีได้เอาชนะโปรตุเกสด้วยคะแนน31ต่อ5 ในกลุ่มc ของการแข่งขันรักบี้เวิลด์คัพปี2007 ที่สนามปาร์กเดแพร็งส์ ที่กรุงปารีส ประเทศฝรั่งเศส',\n",
              "  'vi': 'Ý đã đánh bại Bồ Đào Nha với tỉ số 31-5 ở Bảng C Giải vô địch Rugby thế giới 2007 tại Parc des Princes, Pari, Pháp.',\n",
              "  'zh': '意大利在法国巴黎王子公园体育场举办的2007年橄榄球世界杯C组以31-5击败葡萄牙。'},\n",
              " 'url': 'http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbtN3ZgojCU1"
      },
      "source": [
        "LANG_TOKEN_MAPPING = {\n",
        "    'en': '<en>',\n",
        "    'ja': '<jp>',\n",
        "    'zh': '<zh>'\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUijvQ0SjNvI",
        "outputId": "aaca8a6a-361d-4e0e-90f4-f491800ce632"
      },
      "source": [
        "special_tokens_dict = {'additional_special_tokens': list(LANG_TOKEN_MAPPING.values())}\n",
        "tokenizer.add_special_tokens(special_tokens_dict)\n",
        "model.resize_token_embeddings(len(tokenizer))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Embedding(250103, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5TH169djnjB",
        "outputId": "80bdcabd-e979-48ba-a3fb-82b9b4819b72"
      },
      "source": [
        "token_ids = tokenizer.encode(\n",
        "    example_input_str, return_tensors='pt',\n",
        "    padding='max_length',\n",
        "    truncation=True, max_length=max_seq_len)\n",
        "print(token_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[250101,   1494,    339,    259,    262,   2978,    259,    272,   1982,\n",
            "           1315,    260,      1,      0,      0,      0,      0,      0,      0,\n",
            "              0,      0]])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jryVZU0ukTDB"
      },
      "source": [
        "def encode_input_str(text, target_lang, tokenizer, seq_len,\n",
        "                     lang_token_map=LANG_TOKEN_MAPPING):\n",
        "  target_lang_token = lang_token_map[target_lang]\n",
        "\n",
        "  # Tokenize and add special tokens\n",
        "  input_ids = tokenizer.encode(\n",
        "      text = target_lang_token + text,\n",
        "      return_tensors = 'pt',\n",
        "      padding = 'max_length',\n",
        "      truncation = True,\n",
        "      max_length = seq_len)\n",
        "\n",
        "  return input_ids[0]\n",
        "  \n",
        "def encode_target_str(text, tokenizer, seq_len,\n",
        "                      lang_token_map=LANG_TOKEN_MAPPING):\n",
        "  token_ids = tokenizer.encode(\n",
        "      text = text,\n",
        "      return_tensors = 'pt',\n",
        "      padding = 'max_length',\n",
        "      truncation = True,\n",
        "      max_length = seq_len)\n",
        "  \n",
        "  return token_ids[0]\n",
        "\n",
        "def format_translation_data(translations, lang_token_map,\n",
        "                            tokenizer, seq_len=128):\n",
        "  # Choose a random 2 languages for in i/o\n",
        "  langs = list(lang_token_map.keys())\n",
        "  input_lang, target_lang = np.random.choice(langs, size=2, replace=False)\n",
        "\n",
        "  # Get the translations for the batch\n",
        "  input_text = translations[input_lang]\n",
        "  target_text = translations[target_lang]\n",
        "\n",
        "  if input_text is None or target_text is None:\n",
        "    return None\n",
        "\n",
        "  input_token_ids = encode_input_str(\n",
        "      input_text, target_lang, tokenizer, seq_len, lang_token_map)\n",
        "  \n",
        "  target_token_ids = encode_target_str(\n",
        "      target_text, tokenizer, seq_len, lang_token_map)\n",
        "\n",
        "  return input_token_ids, target_token_ids\n",
        "\n",
        "def transform_batch(batch, lang_token_map, tokenizer):\n",
        "  inputs = []\n",
        "  targets = []\n",
        "  for translation_set in batch['translation']:\n",
        "    formatted_data = format_translation_data(\n",
        "        translation_set, lang_token_map, tokenizer, max_seq_len)\n",
        "    \n",
        "    if formatted_data is None:\n",
        "      continue\n",
        "\n",
        "    input_ids, target_ids = formatted_data\n",
        "    inputs.append(input_ids.unsqueeze(0))\n",
        "    targets.append(target_ids.unsqueeze(0))\n",
        "    \n",
        "  batch_input_ids = torch.cat(inputs).cuda()\n",
        "  batch_target_ids = torch.cat(targets).cuda()\n",
        "\n",
        "  return batch_input_ids, batch_target_ids\n",
        "\n",
        "def get_data_generator(dataset, lang_token_map, tokenizer, batch_size=32):\n",
        "  dataset = dataset.shuffle()\n",
        "  for i in range(0, len(dataset), batch_size):\n",
        "    raw_batch = dataset[i:i+batch_size]\n",
        "    yield transform_batch(raw_batch, lang_token_map, tokenizer)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CffHU9KlobK",
        "outputId": "f2a292b7-bb7c-42d2-8b22-988fa11f3819"
      },
      "source": [
        "in_ids, out_ids = format_translation_data(\n",
        "    train_dataset[1]['translation'], LANG_TOKEN_MAPPING, tokenizer)\n",
        "\n",
        "print(' '.join(tokenizer.convert_ids_to_tokens(in_ids)))\n",
        "print(' '.join(tokenizer.convert_ids_to_tokens(out_ids)))\n",
        "\n",
        "data_gen = get_data_generator(train_dataset, LANG_TOKEN_MAPPING, tokenizer, 8)\n",
        "data_batch = next(data_gen)\n",
        "print('Input shape:', data_batch[0].shape)\n",
        "print('Output shape:', data_batch[1].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<jp> ▁ 安 德里 亚 · 马 西 在 第四 分钟 成功 达 阵 , 意大利 率先 得分 。 </s> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "▁ アンド レア ・ マー ジ が 開始 4 分 後の トライ で イタリア にとって 最初の 得点 を入れ た 。 </s> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n",
            "Input shape: torch.Size([8, 20])\n",
            "Output shape: torch.Size([8, 20])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5AzLam0nxpf"
      },
      "source": [
        "n_epochs = 5\n",
        "batch_size = 16\n",
        "print_freq = 50\n",
        "lr = 5e-4\n",
        "n_batches = int(np.ceil(len(train_dataset) / batch_size))\n",
        "total_steps = n_epochs * n_batches\n",
        "n_warmup_steps = int(total_steps * 0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7QXP16AojFM"
      },
      "source": [
        "# Optimizer\n",
        "optimizer = AdamW(model.parameters(), lr=lr)\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "    optimizer, n_warmup_steps, total_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjGRS1pcoyWu"
      },
      "source": [
        "losses = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVb2I5JPqEvx"
      },
      "source": [
        "def eval_model(model, gdataset, max_iters=8):\n",
        "  test_generator = get_data_generator(gdataset, LANG_TOKEN_MAPPING,\n",
        "                                      tokenizer, batch_size)\n",
        "  eval_losses = []\n",
        "  for i, (input_batch, label_batch) in enumerate(test_generator):\n",
        "    if i >= max_iters:\n",
        "      break\n",
        "\n",
        "    model_out = model.forward(\n",
        "        input_ids = input_batch,\n",
        "        labels = label_batch)\n",
        "    eval_losses.append(model_out.loss.item())\n",
        "\n",
        "  return np.mean(eval_losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYkv-VQ3qa2d"
      },
      "source": [
        "test_loss = eval_model(model, test_dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVx4INnhqcTc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db772756-9859-4a42-ffc8-7c6609ed38a9"
      },
      "source": [
        "test_loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.400395840406418"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772,
          "referenced_widgets": [
            "c97acc3abb5c42c0b448f9278a3ff202",
            "292217e04dfd4c94a3fa56597aeb19b6",
            "37078d3fd7224e01876e76ef555a0d47",
            "380f7f1afa764e2fa48a8bbdc92f1c59",
            "07a06bfb642f4560bc184719e1ddfe4e",
            "539a4ab39985499fbe6bcf7bdbec4d82",
            "47d46da9f7924c05a6f37ca92c00a3b3",
            "000b4db97f614596b14805bbf4ed841a"
          ]
        },
        "id": "CrQS_2Oho0ZR",
        "outputId": "2644e1b8-6c9e-4b12-914d-b41deb648d80"
      },
      "source": [
        "for epoch_idx in range(n_epochs):\n",
        "  # Randomize data order\n",
        "  data_generator = get_data_generator(train_dataset, LANG_TOKEN_MAPPING,\n",
        "                                      tokenizer, batch_size)\n",
        "  \n",
        "  for batch_idx, (input_batch, label_batch) \\\n",
        "      in tqdm_notebook(enumerate(data_generator), total=n_batches):\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "      \n",
        "    # Forward pass\n",
        "    model_out = model.forward(\n",
        "        input_ids = input_batch,\n",
        "        labels = label_batch)\n",
        "    \n",
        "    loss = model_out.loss\n",
        "    losses.append(loss.item())\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "\n",
        "    # Print training update info\n",
        "    if (batch_idx + 1) % print_freq == 0:\n",
        "      avg_loss = np.mean(losses[-print_freq:])\n",
        "      print('Epoch: {} | Step: {} | Avg. loss: {:.3f} | lr: {}'.format(\n",
        "          epoch_idx+1, batch_idx+1, avg_loss, scheduler.get_last_lr()[0]))\n",
        "      \n",
        "  test_loss = eval_model(model, test_dataset)\n",
        "  print('Test loss of {:.3f}'.format(test_loss))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c97acc3abb5c42c0b448f9278a3ff202",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1131.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/alt/alt-parallel/1.0.0/70124cadb11e926eb17904d6edf5f79041aee772bab75a7ef37cb25633fed98f/cache-d639ae06b401a07b.arrow\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1 | Step: 50 | Avg. loss: 13.441 | lr: 0.00044642857142857147\n",
            "Epoch: 1 | Step: 100 | Avg. loss: 6.372 | lr: 0.0004960707269155207\n",
            "Epoch: 1 | Step: 150 | Avg. loss: 5.040 | lr: 0.0004916056438649759\n",
            "Epoch: 1 | Step: 200 | Avg. loss: 4.528 | lr: 0.00048714056081443114\n",
            "Epoch: 1 | Step: 250 | Avg. loss: 4.236 | lr: 0.0004826754777638864\n",
            "Epoch: 1 | Step: 300 | Avg. loss: 4.089 | lr: 0.0004782103947133417\n",
            "Epoch: 1 | Step: 350 | Avg. loss: 3.890 | lr: 0.00047374531166279694\n",
            "Epoch: 1 | Step: 400 | Avg. loss: 3.840 | lr: 0.0004692802286122522\n",
            "Epoch: 1 | Step: 450 | Avg. loss: 3.703 | lr: 0.0004648151455617075\n",
            "Epoch: 1 | Step: 500 | Avg. loss: 3.662 | lr: 0.00046035006251116275\n",
            "Epoch: 1 | Step: 550 | Avg. loss: 3.608 | lr: 0.00045588497946061796\n",
            "Epoch: 1 | Step: 600 | Avg. loss: 3.598 | lr: 0.00045141989641007323\n",
            "Epoch: 1 | Step: 650 | Avg. loss: 3.521 | lr: 0.0004469548133595285\n",
            "Epoch: 1 | Step: 700 | Avg. loss: 3.490 | lr: 0.00044248973030898376\n",
            "Epoch: 1 | Step: 750 | Avg. loss: 3.477 | lr: 0.000438024647258439\n",
            "Epoch: 1 | Step: 800 | Avg. loss: 3.413 | lr: 0.0004335595642078943\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-54-169df05f2155>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\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---> 17\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     18\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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[1;32m     19\u001b[0m     \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    243\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    244\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 245\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    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\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    145\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    146\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--> 147\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}