HW5_RNN.ipynb

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            "_model_name": "LayoutModel",
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            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
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            "_view_module_version": "1.2.0",
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        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mFBc8SFRf_1"
      },
      "source": [
        "# **Homework 5 - Sequence-to-sequence**\n",
        "\n",
        "若有任何問題，歡迎來信至助教信箱 ntu-ml-2021spring-ta@googlegroups.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9Y6zehrRf_3"
      },
      "source": [
        "### (4/21 Updates)\n",
        "1. Link to reference [training curves](https://wandb.ai/george0828zhang/hw5.seq2seq.new).\n",
        "\n",
        "### (4/14 Updates) \n",
        "1. Link to tutorial [video](https://youtu.be/htG5WpZVQPU).\n",
        "2. Now defaults to load `\"avg_last_5_checkpoint.pt\"` to generate prediction.\n",
        "3. Expected run time on Colab with Tesla T4\n",
        "\n",
        "|Baseline|Details|Total Time|\n",
        "|-|:-:|:-:|\n",
        "|Simple|2m 15s $\\times$30 epochs|1hr 8m|\n",
        "|Medium|4m $\\times$30 epochs|2hr|\n",
        "|Strong|8m $\\times$30 epochs (backward)<br>+1hr (back-translation)<br>+15m $\\times$30 epochs (forward)|12hr 30m|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KUaluVrRf_4"
      },
      "source": [
        "# Sequence-to-Sequence 介紹\n",
        "- 大多數常見的 seq2seq model 為 encoder-decoder model，主要由兩個部分組成，分別是 encoder 和 decoder，而這兩個部可以使用 recurrent neural network (RNN)或 transformer 來實作，主要是用來解決輸入和輸出的長度不一樣的情況\n",
        "- **Encoder** 是將一連串的輸入，如文字、影片、聲音訊號等，編碼為單個向量，這單個向量可以想像為是整個輸入的抽象表示，包含了整個輸入的資訊\n",
        "- **Decoder** 是將 encoder 輸出的單個向量逐步解碼，一次輸出一個結果，直到將最後目標輸出被產生出來為止，每次輸出會影響下一次的輸出，一般會在開頭加入 \"< BOS >\" 來表示開始解碼，會在結尾輸出 \"< EOS >\" 來表示輸出結束\n",
        "\n",
        "\n",
        "![seq2seq](https://i.imgur.com/0zeDyuI.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58jr-b00Rf_4"
      },
      "source": [
        "# 作業介紹\n",
        "- 英文翻譯中文\n",
        "  - 輸入： 一句英文 （e.g.\t\ttom is a student .） \n",
        "  - 輸出： 中文翻譯 （e.g. \t\t湯姆 是 個 學生 。）\n",
        "\n",
        "- TODO\n",
        "  - 訓練一個 RNN 模型達到 Seq2seq 翻譯\n",
        "  - 訓練一個 Transformer 大幅提升效能\n",
        "  - 實作 Back-translation 大幅提升效能"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvOJeziqRf_5"
      },
      "source": [
        "# 下載和引入需要的函式庫"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LIIEkh3Rf_5",
        "outputId": "e69595ac-a778-4609-b5cb-04a890b9a59d"
      },
      "source": [
        "!pip install 'torch>=1.6.0' editdistance matplotlib sacrebleu sacremoses sentencepiece tqdm wandb\n",
        "!pip install --upgrade jupyter ipywidgets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully built subprocess32 pathtools\n",
            "Installing collected packages: smmap, gitdb, yaspin, subprocess32, shortuuid, sentry-sdk, portalocker, pathtools, GitPython, docker-pycreds, configparser, colorama, wandb, sentencepiece, sacremoses, sacrebleu\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rj5LPl7kRf_6",
        "outputId": "fd8535b1-4af5-43af-f7cf-efa3b8ff1630"
      },
      "source": [
        "!git clone https://github.com/pytorch/fairseq.git\n",
        "!cd fairseq && git checkout 9a1c497\n",
        "!pip install --upgrade ./fairseq/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'fairseq'...\n",
            "remote: Enumerating objects: 29857, done.\u001b[K\n",
            "remote: Counting objects: 100% (1015/1015), done.\u001b[K\n",
            "remote: Compressing objects: 100% (583/583), done.\u001b[K\n",
            "remote: Total 29857 (delta 488), reused 825 (delta 414), pack-reused 28842\u001b[K\n",
            "Receiving objects: 100% (29857/29857), 14.00 MiB | 5.52 MiB/s, done.\n",
            "Resolving deltas: 100% (22156/22156), done.\n",
            "Note: checking out '9a1c497'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by performing another checkout.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -b with the checkout command again. Example:\n",
            "\n",
            "  git checkout -b <new-branch-name>\n",
            "\n",
            "HEAD is now at 9a1c4970 Make Hydra logging work with DDP (#1568)\n",
            "Processing ./fairseq\n",
            "\u001b[33m  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
            "   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\u001b[0m\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[K     |████████████████████████████████| 123 kB 7.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (1.19.5)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (2019.12.20)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (1.15.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (1.10.0+cu111)\n",
            "Requirement already satisfied: sacrebleu>=1.4.12 in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (2.0.0)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from fairseq==1.0.0a0+9a1c497) (0.29.24)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from hydra-core<1.1->fairseq==1.0.0a0+9a1c497) (5.4.0)\n",
            "Collecting antlr4-python3-runtime==4.8\n",
            "  Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)\n",
            "\u001b[K     |████████████████████████████████| 112 kB 39.9 MB/s \n",
            "\u001b[?25hCollecting PyYAML>=5.1.*\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 40.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from omegaconf<2.1->fairseq==1.0.0a0+9a1c497) (3.10.0.2)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from sacrebleu>=1.4.12->fairseq==1.0.0a0+9a1c497) (0.4.4)\n",
            "Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu>=1.4.12->fairseq==1.0.0a0+9a1c497) (0.8.9)\n",
            "Requirement already satisfied: portalocker in /usr/local/lib/python3.7/dist-packages (from sacrebleu>=1.4.12->fairseq==1.0.0a0+9a1c497) (2.3.2)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi->fairseq==1.0.0a0+9a1c497) (2.21)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources->hydra-core<1.1->fairseq==1.0.0a0+9a1c497) (3.6.0)\n",
            "Building wheels for collected packages: fairseq, antlr4-python3-runtime\n",
            "  Building wheel for fairseq (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fairseq: filename=fairseq-1.0.0a0+9a1c497-cp37-cp37m-linux_x86_64.whl size=2863597 sha256=079a848d08842d69cfaa95054f6f6c5d445c680f9c4f52cf6ff4f8ce4ad108e3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-2dq9h2a4/wheels/7c/35/80/edbd520a1a7e615df007002aeea9f6bf5f3c8f9243e072f6ce\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=100577516b0071101dd5b77bc9306d3dc355c2d7a494bc8bfa8ba0116121501c\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n",
            "Successfully built fairseq antlr4-python3-runtime\n",
            "Installing collected packages: PyYAML, omegaconf, antlr4-python3-runtime, hydra-core, fairseq\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed PyYAML-6.0 antlr4-python3-runtime-4.8 fairseq-1.0.0a0+9a1c497 hydra-core-1.0.7 omegaconf-2.0.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVbZ1jCIRf_6"
      },
      "source": [
        "import sys\n",
        "import pdb\n",
        "import pprint\n",
        "import logging\n",
        "import os\n",
        "import random\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils import data\n",
        "import numpy as np\n",
        "import tqdm.auto as tqdm\n",
        "from pathlib import Path\n",
        "from argparse import Namespace\n",
        "from fairseq import utils\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjLzTCB8Rf_7"
      },
      "source": [
        "# 設定種子"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxuHZyVMRf_7"
      },
      "source": [
        "seed = 73\n",
        "random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "if torch.cuda.is_available():\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)  \n",
        "np.random.seed(seed)  \n",
        "torch.backends.cudnn.benchmark = False\n",
        "torch.backends.cudnn.deterministic = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Lq4oVMNRf_7"
      },
      "source": [
        "# 資料集介紹\n",
        "\n",
        "## 英轉繁雙語資料\n",
        "* [TED2020](#reimers-2020-multilingual-sentence-bert)\n",
        "    - 原始資料量: 398,066句    \n",
        "    - 處理後資料: 393,980句\n",
        "    \n",
        "\n",
        "## 測試資料\n",
        "- 資料量: 4,000句\n",
        "- **中文部分不公開，提供的檔案為假翻譯，全部都是句點。**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVchNCx9Rf_8"
      },
      "source": [
        "# 資料下載"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHvIp7mwRf_8"
      },
      "source": [
        "### 安裝megatools (optional)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGjsJcwaRf_8"
      },
      "source": [
        "#!apt-get install megatools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-kAfySfRf_8"
      },
      "source": [
        "## 下載檔案並解壓縮"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvS7ICHnVsMO",
        "outputId": "f3a437af-0458-4119-ea51-9afdbba0a2f1"
      },
      "source": [
        "!gdown --id '15f5ZghJJ1AQ9rARr4XDNCQcfxGXtzWyt' --output ted2020.tgz\n",
        "!gdown --id '18WjD1jP5p7g6E3YuAJs6PaAADvhKEbuq' --output test.tgz\n",
        "!tar -xvf ted2020.tgz \n",
        "!tar -xvf test.tgz \n",
        "\n",
        "data_dir = './DATA/rawdata'\n",
        "dataset_name = 'ted2020'\n",
        "prefix = Path(data_dir).absolute() / dataset_name\n",
        "prefix.mkdir(parents=True, exist_ok=True)\n",
        "print(prefix)\n",
        "!mv {'raw.en'} {prefix/'train_dev.raw.en'}\n",
        "!mv {'raw.zh'} {prefix/'train_dev.raw.zh'}\n",
        "!mv {'test.en'} {prefix/'test.raw.en'}\n",
        "!mv {'test.zh'} {prefix/'test.raw.zh'}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=15f5ZghJJ1AQ9rARr4XDNCQcfxGXtzWyt\n",
            "To: /content/ted2020.tgz\n",
            "100% 28.6M/28.6M [00:00<00:00, 46.5MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=18WjD1jP5p7g6E3YuAJs6PaAADvhKEbuq\n",
            "To: /content/test.tgz\n",
            "100% 150k/150k [00:00<00:00, 45.7MB/s]\n",
            "raw.en\n",
            "raw.zh\n",
            "test.en\n",
            "test.zh\n",
            "/content/DATA/rawdata/ted2020\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ff0pJiAGRf_8",
        "outputId": "4d959932-5342-4fef-85c3-9bf866ce82f3"
      },
      "source": [
        "'''\n",
        "data_dir = './DATA/rawdata'\n",
        "dataset_name = 'ted2020'\n",
        "urls = (\n",
        "    '\"https://mega.nz/#!vEcTCISJ!3Rw0eHTZWPpdHBTbQEqBDikDEdFPr7fI8WxaXK9yZ9U\"', \n",
        "    '\"https://mega.nz/#!zNcnGIoJ!oPJX9AvVVs11jc0SaK6vxP_lFUNTkEcK2WbxJpvjU5Y\"',\n",
        "# # If the above links die, use the following instead. \n",
        "#     \"https://www.csie.ntu.edu.tw/~r09922057/ML2021-hw5/ted2020.tgz\",\n",
        "#     \"https://www.csie.ntu.edu.tw/~r09922057/ML2021-hw5/test.tgz\",\n",
        "# # If the above links die, use the following instead. \n",
        "#     \"https://mega.nz/#!vEcTCISJ!3Rw0eHTZWPpdHBTbQEqBDikDEdFPr7fI8WxaXK9yZ9U\",\n",
        "#     \"https://mega.nz/#!zNcnGIoJ!oPJX9AvVVs11jc0SaK6vxP_lFUNTkEcK2WbxJpvjU5Y\",\n",
        ")\n",
        "file_names = (\n",
        "    'ted2020.tgz', # train & dev\n",
        "    'test.tgz', # test\n",
        ")\n",
        "prefix = Path(data_dir).absolute() / dataset_name\n",
        "\n",
        "prefix.mkdir(parents=True, exist_ok=True)\n",
        "for u, f in zip(urls, file_names):\n",
        "    path = prefix/f\n",
        "    if not path.exists():\n",
        "        if 'mega' in u:\n",
        "            !megadl {u} --path {path}\n",
        "        else:\n",
        "            !wget {u} -O {path}\n",
        "    if path.suffix == \".tgz\":\n",
        "        !tar -xvf {path} -C {prefix}\n",
        "    elif path.suffix == \".zip\":\n",
        "        !unzip -o {path} -d {prefix}\n",
        "!mv {prefix/'raw.en'} {prefix/'train_dev.raw.en'}\n",
        "!mv {prefix/'raw.zh'} {prefix/'train_dev.raw.zh'}\n",
        "!mv {prefix/'test.en'} {prefix/'test.raw.en'}\n",
        "!mv {prefix/'test.zh'} {prefix/'test.raw.zh'}\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "gzip: stdin: not in gzip format\n",
            "tar: Child returned status 1\n",
            "tar: Error is not recoverable: exiting now\n",
            "\n",
            "gzip: stdin: not in gzip format\n",
            "tar: Child returned status 1\n",
            "tar: Error is not recoverable: exiting now\n",
            "mv: cannot stat '/content/DATA/rawdata/ted2020/raw.en': No such file or directory\n",
            "mv: cannot stat '/content/DATA/rawdata/ted2020/raw.zh': No such file or directory\n",
            "mv: cannot stat '/content/DATA/rawdata/ted2020/test.en': No such file or directory\n",
            "mv: cannot stat '/content/DATA/rawdata/ted2020/test.zh': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8PYnDc8Rf_9"
      },
      "source": [
        "## 設定語言"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efnRtzRIRf_9"
      },
      "source": [
        "src_lang = 'en'\n",
        "tgt_lang = 'zh'\n",
        "\n",
        "data_prefix = f'{prefix}/train_dev.raw'\n",
        "test_prefix = f'{prefix}/test.raw'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e22AGzQKRf_9",
        "outputId": "b77ce270-66a6-4d87-cb9a-816020ace137"
      },
      "source": [
        "!head {data_prefix+'.'+src_lang} -n 5\n",
        "!head {data_prefix+'.'+tgt_lang} -n 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thank you so much, Chris.\n",
            "And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful.\n",
            "I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night.\n",
            "And I say that sincerely, partly because  I need that.\n",
            "Put yourselves in my position.\n",
            "非常謝謝你，克里斯。能有這個機會第二度踏上這個演講台\n",
            "真是一大榮幸。我非常感激。\n",
            "這個研討會給我留下了極為深刻的印象，我想感謝大家 對我之前演講的好評。\n",
            "我是由衷的想這麼說，有部份原因是因為 —— 我真的有需要!\n",
            "請你們設身處地為我想一想！\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwP9Cx9-Rf_9"
      },
      "source": [
        "## 檔案前處理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hfceswn5Rf_-"
      },
      "source": [
        "import re\n",
        "\n",
        "def strQ2B(ustring):\n",
        "    \"\"\"把字串全形轉半形\"\"\"\n",
        "    # 參考來源:https://ithelp.ithome.com.tw/articles/10233122\n",
        "    ss = []\n",
        "    for s in ustring:\n",
        "        rstring = \"\"\n",
        "        for uchar in s:\n",
        "            inside_code = ord(uchar)\n",
        "            if inside_code == 12288:  # 全形空格直接轉換\n",
        "                inside_code = 32\n",
        "            elif (inside_code >= 65281 and inside_code <= 65374):  # 全形字元（除空格）根據關係轉化\n",
        "                inside_code -= 65248\n",
        "            rstring += chr(inside_code)\n",
        "        ss.append(rstring)\n",
        "    return ''.join(ss)\n",
        "                \n",
        "def clean_s(s, lang):\n",
        "    if lang == 'en':\n",
        "        s = re.sub(r\"\\([^()]*\\)\", \"\", s) # remove ([text])\n",
        "        s = s.replace('-', '') # remove '-'\n",
        "        s = re.sub('([.,;!?()\\\"])', r' \\1 ', s) # keep punctuation\n",
        "    elif lang == 'zh':\n",
        "        s = strQ2B(s) # Q2B\n",
        "        s = re.sub(r\"\\([^()]*\\)\", \"\", s) # remove ([text])\n",
        "        s = s.replace(' ', '')\n",
        "        s = s.replace('—', '')\n",
        "        s = s.replace('“', '\"')\n",
        "        s = s.replace('”', '\"')\n",
        "        s = s.replace('_', '')\n",
        "        s = re.sub('([。,;!?()\\\"~「」])', r' \\1 ', s) # keep punctuation\n",
        "    s = ' '.join(s.strip().split())\n",
        "    return s\n",
        "\n",
        "def len_s(s, lang):\n",
        "    if lang == 'zh':\n",
        "        return len(s)\n",
        "    return len(s.split())\n",
        "\n",
        "def clean_corpus(prefix, l1, l2, ratio=9, max_len=1000, min_len=1):\n",
        "    if Path(f'{prefix}.clean.{l1}').exists() and Path(f'{prefix}.clean.{l2}').exists():\n",
        "        print(f'{prefix}.clean.{l1} & {l2} exists. skipping clean.')\n",
        "        return\n",
        "    with open(f'{prefix}.{l1}', 'r') as l1_in_f:\n",
        "        with open(f'{prefix}.{l2}', 'r') as l2_in_f:\n",
        "            with open(f'{prefix}.clean.{l1}', 'w') as l1_out_f:\n",
        "                with open(f'{prefix}.clean.{l2}', 'w') as l2_out_f:\n",
        "                    for s1 in l1_in_f:\n",
        "                        s1 = s1.strip()\n",
        "                        s2 = l2_in_f.readline().strip()\n",
        "                        s1 = clean_s(s1, l1)\n",
        "                        s2 = clean_s(s2, l2)\n",
        "                        s1_len = len_s(s1, l1)\n",
        "                        s2_len = len_s(s2, l2)\n",
        "                        if min_len > 0: # remove short sentence\n",
        "                            if s1_len < min_len or s2_len < min_len:\n",
        "                                continue\n",
        "                        if max_len > 0: # remove long sentence\n",
        "                            if s1_len > max_len or s2_len > max_len:\n",
        "                                continue\n",
        "                        if ratio > 0: # remove by ratio of length\n",
        "                            if s1_len/s2_len > ratio or s2_len/s1_len > ratio:\n",
        "                                continue\n",
        "                        print(s1, file=l1_out_f)\n",
        "                        print(s2, file=l2_out_f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kGEheDhRf_-"
      },
      "source": [
        "clean_corpus(data_prefix, src_lang, tgt_lang)\n",
        "clean_corpus(test_prefix, src_lang, tgt_lang, ratio=-1, min_len=-1, max_len=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsbdOmieRf__",
        "outputId": "7ba838da-0735-4cb7-ed89-43449b0026ec"
      },
      "source": [
        "!head {data_prefix+'.clean.'+src_lang} -n 5\n",
        "!head {data_prefix+'.clean.'+tgt_lang} -n 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thank you so much , Chris .\n",
            "And it's truly a great honor to have the opportunity to come to this stage twice ; I'm extremely grateful .\n",
            "I have been blown away by this conference , and I want to thank all of you for the many nice comments about what I had to say the other night .\n",
            "And I say that sincerely , partly because I need that .\n",
            "Put yourselves in my position .\n",
            "非常謝謝你 , 克里斯 。 能有這個機會第二度踏上這個演講台\n",
            "真是一大榮幸 。 我非常感激 。\n",
            "這個研討會給我留下了極為深刻的印象 , 我想感謝大家對我之前演講的好評 。\n",
            "我是由衷的想這麼說 , 有部份原因是因為我真的有需要 !\n",
            "請你們設身處地為我想一想 !\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ox3OkrcRf__"
      },
      "source": [
        "## 切出 train/valid set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alpRBPVkRf__"
      },
      "source": [
        "valid_ratio = 0.01 # 3000~4000句就夠了\n",
        "train_ratio = 1 - valid_ratio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rr2q-V7lRf__"
      },
      "source": [
        "if (prefix/f'train.clean.{src_lang}').exists() \\\n",
        "and (prefix/f'train.clean.{tgt_lang}').exists() \\\n",
        "and (prefix/f'valid.clean.{src_lang}').exists() \\\n",
        "and (prefix/f'valid.clean.{tgt_lang}').exists():\n",
        "    print(f'train/valid splits exists. skipping split.')\n",
        "else:\n",
        "    line_num = sum(1 for line in open(f'{data_prefix}.clean.{src_lang}'))\n",
        "    labels = list(range(line_num))\n",
        "    random.shuffle(labels)\n",
        "    for lang in [src_lang, tgt_lang]:\n",
        "        train_f = open(os.path.join(data_dir, dataset_name, f'train.clean.{lang}'), 'w')\n",
        "        valid_f = open(os.path.join(data_dir, dataset_name, f'valid.clean.{lang}'), 'w')\n",
        "        count = 0\n",
        "        for line in open(f'{data_prefix}.clean.{lang}', 'r'):\n",
        "            if labels[count]/line_num < train_ratio:\n",
        "                train_f.write(line)\n",
        "            else:\n",
        "                valid_f.write(line)\n",
        "            count += 1\n",
        "        train_f.close()\n",
        "        valid_f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyySXLxNRf__"
      },
      "source": [
        "## Subword Units \n",
        "翻譯存在的一大問題是未登錄詞(out of vocabulary)，可以使用 subword units 作為斷詞單位來解決。\n",
        "- 使用 [sentencepiece](#kudo-richardson-2018-sentencepiece) 套件\n",
        "- 用 unigram 或 byte-pair encoding (BPE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYm2mlYMRgAA"
      },
      "source": [
        "import sentencepiece as spm\n",
        "vocab_size = 8000\n",
        "if (prefix/f'spm{vocab_size}.model').exists():\n",
        "    print(f'{prefix}/spm{vocab_size}.model exists. skipping spm_train.')\n",
        "else:\n",
        "    spm.SentencePieceTrainer.train(\n",
        "        input=','.join([f'{prefix}/train.clean.{src_lang}',\n",
        "                        f'{prefix}/valid.clean.{src_lang}',\n",
        "                        f'{prefix}/train.clean.{tgt_lang}',\n",
        "                        f'{prefix}/valid.clean.{tgt_lang}']),\n",
        "        model_prefix=prefix/f'spm{vocab_size}',\n",
        "        vocab_size=vocab_size,\n",
        "        character_coverage=1,\n",
        "        model_type='unigram', # 'bpe' 也可\n",
        "        input_sentence_size=1e6,\n",
        "        shuffle_input_sentence=True,\n",
        "        normalization_rule_name='nmt_nfkc_cf',\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DBkBnGhRgAA"
      },
      "source": [
        "spm_model = spm.SentencePieceProcessor(model_file=str(prefix/f'spm{vocab_size}.model'))\n",
        "in_tag = {\n",
        "    'train': 'train.clean',\n",
        "    'valid': 'valid.clean',\n",
        "    'test': 'test.raw.clean',\n",
        "}\n",
        "for split in ['train', 'valid', 'test']:\n",
        "    for lang in [src_lang, tgt_lang]:\n",
        "        out_path = prefix/f'{split}.{lang}'\n",
        "        if out_path.exists():\n",
        "            print(f\"{out_path} exists. skipping spm_encode.\")\n",
        "        else:\n",
        "            with open(prefix/f'{split}.{lang}', 'w') as out_f:\n",
        "                with open(prefix/f'{in_tag[split]}.{lang}', 'r') as in_f:\n",
        "                    for line in in_f:\n",
        "                        line = line.strip()\n",
        "                        tok = spm_model.encode(line, out_type=str)\n",
        "                        print(' '.join(tok), file=out_f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsB68oByRgAA",
        "outputId": "399fc12d-8e00-4337-dd78-1665e46d4dbf"
      },
      "source": [
        "!head {data_dir+'/'+dataset_name+'/train.'+src_lang} -n 5\n",
        "!head {data_dir+'/'+dataset_name+'/train.'+tgt_lang} -n 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "▁thank ▁you ▁so ▁much ▁, ▁chris ▁.\n",
            "▁and ▁it ' s ▁ t ru ly ▁a ▁great ▁ho n or ▁to ▁have ▁the ▁ op port un ity ▁to ▁come ▁to ▁this ▁st age ▁ t wi ce ▁; ▁i ' m ▁ex t re me ly ▁gr ate ful ▁.\n",
            "▁i ▁have ▁been ▁ bl ow n ▁away ▁by ▁this ▁con fer ence ▁, ▁and ▁i ▁want ▁to ▁thank ▁all ▁of ▁you ▁for ▁the ▁many ▁ ni ce ▁ com ment s ▁about ▁what ▁i ▁had ▁to ▁say ▁the ▁other ▁night ▁.\n",
            "▁and ▁i ▁say ▁that ▁since re ly ▁, ▁part ly ▁because ▁i ▁need ▁that ▁.\n",
            "▁put ▁your s el ve s ▁in ▁my ▁po s ition ▁.\n",
            "▁ 非常 謝 謝 你 ▁, ▁ 克 里 斯 ▁。 ▁ 能 有 這個 機會 第二 度 踏 上 這個 演講 台\n",
            "▁ 真 是 一 大 榮 幸 ▁。 ▁我 非常 感 激 ▁。\n",
            "▁這個 研 討 會 給我 留 下 了 極 為 深 刻 的 印 象 ▁, ▁我想 感 謝 大家 對我 之前 演講 的 好 評 ▁。\n",
            "▁我 是由 衷 的 想 這麼 說 ▁, ▁有 部份 原因 是因為 我 真的 有 需要 ▁!\n",
            "▁ 請 你們 設 身 處 地 為 我想 一 想 ▁!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnaZ74ToRgAA"
      },
      "source": [
        "## 用 fairseq 將資料轉為 binary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ed3yys60RgAA",
        "outputId": "81539c24-f5ee-477a-80e1-6dc542a441c9"
      },
      "source": [
        "binpath = Path('./DATA/data-bin', dataset_name)\n",
        "if binpath.exists():\n",
        "    print(binpath, \"exists, will not overwrite!\")\n",
        "else:\n",
        "    !python -m fairseq_cli.preprocess \\\n",
        "        --source-lang {src_lang}\\\n",
        "        --target-lang {tgt_lang}\\\n",
        "        --trainpref {prefix/'train'}\\\n",
        "        --validpref {prefix/'valid'}\\\n",
        "        --testpref {prefix/'test'}\\\n",
        "        --destdir {binpath}\\\n",
        "        --joined-dictionary\\\n",
        "        --workers 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:25:10 | INFO | fairseq_cli.preprocess | Namespace(align_suffix=None, alignfile=None, all_gather_list_size=16384, azureml_logging=False, bf16=False, bpe=None, cpu=False, criterion='cross_entropy', dataset_impl='mmap', destdir='DATA/data-bin/ted2020', empty_cache_freq=0, fp16=False, fp16_init_scale=128, fp16_no_flatten_grads=False, fp16_scale_tolerance=0.0, fp16_scale_window=None, joined_dictionary=True, log_format=None, log_interval=100, lr_scheduler='fixed', memory_efficient_bf16=False, memory_efficient_fp16=False, min_loss_scale=0.0001, model_parallel_size=1, no_progress_bar=False, nwordssrc=-1, nwordstgt=-1, only_source=False, optimizer=None, padding_factor=8, profile=False, quantization_config_path=None, reset_logging=False, scoring='bleu', seed=1, source_lang='en', srcdict=None, suppress_crashes=False, target_lang='zh', task='translation', tensorboard_logdir=None, testpref='/content/DATA/rawdata/ted2020/test', tgtdict=None, threshold_loss_scale=None, thresholdsrc=0, thresholdtgt=0, tokenizer=None, tpu=False, trainpref='/content/DATA/rawdata/ted2020/train', user_dir=None, validpref='/content/DATA/rawdata/ted2020/valid', wandb_project=None, workers=2)\n",
            "2021-11-18 09:26:33 | INFO | fairseq_cli.preprocess | [en] Dictionary: 8000 types\n",
            "2021-11-18 09:27:41 | INFO | fairseq_cli.preprocess | [en] /content/DATA/rawdata/ted2020/train.en: 390041 sents, 12215608 tokens, 0.0% replaced by <unk>\n",
            "2021-11-18 09:27:41 | INFO | fairseq_cli.preprocess | [en] Dictionary: 8000 types\n",
            "2021-11-18 09:27:42 | INFO | fairseq_cli.preprocess | [en] /content/DATA/rawdata/ted2020/valid.en: 3939 sents, 122560 tokens, 0.0% replaced by <unk>\n",
            "2021-11-18 09:27:42 | INFO | fairseq_cli.preprocess | [en] Dictionary: 8000 types\n",
            "2021-11-18 09:27:43 | INFO | fairseq_cli.preprocess | [en] /content/DATA/rawdata/ted2020/test.en: 4000 sents, 122894 tokens, 0.0% replaced by <unk>\n",
            "2021-11-18 09:27:43 | INFO | fairseq_cli.preprocess | [zh] Dictionary: 8000 types\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | [zh] /content/DATA/rawdata/ted2020/train.zh: 390041 sents, 9533185 tokens, 0.0% replaced by <unk>\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | [zh] Dictionary: 8000 types\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | [zh] /content/DATA/rawdata/ted2020/valid.zh: 3939 sents, 96124 tokens, 0.0052% replaced by <unk>\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | [zh] Dictionary: 8000 types\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | [zh] /content/DATA/rawdata/ted2020/test.zh: 4000 sents, 8000 tokens, 0.0% replaced by <unk>\n",
            "2021-11-18 09:28:44 | INFO | fairseq_cli.preprocess | Wrote preprocessed data to DATA/data-bin/ted2020\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Es4vh1BPRgAA"
      },
      "source": [
        "# 實驗的參數設定表"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAb7s1XFRgAA"
      },
      "source": [
        "config = Namespace(\n",
        "    datadir = \"./DATA/data-bin/ted2020\",\n",
        "    savedir = \"./checkpoints/rnn\",\n",
        "    source_lang = \"en\",\n",
        "    target_lang = \"zh\",\n",
        "    \n",
        "    # cpu threads when fetching & processing data.\n",
        "    num_workers=2,  \n",
        "    # batch size in terms of tokens. gradient accumulation increases the effective batchsize.\n",
        "    max_tokens=8192,\n",
        "    accum_steps=2,\n",
        "    \n",
        "    # the lr s calculated from Noam lr scheduler. you can tune the maximum lr by this factor.\n",
        "    lr_factor=2.,\n",
        "    lr_warmup=4000,\n",
        "    \n",
        "    # clipping gradient norm helps alleviate gradient exploding\n",
        "    clip_norm=1.0,\n",
        "    \n",
        "    # maximum epochs for training\n",
        "    max_epoch=30,\n",
        "    start_epoch=1,\n",
        "    \n",
        "    # beam size for beam search\n",
        "    beam=5, \n",
        "    # generate sequences of maximum length ax + b, where x is the source length\n",
        "    max_len_a=1.2, \n",
        "    max_len_b=10,\n",
        "    # when decoding, post process sentence by removing sentencepiece symbols.\n",
        "    post_process = \"sentencepiece\",\n",
        "    \n",
        "    # checkpoints\n",
        "    keep_last_epochs=5,\n",
        "    resume=None, # if resume from checkpoint name (under config.savedir)\n",
        "    \n",
        "    # logging\n",
        "    use_wandb=False,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niamOKsURgAB"
      },
      "source": [
        "# Logging\n",
        "- logging 套件紀錄一般訊息\n",
        "- wandb 紀錄續練過程 loss, bleu, model weight 等等"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "VmpRRccMRgAB"
      },
      "source": [
        "logging.basicConfig(\n",
        "    format=\"%(asctime)s | %(levelname)s | %(name)s | %(message)s\",\n",
        "    datefmt=\"%Y-%m-%d %H:%M:%S\",\n",
        "    level=\"INFO\", # \"DEBUG\" \"WARNING\" \"ERROR\"\n",
        "    stream=sys.stdout,\n",
        ")\n",
        "proj = \"hw5.seq2seq\"\n",
        "logger = logging.getLogger(proj)\n",
        "if config.use_wandb:\n",
        "    import wandb\n",
        "    wandb.init(project=proj, name=Path(config.savedir).stem, config=config)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mmwgT_RBRgAC"
      },
      "source": [
        "# CUDA環境"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yrzcd3gdRgAC",
        "outputId": "0c64029a-494b-48a3-f800-e8b8fe11b46d"
      },
      "source": [
        "cuda_env = utils.CudaEnvironment()\n",
        "utils.CudaEnvironment.pretty_print_cuda_env_list([cuda_env])\n",
        "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:28:45 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************\n",
            "2021-11-18 09:28:45 | INFO | fairseq.utils | rank   0: capabilities =  3.7  ; total memory = 11.173 GB ; name = Tesla K80                               \n",
            "2021-11-18 09:28:45 | INFO | fairseq.utils | ***********************CUDA enviroments for all 1 workers***********************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxW8HPRBRgAC"
      },
      "source": [
        "# 讀取資料集"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2j0L1I8RgAC"
      },
      "source": [
        "## 借用 fairseq 的 TranslationTask\n",
        "* 用來讀進上面 binarized 的檔案\n",
        "* 有現成的 data iterator (dataloader)\n",
        "* 字典 task.source_dictionary 和 task.target_dictionary 也很好用 \n",
        "* 有實做 beam search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvTywNGURgAC",
        "outputId": "52e5b276-9967-48eb-ddcb-077e11a45f8a"
      },
      "source": [
        "from fairseq.tasks.translation import TranslationConfig, TranslationTask\n",
        "\n",
        "## setup task\n",
        "task_cfg = TranslationConfig(\n",
        "    data=config.datadir,\n",
        "    source_lang=config.source_lang,\n",
        "    target_lang=config.target_lang,\n",
        "    train_subset=\"train\",\n",
        "    required_seq_len_multiple=8,\n",
        "    dataset_impl=\"mmap\",\n",
        "    upsample_primary=1,\n",
        ")\n",
        "task = TranslationTask.setup_task(task_cfg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:28:45 | INFO | fairseq.tasks.translation | [en] dictionary: 8000 types\n",
            "2021-11-18 09:28:45 | INFO | fairseq.tasks.translation | [zh] dictionary: 8000 types\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fwJCRrdRgAC",
        "outputId": "8a20a2a9-a1a4-4b7d-a0f9-289910aacf6c"
      },
      "source": [
        "logger.info(\"loading data for epoch 1\")\n",
        "task.load_dataset(split=\"train\", epoch=1, combine=True) # combine if you have back-translation data.\n",
        "task.load_dataset(split=\"valid\", epoch=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:28:45 | INFO | hw5.seq2seq | loading data for epoch 1\n",
            "2021-11-18 09:28:45 | INFO | fairseq.data.data_utils | loaded 390,041 examples from: ./DATA/data-bin/ted2020/train.en-zh.en\n",
            "2021-11-18 09:28:45 | INFO | fairseq.data.data_utils | loaded 390,041 examples from: ./DATA/data-bin/ted2020/train.en-zh.zh\n",
            "2021-11-18 09:28:45 | INFO | fairseq.tasks.translation | ./DATA/data-bin/ted2020 train en-zh 390041 examples\n",
            "2021-11-18 09:28:45 | INFO | fairseq.data.data_utils | loaded 3,939 examples from: ./DATA/data-bin/ted2020/valid.en-zh.en\n",
            "2021-11-18 09:28:45 | INFO | fairseq.data.data_utils | loaded 3,939 examples from: ./DATA/data-bin/ted2020/valid.en-zh.zh\n",
            "2021-11-18 09:28:45 | INFO | fairseq.tasks.translation | ./DATA/data-bin/ted2020 valid en-zh 3939 examples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyRGxfBjRgAD",
        "outputId": "ef3a2d6d-97da-4490-a355-9b8a943791e8"
      },
      "source": [
        "sample = task.dataset(\"valid\")[1]\n",
        "pprint.pprint(sample)\n",
        "pprint.pprint(\n",
        "    \"Source: \" + \\\n",
        "    task.source_dictionary.string(\n",
        "        sample['source'],\n",
        "        config.post_process,\n",
        "    )\n",
        ")\n",
        "pprint.pprint(\n",
        "    \"Target: \" + \\\n",
        "    task.target_dictionary.string(\n",
        "        sample['target'],\n",
        "        config.post_process,\n",
        "    )\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': 1,\n",
            " 'source': tensor([  18,   14,    6, 2234,   60,   19,   80,    5,  256,   16,  405, 1407,\n",
            "        1706,    7,    2]),\n",
            " 'target': tensor([ 140,  690,   28,  270,   45,  151, 1142,  660,  606,  369, 3114, 2434,\n",
            "        1434,  192,    2])}\n",
            "\"Source: that's exactly what i do optical mind control .\"\n",
            "'Target: 這實在就是我所做的--光學操控思想'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9vwRR_MRgAD"
      },
      "source": [
        "## Dataset Iterator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qX6H7Ns6RgAD"
      },
      "source": [
        "* 將每個 batch 控制在 N 個 token 讓 GPU 記憶體更有效被利用\n",
        "* 讓 training set 每個 epoch 有不同 shuffling\n",
        "* 濾掉長度太長的句子\n",
        "* 將每個 batch 內的句子 pad 成一樣長，好讓 GPU 平行運算\n",
        "* 加上 eos 並 shift 一格\n",
        "    - teacher forcing: 為了訓練模型根據prefix生成下個字，decoder的輸入會是輸出目標序列往右shift一格。\n",
        "    - 一般是會在輸入開頭加個bos token (如下圖)\n",
        "![seq2seq](https://i.imgur.com/0zeDyuI.png)\n",
        "    - fairseq 則是直接把 eos 挪到 beginning，訓練起來效果其實差不多。例如: \n",
        "    ```\n",
        "    # 輸出目標 (target) 和 Decoder輸入 (prev_output_tokens): \n",
        "                   eos = 2\n",
        "                target = 419,  711,  238,  888,  792,   60,  968,    8,    2\n",
        "    prev_output_tokens = 2,  419,  711,  238,  888,  792,   60,  968,    8\n",
        "    ```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llT1AMtcRgAD",
        "outputId": "e762c316-60b7-42d5-dccd-d883aab3a553"
      },
      "source": [
        "def load_data_iterator(task, split, epoch=1, max_tokens=4000, num_workers=1, cached=True):\n",
        "    batch_iterator = task.get_batch_iterator(\n",
        "        dataset=task.dataset(split),\n",
        "        max_tokens=max_tokens,\n",
        "        max_sentences=None,\n",
        "        max_positions=utils.resolve_max_positions(\n",
        "            task.max_positions(),\n",
        "            max_tokens,\n",
        "        ),\n",
        "        ignore_invalid_inputs=True,\n",
        "        seed=seed,\n",
        "        num_workers=num_workers,\n",
        "        epoch=epoch,\n",
        "        disable_iterator_cache=not cached,\n",
        "        # Set this to False to speed up. However, if set to False, changing max_tokens beyond \n",
        "        # first call of this method has no effect. \n",
        "    )\n",
        "    return batch_iterator\n",
        "\n",
        "demo_epoch_obj = load_data_iterator(task, \"valid\", epoch=1, max_tokens=20, num_workers=1, cached=False)\n",
        "demo_iter = demo_epoch_obj.next_epoch_itr(shuffle=True)\n",
        "sample = next(demo_iter)\n",
        "sample"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:28:45 | WARNING | fairseq.tasks.fairseq_task | 2,532 samples have invalid sizes and will be skipped, max_positions=(20, 20), first few sample ids=[29, 135, 2444, 3058, 682, 731, 235, 1558, 3383, 559]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': tensor([723]),\n",
              " 'net_input': {'prev_output_tokens': tensor([[   2,  140,  296,  318, 1560,   51,  568,  316,  225, 1952,  254,   78,\n",
              "            151, 2691,    9,  215, 1680,   10,    1,    1,    1,    1,    1,    1]]),\n",
              "  'src_lengths': tensor([19]),\n",
              "  'src_tokens': tensor([[   1,    1,    1,    1,    1,   18,   26,   82,    8,  480,   15,  651,\n",
              "           1361,   38,    6,  176, 2696,   39,    5,  822,   92,  260,    7,    2]])},\n",
              " 'nsentences': 1,\n",
              " 'ntokens': 18,\n",
              " 'target': tensor([[ 140,  296,  318, 1560,   51,  568,  316,  225, 1952,  254,   78,  151,\n",
              "          2691,    9,  215, 1680,   10,    2,    1,    1,    1,    1,    1,    1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brslBEMVRgAD"
      },
      "source": [
        "* 每個 batch 是一個字典，key 是字串，value 是 Tensor，內容說明如下\n",
        "```python\n",
        "batch = {\n",
        "    \"id\": id, # 每個 example 的 id\n",
        "    \"nsentences\": len(samples), # batch size 句子數\n",
        "    \"ntokens\": ntokens, # batch size 字數\n",
        "    \"net_input\": {\n",
        "        \"src_tokens\": src_tokens, # 來源語言的序列\n",
        "        \"src_lengths\": src_lengths, # 每句話沒有 pad 過的長度\n",
        "        \"prev_output_tokens\": prev_output_tokens, # 上面提到右 shift 一格後的目標序列\n",
        "    },\n",
        "    \"target\": target, # 目標序列\n",
        "}\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F7MjFLD5RgAD"
      },
      "source": [
        "# 定義模型架構\n",
        "* 我們一樣繼承 fairseq 的 encoder, decoder 和 model, 這樣測試階段才能直接用他寫好的 beam search 函式"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq-QuGyfRgAD"
      },
      "source": [
        "from fairseq.models import (\n",
        "    FairseqEncoder, \n",
        "    FairseqIncrementalDecoder,\n",
        "    FairseqEncoderDecoderModel\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJ6cvkmtRgAD"
      },
      "source": [
        "## Encoder 編碼器"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21JPCzibRgAD"
      },
      "source": [
        "- seq2seq 模型的編碼器為 RNN 或 Transformer Encoder，以下說明以 RNN 為例，Transformer 略有不同。對於每個輸入，Encoder 會輸出一個向量和一個隱藏狀態(hidden state)，並將隱藏狀態用於下一個輸入。換句話說，Encoder 會逐步讀取輸入序列，並在每個 timestep 輸出單個向量，以及在最後 timestep 輸出最終隱藏狀態(content vector)\n",
        "- 參數:\n",
        "  - *args*\n",
        "      - encoder_embed_dim 是 embedding 的維度，主要將 one-hot vector 的單詞向量壓縮到指定的維度，主要是為了降維和濃縮資訊的功用\n",
        "      - encoder_ffn_embed_dim 是 RNN 輸出和隱藏狀態的維度(hidden dimension)\n",
        "      - encoder_layers 是 RNN 要疊多少層\n",
        "      - dropout 是決定有多少的機率會將某個節點變為 0，主要是為了防止 overfitting ，一般來說是在訓練時使用，測試時則不使用\n",
        "  - *dictionary*: fairseq 幫我們做好的 dictionary. 在此用來得到 padding index，好用來得到 encoder padding mask. \n",
        "  - *embed_tokens*: 事先做好的詞嵌入 (nn.Embedding)\n",
        "\n",
        "- 輸入: \n",
        "    - *src_tokens*: 英文的整數序列 e.g. 1, 28, 29, 205, 2 \n",
        "- 輸出: \n",
        "    - *outputs*: 最上層 RNN 每個 timestep 的輸出，後續可以用 Attention 再進行處理\n",
        "    - *final_hiddens*: 每層最終 timestep 的隱藏狀態，將傳遞到 Decoder 進行解碼\n",
        "    - *encoder_padding_mask*: 告訴我們哪些是位置的資訊不重要。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVgGotzjRgAE"
      },
      "source": [
        "class RNNEncoder(FairseqEncoder):\n",
        "    def __init__(self, args, dictionary, embed_tokens):\n",
        "        super().__init__(dictionary)\n",
        "        self.embed_tokens = embed_tokens\n",
        "        \n",
        "        self.embed_dim = args.encoder_embed_dim\n",
        "        self.hidden_dim = args.encoder_ffn_embed_dim\n",
        "        self.num_layers = args.encoder_layers\n",
        "        \n",
        "        self.dropout_in_module = nn.Dropout(args.dropout)\n",
        "        self.rnn = nn.GRU(\n",
        "            self.embed_dim, \n",
        "            self.hidden_dim, \n",
        "            self.num_layers, \n",
        "            dropout=args.dropout, \n",
        "            batch_first=False, \n",
        "            bidirectional=True\n",
        "        )\n",
        "        self.dropout_out_module = nn.Dropout(args.dropout)\n",
        "        \n",
        "        self.padding_idx = dictionary.pad()\n",
        "        \n",
        "    def combine_bidir(self, outs, bsz: int):\n",
        "        out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()\n",
        "        return out.view(self.num_layers, bsz, -1)\n",
        "\n",
        "    def forward(self, src_tokens, **unused):\n",
        "        bsz, seqlen = src_tokens.size()\n",
        "        \n",
        "        # get embeddings\n",
        "        x = self.embed_tokens(src_tokens)\n",
        "        x = self.dropout_in_module(x)\n",
        "\n",
        "        # B x T x C -> T x B x C\n",
        "        x = x.transpose(0, 1)\n",
        "        \n",
        "        # 過雙向RNN\n",
        "        h0 = x.new_zeros(2 * self.num_layers, bsz, self.hidden_dim)\n",
        "        x, final_hiddens = self.rnn(x, h0)\n",
        "        outputs = self.dropout_out_module(x)\n",
        "        # outputs = [sequence len, batch size, hid dim * directions] 是最上層RNN的輸出\n",
        "        # hidden =  [num_layers * directions, batch size  , hid dim]\n",
        "        \n",
        "        # 因為 Encoder 是雙向的RNN，所以需要將同一層兩個方向的 hidden state 接在一起\n",
        "        final_hiddens = self.combine_bidir(final_hiddens, bsz)\n",
        "        # hidden =  [num_layers x batch x num_directions*hidden]\n",
        "        \n",
        "        encoder_padding_mask = src_tokens.eq(self.padding_idx).t()\n",
        "        return tuple(\n",
        "            (\n",
        "                outputs,  # seq_len x batch x hidden\n",
        "                final_hiddens,  # num_layers x batch x num_directions*hidden\n",
        "                encoder_padding_mask,  # seq_len x batch\n",
        "            )\n",
        "        )\n",
        "    \n",
        "    def reorder_encoder_out(self, encoder_out, new_order):\n",
        "        # 這個beam search時會用到，意義並不是很重要\n",
        "        return tuple(\n",
        "            (\n",
        "                encoder_out[0].index_select(1, new_order),\n",
        "                encoder_out[1].index_select(1, new_order),\n",
        "                encoder_out[2].index_select(1, new_order),\n",
        "            )\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BkCWsRMRgAE"
      },
      "source": [
        "## Attention"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izxgPOAVRgAE"
      },
      "source": [
        "- 當輸入過長，或是單獨靠 “content vector” 無法取得整個輸入的意思時，用 Attention Mechanism 來提供 Decoder 更多的資訊\n",
        "- 根據現在 **Decoder embeddings** ，去計算在 **Encoder outputs** 中，那些與其有較高的關係，根據關係的數值來把 Encoder outputs 平均起來作為 **Decoder** RNN 的輸入 \n",
        "- 常見 Attention 的實作是用 Neural Network / Dot Product 來算 **query** (decoder embeddings) 和 **key** (Encoder outputs) 之間的關係，再對所有算出來的數值做 **softmax** 得到分佈，最後根據這個分佈對 **values** (Encoder outputs) 做 **weight sum**\n",
        "\n",
        "- 參數:\n",
        "  - *input_embed_dim*: key 的維度，應是 decoder 要做 attend 時的向量的維度\n",
        "  - *source_embed_dim*: query 的維度，應是要被 attend 的向量(encoder outputs)的維度\n",
        "  - *output_embed_dim*: value 的維度，應是做完 attention 後，下一層預期的向量維度\n",
        "\n",
        "- 輸入: \n",
        "    - *inputs*: 就是 key，要 attend 別人的向量\n",
        "    - *encoder_outputs*: 是 query/value，被 attend 的向量\n",
        "    - *encoder_padding_mask*: 告訴我們哪些是位置的資訊不重要。\n",
        "- 輸出: \n",
        "    - *output*: 做完 attention 後的 context vector\n",
        "    - *attention score*: attention 的分布\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGAhZYweRgAE"
      },
      "source": [
        "class AttentionLayer(nn.Module):\n",
        "    def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):\n",
        "        super().__init__()\n",
        "\n",
        "        self.input_proj = nn.Linear(input_embed_dim, source_embed_dim, bias=bias)\n",
        "        self.output_proj = nn.Linear(\n",
        "            input_embed_dim + source_embed_dim, output_embed_dim, bias=bias\n",
        "        )\n",
        "\n",
        "    def forward(self, inputs, encoder_outputs, encoder_padding_mask):\n",
        "        # inputs: T, B, dim\n",
        "        # encoder_outputs: S x B x dim\n",
        "        # padding mask:  S x B\n",
        "        \n",
        "        # convert all to batch first\n",
        "        inputs = inputs.transpose(1,0) # B, T, dim\n",
        "        encoder_outputs = encoder_outputs.transpose(1,0) # B, S, dim\n",
        "        encoder_padding_mask = encoder_padding_mask.transpose(1,0) # B, S\n",
        "        \n",
        "        # 投影到encoder_outputs的維度\n",
        "        x = self.input_proj(inputs)\n",
        "\n",
        "        # 計算attention\n",
        "        # (B, T, dim) x (B, dim, S) = (B, T, S)\n",
        "        attn_scores = torch.bmm(x, encoder_outputs.transpose(1,2))\n",
        "\n",
        "        # 擋住padding位置的attention\n",
        "        if encoder_padding_mask is not None:\n",
        "            # 利用broadcast  B, S -> (B, 1, S)\n",
        "            encoder_padding_mask = encoder_padding_mask.unsqueeze(1)\n",
        "            attn_scores = (\n",
        "                attn_scores.float()\n",
        "                .masked_fill_(encoder_padding_mask, float(\"-inf\"))\n",
        "                .type_as(attn_scores)\n",
        "            )  # FP16 support: cast to float and back\n",
        "\n",
        "        # 在source對應維度softmax\n",
        "        attn_scores = F.softmax(attn_scores, dim=-1)\n",
        "\n",
        "        # 形狀 (B, T, S) x (B, S, dim) = (B, T, dim) 加權平均\n",
        "        x = torch.bmm(attn_scores, encoder_outputs)\n",
        "\n",
        "        # (B, T, dim)\n",
        "        x = torch.cat((x, inputs), dim=-1)\n",
        "        x = torch.tanh(self.output_proj(x)) # concat + linear + tanh\n",
        "        \n",
        "        # 回復形狀 (B, T, dim) -> (T, B, dim)\n",
        "        return x.transpose(1,0), attn_scores"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osesj1_KRgAE"
      },
      "source": [
        "## Decoder 解碼器"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mdC6468RgAE"
      },
      "source": [
        "* 解碼器的 hidden states 會用編碼器最終隱藏狀態來初始化(content vector)\n",
        "* 解碼器同時也根據目前 timestep 的輸入(也就是前幾個 timestep 的 output)，改變 hidden states，並輸出結果 \n",
        "* 如果加入 attention 可以使表現更好\n",
        "* 我們把 seq2seq 步驟寫在解碼器裡，好讓等等 Seq2Seq 這個型別可以通用 RNN 和 Transformer，而不用再改寫\n",
        "- 參數:\n",
        "  - *args*\n",
        "      - decoder_embed_dim 是解碼器 embedding 的維度，類同 encoder_embed_dim，\n",
        "      - decoder_ffn_embed_dim 是解碼器 RNN 的隱藏維度，類同 encoder_ffn_embed_dim\n",
        "      - decoder_layers 解碼器 RNN 的層數\n",
        "      - share_decoder_input_output_embed 通常 decoder 最後輸出的投影矩陣會和輸入 embedding 共用參數\n",
        "  - *dictionary*: fairseq 幫我們做好的 dictionary.\n",
        "  - *embed_tokens*: 事先做好的詞嵌入(nn.Embedding)\n",
        "- 輸入: \n",
        "    - *prev_output_tokens*: 英文的整數序列 e.g. 1, 28, 29, 205, 2 已經 shift 一格的 target\n",
        "    - *encoder_out*: 編碼器的輸出\n",
        "    - *incremental_state*: 這是測試階段為了加速，所以會記錄每個 timestep 的 hidden state 詳見 forward\n",
        "- 輸出: \n",
        "    - *outputs*: decoder 每個 timestep 的 logits，還沒經過 softmax 的分布\n",
        "    - *extra*: 沒用到"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZrwLNchRgAE"
      },
      "source": [
        "class RNNDecoder(FairseqIncrementalDecoder):\n",
        "    def __init__(self, args, dictionary, embed_tokens):\n",
        "        super().__init__(dictionary)\n",
        "        self.embed_tokens = embed_tokens\n",
        "        \n",
        "        assert args.decoder_layers == args.encoder_layers, f\"\"\"seq2seq rnn requires that encoder \n",
        "        and decoder have same layers of rnn. got: {args.encoder_layers, args.decoder_layers}\"\"\"\n",
        "        assert args.decoder_ffn_embed_dim == args.encoder_ffn_embed_dim*2, f\"\"\"seq2seq-rnn requires \n",
        "        that decoder hidden to be 2*encoder hidden dim. got: {args.decoder_ffn_embed_dim, args.encoder_ffn_embed_dim*2}\"\"\"\n",
        "        \n",
        "        self.embed_dim = args.decoder_embed_dim\n",
        "        self.hidden_dim = args.decoder_ffn_embed_dim\n",
        "        self.num_layers = args.decoder_layers\n",
        "        \n",
        "        \n",
        "        self.dropout_in_module = nn.Dropout(args.dropout)\n",
        "        self.rnn = nn.GRU(\n",
        "            self.embed_dim, \n",
        "            self.hidden_dim, \n",
        "            self.num_layers, \n",
        "            dropout=args.dropout, \n",
        "            batch_first=False, \n",
        "            bidirectional=False\n",
        "        )\n",
        "        self.attention = AttentionLayer(\n",
        "            self.embed_dim, self.hidden_dim, self.embed_dim, bias=False\n",
        "        ) \n",
        "        # self.attention = None\n",
        "        self.dropout_out_module = nn.Dropout(args.dropout)\n",
        "        \n",
        "        if self.hidden_dim != self.embed_dim:\n",
        "            self.project_out_dim = nn.Linear(self.hidden_dim, self.embed_dim)\n",
        "        else:\n",
        "            self.project_out_dim = None\n",
        "        \n",
        "        if args.share_decoder_input_output_embed:\n",
        "            self.output_projection = nn.Linear(\n",
        "                self.embed_tokens.weight.shape[1],\n",
        "                self.embed_tokens.weight.shape[0],\n",
        "                bias=False,\n",
        "            )\n",
        "            self.output_projection.weight = self.embed_tokens.weight\n",
        "        else:\n",
        "            self.output_projection = nn.Linear(\n",
        "                self.output_embed_dim, len(dictionary), bias=False\n",
        "            )\n",
        "            nn.init.normal_(\n",
        "                self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5\n",
        "            )\n",
        "        \n",
        "    def forward(self, prev_output_tokens, encoder_out, incremental_state=None, **unused):\n",
        "        # 取出encoder的輸出\n",
        "        encoder_outputs, encoder_hiddens, encoder_padding_mask = encoder_out\n",
        "        # outputs:          seq_len x batch x num_directions*hidden\n",
        "        # encoder_hiddens:  num_layers x batch x num_directions*encoder_hidden\n",
        "        # padding_mask:     seq_len x batch\n",
        "        \n",
        "        if incremental_state is not None and len(incremental_state) > 0:\n",
        "            # 有上個timestep留下的資訊，讀進來就可以繼續decode，不用從bos重來\n",
        "            prev_output_tokens = prev_output_tokens[:, -1:]\n",
        "            cache_state = self.get_incremental_state(incremental_state, \"cached_state\")\n",
        "            prev_hiddens = cache_state[\"prev_hiddens\"]\n",
        "        else:\n",
        "            # 沒有incremental state代表這是training或者是test time時的第一步\n",
        "            # 準備seq2seq: 把encoder_hiddens pass進去decoder的hidden states\n",
        "            prev_hiddens = encoder_hiddens\n",
        "        \n",
        "        bsz, seqlen = prev_output_tokens.size()\n",
        "        \n",
        "        # embed tokens\n",
        "        x = self.embed_tokens(prev_output_tokens)\n",
        "        x = self.dropout_in_module(x)\n",
        "\n",
        "        # B x T x C -> T x B x C\n",
        "        x = x.transpose(0, 1)\n",
        "                \n",
        "        # 做decoder-to-encoder attention\n",
        "        if self.attention is not None:\n",
        "            x, attn = self.attention(x, encoder_outputs, encoder_padding_mask)\n",
        "                        \n",
        "        # 過單向RNN\n",
        "        x, final_hiddens = self.rnn(x, prev_hiddens)\n",
        "        # outputs = [sequence len, batch size, hid dim]\n",
        "        # hidden =  [num_layers * directions, batch size  , hid dim]\n",
        "        x = self.dropout_out_module(x)\n",
        "                \n",
        "        # 投影到embedding size (如果hidden 和embed size不一樣，然後share_embedding又設成True,需要額外project一次)\n",
        "        if self.project_out_dim != None:\n",
        "            x = self.project_out_dim(x)\n",
        "        \n",
        "        # 投影到vocab size 的分佈\n",
        "        x = self.output_projection(x)\n",
        "        \n",
        "        # T x B x C -> B x T x C\n",
        "        x = x.transpose(1, 0)\n",
        "        \n",
        "        # 如果是Incremental, 記錄這個timestep的hidden states, 下個timestep讀回來\n",
        "        cache_state = {\n",
        "            \"prev_hiddens\": final_hiddens,\n",
        "        }\n",
        "        self.set_incremental_state(incremental_state, \"cached_state\", cache_state)\n",
        "        \n",
        "        return x, None\n",
        "    \n",
        "    def reorder_incremental_state(\n",
        "        self,\n",
        "        incremental_state,\n",
        "        new_order,\n",
        "    ):\n",
        "        # 這個beam search時會用到，意義並不是很重要\n",
        "        cache_state = self.get_incremental_state(incremental_state, \"cached_state\")\n",
        "        prev_hiddens = cache_state[\"prev_hiddens\"]\n",
        "        prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens]\n",
        "        cache_state = {\n",
        "            \"prev_hiddens\": torch.stack(prev_hiddens),\n",
        "        }\n",
        "        self.set_incremental_state(incremental_state, \"cached_state\", cache_state)\n",
        "        return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ZsrA8FoRgAF"
      },
      "source": [
        "## Seq2Seq\n",
        "- 由 **Encoder** 和 **Decoder** 組成\n",
        "- 接收輸入並傳給 **Encoder** \n",
        "- 將 **Encoder** 的輸出傳給 **Decoder**\n",
        "- **Decoder** 根據前幾個 timestep 的輸出和 **Encoder** 輸出進行解碼  \n",
        "- 當解碼完成後，將 **Decoder** 的輸出傳回 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHAgW5hdRgAF"
      },
      "source": [
        "class Seq2Seq(FairseqEncoderDecoderModel):\n",
        "    def __init__(self, args, encoder, decoder):\n",
        "        super().__init__(encoder, decoder)\n",
        "        self.args = args\n",
        "    \n",
        "    def forward(\n",
        "        self,\n",
        "        src_tokens,\n",
        "        src_lengths,\n",
        "        prev_output_tokens,\n",
        "        return_all_hiddens: bool = True,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Run the forward pass for an encoder-decoder model.\n",
        "        \"\"\"\n",
        "        encoder_out = self.encoder(\n",
        "            src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens\n",
        "        )\n",
        "        logits, extra = self.decoder(\n",
        "            prev_output_tokens,\n",
        "            encoder_out=encoder_out,\n",
        "            src_lengths=src_lengths,\n",
        "            return_all_hiddens=return_all_hiddens,\n",
        "        )\n",
        "        return logits, extra"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEsQPWz4RgAF"
      },
      "source": [
        "# 模型初始化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfKelefVRgAF"
      },
      "source": [
        "# # HINT: transformer 架構\n",
        "# from fairseq.models.transformer import (\n",
        "#     TransformerEncoder, \n",
        "#     TransformerDecoder,\n",
        "# )\n",
        "\n",
        "def build_model(args, task):\n",
        "    \"\"\" 按照參數設定建置模型 \"\"\"\n",
        "    src_dict, tgt_dict = task.source_dictionary, task.target_dictionary\n",
        "\n",
        "    # 詞嵌入\n",
        "    encoder_embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim, src_dict.pad())\n",
        "    decoder_embed_tokens = nn.Embedding(len(tgt_dict), args.decoder_embed_dim, tgt_dict.pad())\n",
        "    \n",
        "    # 編碼器與解碼器\n",
        "    # TODO: 替換成 TransformerEncoder 和 TransformerDecoder\n",
        "    encoder = RNNEncoder(args, src_dict, encoder_embed_tokens)\n",
        "    decoder = RNNDecoder(args, tgt_dict, decoder_embed_tokens)\n",
        "    \n",
        "    # 序列到序列模型\n",
        "    model = Seq2Seq(args, encoder, decoder)\n",
        "    \n",
        "    # 序列到序列模型的初始化很重要 需要特別處理\n",
        "    def init_params(module):\n",
        "        from fairseq.modules import MultiheadAttention\n",
        "        if isinstance(module, nn.Linear):\n",
        "            module.weight.data.normal_(mean=0.0, std=0.02)\n",
        "            if module.bias is not None:\n",
        "                module.bias.data.zero_()\n",
        "        if isinstance(module, nn.Embedding):\n",
        "            module.weight.data.normal_(mean=0.0, std=0.02)\n",
        "            if module.padding_idx is not None:\n",
        "                module.weight.data[module.padding_idx].zero_()\n",
        "        if isinstance(module, MultiheadAttention):\n",
        "            module.q_proj.weight.data.normal_(mean=0.0, std=0.02)\n",
        "            module.k_proj.weight.data.normal_(mean=0.0, std=0.02)\n",
        "            module.v_proj.weight.data.normal_(mean=0.0, std=0.02)\n",
        "        if isinstance(module, nn.RNNBase):\n",
        "            for name, param in module.named_parameters():\n",
        "                if \"weight\" in name or \"bias\" in name:\n",
        "                    param.data.uniform_(-0.1, 0.1)\n",
        "            \n",
        "    # 初始化模型\n",
        "    model.apply(init_params)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_7VlmlhRgAF"
      },
      "source": [
        "## 設定模型相關參數\n",
        "參考參數\n",
        "\n",
        "|model|embedding dim|encoder ffn|encoder layers|decoder ffn|decoder layers|\n",
        "|-|-|-|-|-|-|\n",
        "|RNN|256|512|1|1024|1|\n",
        "|Transformer|256|1024|4|1024|4|\n",
        "\n",
        "Strong baseline 用的參數可以參考 [Attention is all you need](#vaswani2017) 的 Table 3 的 transformer-base"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtV0uOXARgAF"
      },
      "source": [
        "arch_args = Namespace(\n",
        "    encoder_embed_dim=256,\n",
        "    encoder_ffn_embed_dim=512,\n",
        "    encoder_layers=1,\n",
        "    decoder_embed_dim=256,\n",
        "    decoder_ffn_embed_dim=1024,\n",
        "    decoder_layers=1,\n",
        "    share_decoder_input_output_embed=True,\n",
        "    dropout=0.3,\n",
        ")\n",
        "\n",
        "# # HINT: 補上Transformer用的參數\n",
        "# def add_transformer_args(args):\n",
        "#     args.encoder_attention_heads=4\n",
        "#     args.encoder_normalize_before=True\n",
        "    \n",
        "#     args.decoder_attention_heads=4\n",
        "#     args.decoder_normalize_before=True\n",
        "    \n",
        "#     args.activation_fn=\"relu\"\n",
        "#     args.max_source_positions=1024\n",
        "#     args.max_target_positions=1024\n",
        "    \n",
        "#     # 補上我們沒有設定的Transformer預設參數\n",
        "#     from fairseq.models.transformer import base_architecture \n",
        "#     base_architecture(arch_args)\n",
        "\n",
        "# add_transformer_args(arch_args)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0zCw6lofRgAG"
      },
      "source": [
        "if config.use_wandb:\n",
        "    wandb.config.update(vars(arch_args))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xLsO1-URgAG",
        "outputId": "00f1f9d0-3fd5-4867-a53e-29126d7d4656"
      },
      "source": [
        "model = build_model(arch_args, task)\n",
        "logger.info(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.3 and num_layers=1\n",
            "  \"num_layers={}\".format(dropout, num_layers))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:28:46 | INFO | hw5.seq2seq | Seq2Seq(\n",
            "  (encoder): RNNEncoder(\n",
            "    (embed_tokens): Embedding(8000, 256, padding_idx=1)\n",
            "    (dropout_in_module): Dropout(p=0.3, inplace=False)\n",
            "    (rnn): GRU(256, 512, dropout=0.3, bidirectional=True)\n",
            "    (dropout_out_module): Dropout(p=0.3, inplace=False)\n",
            "  )\n",
            "  (decoder): RNNDecoder(\n",
            "    (embed_tokens): Embedding(8000, 256, padding_idx=1)\n",
            "    (dropout_in_module): Dropout(p=0.3, inplace=False)\n",
            "    (rnn): GRU(256, 1024, dropout=0.3)\n",
            "    (attention): AttentionLayer(\n",
            "      (input_proj): Linear(in_features=256, out_features=1024, bias=False)\n",
            "      (output_proj): Linear(in_features=1280, out_features=256, bias=False)\n",
            "    )\n",
            "    (dropout_out_module): Dropout(p=0.3, inplace=False)\n",
            "    (project_out_dim): Linear(in_features=1024, out_features=256, bias=True)\n",
            "    (output_projection): Linear(in_features=256, out_features=8000, bias=False)\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NP9lEWwLRgAG"
      },
      "source": [
        "# Optimization 最佳化"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EadaZB2lRgAG"
      },
      "source": [
        "## Loss: Label Smoothing Regularization\n",
        "* 讓模型學習輸出較不集中的分佈，防止模型過度自信\n",
        "* 有時候Ground Truth並非唯一答案，所以在算loss時，我們會保留一部份機率給正確答案以外的label\n",
        "* 可以有效防止過度擬合\n",
        "\n",
        "code [source](https://fairseq.readthedocs.io/en/latest/_modules/fairseq/criterions/label_smoothed_cross_entropy.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "858Lp3YbRgAG"
      },
      "source": [
        "class LabelSmoothedCrossEntropyCriterion(nn.Module):\n",
        "    def __init__(self, smoothing, ignore_index=None, reduce=True):\n",
        "        super().__init__()\n",
        "        self.smoothing = smoothing\n",
        "        self.ignore_index = ignore_index\n",
        "        self.reduce = reduce\n",
        "    \n",
        "    def forward(self, lprobs, target):\n",
        "        if target.dim() == lprobs.dim() - 1:\n",
        "            target = target.unsqueeze(-1)\n",
        "        # nll: Negative log likelihood，當目標是one-hot時的cross-entropy loss. 以下同 F.nll_loss\n",
        "        nll_loss = -lprobs.gather(dim=-1, index=target)\n",
        "        # 將一部分正確答案的機率分配給其他label 所以當計算cross-entropy時等於把所有label的log prob加起來\n",
        "        smooth_loss = -lprobs.sum(dim=-1, keepdim=True)\n",
        "        if self.ignore_index is not None:\n",
        "            pad_mask = target.eq(self.ignore_index)\n",
        "            nll_loss.masked_fill_(pad_mask, 0.0)\n",
        "            smooth_loss.masked_fill_(pad_mask, 0.0)\n",
        "        else:\n",
        "            nll_loss = nll_loss.squeeze(-1)\n",
        "            smooth_loss = smooth_loss.squeeze(-1)\n",
        "        if self.reduce:\n",
        "            nll_loss = nll_loss.sum()\n",
        "            smooth_loss = smooth_loss.sum()\n",
        "        # 計算cross-entropy時 加入分配給其他label的loss\n",
        "        eps_i = self.smoothing / lprobs.size(-1)\n",
        "        loss = (1.0 - self.smoothing) * nll_loss + eps_i * smooth_loss\n",
        "        return loss\n",
        "\n",
        "# 一般都用0.1效果就很好了\n",
        "criterion = LabelSmoothedCrossEntropyCriterion(\n",
        "    smoothing=0.1,\n",
        "    ignore_index=task.target_dictionary.pad(),\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtLFVN_9RgAH"
      },
      "source": [
        "## Optimizer: Adam + lr scheduling\n",
        "Inverse square root 排程對於訓練 Transformer 時的穩定性很重要，後來也用在 RNN 上。\n",
        "根據底下公式來更新 learning rate，前期線性增長，後期根據更新步數方根的倒數來遞減。\n",
        "$$lrate = d_{\\text{model}}^{-0.5}\\cdot\\min({step\\_num}^{-0.5},{step\\_num}\\cdot{warmup\\_steps}^{-1.5})$$\n",
        "code [source](https://nlp.seas.harvard.edu/2018/04/03/attention.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "seuXl85WRgAH"
      },
      "source": [
        "class NoamOpt:\n",
        "    \"Optim wrapper that implements rate.\"\n",
        "    def __init__(self, model_size, factor, warmup, optimizer):\n",
        "        self.optimizer = optimizer\n",
        "        self._step = 0\n",
        "        self.warmup = warmup\n",
        "        self.factor = factor\n",
        "        self.model_size = model_size\n",
        "        self._rate = 0\n",
        "    \n",
        "    @property\n",
        "    def param_groups(self):\n",
        "        return self.optimizer.param_groups\n",
        "        \n",
        "    def multiply_grads(self, c):\n",
        "        \"\"\"Multiplies grads by a constant *c*.\"\"\"                \n",
        "        for group in self.param_groups:\n",
        "            for p in group['params']:\n",
        "                if p.grad is not None:\n",
        "                    p.grad.data.mul_(c)\n",
        "        \n",
        "    def step(self):\n",
        "        \"Update parameters and rate\"\n",
        "        self._step += 1\n",
        "        rate = self.rate()\n",
        "        for p in self.param_groups:\n",
        "            p['lr'] = rate\n",
        "        self._rate = rate\n",
        "        self.optimizer.step()\n",
        "        \n",
        "    def rate(self, step = None):\n",
        "        \"Implement `lrate` above\"\n",
        "        if step is None:\n",
        "            step = self._step\n",
        "        return 0 if not step else self.factor * \\\n",
        "            (self.model_size ** (-0.5) *\n",
        "            min(step ** (-0.5), step * self.warmup ** (-1.5)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSUx2dnlRgAH"
      },
      "source": [
        "## 排程視覺化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "yCO6E_tlRgAH",
        "outputId": "e1cec7d7-ca84-40bb-e6f2-cb5199698b22"
      },
      "source": [
        "optimizer = NoamOpt(\n",
        "    model_size=arch_args.encoder_embed_dim, \n",
        "    factor=config.lr_factor, \n",
        "    warmup=config.lr_warmup, \n",
        "    optimizer=torch.optim.AdamW(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=0.0001))\n",
        "plt.plot(np.arange(1, 100000), [optimizer.rate(i) for i in range(1, 100000)])\n",
        "plt.legend([f\"{optimizer.model_size}:{optimizer.warmup}\"])\n",
        "None"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7O1771PRgAH"
      },
      "source": [
        "# 訓練步驟"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRiICusoRgAH"
      },
      "source": [
        "## Training 訓練"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayT4cMFJRgAH"
      },
      "source": [
        "from fairseq.data import iterators\n",
        "from torch.cuda.amp import GradScaler, autocast\n",
        "\n",
        "def train_one_epoch(epoch_itr, model, task, criterion, optimizer, accum_steps=1):\n",
        "    itr = epoch_itr.next_epoch_itr(shuffle=True)\n",
        "    itr = iterators.GroupedIterator(itr, accum_steps) # 梯度累積: 每 accum_steps 個 sample 更新一次\n",
        "    \n",
        "    stats = {\"loss\": []}\n",
        "    scaler = GradScaler() # 混和精度訓練 automatic mixed precision (amp) \n",
        "    \n",
        "    model.train()\n",
        "    progress = tqdm.tqdm(itr, desc=f\"train epoch {epoch_itr.epoch}\", leave=False)\n",
        "    for samples in progress:\n",
        "        model.zero_grad()\n",
        "        accum_loss = 0\n",
        "        sample_size = 0\n",
        "        # 梯度累積: 每 accum_steps 個 sample 更新一次\n",
        "        for i, sample in enumerate(samples):\n",
        "            if i == 1:\n",
        "                # emptying the CUDA cache after the first step can reduce the chance of OOM\n",
        "                torch.cuda.empty_cache()\n",
        "\n",
        "            sample = utils.move_to_cuda(sample, device=device)\n",
        "            target = sample[\"target\"]\n",
        "            sample_size_i = sample[\"ntokens\"]\n",
        "            sample_size += sample_size_i\n",
        "            \n",
        "            # 混和精度訓練 \n",
        "            with autocast():\n",
        "                net_output = model.forward(**sample[\"net_input\"])\n",
        "                lprobs = F.log_softmax(net_output[0], -1)            \n",
        "                loss = criterion(lprobs.view(-1, lprobs.size(-1)), target.view(-1))\n",
        "                \n",
        "                # logging\n",
        "                accum_loss += loss.item()\n",
        "                # back-prop\n",
        "                scaler.scale(loss).backward()                \n",
        "        \n",
        "        scaler.unscale_(optimizer)\n",
        "        optimizer.multiply_grads(1 / (sample_size or 1.0)) # (sample_size or 1.0) handles the case of a zero gradient\n",
        "        gnorm = nn.utils.clip_grad_norm_(model.parameters(), config.clip_norm) # 梯度裁剪 防止梯度爆炸\n",
        "        \n",
        "        scaler.step(optimizer)\n",
        "        scaler.update()\n",
        "        \n",
        "        # logging\n",
        "        loss_print = accum_loss/sample_size\n",
        "        stats[\"loss\"].append(loss_print)\n",
        "        progress.set_postfix(loss=loss_print)\n",
        "        if config.use_wandb:\n",
        "            wandb.log({\n",
        "                \"train/loss\": loss_print,\n",
        "                \"train/grad_norm\": gnorm.item(),\n",
        "                \"train/lr\": optimizer.rate(),\n",
        "                \"train/sample_size\": sample_size,\n",
        "            })\n",
        "        \n",
        "    loss_print = np.mean(stats[\"loss\"])\n",
        "    logger.info(f\"training loss: {loss_print:.4f}\")\n",
        "    return stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VI0izb1uRgAH"
      },
      "source": [
        "## Validation & Inference 檢驗和推論\n",
        "為防止訓練發生過度擬合，每過一段時間要做一次檢測，計算模型在未看過的資料上的表現。\n",
        "- 過程基本上和training一樣，另外加上 inference\n",
        "- 檢驗完畢可順便儲存模型參數\n",
        "\n",
        "單看 validation loss，我們很難知道模型真實的效能\n",
        "- 直接用當前模型去生成翻譯結果 (hypothesis)，再和正確答案 (reference) 計算 BLEU score\n",
        "- 也可用肉眼看翻譯結果的好壞\n",
        "- 我們用 fairseq 寫好的 sequence generator 來進行 beam search 生成翻譯結果"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFZH2Pf6RgAH"
      },
      "source": [
        "# fairseq 的 beam search generator\n",
        "# 給定模型和輸入序列，用 beam search 生成翻譯結果\n",
        "sequence_generator = task.build_generator([model], config)\n",
        "\n",
        "def decode(toks, dictionary):\n",
        "    # 從 Tensor 轉成人看得懂的句子\n",
        "    s = dictionary.string(\n",
        "        toks.int().cpu(),\n",
        "        config.post_process,\n",
        "    )\n",
        "    return s if s else \"<unk>\"\n",
        "\n",
        "def inference_step(sample, model):\n",
        "    gen_out = sequence_generator.generate([model], sample)\n",
        "    srcs = []\n",
        "    hyps = []\n",
        "    refs = []\n",
        "    for i in range(len(gen_out)):\n",
        "        # 對於每個 sample, 收集輸入，輸出和參考答案，稍後計算 BLEU\n",
        "        srcs.append(decode(\n",
        "            utils.strip_pad(sample[\"net_input\"][\"src_tokens\"][i], task.source_dictionary.pad()), \n",
        "            task.source_dictionary,\n",
        "        ))\n",
        "        hyps.append(decode(\n",
        "            gen_out[i][0][\"tokens\"], # 0 代表取出 beam 內分數第一的輸出結果\n",
        "            task.target_dictionary,\n",
        "        ))\n",
        "        refs.append(decode(\n",
        "            utils.strip_pad(sample[\"target\"][i], task.target_dictionary.pad()), \n",
        "            task.target_dictionary,\n",
        "        ))\n",
        "    return srcs, hyps, refs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r94mllpjRgAI"
      },
      "source": [
        "import shutil\n",
        "import sacrebleu\n",
        "\n",
        "def validate(model, task, criterion, log_to_wandb=True):\n",
        "    logger.info('begin validation')\n",
        "    itr = load_data_iterator(task, \"valid\", 1, config.max_tokens, config.num_workers).next_epoch_itr(shuffle=False)\n",
        "    \n",
        "    stats = {\"loss\":[], \"bleu\": 0, \"srcs\":[], \"hyps\":[], \"refs\":[]}\n",
        "    srcs = []\n",
        "    hyps = []\n",
        "    refs = []\n",
        "    \n",
        "    model.eval()\n",
        "    progress = tqdm.tqdm(itr, desc=f\"validation\", leave=False)\n",
        "    with torch.no_grad():\n",
        "        for i, sample in enumerate(progress):\n",
        "            # validation loss\n",
        "            sample = utils.move_to_cuda(sample, device=device)\n",
        "            net_output = model.forward(**sample[\"net_input\"])\n",
        "\n",
        "            lprobs = F.log_softmax(net_output[0], -1)\n",
        "            target = sample[\"target\"]\n",
        "            sample_size = sample[\"ntokens\"]\n",
        "            loss = criterion(lprobs.view(-1, lprobs.size(-1)), target.view(-1)) / sample_size\n",
        "            progress.set_postfix(valid_loss=loss.item())\n",
        "            stats[\"loss\"].append(loss)\n",
        "            \n",
        "            # 進行推論\n",
        "            s, h, r = inference_step(sample, model)\n",
        "            srcs.extend(s)\n",
        "            hyps.extend(h)\n",
        "            refs.extend(r)\n",
        "            \n",
        "    tok = 'zh' if task.cfg.target_lang == 'zh' else '13a'\n",
        "    stats[\"loss\"] = torch.stack(stats[\"loss\"]).mean().item()\n",
        "    stats[\"bleu\"] = sacrebleu.corpus_bleu(hyps, [refs], tokenize=tok) # 計算BLEU score\n",
        "    stats[\"srcs\"] = srcs\n",
        "    stats[\"hyps\"] = hyps\n",
        "    stats[\"refs\"] = refs\n",
        "    \n",
        "    if config.use_wandb and log_to_wandb:\n",
        "        wandb.log({\n",
        "            \"valid/loss\": stats[\"loss\"],\n",
        "            \"valid/bleu\": stats[\"bleu\"].score,\n",
        "        }, commit=False)\n",
        "    \n",
        "    showid = np.random.randint(len(hyps))\n",
        "    logger.info(\"example source: \" + srcs[showid])\n",
        "    logger.info(\"example hypothesis: \" + hyps[showid])\n",
        "    logger.info(\"example reference: \" + refs[showid])\n",
        "    \n",
        "    # show bleu results\n",
        "    logger.info(f\"validation loss:\\t{stats['loss']:.4f}\")\n",
        "    logger.info(stats[\"bleu\"].format())\n",
        "    return stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soCOXVRJRgAI"
      },
      "source": [
        "# 儲存及載入模型參數"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMi2N_iXRgAI"
      },
      "source": [
        "def validate_and_save(model, task, criterion, optimizer, epoch, save=True):   \n",
        "    stats = validate(model, task, criterion)\n",
        "    bleu = stats['bleu']\n",
        "    loss = stats['loss']\n",
        "    if save:\n",
        "        # save epoch checkpoints\n",
        "        savedir = Path(config.savedir).absolute()\n",
        "        savedir.mkdir(parents=True, exist_ok=True)\n",
        "        \n",
        "        check = {\n",
        "            \"model\": model.state_dict(),\n",
        "            \"stats\": {\"bleu\": bleu.score, \"loss\": loss},\n",
        "            \"optim\": {\"step\": optimizer._step}\n",
        "        }\n",
        "        torch.save(check, savedir/f\"checkpoint{epoch}.pt\")\n",
        "        shutil.copy(savedir/f\"checkpoint{epoch}.pt\", savedir/f\"checkpoint_last.pt\")\n",
        "        logger.info(f\"saved epoch checkpoint: {savedir}/checkpoint{epoch}.pt\")\n",
        "    \n",
        "        # save epoch samples\n",
        "        with open(savedir/f\"samples{epoch}.{config.source_lang}-{config.target_lang}.txt\", \"w\") as f:\n",
        "            for s, h in zip(stats[\"srcs\"], stats[\"hyps\"]):\n",
        "                f.write(f\"{s}\\t{h}\\n\")\n",
        "\n",
        "        # get best valid bleu    \n",
        "        if getattr(validate_and_save, \"best_bleu\", 0) < bleu.score:\n",
        "            validate_and_save.best_bleu = bleu.score\n",
        "            torch.save(check, savedir/f\"checkpoint_best.pt\")\n",
        "            \n",
        "        del_file = savedir / f\"checkpoint{epoch - config.keep_last_epochs}.pt\"\n",
        "        if del_file.exists():\n",
        "            del_file.unlink()\n",
        "    \n",
        "    return stats\n",
        "\n",
        "def try_load_checkpoint(model, optimizer=None, name=None):\n",
        "    name = name if name else \"checkpoint_last.pt\"\n",
        "    checkpath = Path(config.savedir)/name\n",
        "    if checkpath.exists():\n",
        "        check = torch.load(checkpath)\n",
        "        model.load_state_dict(check[\"model\"])\n",
        "        stats = check[\"stats\"]\n",
        "        step = \"unknown\"\n",
        "        if optimizer != None:\n",
        "            optimizer._step = step = check[\"optim\"][\"step\"]\n",
        "        logger.info(f\"loaded checkpoint {checkpath}: step={step} loss={stats['loss']} bleu={stats['bleu']}\")\n",
        "    else:\n",
        "        logger.info(f\"no checkpoints found at {checkpath}!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3v25GITRgAI"
      },
      "source": [
        "# 主程式\n",
        "## 訓練迴圈"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6etVCy5RgAI"
      },
      "source": [
        "model = model.to(device=device)\n",
        "criterion = criterion.to(device=device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTy3bKyFRgAI",
        "outputId": "cc8ff780-65e2-4b87-836a-1bd58b04e232"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Nov 18 09:29:02 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   38C    P0    57W / 149W |    585MiB / 11441MiB |      4%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2227LSBRgAI",
        "outputId": "99812ba1-2ab8-48f7-9be6-0c53de5ef3bd"
      },
      "source": [
        "logger.info(\"task: {}\".format(task.__class__.__name__))\n",
        "logger.info(\"encoder: {}\".format(model.encoder.__class__.__name__))\n",
        "logger.info(\"decoder: {}\".format(model.decoder.__class__.__name__))\n",
        "logger.info(\"criterion: {}\".format(criterion.__class__.__name__))\n",
        "logger.info(\"optimizer: {}\".format(optimizer.__class__.__name__))\n",
        "logger.info(\n",
        "    \"num. model params: {:,} (num. trained: {:,})\".format(\n",
        "        sum(p.numel() for p in model.parameters()),\n",
        "        sum(p.numel() for p in model.parameters() if p.requires_grad),\n",
        "    )\n",
        ")\n",
        "logger.info(f\"max tokens per batch = {config.max_tokens}, accumulate steps = {config.accum_steps}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | task: TranslationTask\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | encoder: RNNEncoder\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | decoder: RNNDecoder\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | criterion: LabelSmoothedCrossEntropyCriterion\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | optimizer: NoamOpt\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | num. model params: 11,251,968 (num. trained: 11,251,968)\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | max tokens per batch = 8192, accumulate steps = 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
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          "height": 1000,
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          ]
        },
        "id": "BZH_m7fERgAI",
        "outputId": "43864583-1273-4815-ac46-c2d301f6a99b"
      },
      "source": [
        "epoch_itr = load_data_iterator(task, \"train\", config.start_epoch, config.max_tokens, config.num_workers)\n",
        "try_load_checkpoint(model, optimizer, name=config.resume)\n",
        "while epoch_itr.next_epoch_idx <= config.max_epoch:\n",
        "    # train for one epoch\n",
        "    train_one_epoch(epoch_itr, model, task, criterion, optimizer, config.accum_steps)\n",
        "    stats = validate_and_save(model, task, criterion, optimizer, epoch=epoch_itr.epoch)\n",
        "    logger.info(\"end of epoch {}\".format(epoch_itr.epoch))    \n",
        "    epoch_itr = load_data_iterator(task, \"train\", epoch_itr.next_epoch_idx, config.max_tokens, config.num_workers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:29:02 | WARNING | fairseq.tasks.fairseq_task | 1 samples have invalid sizes and will be skipped, max_positions=(1024, 1024), first few sample ids=[326674]\n",
            "2021-11-18 09:29:02 | INFO | hw5.seq2seq | no checkpoints found at checkpoints/rnn/checkpoint_last.pt!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbb9d4cbcbf74f90ae18e6c2f8d44b26",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 1:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:44:42 | INFO | hw5.seq2seq | training loss: 7.0856\n",
            "2021-11-18 09:44:42 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ca5e4b7362846849b551417263cbc34",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/fairseq/search.py:140: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  beams_buf = indices_buf // vocab_size\n",
            "/usr/local/lib/python3.7/dist-packages/fairseq/sequence_generator.py:657: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  unfin_idx = idx // beam_size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | example source: but the point is , it doesn't feel that way .\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | example hypothesis: 但 , 一個 ,  , 我 , 你 , 你 。 」\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | example reference: 但重點是 , 感覺起來卻不是那麼回事呀\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | validation loss:\t6.5407\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | BLEU = 0.10 11.7/0.7/0.0/0.0 (BP = 0.872 ratio = 0.879 hyp_len = 98331 ref_len = 111811)\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint1.pt\n",
            "2021-11-18 09:45:23 | INFO | hw5.seq2seq | end of epoch 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b9ca092910649fd8084e53afb4dfd95",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 2:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:01:04 | INFO | hw5.seq2seq | training loss: 6.3884\n",
            "2021-11-18 10:01:05 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e35b5d271be4c9892c932d14ac42ad9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | example source: we can push through that .\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | example hypothesis: 所以 , 很重要的 。\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | example reference: 我們可以衝破這些限制 。\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | validation loss:\t6.0794\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | BLEU = 0.78 18.3/2.1/0.4/0.1 (BP = 0.799 ratio = 0.817 hyp_len = 91365 ref_len = 111811)\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint2.pt\n",
            "2021-11-18 10:01:39 | INFO | hw5.seq2seq | end of epoch 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef305d62472b4d1eb449b805f7ae5755",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 3:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:17:22 | INFO | hw5.seq2seq | training loss: 5.8970\n",
            "2021-11-18 10:17:22 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3dfac348218b4153a357c9a16bc4f9bc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | example source: thank you .\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | example hypothesis: 謝謝 。\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | example reference: 謝謝 。\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | validation loss:\t5.5087\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | BLEU = 1.52 19.0/3.0/0.7/0.2 (BP = 0.943 ratio = 0.944 hyp_len = 105580 ref_len = 111811)\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint3.pt\n",
            "2021-11-18 10:18:01 | INFO | hw5.seq2seq | end of epoch 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "33c752fdfd1e4b158827f79d1e41e1c2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 4:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:33:44 | INFO | hw5.seq2seq | training loss: 5.4506\n",
            "2021-11-18 10:33:44 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "543a4e94f790467999541152535ea405",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | example source: those men would eventually be convicted of placing a van filled with 1 , 500 pounds of explosives into the sublevel parking lot of the world trade center's north tower , causing an explosion that killed six people and injured over 1 , 000 others .\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | example hypothesis: 有些人認為 , 美國人工智慧的數量 , 大量的數量 , 超過1500英里的路徑 , 大量的數量 , 大量的數量 , 大量的數量 , 大量的數量 , 大量的黑人 , 大量的黑人 , 大量的黑人 , 大量的黑人 , 大量的黑人 , 大量的數量 , 超過25英尺的路徑 , 由於南美洲的黑人 。\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | example reference: 這群男人最終被指控將滿載1500磅重的炸彈的廂式貨車停在世界貿易中心北塔的地下停車場 , 爆炸造成6人死亡 , 同時致使超過1000人受傷 。\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | validation loss:\t5.1524\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | BLEU = 2.62 19.4/4.5/1.3/0.4 (BP = 1.000 ratio = 1.226 hyp_len = 137127 ref_len = 111811)\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint4.pt\n",
            "2021-11-18 10:34:28 | INFO | hw5.seq2seq | end of epoch 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc428c11f7c040f0bae00893bcde11b4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 5:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:50:11 | INFO | hw5.seq2seq | training loss: 5.1662\n",
            "2021-11-18 10:50:11 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e9044af87624ba7b6e529d9d144b180",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | example source: but this is really how i go about creating these photographs .\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | example hypothesis: 但這讓我感到驚訝 。\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | example reference: 但是我真的是這樣做出這些相片的 。\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | validation loss:\t4.8502\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | BLEU = 6.04 34.8/11.7/4.8/2.1 (BP = 0.759 ratio = 0.784 hyp_len = 87631 ref_len = 111811)\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint5.pt\n",
            "2021-11-18 10:50:45 | INFO | hw5.seq2seq | end of epoch 5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3730641f9817479b904255ad3b86cefe",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 6:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:06:24 | INFO | hw5.seq2seq | training loss: 4.8707\n",
            "2021-11-18 11:06:24 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e38a72e50ce140f6afe5c6e3c55b7538",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | example source: so that is a fact .\n",
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | example hypothesis: 所以這是一個事實 。\n",
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | example reference: 這就是事實\n",
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | validation loss:\t4.5166\n",
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | BLEU = 9.35 31.5/12.9/6.1/3.1 (BP = 1.000 ratio = 1.124 hyp_len = 125712 ref_len = 111811)\n",
            "2021-11-18 11:07:04 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint6.pt\n",
            "2021-11-18 11:07:05 | INFO | hw5.seq2seq | end of epoch 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e854af34f9f14d6287b84871b3957a38",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 7:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:22:44 | INFO | hw5.seq2seq | training loss: 4.6555\n",
            "2021-11-18 11:22:44 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4e929e2bca564c56b1823bebf03fa55f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:23:24 | INFO | hw5.seq2seq | example source: thank you so much everyone .\n",
            "2021-11-18 11:23:24 | INFO | hw5.seq2seq | example hypothesis: 非常感謝大家 。\n",
            "2021-11-18 11:23:24 | INFO | hw5.seq2seq | example reference: 謝謝大家 !\n",
            "2021-11-18 11:23:24 | INFO | hw5.seq2seq | validation loss:\t4.3725\n",
            "2021-11-18 11:23:24 | INFO | hw5.seq2seq | BLEU = 10.70 33.4/14.5/7.2/3.8 (BP = 1.000 ratio = 1.123 hyp_len = 125550 ref_len = 111811)\n",
            "2021-11-18 11:23:25 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint7.pt\n",
            "2021-11-18 11:23:25 | INFO | hw5.seq2seq | end of epoch 7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5586d9fe48241239fe8ef4abe89446c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 8:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:39:02 | INFO | hw5.seq2seq | training loss: 4.5275\n",
            "2021-11-18 11:39:02 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6bd9f9a3eee04b118bf5097769d5f4f4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | example source: these differences became a source of inspired artistic celebration .\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | example hypothesis: 這些差異都變成了藝術的優勢 。\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | example reference: 這些差異變成了一個源頭 , 富有靈感之藝術讚頌的源頭 。\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | validation loss:\t4.2527\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | BLEU = 12.86 39.4/18.2/9.2/4.9 (BP = 0.961 ratio = 0.961 hyp_len = 107493 ref_len = 111811)\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint8.pt\n",
            "2021-11-18 11:39:40 | INFO | hw5.seq2seq | end of epoch 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86bba8b08b42430fb1b691370e6ffdde",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 9:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:55:19 | INFO | hw5.seq2seq | training loss: 4.4350\n",
            "2021-11-18 11:55:19 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0489f0380202446a81824d04e7682c0c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | example source: it's not .\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | example hypothesis: 不是 。\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | example reference: 不是\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | validation loss:\t4.1861\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | BLEU = 13.66 41.2/19.2/9.8/5.3 (BP = 0.962 ratio = 0.962 hyp_len = 107588 ref_len = 111811)\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint9.pt\n",
            "2021-11-18 11:55:56 | INFO | hw5.seq2seq | end of epoch 9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "71154ca3c23340a19a332c35e0506495",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 10:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 12:11:35 | INFO | hw5.seq2seq | training loss: 4.3675\n",
            "2021-11-18 12:11:35 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2dec96360cd45a1a5a1caee1db34900",
              "version_minor": 0,
              "version_major": 2
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        {
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          "name": "stdout",
          "text": [
            "2021-11-18 12:12:12 | INFO | hw5.seq2seq | example source: so think about when you have friends , families and coworkers in california , on the west coast or in other parts of the world .\n",
            "2021-11-18 12:12:12 | INFO | hw5.seq2seq | example hypothesis: 所以 , 想想看 , 在加州州 , 在加州州 , 在加州州 , 在加州州 , 在加州州 , 在加州州 , 在加州州 , 在加州州 , 在加州 , 在加州州 , 在加州 , 在加州州 , 在加州\n",
            "2021-11-18 12:12:12 | INFO | hw5.seq2seq | example reference: 所以 , 想像當你嘗試聯繫在加州 , 在西海岸或者在世界的另一面\n",
            "2021-11-18 12:12:12 | INFO | hw5.seq2seq | validation loss:\t4.1295\n",
            "2021-11-18 12:12:12 | INFO | hw5.seq2seq | BLEU = 14.28 40.6/19.2/10.0/5.4 (BP = 0.995 ratio = 0.995 hyp_len = 111231 ref_len = 111811)\n",
            "2021-11-18 12:12:13 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint10.pt\n",
            "2021-11-18 12:12:13 | INFO | hw5.seq2seq | end of epoch 10\n"
          ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 12:27:54 | INFO | hw5.seq2seq | training loss: 4.3135\n",
            "2021-11-18 12:27:54 | INFO | hw5.seq2seq | begin validation\n"
          ]
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        {
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          "text": [
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | example source: and i was asked to move out because one of my roommates had shared my status with her parents .\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | example hypothesis: 我被要求搬出去 , 因為我的房間裡有我和她的父母一樣 。\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | example reference: 我會被要求搬出去 , 是因為其中一名室友和她的父母談了我的狀況 。\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | validation loss:\t4.0846\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | BLEU = 15.05 42.5/20.5/10.7/5.9 (BP = 0.982 ratio = 0.983 hyp_len = 109870 ref_len = 111811)\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint11.pt\n",
            "2021-11-18 12:28:31 | INFO | hw5.seq2seq | end of epoch 11\n"
          ]
        },
        {
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            "text/plain": [
              "train epoch 12:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 12:44:11 | INFO | hw5.seq2seq | training loss: 4.2706\n",
            "2021-11-18 12:44:11 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | example source: and i want to share with you a quick video from our facility that gives you a sense of how this looks at scale .\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | example hypothesis: 我想要和各位分享一個快速的影片 , 給你們看一下這個觀點 。\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | example reference: 我想與你們快速分享一段關於我們設備的影片可以瞭解一下它的規模\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | validation loss:\t4.0560\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | BLEU = 15.12 44.9/21.8/11.5/6.3 (BP = 0.927 ratio = 0.930 hyp_len = 103971 ref_len = 111811)\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint12.pt\n",
            "2021-11-18 12:44:48 | INFO | hw5.seq2seq | end of epoch 12\n"
          ]
        },
        {
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            "text/plain": [
              "train epoch 13:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 13:00:37 | INFO | hw5.seq2seq | training loss: 4.2342\n",
            "2021-11-18 13:00:37 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | example source: it's a distinction perhaps we might make between two nobel laureates , richard feynman and john nash .\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | example hypothesis: 這是一個區別 , 我們可能在兩個諾貝爾雷德和約翰納希 。\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | example reference: 我們或許可以從兩位諾貝爾獎得主 , 理查費曼和約翰奈許之間看出這樣的差異 。\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | validation loss:\t4.0366\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | BLEU = 15.50 42.9/21.0/11.1/6.1 (BP = 0.987 ratio = 0.987 hyp_len = 110343 ref_len = 111811)\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint13.pt\n",
            "2021-11-18 13:01:15 | INFO | hw5.seq2seq | end of epoch 13\n"
          ]
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            "text/plain": [
              "train epoch 14:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 13:16:59 | INFO | hw5.seq2seq | training loss: 4.2042\n",
            "2021-11-18 13:16:59 | INFO | hw5.seq2seq | begin validation\n"
          ]
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          "text": [
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | example source: and therein lie the words of wisdom of a young girl whose brief life forever impacted mine .\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | example hypothesis: 無論如何 , 年輕的年輕女子 , 一直受到影響我所謂的智慧 。\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | example reference: 她說的話對我有很大啟發 。 這一個年幼女孩短暫的一生永遠影響著我 。\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | validation loss:\t4.0238\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | BLEU = 14.85 49.5/24.7/13.0/7.2 (BP = 0.806 ratio = 0.822 hyp_len = 91931 ref_len = 111811)\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint14.pt\n",
            "2021-11-18 13:17:32 | INFO | hw5.seq2seq | end of epoch 14\n"
          ]
        },
        {
          "output_type": "display_data",
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              "version_major": 2
            },
            "text/plain": [
              "train epoch 15:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 13:33:13 | INFO | hw5.seq2seq | training loss: 4.1817\n",
            "2021-11-18 13:33:13 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
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          "output_type": "display_data",
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          "text": [
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | example source: and we've started making curtains , and not only is it beautiful , but people can see status that you care about your children .\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | example hypothesis: 我們開始製作曲線 , 不僅是美麗的 , 還能看到你在乎你的小孩 。\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | example reference: 我們也開始生産窗簾不止因爲窗簾看起來漂亮 , 人們也可以看到你的地位看得出你很關心小孩\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | validation loss:\t3.9847\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | BLEU = 15.87 44.2/21.6/11.5/6.4 (BP = 0.975 ratio = 0.976 hyp_len = 109102 ref_len = 111811)\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint15.pt\n",
            "2021-11-18 13:33:50 | INFO | hw5.seq2seq | end of epoch 15\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "train epoch 16:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 13:49:32 | INFO | hw5.seq2seq | training loss: 4.1524\n",
            "2021-11-18 13:49:32 | INFO | hw5.seq2seq | begin validation\n"
          ]
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          "text": [
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | example source: now , i'm not suggesting we want to raise our babies in our stomach , but i am suggesting it's possible we might want to manage gastric secretion in the gut .\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | example hypothesis: 現在 , 我並不是建議我們想要培養我們的嬰兒 , 但我建議的是 , 我們想要培養我們的嬰兒 , 但是我建議我們要培養我們的嬰兒 , 但是我建議的是 , 我們要培養我們的嬰兒 。\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | example reference: 我並不是說要把嬰兒養在胃裡而是我們可能可以了解胃在內臟裡的分泌物\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | validation loss:\t3.9651\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | BLEU = 16.28 43.7/21.6/11.5/6.5 (BP = 1.000 ratio = 1.004 hyp_len = 112241 ref_len = 111811)\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint16.pt\n",
            "2021-11-18 13:50:10 | INFO | hw5.seq2seq | end of epoch 16\n"
          ]
        },
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              "train epoch 17:   0%|          | 0/791 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 14:05:52 | INFO | hw5.seq2seq | training loss: 4.1343\n",
            "2021-11-18 14:05:52 | INFO | hw5.seq2seq | begin validation\n"
          ]
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          "output_type": "display_data",
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          "text": [
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | example source: so we're hoping that's what they'll do .\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | example hypothesis: 我們希望他們能做些什麼 。\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | example reference: 所以 , 我們希望它們能夠幫忙 。\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | validation loss:\t3.9475\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | BLEU = 15.88 43.0/21.1/11.2/6.2 (BP = 1.000 ratio = 1.036 hyp_len = 115807 ref_len = 111811)\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint17.pt\n",
            "2021-11-18 14:06:30 | INFO | hw5.seq2seq | end of epoch 17\n"
          ]
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 14:22:11 | INFO | hw5.seq2seq | training loss: 4.1143\n",
            "2021-11-18 14:22:11 | INFO | hw5.seq2seq | begin validation\n"
          ]
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          "text": [
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | example source: i remember so vividly that day .\n",
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | example hypothesis: 我還記得那天 。\n",
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | example reference: 我對那一天還記憶猶新 。\n",
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | validation loss:\t3.9466\n",
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | BLEU = 16.37 46.4/23.0/12.3/7.0 (BP = 0.941 ratio = 0.943 hyp_len = 105434 ref_len = 111811)\n",
            "2021-11-18 14:22:47 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint18.pt\n",
            "2021-11-18 14:22:48 | INFO | hw5.seq2seq | end of epoch 18\n"
          ]
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            "text/plain": [
              "train epoch 19:   0%|          | 0/791 [00:00<?, ?it/s]"
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          },
          "metadata": {}
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 14:38:28 | INFO | hw5.seq2seq | training loss: 4.0975\n",
            "2021-11-18 14:38:28 | INFO | hw5.seq2seq | begin validation\n"
          ]
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          "text": [
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | example source: the next step is to make compliant toes , and try to add spines and claws and set it for dry adhesives .\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | example hypothesis: 下一個步驟是要做成熟的物體 , 試圖加入細菌和爪子 , 試圖加入細菌和爪子 。\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | example reference: 下一步是要製造順從聽話的腳趾頭 。 然後加上刺和爪子 , 再加上乾膠 。\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | validation loss:\t3.9373\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | BLEU = 16.89 46.0/23.0/12.4/7.1 (BP = 0.968 ratio = 0.968 hyp_len = 108273 ref_len = 111811)\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint19.pt\n",
            "2021-11-18 14:39:05 | INFO | hw5.seq2seq | end of epoch 19\n"
          ]
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dfb312f89c0a40f280e3ea3785c54b67",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 20:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 14:54:47 | INFO | hw5.seq2seq | training loss: 4.0803\n",
            "2021-11-18 14:54:47 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "61f1f5994b89459e8fbd1a2eed32150c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | example source: and got the cord off of the baby's neck , and a healthy screaming , kicking baby arrived , just as the dad ran in from the parking lot , \" hi , you have a son , i'm dr . darria .\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | example hypothesis: 把嬰兒的脖子拿出來 , 把寶寶拿到嬰兒的脖子裡 , 爸爸從停車場出生 , 說: 「 嗨 , 你有兒子 , 我是drria 。\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | example reference: 我把臍帶從嬰兒的脖子繞開 , 接著 , 一個健康哭鬧的嬰兒誕生了 。 同時 , 他的父親剛從停車場趕來 。 「 你好 , 是一個男孩 。 我是達里亞醫生 。\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | validation loss:\t3.9187\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | BLEU = 16.83 45.0/22.3/11.9/6.7 (BP = 1.000 ratio = 1.000 hyp_len = 111761 ref_len = 111811)\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint20.pt\n",
            "2021-11-18 14:55:25 | INFO | hw5.seq2seq | end of epoch 20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b32efacf458b416fbaad6ca2d137f24b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 21:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 15:11:06 | INFO | hw5.seq2seq | training loss: 4.0703\n",
            "2021-11-18 15:11:06 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c2bf2f959ff84e99b9d03addc48768f5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | example source: no , but the issue of city building in democracy is interesting because it creates chaos , right ?\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | example hypothesis: 不 , 民主是有趣的 , 因為它創造混亂 , 對吧 ?\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | example reference: 不 , 但民主的城市建設很有趣 , 因為會造成混亂 , 是嗎 ?\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | validation loss:\t3.9114\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | BLEU = 16.96 46.1/23.0/12.4/7.0 (BP = 0.974 ratio = 0.974 hyp_len = 108929 ref_len = 111811)\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint21.pt\n",
            "2021-11-18 15:11:42 | INFO | hw5.seq2seq | end of epoch 21\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35fbf66d48f74bc3afa9b13905a3c4fd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 22:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 15:27:25 | INFO | hw5.seq2seq | training loss: 4.0516\n",
            "2021-11-18 15:27:25 | INFO | hw5.seq2seq | begin validation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "02aee7704bf64020a401f12643916924",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "validation:   0%|          | 0/22 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | example source: so i'm going to show you a demo of this .\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | example hypothesis: 現在我要展示一個關於這個例子的例子 。\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | example reference: 現在請各位看示範\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | validation loss:\t3.9076\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | BLEU = 17.21 45.9/23.0/12.4/7.0 (BP = 0.987 ratio = 0.987 hyp_len = 110365 ref_len = 111811)\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | saved epoch checkpoint: /content/checkpoints/rnn/checkpoint22.pt\n",
            "2021-11-18 15:28:02 | INFO | hw5.seq2seq | end of epoch 22\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fe1a4495a9e4ee6a64928f7dac55ce5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "train epoch 23:   0%|          | 0/791 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrEa7d_jRgAI"
      },
      "source": [
        "# Submission 繳交檔案"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZQwlmqARgAI"
      },
      "source": [
        "# 把幾個 checkpoint 平均起來可以達到 ensemble 的效果\n",
        "checkdir=config.savedir\n",
        "!python ./fairseq/scripts/average_checkpoints.py \\\n",
        "--inputs {checkdir} \\\n",
        "--num-epoch-checkpoints 5 \\\n",
        "--output {checkdir}/avg_last_5_checkpoint.pt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7s6TJJgJRgAJ"
      },
      "source": [
        "## 確認生成繳交檔案的模型參數"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Q-xHf40RgAJ"
      },
      "source": [
        "# checkpoint_last.pt : 最後一次檢驗的檔案\n",
        "# checkpoint_best.pt : 檢驗 BLEU 最高的檔案\n",
        "# avg_last_5_checkpoint.pt:　最5後個檔案平均\n",
        "try_load_checkpoint(model, name=\"avg_last_5_checkpoint.pt\")\n",
        "validate(model, task, criterion, log_to_wandb=False)\n",
        "None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haiUc_9zRgAJ"
      },
      "source": [
        "## 進行預測"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToeSiNSsRgAJ"
      },
      "source": [
        "def generate_prediction(model, task, split=\"test\", outfile=\"./prediction.txt\"):    \n",
        "    task.load_dataset(split=split, epoch=1)\n",
        "    itr = load_data_iterator(task, split, 1, config.max_tokens, config.num_workers).next_epoch_itr(shuffle=False)\n",
        "    \n",
        "    idxs = []\n",
        "    hyps = []\n",
        "\n",
        "    model.eval()\n",
        "    progress = tqdm.tqdm(itr, desc=f\"prediction\")\n",
        "    with torch.no_grad():\n",
        "        for i, sample in enumerate(progress):\n",
        "            # validation loss\n",
        "            sample = utils.move_to_cuda(sample, device=device)\n",
        "\n",
        "            # 進行推論\n",
        "            s, h, r = inference_step(sample, model)\n",
        "            \n",
        "            hyps.extend(h)\n",
        "            idxs.extend(list(sample['id']))\n",
        "            \n",
        "    # 根據 preprocess 時的順序排列\n",
        "    hyps = [x for _,x in sorted(zip(idxs,hyps))]\n",
        "    \n",
        "    with open(outfile, \"w\") as f:\n",
        "        for h in hyps:\n",
        "            f.write(h+\"\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fntZtAH5RgAJ"
      },
      "source": [
        "generate_prediction(model, task)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLxPasgcRgAJ"
      },
      "source": [
        "raise"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pFgIxZ1RgAJ"
      },
      "source": [
        "# Back-translation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G06XYUWDRgAJ"
      },
      "source": [
        "## 訓練一個反向的翻譯模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aIJ8Zh6_RgAJ"
      },
      "source": [
        "1. 將實驗的參數設定表中(config)的source_lang與target_lang互相交換\n",
        "2. 將實驗的參數設定表中(config)的savedir更改(ex. \"./checkpoints/rnn-back\")\n",
        "3. 訓練一個反向模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4Y2DBd2RgAJ"
      },
      "source": [
        "## 利用反向模型生成額外資料"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lP3x7KDARgAJ"
      },
      "source": [
        "### 下載 monolingual data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaBeJACxRgAJ"
      },
      "source": [
        "mono_dataset_name = 'mono'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ves8FGlNRgAK"
      },
      "source": [
        "mono_prefix = Path(data_dir).absolute() / mono_dataset_name\n",
        "mono_prefix.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "urls = (\n",
        "    '\"https://onedrive.live.com/download?cid=3E549F3B24B238B4&resid=3E549F3B24B238B4%214986&authkey=AANUKbGfZx0kM80\"',\n",
        "# # If the above links die, use the following instead. \n",
        "#     \"https://www.csie.ntu.edu.tw/~r09922057/ML2021-hw5/ted_zh_corpus.deduped.gz\",\n",
        "# # If the above links die, use the following instead. \n",
        "#     \"https://mega.nz/#!vMNnDShR!4eHDxzlpzIpdpeQTD-htatU_C7QwcBTwGDaSeBqH534\",\n",
        ")\n",
        "file_names = (\n",
        "    'ted_zh_corpus.deduped.gz',\n",
        ")\n",
        "\n",
        "for u, f in zip(urls, file_names):\n",
        "    path = mono_prefix/f\n",
        "    if not path.exists():\n",
        "        if 'mega' in u:\n",
        "            !megadl {u} --path {path}\n",
        "        else:\n",
        "            !wget {u} -O {path}\n",
        "    else:\n",
        "        print(f'{f} is exist, skip downloading')\n",
        "    if path.suffix == \".tgz\":\n",
        "        !tar -xvf {path} -C {prefix}\n",
        "    elif path.suffix == \".zip\":\n",
        "        !unzip -o {path} -d {prefix}\n",
        "    elif path.suffix == \".gz\":\n",
        "        !gzip -fkd {path}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFZB5-bdRgAK"
      },
      "source": [
        "### TODO: 清理資料集\n",
        "\n",
        "1. 將太長、太短的句子移除\n",
        "2. 統一標點符號\n",
        "\n",
        "hint: 可以使用clean_s()來協助"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZk_UoLCRgAK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nzZiXIBRgAL"
      },
      "source": [
        "### TODO: Subword Units\n",
        "\n",
        "用反向模型的 spm model 將資料切成 subword units\n",
        "\n",
        "hint: spm model 的路徑為 DATA/raw-data/\\[dataset\\]/spm\\[vocab_num\\].model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raaXd0s-RgAL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lh4_NeQkRgAL"
      },
      "source": [
        "### Binarize\n",
        "\n",
        "使用fairseq將資料轉為binary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwH2Xek6RgAM"
      },
      "source": [
        "binpath = Path('./DATA/data-bin', mono_dataset_name)\n",
        "src_dict_file = './DATA/data-bin/ted2020/dict.en.txt'\n",
        "tgt_dict_file = src_dict_file\n",
        "monopref = str(mono_prefix/\"mono.tok\") # whatever filepath you get after applying subword tokenization\n",
        "if binpath.exists():\n",
        "    print(binpath, \"exists, will not overwrite!\")\n",
        "else:\n",
        "    !python -m fairseq_cli.preprocess\\\n",
        "        --source-lang 'zh'\\\n",
        "        --target-lang 'en'\\\n",
        "        --trainpref {monopref}\\\n",
        "        --destdir {binpath}\\\n",
        "        --srcdict {src_dict_file}\\\n",
        "        --tgtdict {tgt_dict_file}\\\n",
        "        --workers 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VfeRKRnFRgAM"
      },
      "source": [
        "### TODO: 生成反向翻譯資料\n",
        "\n",
        "將 binarized data 加入原本的資料夾中並用一個 split_name 取名\n",
        "\n",
        "ex. ./DATA/data-bin/ted2020/\\[split_name\\].zh-en.\\[\"en\", \"zh\"\\].\\[\"bin\", \"idx\"\\]\n",
        "\n",
        "便可以使用 generate_prediction(model, task, split=\"split_name\")來產生翻譯資料"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X66h01gpRgAM"
      },
      "source": [
        "# 將 binarized data 加入原本的資料夾中並用一個 split_name 取名\n",
        "# ex. ./DATA/data-bin/ted2020/\\[split_name\\].zh-en.\\[\"en\", \"zh\"\\].\\[\"bin\", \"idx\"\\]\n",
        "!cp ./DATA/data-bin/mono/train.zh-en.zh.bin ./DATA/data-bin/ted2020/mono.zh-en.zh.bin\n",
        "!cp ./DATA/data-bin/mono/train.zh-en.zh.idx ./DATA/data-bin/ted2020/mono.zh-en.zh.idx\n",
        "!cp ./DATA/data-bin/mono/train.zh-en.en.bin ./DATA/data-bin/ted2020/mono.zh-en.en.bin\n",
        "!cp ./DATA/data-bin/mono/train.zh-en.en.idx ./DATA/data-bin/ted2020/mono.zh-en.en.idx"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0TdWjdbRgAM"
      },
      "source": [
        "# hint: 用反向模型在 split='mono' 上進行預測，生成 prediction_file\n",
        "# generate_prediction( ... ,split=... ,outfile=... )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNxvIzxORgAM"
      },
      "source": [
        "### TODO: 產生新的dataset\n",
        "\n",
        "1. 將翻譯出來的資料與原先的訓練資料結合\n",
        "2. 使用之前的spm model切出成Subword Units\n",
        "3. 重新使用fairseq將資料轉為binary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bcxJ78iRgAM"
      },
      "source": [
        "# 合併剛剛生成的 prediction_file (.en) 以及中文 mono.zh (.zh)\n",
        "# \n",
        "# hint: 在此用剛剛的 spm model 對 prediction_file 進行切斷詞\n",
        "# spm_model.encode(line, out_type=str)\n",
        "# output: ./DATA/rawdata/mono/mono.tok.en & mono.tok.zh\n",
        "#\n",
        "# hint: 在此用 fairseq 把這些檔案再 binarize\n",
        "# binpath = Path('./DATA/data-bin/synthetic')\n",
        "# src_dict_file = './DATA/data-bin/ted2020/dict.en.txt'\n",
        "# tgt_dict_file = src_dict_file\n",
        "# monopref = ./DATA/rawdata/mono/mono.tok # or whatever path after applying subword tokenization, w/o the suffix (.zh/.en)\n",
        "# if binpath.exists():\n",
        "#     print(binpath, \"exists, will not overwrite!\")\n",
        "# else:\n",
        "#     !python -m fairseq_cli.preprocess\\\n",
        "#         --source-lang 'zh'\\\n",
        "#         --target-lang 'en'\\\n",
        "#         --trainpref {monopref}\\\n",
        "#         --destdir {binpath}\\\n",
        "#         --srcdict {src_dict_file}\\\n",
        "#         --tgtdict {tgt_dict_file}\\\n",
        "#         --workers 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3UIojNgRgAM"
      },
      "source": [
        "# 這裡用剛剛準備的檔案合併原先 ted2020 來生成最終 back-translation 的資料\n",
        "!cp -r ./DATA/data-bin/ted2020/ ./DATA/data-bin/ted2020_with_mono/\n",
        "\n",
        "!cp ./DATA/data-bin/synthetic/train.zh-en.zh.bin ./DATA/data-bin/ted2020_with_mono/train1.en-zh.zh.bin\n",
        "!cp ./DATA/data-bin/synthetic/train.zh-en.zh.idx ./DATA/data-bin/ted2020_with_mono/train1.en-zh.zh.idx\n",
        "!cp ./DATA/data-bin/synthetic/train.zh-en.en.bin ./DATA/data-bin/ted2020_with_mono/train1.en-zh.en.bin\n",
        "!cp ./DATA/data-bin/synthetic/train.zh-en.en.idx ./DATA/data-bin/ted2020_with_mono/train1.en-zh.en.idx"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48vGBjZJRgAM"
      },
      "source": [
        "### TODO: 重新訓練\n",
        "\n",
        "當已經產生新的資料集\n",
        "\n",
        "1. 將實驗的參數設定表(config)中的datadir改為新的資料集(\"./DATA/data-bin/ted2020_with_mono\")\n",
        "2. 將實驗的參數設定表(config)中的source_lang與target_lang設定還原(\"en\", \"zh\")\n",
        "3. 將實驗的參數設定表(config)中的savedir更改(ex. \"./checkpoints/rnn-bt\")\n",
        "4. 重新訓練"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bE6FN22dRgAM"
      },
      "source": [
        "# References"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmoErD0FRgAM"
      },
      "source": [
        "1. <a name=ott2019fairseq></a>Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., ... & Auli, M. (2019, June). fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations) (pp. 48-53).\n",
        "2. <a name=vaswani2017></a>Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017, December). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000-6010).\n",
        "3. <a name=reimers-2020-multilingual-sentence-bert></a>Reimers, N., & Gurevych, I. (2020, November). Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4512-4525).\n",
        "4. <a name=tiedemann2012parallel></a>Tiedemann, J. (2012, May). Parallel Data, Tools and Interfaces in OPUS. In Lrec (Vol. 2012, pp. 2214-2218).\n",
        "5. <a name=kudo-richardson-2018-sentencepiece></a>Kudo, T., & Richardson, J. (2018, November). SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 66-71).\n",
        "6. <a name=sennrich-etal-2016-improving></a>Sennrich, R., Haddow, B., & Birch, A. (2016, August). Improving Neural Machine Translation Models with Monolingual Data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 86-96).\n",
        "7. <a name=edunov-etal-2018-understanding></a>Edunov, S., Ott, M., Auli, M., & Grangier, D. (2018). Understanding Back-Translation at Scale. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 489-500).\n",
        "8. https://github.com/ajinkyakulkarni14/TED-Multilingual-Parallel-Corpus\n",
        "9. https://ithelp.ithome.com.tw/articles/10233122\n",
        "10. https://nlp.seas.harvard.edu/2018/04/03/attention.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNokqSVURgAN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}