Question_Generation_Playground.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "62fcc456",
      "metadata": {
        "id": "62fcc456"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers\n",
        "!pip install sentencepiece\n",
        "!pip install datasets\n",
        "!pip install sentence_transformers\n",
        "!pip install gdown\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "ea30ba86",
      "metadata": {
        "id": "ea30ba86"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pickle as pkl\n",
        "from tqdm import tqdm\n",
        "\n",
        "# pytorch\n",
        "import torch\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "# model config\n",
        "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, RobertaModel, T5Config, T5ForConditionalGeneration, T5Tokenizer, T5Model\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# model optim\n",
        "from torch.optim import AdamW, SGD\n",
        "\n",
        "# lr schedulers\n",
        "from transformers import get_cosine_schedule_with_warmup, get_linear_schedule_with_warmup, \\\n",
        "    get_cosine_with_hard_restarts_schedule_with_warmup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "62bc3550",
      "metadata": {
        "id": "62bc3550"
      },
      "outputs": [],
      "source": [
        "# file dependency (don't forget to upload utils.py)\n",
        "from utils import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "81228f5d",
      "metadata": {
        "id": "81228f5d"
      },
      "outputs": [],
      "source": [
        "# specify device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "# checkpoint -> pretrained model\n",
        "checkpoint = 't5-base'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "8d1f434d",
      "metadata": {
        "id": "8d1f434d",
        "outputId": "8b61b49a-10f3-4937-d3a0-308f02ecb137",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py:240: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "# load tokenizer and model\n",
        "processer = get_tokenizer(checkpoint)\n",
        "model = get_model(checkpoint, device, processer).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "1532c37d",
      "metadata": {
        "id": "1532c37d"
      },
      "outputs": [],
      "source": [
        "# IR encoder -> T-5 sentence dense embeddings\n",
        "encoder_model = SentenceTransformer('sentence-transformers/sentence-t5-base')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jREKrixxbq3J"
      },
      "id": "jREKrixxbq3J"
    },
    {
      "cell_type": "markdown",
      "id": "2a391513",
      "metadata": {
        "id": "2a391513"
      },
      "source": [
        "### Data processing pipeline for fine-tuning\n",
        "1. check if feature file exists\n",
        "2. exists: load dataset from FeatureData\n",
        "3. doesn't exist: load raw dataset from CustomData, call prepare_features, then do 2.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "09cdd035",
      "metadata": {
        "id": "09cdd035",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdf7b2e5-f923-4280-a205-d29de5ab6c48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  Question_Answer_Dataset_v1.2.zip\n",
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          ]
        }
      ],
      "source": [
        "# load dataset zip file to current directory and unzip\n",
        "!unzip Question_Answer_Dataset_v1.2.zip\n",
        "\n",
        "# define data_path for raw input and feature_path for feature input\n",
        "data_path = 'Question_Answer_Dataset_v1.2'\n",
        "feature_cache_path = 'Question_Answer_Dataset_v1.2/features_questions'\n",
        "raw_path = os.path.join(data_path, \"raw\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf $feature_cache_path"
      ],
      "metadata": {
        "id": "BXO5UIGJAikw"
      },
      "id": "BXO5UIGJAikw",
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ls $data_path"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7W0DOqzohb9",
        "outputId": "8760a4a6-6f9b-4225-be13-bb8ad7fc853a"
      },
      "id": "g7W0DOqzohb9",
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LICENSE-S08,S09  raw  \u001b[0m\u001b[01;32mREADME.v1.2\u001b[0m*  \u001b[01;34mS08\u001b[0m/  \u001b[01;34mS09\u001b[0m/  \u001b[01;34mS10\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "7528f775",
      "metadata": {
        "id": "7528f775",
        "outputId": "c2a84065-08a8-4589-b384-41da2379f074",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processing raw dataset... \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1715/1715 [02:36<00:00, 10.96it/s]\n",
            "100%|██████████| 826/826 [01:33<00:00,  8.87it/s]\n",
            "100%|██████████| 1459/1459 [02:24<00:00, 10.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "length of dataset:  2684\n",
            "computing features...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2684/2684 [00:02<00:00, 932.98it/s] \n"
          ]
        }
      ],
      "source": [
        "# prepare feature data if not yet exist\n",
        "if not (os.path.exists(feature_cache_path) and os.path.isfile(feature_cache_path)):\n",
        "    # use the encoder to get the raw dataset (context are extracted by IR with the K-NN sentence to the QA pair)\n",
        "    print(\"processing raw dataset... \")\n",
        "    raw_dataset = CustomData(data_path, encoder_model, k=1)\n",
        "    print(\"computing features...\")\n",
        "    # tokenize\n",
        "    prepare_features_q(raw_dataset, feature_cache_path, processer, max_len_inp=512,max_len_out=512)\n",
        "else:\n",
        "    print(\"features exists\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "3bba5eba",
      "metadata": {
        "id": "3bba5eba",
        "outputId": "c3b59f26-7eb4-4472-d61a-762fa4359b51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "length of feature train set:  2259\n",
            "length of feature test set:  425\n"
          ]
        }
      ],
      "source": [
        "# feature dataset\n",
        "# leave 425 points for testing\n",
        "test_points = 425\n",
        "train_dataset = FeatureData(feature_cache_path, 'train', test_points)\n",
        "test_dataset = FeatureData(feature_cache_path, 'test', test_points)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "0bca0e19",
      "metadata": {
        "id": "0bca0e19",
        "outputId": "40b22fae-4be4-4aea-b154-d6d36c96e44e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input ids shape:  torch.Size([512])\n",
            "question ids shape:  torch.Size([512])\n"
          ]
        }
      ],
      "source": [
        "# check what's in the dataset\n",
        "input_dict = train_dataset[0]\n",
        "print(\"input ids shape: \", input_dict['input_ids'].size())\n",
        "print(\"question ids shape: \", input_dict['target_ids'].size())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "94b15d9e",
      "metadata": {
        "id": "94b15d9e",
        "outputId": "6fac5978-a3bf-47e5-fcc2-65c461b5bbac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded train feature data with 2259 batches\n",
            "Loaded test feature data with 425 batches\n"
          ]
        }
      ],
      "source": [
        "# default split point: 425 -> samples after the split point will be in the test set\n",
        "dataloader_train, dataloader_test = get_dataloaders(train_dataset, test_dataset, batch_size=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "c4a40753",
      "metadata": {
        "id": "c4a40753",
        "outputId": "53c9f6d2-1b8a-4087-eaae-42e0f6168fd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Is Calvin Jr. older than John Coolidge?',\n",
              " 'No',\n",
              " 'They had two sons; John Coolidge, born in 1906, and Calvin Jr., born in 1908.')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "raw_dataset[110] # this is a hard question -> chain of thoughts/verification might be useful"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[110].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BK79lTqR_zTt",
        "outputId": "7f44d2fe-2efb-4f82-c478-f07a53db2a3c"
      },
      "id": "BK79lTqR_zTt",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'input_mask', 'target_ids', 'target_mask', 'labels'])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17e74ffa",
      "metadata": {
        "id": "17e74ffa"
      },
      "source": [
        "### Training Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# saves trained model\n",
        "def __save_model(model_dir, model, model_type='latest'):\n",
        "\n",
        "    if model_type == \"latest\":\n",
        "        saved_name = 'latest_model.pt'\n",
        "    else:\n",
        "        saved_name = 'best_model.pt'\n",
        "\n",
        "    root_model_path = os.path.join(model_dir, saved_name)\n",
        "    state_dict = {'weights': model.state_dict(),\n",
        "                  'optimizer': model.optimizer.state_dict(),\n",
        "                  'scheduler': model.scheduler.state_dict()}\n",
        "    torch.save(state_dict, root_model_path)"
      ],
      "metadata": {
        "id": "0NwOgAgcWgxm"
      },
      "id": "0NwOgAgcWgxm",
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loads the experiment data if exists to resume training from last saved checkpoint.\n",
        "def __load_experiment(model_dir, model, model_type='latest'):\n",
        "\n",
        "    if model_type == \"latest\":\n",
        "        saved_name = 'latest_model.pt'\n",
        "    else:\n",
        "        saved_name = 'best_model.pt'\n",
        "\n",
        "    if os.path.exists(os.path.join(model_dir, 'train.log')):\n",
        "        # get current epoch\n",
        "        current_epoch = 0\n",
        "        with open(os.path.join(model_dir, 'train.log')) as f:\n",
        "            for line in f:\n",
        "                current_epoch += 1\n",
        "        # get the latest model\n",
        "        state_dict = torch.load(os.path.join(model_dir, saved_name), map_location=device.type)\n",
        "        model.load_state_dict(state_dict['weights'])\n",
        "        model.optimizer.load_state_dict(state_dict['optimizer'])\n",
        "        model.scheduler.load_state_dict(state_dict['scheduler'])\n",
        "    else:\n",
        "        current_epoch = 0\n",
        "\n",
        "    return model, current_epoch\n"
      ],
      "metadata": {
        "id": "anQu63nhWxLk"
      },
      "id": "anQu63nhWxLk",
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creates log file with loss at each epoch\n",
        "def log(output_dir, log_str, file_name=None):\n",
        "    if file_name is None:\n",
        "        file_name = \"all.log\"\n",
        "    output_file = os.path.join(output_dir, file_name)\n",
        "    with open(output_file, 'a') as f:\n",
        "        f.write(log_str + '\\n')"
      ],
      "metadata": {
        "id": "XygG-_wYXATk"
      },
      "id": "XygG-_wYXATk",
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training loop\n",
        "def train(model, dataloader_train, n_epochs, model_dir, log_file):\n",
        "\n",
        "    model.train() # put to train mode\n",
        "\n",
        "    # load current model if exist\n",
        "    model, current_epoch = __load_experiment(model_dir, model)\n",
        "\n",
        "    all_losses = []\n",
        "\n",
        "    for e in range(current_epoch, n_epochs):\n",
        "\n",
        "        losses = 0\n",
        "        for step, batch in tqdm(enumerate(dataloader_train), total=len(dataloader_train)):\n",
        "\n",
        "            outputs = model(\n",
        "                input_ids=batch['input_ids'].to(device),\n",
        "                attention_mask=batch['input_mask'].to(device),\n",
        "                decoder_input_ids=batch['target_ids'].to(device),\n",
        "                decoder_attention_mask=batch['target_mask'].to(device),\n",
        "                labels = batch['labels'].to(device)\n",
        "            )\n",
        "\n",
        "            loss = outputs[0]\n",
        "\n",
        "            model.optimizer.zero_grad() # clear loss\n",
        "            loss.backward()\n",
        "            model.optimizer.step()  # backprop to update the weights\n",
        "\n",
        "            if model.scheduler is not None:\n",
        "                model.scheduler.step()  # update learning rate schedule\n",
        "\n",
        "            # log losses\n",
        "            loss /= len(dataloader_train) # already average across batch in nn.CrossEntropy\n",
        "            losses += loss.item()\n",
        "\n",
        "        # output stats\n",
        "        print(f\"Epoch {e}; loss {losses}\")\n",
        "        log(model_dir, \"Epoch \" + str(e+1) + \"; loss \" + str(losses), log_file)\n",
        "        all_losses.append(losses)\n",
        "        # save model\n",
        "        __save_model(model_dir, model) # save latest\n",
        "        if (e > current_epoch and losses < all_losses[-1]):\n",
        "            __save_model(model_dir, model, model_type='best') # save best model\n",
        "\n",
        "\n",
        "\n",
        "def test(model, dataloader_test, model_dir, log_file):\n",
        "\n",
        "    model, e = __load_experiment(model_dir, model, model_type='latest')\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    losses = 0\n",
        "    for step, batch in tqdm(enumerate(dataloader_test), total=len(dataloader_test)):\n",
        "\n",
        "        outputs = model(\n",
        "            input_ids=batch['input_ids'].to(device),\n",
        "            attention_mask=batch['input_mask'].to(device),\n",
        "            decoder_input_ids=batch['target_ids'].to(device),\n",
        "            decoder_attention_mask=batch['target_mask'].to(device),\n",
        "            labels = batch['labels'].to(device)\n",
        "        )\n",
        "\n",
        "        loss = outputs[0]\n",
        "\n",
        "        # log losses\n",
        "        loss /= len(dataloader_test)\n",
        "        losses += loss.item()\n",
        "\n",
        "    # output stats\n",
        "    print(f\"Validation loss {losses}\")\n",
        "    log(model_dir, \"Validation loss \" + str(losses), log_file)"
      ],
      "metadata": {
        "id": "xcluL7JSCPso"
      },
      "id": "xcluL7JSCPso",
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "688ad709",
      "metadata": {
        "id": "688ad709"
      },
      "outputs": [],
      "source": [
        "def get_optimizer(model, opt_name, lr, eps):\n",
        "    if opt_name == 'Adam':\n",
        "        return AdamW(model.parameters(), lr=lr, eps=eps)\n",
        "    elif opt_name == 'SGD':\n",
        "        return SGD(model.parameters(), lr=lr, eps=eps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "9da7b630",
      "metadata": {
        "id": "9da7b630"
      },
      "outputs": [],
      "source": [
        "def get_scheduler(model, scheduler, n_batches, n_epochs, warmup_portion=0.1):\n",
        "    train_steps = n_epochs*n_batches\n",
        "    warm_step = int(train_steps*warmup_portion)\n",
        "    if scheduler == \"linear\":\n",
        "        return get_linear_schedule_with_warmup(model.optimizer, num_warmup_steps=warm_step,num_training_steps=train_steps)\n",
        "    elif scheduler == \"cosine\":\n",
        "        return get_cosine_schedule_with_warmup(model.optimizer, num_warmup_steps=warm_step,num_training_steps=train_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02182fc3",
      "metadata": {
        "id": "02182fc3"
      },
      "source": [
        "#### configure hyperparameters\n",
        "n_epoch\n",
        "\n",
        "lr\n",
        "\n",
        "weight_decay\n",
        "\n",
        "optimizer\n",
        "\n",
        "warmup_steps\n",
        "\n",
        "lr_scheduler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "da9568ac",
      "metadata": {
        "id": "da9568ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abed412a-8f43-48d5-9190-097d03947a80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded train feature data with 2259 batches\n",
            "Loaded test feature data with 425 batches\n",
            "t5-base\n"
          ]
        }
      ],
      "source": [
        "# hyperparameters\n",
        "n_epochs = 10\n",
        "lr = 1e-5\n",
        "weight_decay = 5e-5\n",
        "batch_size = 1\n",
        "\n",
        "# dataloaders\n",
        "# default split point: 425 -> samples after the split point will be in the test set\n",
        "dataloader_train, dataloader_test = get_dataloaders(train_dataset, test_dataset, batch_size=batch_size)\n",
        "\n",
        "# model optimizer\n",
        "model.optimizer = get_optimizer(model, \"Adam\", lr, weight_decay)\n",
        "\n",
        "# learning rate scheduler\n",
        "# i.e. there are two types of scheduler: linear and cosine. I was confused what\n",
        "# to choose so I chose linear\n",
        "model.scheduler = get_scheduler(model, \"linear\", len(dataloader_train), n_epochs)\n",
        "\n",
        "name = checkpoint.split('/')[-1]\n",
        "print(name)\n",
        "\n",
        "# model state_dict\n",
        "model_dir = f\"{name}_e{n_epochs}_lr{lr}_eps{weight_decay}_Adam_linearS_batch{batch_size}\"\n",
        "if not os.path.isdir(model_dir):\n",
        "    os.mkdir(model_dir)\n",
        "\n",
        "# log file\n",
        "log_file = \"train.log\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "fd7a22e6",
      "metadata": {
        "id": "fd7a22e6",
        "outputId": "aebd11f7-f6a6-4164-aef1-6f499125272c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:03<00:00,  2.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0; loss 4.403209098178195\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:03<00:00,  2.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1; loss 0.7320343435912946\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:03<00:00,  2.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2; loss 0.22169073486747948\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:03<00:00,  2.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3; loss 0.10549498569642424\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:02<00:00,  2.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4; loss 0.06360594212611659\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:03<00:00,  2.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5; loss 0.04484260515624783\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:02<00:00,  2.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6; loss 0.033844294625680504\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:02<00:00,  2.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7; loss 0.02794611336623376\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:02<00:00,  2.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8; loss 0.024307027566472073\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2259/2259 [13:02<00:00,  2.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9; loss 0.023297698944418244\n"
          ]
        }
      ],
      "source": [
        "train(model, dataloader_train, n_epochs, model_dir, log_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###load trained model###"
      ],
      "metadata": {
        "id": "Pyd-xefU2tnt"
      },
      "id": "Pyd-xefU2tnt"
    },
    {
      "cell_type": "code",
      "source": [
        "model, current_epoch = __load_experiment(model_dir, model, model_type='latest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "s8oOiFvz4hRS",
        "outputId": "d27bf8da-a645-4675-bbd9-c88daf9ef67e"
      },
      "id": "s8oOiFvz4hRS",
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-109-ddc7077131bf>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__load_experiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'latest'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-98-fabb3101a309>\u001b[0m in \u001b[0;36m__load_experiment\u001b[0;34m(model_dir, model, model_type)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;31m# get the latest model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaved_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weights'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'optimizer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scheduler'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'load_state_dict'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model[0].eval()"
      ],
      "metadata": {
        "id": "wJnTvH0VGxdv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1109b1c6-d236-4bc7-cff4-0eb9effccb6c"
      },
      "id": "wJnTvH0VGxdv",
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T5ForConditionalGeneration(\n",
              "  (shared): Embedding(32100, 768)\n",
              "  (encoder): T5Stack(\n",
              "    (embed_tokens): Embedding(32100, 768)\n",
              "    (block): ModuleList(\n",
              "      (0): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 12)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseActDense(\n",
              "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): ReLU()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1-11): 11 x T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseActDense(\n",
              "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): ReLU()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): T5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (decoder): T5Stack(\n",
              "    (embed_tokens): Embedding(32100, 768)\n",
              "    (block): ModuleList(\n",
              "      (0): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 12)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseActDense(\n",
              "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): ReLU()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1-11): 11 x T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseActDense(\n",
              "              (wi): Linear(in_features=768, out_features=3072, bias=False)\n",
              "              (wo): Linear(in_features=3072, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): ReLU()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): T5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=32100, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "id": "0cedcf79",
      "metadata": {
        "id": "0cedcf79"
      },
      "outputs": [],
      "source": [
        "context = r\"\"\"Giraffes can inhabit savannas, grasslands, or open woodlands. They prefer areas enriched with acacia growth. They drink large quantities of water and, as a result, they can spend long periods of time in dry, arid areas. When searching for more food they will venture into areas with denser foliage.\"\"\".replace('\\n', ' ')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = processer(context, return_tensors='pt')"
      ],
      "metadata": {
        "id": "HMMTPA8A1gPL"
      },
      "id": "HMMTPA8A1gPL",
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs.keys()"
      ],
      "metadata": {
        "id": "_JlbWA31HKUH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68a339ee-0a60-485b-d18c-7d4751dffe78"
      },
      "id": "_JlbWA31HKUH",
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids = inputs['input_ids'].to(device)"
      ],
      "metadata": {
        "id": "mlErhDo7HL-A"
      },
      "id": "mlErhDo7HL-A",
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model[0].generate(input_ids, max_new_tokens=512)\n",
        "print(outputs)"
      ],
      "metadata": {
        "id": "dT2p-BnHHNUY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46c4b88f-fe0a-463f-ce68-5bc04b9b7460"
      },
      "id": "dT2p-BnHHNUY",
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[    0, 32099,   328,  3281,   508,  6201,    13,   387,    11,     6,\n",
            "          2459,     6,    79,    54,  1492,   307,  8811,    13,    97,    16,\n",
            "          2192,     6,     3,     9,  4055,   844,     5,   366,  9601,    21,\n",
            "           542,    79,    56,  6086,   139,   844,    28, 13809, 25840,     5,\n",
            "             1]], device='cuda:0')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question_answer = processer.decode(outputs[0], skip_special_tokens=False)\n",
        "print(question_answer)\n",
        "question_answer = question_answer.replace(processer.pad_token, \"\").replace(processer.eos_token, \"\")\n",
        "print(question_answer)\n",
        "print(question_answer.split(processer.sep_token))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFcavNf46db7",
        "outputId": "c2574e07-2c24-469b-99cd-248804d0fffe"
      },
      "id": "FFcavNf46db7",
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pad> <extra_id_0> They drink large amounts of water and, therefore, they can spend long periods of time in dry, arid areas. When hunting for food they will venture into areas with dense foliage.</s>\n",
            " <extra_id_0> They drink large amounts of water and, therefore, they can spend long periods of time in dry, arid areas. When hunting for food they will venture into areas with dense foliage.\n",
            "['<extra_id_0>', 'They', 'drink', 'large', 'amounts', 'of', 'water', 'and,', 'therefore,', 'they', 'can', 'spend', 'long', 'periods', 'of', 'time', 'in', 'dry,', 'arid', 'areas.', 'When', 'hunting', 'for', 'food', 'they', 'will', 'venture', 'into', 'areas', 'with', 'dense', 'foliage.']\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.13"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}