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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "id": "ldtfNeYAmEw1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "id": "JH0Tv5hknAle"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch_lightning==1.8.0"
      ],
      "metadata": {
        "id": "V-qjWgi6nIg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xADwTIg5jcKo"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchmetrics.functional as tm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "from tokenizers import Tokenizer\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "from transformers import BertForSequenceClassification, BertConfig\n",
        "\n",
        "from pytorch_lightning.callbacks import ModelCheckpoint\n",
        "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "from IPython.display import display\n",
        "from typing import List, Dict, Any, Tuple\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class ApeachDataset(Dataset):\n",
        "    def __init__(self,\n",
        "                 split: str,\n",
        "                 tokenizer: Tokenizer, \n",
        "                 max_length: int = 256,\n",
        "                 padding: str = \"max_length\") -> None:\n",
        "        super().__init__()\n",
        "        dataset = load_dataset(\"jason9693/APEACH\")\n",
        "        texts = dataset[split]['text']\n",
        "        inputs = tokenizer(texts, padding=padding, max_length=max_length, truncation=True, return_tensors=\"pt\")\n",
        "        \n",
        "        self.input_ids = inputs[\"input_ids\"]\n",
        "        self.attention_masks = inputs[\"attention_mask\"]\n",
        "        \n",
        "        labels = dataset[split]['class']\n",
        "        self.labels = torch.tensor(labels, dtype=torch.float32)\n",
        "        \n",
        "    def __len__(self):\n",
        "        return self.input_ids.shape[0]\n",
        "        \n",
        "    def __getitem__(self, index: Any) -> Dict:\n",
        "        return self.input_ids[index], self.attention_masks[index], self.labels[index]\n",
        "\n",
        "    def dataloader(self, **kwargs) -> DataLoader:\n",
        "        return DataLoader(self, **kwargs)"
      ],
      "metadata": {
        "id": "hPyRyDKPlepz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "monologg/koelectra-small-v3-discriminator\n",
        "beomi/KcELECTRA-base\n",
        "beomi/kcbert-base\n",
        "beomi/kcbert-large\n",
        "\"\"\"\n",
        "\n",
        "huggingface_model_name = \"monologg/koelectra-small-v3-discriminator\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(huggingface_model_name)"
      ],
      "metadata": {
        "id": "FS7LlS-znspc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 64\n",
        "batch_size = 128\n",
        "labels = ['hate']\n",
        "train_dl = ApeachDataset(\"train\", tokenizer, max_length=max_length).dataloader(batch_size=batch_size)\n",
        "val_dl = ApeachDataset(\"test\", tokenizer, max_length=max_length).dataloader(batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
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        },
        "id": "-BiGWGCUnw7g",
        "outputId": "6e9269d0-1402-436d-cc15-a47b60072c96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/jason9693___csv/default-13a19883aaaad80d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
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            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/jason9693___csv/default-13a19883aaaad80d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "def join_step_outputs(outputs):\n",
        "    result = {}\n",
        "    keys = outputs[0].keys()\n",
        "    for k in keys:\n",
        "        X = [x[k] for x in outputs]\n",
        "        if X[0].dim() == 0: # zero-dim tensor\n",
        "            result[k] = torch.stack(X)\n",
        "        else:\n",
        "            result[k] = torch.cat(X, dim=0)\n",
        "    return result\n",
        "\n",
        "class TextClassificationModule(pl.LightningModule):\n",
        "    def __init__(self, huggingface_model_name, labels, lr=5e-4):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters()\n",
        "        self.model = AutoModelForSequenceClassification.from_pretrained(huggingface_model_name, num_labels=len(labels))\n",
        "        # config = {\n",
        "        #     \"max_position_embeddings\": 300,\n",
        "        #     \"hidden_dropout_prob\": 0.1,\n",
        "        #     \"hidden_act\": \"gelu\",\n",
        "        #     \"initializer_range\": 0.02, # 12 to 2\n",
        "        #     \"num_hidden_layers\": 2,\n",
        "        #     \"pooler_num_attention_heads\": 12,\n",
        "        #     \"type_vocab_size\": 2,\n",
        "        #     \"vocab_size\": 30000,\n",
        "        #     \"hidden_size\": 128, # 768 to 128\n",
        "        #     \"attention_probs_dropout_prob\": 0.1,\n",
        "        #     \"num_attention_heads\": 2, # 12 to 2\n",
        "        #     \"intermediate_size\": 512, # 3072 to 512,\n",
        "        #     \"num_labels\": len(labels)\n",
        "        # }\n",
        "        # self.model = BertForSequenceClassification(\n",
        "        #     BertConfig(**config)\n",
        "        # )\n",
        "        self.multiclass = len(labels) > 1\n",
        "        self.criterion = nn.CrossEntropyLoss() if self.multiclass else nn.BCELoss()\n",
        "        self.labels = labels\n",
        "            \n",
        "    def configure_optimizers(self):\n",
        "        return optim.Adam(self.parameters(), lr=self.hparams.lr)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask=None):\n",
        "        logits = self.model(input_ids, attention_mask=attention_mask).logits\n",
        "        if self.multiclass:\n",
        "            logits = logits.softmax(dim=-1)\n",
        "        else:\n",
        "            logits = logits.sigmoid().squeeze(1).float()\n",
        "        return logits\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        ids, masks, labels = batch\n",
        "        \n",
        "        logits = self(ids, masks)\n",
        "        loss = self.criterion(logits, labels)\n",
        "        output = {\"loss\": loss, \"logits\": logits, \"labels\": labels}\n",
        "        return output\n",
        "    \n",
        "    def training_epoch_end(self, outputs):\n",
        "        outputs = join_step_outputs(outputs)\n",
        "        loss = outputs[\"loss\"].mean()\n",
        "        self.log(\"train_epoch_loss\", loss)\n",
        "\n",
        "        logits = outputs[\"logits\"]\n",
        "        labels = outputs[\"labels\"]\n",
        "        acc = tm.accuracy(logits, labels.int(), task = 'binary')\n",
        "        self.log(f\"train_acc\", acc, prog_bar=True)\n",
        "        \n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        ids, masks, labels = batch\n",
        "        logits = self(ids, masks)\n",
        "        loss = self.criterion(logits, labels)\n",
        "        output = {\"loss\": loss, \"logits\": logits, \"labels\": labels}\n",
        "        return output\n",
        "    \n",
        "    def validation_epoch_end(self, outputs):\n",
        "        outputs = join_step_outputs(outputs)\n",
        "        loss = outputs[\"loss\"].mean()\n",
        "        self.log(\"val_epoch_loss\", loss, prog_bar=True)\n",
        "\n",
        "        logits = outputs[\"logits\"]\n",
        "        labels = outputs[\"labels\"]\n",
        "        acc = tm.accuracy(logits, labels.int(), task = 'binary')\n",
        "        self.log(f\"val_acc\", acc, prog_bar=True)"
      ],
      "metadata": {
        "id": "MAzEKiXbn3yw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# val every n steps\n",
        "# https://github.com/PyTorchLightning/pytorch-lightning/issues/2534\n",
        "\n",
        "logger = pl.loggers.TensorBoardLogger(\n",
        "    save_dir='.',\n",
        "    name='lightning_logs'\n",
        ")\n",
        "\n",
        "save_dir = \"./ckpt/\"\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    monitor=\"val_epoch_loss\",\n",
        "    dirpath=save_dir,\n",
        "    filename=f\"hate_{logger.version}_\" + \"{val_acc:.4f}\",\n",
        "    mode=\"min\",\n",
        ")\n",
        "\n",
        "module = TextClassificationModule(\n",
        "    huggingface_model_name,\n",
        "    labels=labels,\n",
        "    lr=1e-4\n",
        ")\n",
        "callbacks = [\n",
        "    EarlyStopping(\"val_epoch_loss\", mode=\"min\", patience=10),\n",
        "    checkpoint_callback\n",
        "]\n",
        "\n",
        "trainer = pl.Trainer(max_epochs=1000, \n",
        "                logger=logger,\n",
        "                accelerator=\"gpu\",\n",
        "                val_check_interval=20,\n",
        "                callbacks=callbacks)\n",
        "trainer.fit(module, train_dl, val_dl)"
      ],
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        "id": "bjMou1kdoJkm",
        "outputId": "d7256e83-2c23-463c-cf54-264cf3030bf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at monologg/koelectra-small-v3-discriminator were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\n",
            "- This IS expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at monologg/koelectra-small-v3-discriminator and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type                             | Params\n",
            "---------------------------------------------------------------\n",
            "0 | model     | ElectraForSequenceClassification | 14.1 M\n",
            "1 | criterion | BCELoss                          | 0     \n",
            "---------------------------------------------------------------\n",
            "14.1 M    Trainable params\n",
            "0         Non-trainable params\n",
            "14.1 M    Total params\n",
            "56.489    Total estimated model params size (MB)\n"
          ]
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