training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "foRIu3laOsgs"
      },
      "source": [
        "__Overall method__: https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gYD43cIfadX7",
        "outputId": "355a0c20-756e-4c3e-9e8d-d2e90f00008b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.22.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.12.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.9.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pickle5 in /usr/local/lib/python3.7/dist-packages (0.0.12)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers \n",
        "!pip3 install pickle5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iOhenVIVbZGq"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import logging\n",
        "import os\n",
        "import pickle5 as pickle\n",
        "import random\n",
        "import re\n",
        "import shutil\n",
        "from typing import Dict, List, Tuple\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
        "from torch.utils.data.distributed import DistributedSampler\n",
        "from tqdm.notebook import tqdm, trange\n",
        "\n",
        "from transformers import (\n",
        "    MODEL_WITH_LM_HEAD_MAPPING,\n",
        "    WEIGHTS_NAME,\n",
        "    AdamW,\n",
        "    AutoConfig,\n",
        "    AutoModelWithLMHead,\n",
        "    AutoTokenizer,\n",
        "    PreTrainedModel,\n",
        "    PreTrainedTokenizer,\n",
        "    get_linear_schedule_with_warmup,\n",
        ")\n",
        "\n",
        "try:\n",
        "    from torch.utils.tensorboard import SummaryWriter\n",
        "except ImportError:\n",
        "    from tensorboardX import SummaryWriter\n",
        "\n",
        "# Configs\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n",
        "MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "_Seq3ZNJ_R1N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f5db30d-7576-40fb-8cb4-077872f60951"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "x-eLGxy8_sJX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f4c0a7c-baeb-443e-e17f-d23a32cffe5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive\n"
          ]
        }
      ],
      "source": [
        "cd drive/MyDrive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mkdir output"
      ],
      "metadata": {
        "id": "l51BMZTmP763",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5cec3af-5d32-4692-9339-166f5b7d2178"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘output’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szZBBZ-1adX_"
      },
      "source": [
        "__Overall method__: https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb\n",
        "\n",
        "__Conversational datasets__: https://github.com/PolyAI-LDN/conversational-datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "YtnRh4sZadYH"
      },
      "outputs": [],
      "source": [
        "#collapse\n",
        "# Args to allow for easy convertion of python script to notebook\n",
        "class Args():\n",
        "    def __init__(self):\n",
        "        self.output_dir = 'output'\n",
        "        self.model_type = 'gpt2'\n",
        "        self.model_name_or_path = 'microsoft/DialoGPT-medium'\n",
        "        self.config_name = 'microsoft/DialoGPT-medium'\n",
        "        self.tokenizer_name = 'microsoft/DialoGPT-medium'\n",
        "        self.cache_dir = 'cached'\n",
        "        self.block_size = 256\n",
        "        self.do_train = True\n",
        "        self.do_eval = True\n",
        "        self.evaluate_during_training = False\n",
        "        self.per_gpu_train_batch_size = 1\n",
        "        self.per_gpu_eval_batch_size = 1\n",
        "        self.gradient_accumulation_steps = 1\n",
        "        self.learning_rate = 5e-5\n",
        "        self.weight_decay = 0.0\n",
        "        self.adam_epsilon = 1e-8\n",
        "        self.max_grad_norm = 1.0\n",
        "        self.num_train_epochs = 3\n",
        "        self.max_steps = -1\n",
        "        self.warmup_steps = 0\n",
        "        self.logging_steps = 1000\n",
        "        self.save_steps = 3500\n",
        "        self.save_total_limit = None\n",
        "        self.eval_all_checkpoints = False\n",
        "        self.no_cuda = False\n",
        "        self.overwrite_output_dir = True\n",
        "        self.overwrite_cache = True\n",
        "        self.should_continue = False\n",
        "        self.seed = 42\n",
        "        self.local_rank = -1\n",
        "        self.fp16 = False\n",
        "        self.fp16_opt_level = 'O1'\n",
        "\n",
        "args = Args()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "3mpK-J31adYJ"
      },
      "outputs": [],
      "source": [
        "with open(\"infj_conversations.pickle\", \"rb\") as fh:\n",
        "  conversations = pickle.load(fh)\n",
        "conversations.drop(columns = [\"id\"], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Bq064O4badYK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "f9eca4f8-f7ac-4a34-db2f-111ac673e82b"
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        {
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              "                                                 response  \\\n",
              "249567  This test and personality tests in general (ba...   \n",
              "533212                        Is that just a random user?   \n",
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              "                                                  context  \\\n",
              "249567  Dear redditors of r/infj,\\n\\nLook, I know that...   \n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "trn_df, val_df = train_test_split(conversations, test_size = 0.2)\n",
        "trn_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "AhVPvwKhadYL"
      },
      "outputs": [],
      "source": [
        "#collapse\n",
        "def construct_conv(row, tokenizer, eos = True):\n",
        "    # from: https://stackoverflow.com/questions/952914/how-to-make-a-flat-list-out-of-list-of-lists\n",
        "    flatten = lambda l: [item for sublist in l for item in sublist]\n",
        "    conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n",
        "    conv = flatten(conv)\n",
        "    return conv\n",
        "\n",
        "class ConversationDataset(Dataset):\n",
        "    def __init__(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n",
        "\n",
        "        block_size = block_size - (tokenizer.model_max_length- tokenizer.max_len_single_sentence)\n",
        "\n",
        "        directory = args.cache_dir\n",
        "        cached_features_file = os.path.join(\n",
        "            directory, args.model_type + \"_cached_lm_\" + str(block_size)\n",
        "        )\n",
        "\n",
        "        if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
        "            logger.info(\"Loading features from cached file %s\", cached_features_file)\n",
        "            with open(cached_features_file, \"rb\") as handle:\n",
        "                self.examples = pickle.load(handle)\n",
        "        else:\n",
        "            logger.info(\"Creating features from dataset file at %s\", directory)\n",
        "\n",
        "            self.examples = []\n",
        "            df = df.dropna()\n",
        "            for _, row in df.iterrows():\n",
        "                conv = construct_conv(row, tokenizer)\n",
        "                if len(conv) > block_size: continue\n",
        "                self.examples.append(conv)\n",
        "\n",
        "            # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)\n",
        "            # If your dataset is small, first you should loook for a bigger one :-) and second you\n",
        "            # can change this behavior by adding (model specific) padding.\n",
        "\n",
        "            logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
        "            with open(cached_features_file, \"wb\") as handle:\n",
        "                pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.examples)\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        return torch.tensor(self.examples[item], dtype=torch.long)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "jQowD3AuadYM"
      },
      "outputs": [],
      "source": [
        "#hide\n",
        "# Cacheing and storing of data/checkpoints\n",
        "\n",
        "def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n",
        "    return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n",
        "\n",
        "\n",
        "def set_seed(args):\n",
        "    random.seed(args.seed)\n",
        "    np.random.seed(args.seed)\n",
        "    torch.manual_seed(args.seed)\n",
        "    if args.n_gpu > 0:\n",
        "        torch.cuda.manual_seed_all(args.seed)\n",
        "\n",
        "\n",
        "def _sorted_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> List[str]:\n",
        "    ordering_and_checkpoint_path = []\n",
        "\n",
        "    glob_checkpoints = glob.glob(os.path.join(args.output_dir, \"{}-*\".format(checkpoint_prefix)))\n",
        "\n",
        "    for path in glob_checkpoints:\n",
        "        if use_mtime:\n",
        "            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n",
        "        else:\n",
        "            regex_match = re.match(\".*{}-([0-9]+)\".format(checkpoint_prefix), path)\n",
        "            if regex_match and regex_match.groups():\n",
        "                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n",
        "\n",
        "    checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n",
        "    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n",
        "    return checkpoints_sorted\n",
        "\n",
        "\n",
        "def _rotate_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> None:\n",
        "    if not args.save_total_limit:\n",
        "        return\n",
        "    if args.save_total_limit <= 0:\n",
        "        return\n",
        "\n",
        "    # Check if we should delete older checkpoint(s)\n",
        "    checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n",
        "    if len(checkpoints_sorted) <= args.save_total_limit:\n",
        "        return\n",
        "\n",
        "    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n",
        "    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n",
        "    for checkpoint in checkpoints_to_be_deleted:\n",
        "        logger.info(\"Deleting older checkpoint [{}] due to args.save_total_limit\".format(checkpoint))\n",
        "        shutil.rmtree(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Zc1apoQxadYO"
      },
      "outputs": [],
      "source": [
        "#collapse\n",
        "# Training of model\n",
        "\n",
        "def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n",
        "    \"\"\" Train the model \"\"\"\n",
        "    if args.local_rank in [-1, 0]:\n",
        "        tb_writer = SummaryWriter()\n",
        "\n",
        "    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n",
        "\n",
        "    def collate(examples: List[torch.Tensor]):\n",
        "        if tokenizer._pad_token is None:\n",
        "            return pad_sequence(examples, batch_first=True)\n",
        "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
        "\n",
        "    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n",
        "    train_dataloader = DataLoader(\n",
        "        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n",
        "    )\n",
        "\n",
        "    if args.max_steps > 0:\n",
        "        t_total = args.max_steps\n",
        "        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n",
        "    else:\n",
        "        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n",
        "\n",
        "    model = model.module if hasattr(model, \"module\") else model  # Take care of distributed/parallel training\n",
        "    model.resize_token_embeddings(len(tokenizer))\n",
        "    # add_special_tokens_(model, tokenizer)\n",
        "\n",
        "\n",
        "    # Prepare optimizer and schedule (linear warmup and decay)\n",
        "    no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
        "    optimizer_grouped_parameters = [\n",
        "        {\n",
        "            \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
        "            \"weight_decay\": args.weight_decay,\n",
        "        },\n",
        "        {\"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n",
        "    ]\n",
        "    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n",
        "    scheduler = get_linear_schedule_with_warmup(\n",
        "        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n",
        "    )\n",
        "\n",
        "    # Check if saved optimizer or scheduler states exist\n",
        "    if (\n",
        "        args.model_name_or_path\n",
        "        and os.path.isfile(os.path.join(args.model_name_or_path, \"optimizer.pt\"))\n",
        "        and os.path.isfile(os.path.join(args.model_name_or_path, \"scheduler.pt\"))\n",
        "    ):\n",
        "        # Load in optimizer and scheduler states\n",
        "        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"optimizer.pt\")))\n",
        "        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"scheduler.pt\")))\n",
        "\n",
        "    if args.fp16:\n",
        "        try:\n",
        "            from apex import amp\n",
        "        except ImportError:\n",
        "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n",
        "        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n",
        "\n",
        "    # multi-gpu training (should be after apex fp16 initialization)\n",
        "    if args.n_gpu > 1:\n",
        "        model = torch.nn.DataParallel(model)\n",
        "\n",
        "    # Distributed training (should be after apex fp16 initialization)\n",
        "    if args.local_rank != -1:\n",
        "        model = torch.nn.parallel.DistributedDataParallel(\n",
        "            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n",
        "        )\n",
        "\n",
        "    # Train!\n",
        "    logger.info(\"***** Running training *****\")\n",
        "    logger.info(\"  Num examples = %d\", len(train_dataset))\n",
        "    logger.info(\"  Num Epochs = %d\", args.num_train_epochs)\n",
        "    logger.info(\"  Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n",
        "    logger.info(\n",
        "        \"  Total train batch size (w. parallel, distributed & accumulation) = %d\",\n",
        "        args.train_batch_size\n",
        "        * args.gradient_accumulation_steps\n",
        "        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n",
        "    )\n",
        "    logger.info(\"  Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n",
        "    logger.info(\"  Total optimization steps = %d\", t_total)\n",
        "\n",
        "    global_step = 0\n",
        "    epochs_trained = 0\n",
        "    steps_trained_in_current_epoch = 0\n",
        "    # Check if continuing training from a checkpoint\n",
        "    if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n",
        "        try:\n",
        "            # set global_step to gobal_step of last saved checkpoint from model path\n",
        "            checkpoint_suffix = args.model_name_or_path.split(\"-\")[-1].split(\"/\")[0]\n",
        "            global_step = int(checkpoint_suffix)\n",
        "            epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n",
        "            steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n",
        "\n",
        "            logger.info(\"  Continuing training from checkpoint, will skip to saved global_step\")\n",
        "            logger.info(\"  Continuing training from epoch %d\", epochs_trained)\n",
        "            logger.info(\"  Continuing training from global step %d\", global_step)\n",
        "            logger.info(\"  Will skip the first %d steps in the first epoch\", steps_trained_in_current_epoch)\n",
        "        except ValueError:\n",
        "            logger.info(\"  Starting fine-tuning.\")\n",
        "\n",
        "    tr_loss, logging_loss = 0.0, 0.0\n",
        "\n",
        "    model.zero_grad()\n",
        "    train_iterator = trange(\n",
        "        epochs_trained, int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0]\n",
        "    )\n",
        "    set_seed(args)  # Added here for reproducibility\n",
        "    for _ in train_iterator:\n",
        "        epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n",
        "        for step, batch in enumerate(epoch_iterator):\n",
        "\n",
        "            # Skip past any already trained steps if resuming training\n",
        "            if steps_trained_in_current_epoch > 0:\n",
        "                steps_trained_in_current_epoch -= 1\n",
        "                continue\n",
        "\n",
        "            inputs, labels = (batch, batch)\n",
        "            if inputs.shape[1] > 1024: continue\n",
        "            inputs = inputs.to(args.device)\n",
        "            labels = labels.to(args.device)\n",
        "            model.train()\n",
        "            outputs = model(inputs, labels=labels)\n",
        "            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)\n",
        "\n",
        "            if args.n_gpu > 1:\n",
        "                loss = loss.mean()  # mean() to average on multi-gpu parallel training\n",
        "            if args.gradient_accumulation_steps > 1:\n",
        "                loss = loss / args.gradient_accumulation_steps\n",
        "\n",
        "            if args.fp16:\n",
        "                with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
        "                    scaled_loss.backward()\n",
        "            else:\n",
        "                loss.backward()\n",
        "\n",
        "            tr_loss += loss.item()\n",
        "            if (step + 1) % args.gradient_accumulation_steps == 0:\n",
        "                if args.fp16:\n",
        "                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n",
        "                else:\n",
        "                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n",
        "                optimizer.step()\n",
        "                scheduler.step()  # Update learning rate schedule\n",
        "                model.zero_grad()\n",
        "                global_step += 1\n",
        "\n",
        "                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n",
        "                    # Log metrics\n",
        "                    if (\n",
        "                        args.local_rank == -1 and args.evaluate_during_training\n",
        "                    ):  # Only evaluate when single GPU otherwise metrics may not average well\n",
        "                        results = evaluate(args, model, tokenizer)\n",
        "                        for key, value in results.items():\n",
        "                            tb_writer.add_scalar(\"eval_{}\".format(key), value, global_step)\n",
        "                    tb_writer.add_scalar(\"lr\", scheduler.get_lr()[0], global_step)\n",
        "                    tb_writer.add_scalar(\"loss\", (tr_loss - logging_loss) / args.logging_steps, global_step)\n",
        "                    logging_loss = tr_loss\n",
        "\n",
        "                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n",
        "                    checkpoint_prefix = \"checkpoint\"\n",
        "                    # Save model checkpoint\n",
        "                    output_dir = os.path.join(args.output_dir, \"{}-{}\".format(checkpoint_prefix, global_step))\n",
        "                    os.makedirs(output_dir, exist_ok=True)\n",
        "                    model_to_save = (\n",
        "                        model.module if hasattr(model, \"module\") else model\n",
        "                    )  # Take care of distributed/parallel training\n",
        "                    model_to_save.save_pretrained(output_dir)\n",
        "                    tokenizer.save_pretrained(output_dir)\n",
        "\n",
        "                    torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n",
        "                    logger.info(\"Saving model checkpoint to %s\", output_dir)\n",
        "\n",
        "                    _rotate_checkpoints(args, checkpoint_prefix)\n",
        "\n",
        "                    torch.save(optimizer.state_dict(), os.path.join(output_dir, \"optimizer.pt\"))\n",
        "                    torch.save(scheduler.state_dict(), os.path.join(output_dir, \"scheduler.pt\"))\n",
        "                    logger.info(\"Saving optimizer and scheduler states to %s\", output_dir)\n",
        "\n",
        "            if args.max_steps > 0 and global_step > args.max_steps:\n",
        "                epoch_iterator.close()\n",
        "                break\n",
        "        if args.max_steps > 0 and global_step > args.max_steps:\n",
        "            train_iterator.close()\n",
        "            break\n",
        "\n",
        "    if args.local_rank in [-1, 0]:\n",
        "        tb_writer.close()\n",
        "\n",
        "    return global_step, tr_loss / global_step\n",
        "\n",
        "# Evaluation of some model\n",
        "\n",
        "def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix=\"\") -> Dict:\n",
        "    # Loop to handle MNLI double evaluation (matched, mis-matched)\n",
        "    eval_output_dir = args.output_dir\n",
        "\n",
        "    eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n",
        "    os.makedirs(eval_output_dir, exist_ok=True)\n",
        "    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n",
        "    # Note that DistributedSampler samples randomly\n",
        "\n",
        "    def collate(examples: List[torch.Tensor]):\n",
        "        if tokenizer._pad_token is None:\n",
        "            return pad_sequence(examples, batch_first=True)\n",
        "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
        "\n",
        "    eval_sampler = SequentialSampler(eval_dataset)\n",
        "    eval_dataloader = DataLoader(\n",
        "        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n",
        "    )\n",
        "\n",
        "    # multi-gpu evaluate\n",
        "    if args.n_gpu > 1:\n",
        "        model = torch.nn.DataParallel(model)\n",
        "\n",
        "    # Eval!\n",
        "    logger.info(\"***** Running evaluation {} *****\".format(prefix))\n",
        "    logger.info(\"  Num examples = %d\", len(eval_dataset))\n",
        "    logger.info(\"  Batch size = %d\", args.eval_batch_size)\n",
        "    eval_loss = 0.0\n",
        "    nb_eval_steps = 0\n",
        "    model.eval()\n",
        "\n",
        "    for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
        "        inputs, labels = (batch, batch)\n",
        "        inputs = inputs.to(args.device)\n",
        "        labels = labels.to(args.device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(inputs, labels=labels)\n",
        "            lm_loss = outputs[0]\n",
        "            eval_loss += lm_loss.mean().item()\n",
        "        nb_eval_steps += 1\n",
        "\n",
        "    eval_loss = eval_loss / nb_eval_steps\n",
        "    perplexity = torch.exp(torch.tensor(eval_loss))\n",
        "\n",
        "    result = {\"perplexity\": perplexity}\n",
        "\n",
        "    output_eval_file = os.path.join(eval_output_dir, prefix, \"eval_results.txt\")\n",
        "    with open(output_eval_file, \"w\") as writer:\n",
        "        logger.info(\"***** Eval results {} *****\".format(prefix))\n",
        "        for key in sorted(result.keys()):\n",
        "            logger.info(\"  %s = %s\", key, str(result[key]))\n",
        "            writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "3dur4zAsadYR"
      },
      "outputs": [],
      "source": [
        "#collapse\n",
        "# Main show runner\n",
        "\n",
        "def main(df_trn, df_val):\n",
        "    args = Args()\n",
        "    \n",
        "    if args.should_continue:\n",
        "        sorted_checkpoints = _sorted_checkpoints(args)\n",
        "        if len(sorted_checkpoints) == 0:\n",
        "            raise ValueError(\"Used --should_continue but no checkpoint was found in --output_dir.\")\n",
        "        else:\n",
        "            args.model_name_or_path = sorted_checkpoints[-1]\n",
        "\n",
        "    if (\n",
        "        os.path.exists(args.output_dir)\n",
        "        and os.listdir(args.output_dir)\n",
        "        and args.do_train\n",
        "        and not args.overwrite_output_dir\n",
        "        and not args.should_continue\n",
        "    ):\n",
        "        raise ValueError(\n",
        "            \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n",
        "                args.output_dir\n",
        "            )\n",
        "        )\n",
        "\n",
        "    # Setup CUDA, GPU & distributed training\n",
        "    device = torch.device(\"cuda\")\n",
        "    args.n_gpu = torch.cuda.device_count()\n",
        "    args.device = device\n",
        "\n",
        "    # Setup logging\n",
        "    logging.basicConfig(\n",
        "        format=\"%(asctime)s - %(levelname)s - %(name)s -   %(message)s\",\n",
        "        datefmt=\"%m/%d/%Y %H:%M:%S\",\n",
        "        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n",
        "    )\n",
        "    logger.warning(\n",
        "        \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n",
        "        args.local_rank,\n",
        "        device,\n",
        "        args.n_gpu,\n",
        "        bool(args.local_rank != -1),\n",
        "        args.fp16,\n",
        "    )\n",
        "\n",
        "    # Set seed\n",
        "    set_seed(args)\n",
        "\n",
        "    config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n",
        "    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n",
        "    model = AutoModelWithLMHead.from_pretrained(\n",
        "        args.model_name_or_path,\n",
        "        from_tf=False,\n",
        "        config=config,\n",
        "        cache_dir=args.cache_dir,\n",
        "    )\n",
        "    model.to(args.device)\n",
        "    \n",
        "    logger.info(\"Training/evaluation parameters %s\", args)\n",
        "\n",
        "    # Training\n",
        "    if args.do_train:\n",
        "        train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n",
        "\n",
        "        global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
        "        logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n",
        "\n",
        "    # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n",
        "    if args.do_train:\n",
        "        # Create output directory if needed\n",
        "        os.makedirs(args.output_dir, exist_ok=True)\n",
        "\n",
        "        logger.info(\"Saving model checkpoint to %s\", args.output_dir)\n",
        "        # Save a trained model, configuration and tokenizer using `save_pretrained()`.\n",
        "        # They can then be reloaded using `from_pretrained()`\n",
        "        model_to_save = (\n",
        "            model.module if hasattr(model, \"module\") else model\n",
        "        )  # Take care of distributed/parallel training\n",
        "        model_to_save.save_pretrained(args.output_dir)\n",
        "        tokenizer.save_pretrained(args.output_dir)\n",
        "\n",
        "        # Good practice: save your training arguments together with the trained model\n",
        "        torch.save(args, os.path.join(args.output_dir, \"training_args.bin\"))\n",
        "\n",
        "        # Load a trained model and vocabulary that you have fine-tuned\n",
        "        model = AutoModelWithLMHead.from_pretrained(args.output_dir)\n",
        "        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n",
        "        model.to(args.device)\n",
        "\n",
        "    # Evaluation\n",
        "    results = {}\n",
        "    if args.do_eval and args.local_rank in [-1, 0]:\n",
        "        checkpoints = [args.output_dir]\n",
        "        if args.eval_all_checkpoints:\n",
        "            checkpoints = list(\n",
        "                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + \"/**/\" + WEIGHTS_NAME, recursive=True))\n",
        "            )\n",
        "            logging.getLogger(\"transformers.modeling_utils\").setLevel(logging.WARN)  # Reduce logging\n",
        "        logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n",
        "        for checkpoint in checkpoints:\n",
        "            global_step = checkpoint.split(\"-\")[-1] if len(checkpoints) > 1 else \"\"\n",
        "            prefix = checkpoint.split(\"/\")[-1] if checkpoint.find(\"checkpoint\") != -1 else \"\"\n",
        "\n",
        "            model = AutoModelWithLMHead.from_pretrained(checkpoint)\n",
        "            model.to(args.device)\n",
        "            result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n",
        "            result = dict((k + \"_{}\".format(global_step), v) for k, v in result.items())\n",
        "            results.update(result)\n",
        "\n",
        "    return results"
      ]
    },
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            "WARNING:__main__:Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:1066: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1211 > 1024). Running this sequence through the model will result in indexing errors\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
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              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
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            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:249: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n"
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